system

Abstract

We provide the system. [Solution] A means for receiving the industry and job content selection from the user, Based on the aforementioned industry and business content, a means for sending a request to the server, A means for identifying the target industry from the aforementioned request content and selecting the appropriate prompt by referring to a database, A system including means for responding to the user with a selected prompt.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the operations of various industries, it is required to improve the quality of operations by generating and providing efficient and appropriate prompts to users. However, with conventional methods, it is difficult to quickly generate and provide appropriate prompts according to the operation content. Due to this problem, users need to create prompts manually, which takes time and effort and reduces operation efficiency. Also, manually created prompts may lack consistency and their quality may vary. Therefore, there is a need for a system that automatically generates and provides prompts suitable for the operations of various industries to users.

Means for Solving the Problems

[0005] This invention provides a system that receives industry and work content selections from a user, sends a request to a server, the server analyzes the request content, selects an appropriate prompt from a database, and sends it back to the user. Specifically, the user selects industry and work content via a terminal and sends it to the server. The server analyzes the request content, identifies the target industry, refers to the database, and selects the corresponding prompt. The server then formats the selected prompt in JSON format and sends it back to the terminal as an HTTP response. The terminal displays the received prompt to the user, who can then use it to perform their work. In this way, prompts suitable for the work can be provided quickly and consistently, improving work efficiency and ensuring uniform quality.

[0006] A "user" refers to an individual user who uses the system to obtain prompts appropriate for their work.

[0007] A "terminal" refers to a device used by a user, such as a computer, smartphone, or tablet, that displays a user interface, accepts input, sends requests, receives and displays responses.

[0008] A "server" is a computer system that receives requests from users, performs industry identification, looks up databases, selects prompts, and responds.

[0009] A "request" is data that a user sends to a server via their device, containing information about their industry and business operations.

[0010] The term "industry" refers to a set of activities or specialized fields within a particular area, such as healthcare, education, or manufacturing.

[0011] "Job description" refers to the specific tasks and duties that a user performs within a particular industry.

[0012] A "database" is a collection of data that stores prompt information specific to each industry.

[0013] A "prompt" refers to information such as instructions, guidelines, and example questions that a system provides to support the user's work.

[0014] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and is a lightweight data exchange format for structuring and representing data.

[0015] An "HTTP response" is the response data that a server sends back to a client via the HTTP protocol.

[0016] "AJAX request" is an abbreviation for Asynchronous JavaScript and XML, and is a technology that uses JavaScript to asynchronously exchange data with a server. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention relates to a system that automatically generates and provides users with prompts tailored to the specific tasks of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[0039] Overall system configuration

[0040] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via a terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, selects an appropriate prompt from its database, and sends it back to the terminal.

[0041] User actions

[0042] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[0043] Terminal operation

[0044] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user.

[0045] Server operation

[0046] When the server receives a request, it first parses the request content to identify the industry and the nature of the work. For example, the request might provide information about the "healthcare industry" and "creating medical records." The server then consults its database to find the most suitable prompt for the relevant industry and work content. This information is obtained using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[0047] Specific example

[0048] For example, if a user selects "patient interview" in the medical field, the server selects prompts from the database that include "detailed methods for recording patient symptoms" and "examples of important questions to ask during the interview." The selected prompts are returned in JSON format and displayed on the terminal. The user can then use these prompts to efficiently conduct the actual interview.

[0049] Usage scenarios and benefits

[0050] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This results in significant time and effort savings, and a substantial improvement in work efficiency.

[0051] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, thereby significantly improving the quality and efficiency of work in various industries.

[0052] The following describes the processing flow.

[0053] Step 1:

[0054] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[0055] Step 2:

[0056] The terminal retrieves the user's selections and creates an AJAX request. The request data includes the selected industry and job description.

[0057] Step 3:

[0058] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[0059] Step 4:

[0060] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[0061] Step 5:

[0062] The server analyzes the request content to identify the industry and type of work. For example, it might extract information about the "healthcare industry" and "creating medical records" from the request data.

[0063] Step 6:

[0064] The server references the database based on the identified industry and business content. It creates an SQL query and searches the database for the corresponding prompt.

[0065] Step 7:

[0066] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[0067] Step 8:

[0068] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[0069] Step 9:

[0070] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[0071] Step 10:

[0072] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[0073] Step 11:

[0074] The terminal displays the analyzed prompt data in the user interface. Users can then review the prompts and use them for their work.

[0075] Step 12:

[0076] Users perform tasks using the displayed prompts. For example, when creating a medical record in the healthcare industry, they refer to the provided prompts to create the appropriate record.

[0077] (Example 1)

[0078] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0079] Traditional systems made it difficult for users to quickly obtain appropriate prompts to improve work efficiency. Furthermore, the consistency and quality of prompts were not guaranteed, leading to inconsistencies in the quality of work. Additionally, the lack of functionality to automatically generate custom prompts for different industries and tasks meant users had to handle them manually, resulting in significant time and effort.

[0080] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0081] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for transmitting a request to an information processing device based on the industry and business content; means for identifying the target industry from the request content, referring to a data storage device to select a corresponding generating AI model; and means for generating an optimal prompt statement from the selected generating AI model and responding to the user. This enables the user to quickly and consistently obtain the optimal prompt to improve business efficiency.

[0082] A "user" is an individual or group that operates the system and makes selections regarding industry and business content.

[0083] "Industry" refers to a specific business field in which certain occupations or job roles exist.

[0084] "Job description" refers to the specific tasks or work that a user is required to perform within the industry to which they belong.

[0085] An "information processing device" refers to a device that has the function of receiving requests from users, analyzing them, and making necessary queries to data storage devices.

[0086] A "data storage device" is a storage means that stores necessary data such as prompt statements and generated AI models, and provides it to an information processing device in response to a request.

[0087] A "generative AI model" is an artificial intelligence algorithm that automatically generates prompt messages that are optimal for a specific industry or job function.

[0088] A "prompt message" is a document containing specific instructions or guidelines that users should refer to in order to perform their tasks efficiently.

[0089] JSON format is a lightweight data exchange format for structuring and representing data.

[0090] A "structured query language" is a query language used for database management and data retrieval, and primarily refers to SQL (Structured Query Language).

[0091] "Asynchronous JavaScript and XML" refers to a technology for asynchronously exchanging data between a server and a client, specifically AJAX (Asynchronous JavaScript and XML).

[0092] "Selection" is the process of choosing the generation AI model and prompt sentences that best suit the user's requirements from the data storage device.

[0093] This invention relates to a system that automatically generates and provides prompt messages suitable for the operations of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[0094] Overall system configuration

[0095] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via the terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, generates a prompt using an appropriate generative AI model, and sends it back to the terminal.

[0096] User actions

[0097] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[0098] Terminal operation

[0099] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an asynchronous JavaScript and XML (AJAX) request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt message to the user.

[0100] Server operation

[0101] When the server receives a request, it first analyzes the request content to identify the industry and business content. For example, it might obtain information about the "healthcare industry" and "creation of medical records" from the request. The server then refers to the data storage device to search for the corresponding generative AI model and uses this model to generate the optimal prompt message. This information is obtained using a Structured Query Language (SQL) query, the generated prompt message is formatted into Structured Data Format (JSON), and sent back to the terminal as an HTTP response.

[0102] Specific example

[0103] For example, if the user selects "Patient Interview" in the medical field, the server uses a generative AI model to generate a prompt message like the following:

[0104] "Methods for recording detailed patient symptoms":

[0105] 1. Basic patient information (name, age, gender)

[0106] 2. Chief complaint (current symptoms and their progression)

[0107] 3. Present illness (chronology and background of symptoms)

[0108] 4. Family history (health information related to family members)

[0109] 5. Medical history (past illnesses and surgeries)

[0110] 6. Current treatment methods (medications and treatments currently being used)

[0111] Examples of important questions to ask during a medical interview:

[0112] 1. How many days ago did the symptoms first appear?

[0113] 2. Are there any specific factors that cause symptoms to worsen or improve?

[0114] 3. Are there any other symptoms you are experiencing?

[0115] 4. How is this affecting your daily life?

[0116] Users can use these prompts to efficiently conduct the actual medical interview.

[0117] Usage scenarios and benefits

[0118] This system allows users to quickly obtain prompts optimized for their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from data storage devices using an AI model, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. This system has the potential to dramatically improve the quality and efficiency of work across various industries.

[0119] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0120] Step 1: User selects industry and job content.

[0121] The user interacts with an interface on their device to select their industry and job duties. For example, if the user works in the medical industry, they might select "Create patient records." This selection then becomes the input for the next step.

[0122] Input: User's selected industry and job duties (e.g., healthcare industry, creating medical records)

[0123] Output: Selections sent to the terminal

[0124] Specific operation: The user opens a browser and uses a UI to select their industry and job description. Once the selection is confirmed, the input data is sent to the terminal.

[0125] Step 2: The terminal generates and sends the request.

[0126] The terminal retrieves the user's selections and sends them to the server as asynchronous JavaScript and XML (AJAX) requests. These requests include the user's industry and job description.

[0127] Input: User's selected industry and job description

[0128] Output: AJAX request sent to the server

[0129] Specific operation: The terminal uses JavaScript to generate an AJAX request containing the user's selections. This request is sent to the server using the HTTP POST method.

[0130] Step 3: Server receives and parses the request

[0131] The server receives the AJAX request sent from the terminal and parses the request content. It extracts the industry and business details and prepares to query the data storage device.

[0132] Input: AJAX request sent from the terminal

[0133] Output: Analyzed industries and business operations

[0134] Specific operation: After receiving a request, the server uses an analysis module to extract the industry and business content. For example, it might extract "healthcare industry" and "creation of medical records."

[0135] Step 4: Reference to data storage device and selection of generated AI model

[0136] Based on the analysis results, the server consults the data storage device and searches for the corresponding generation AI model. It then prepares to generate the optimal prompt sentence using this model.

[0137] Input: Analyzed industry and business content

[0138] Output: Selected generative AI models

[0139] Specific operation: The server generates an SQL query to search for generated AI models from the data storage device. For example, to retrieve models for "healthcare industry" and "medical record creation," it executes "SELECT FROM models WHERE industry='healthcare' AND task='medical record creation';".

[0140] Step 5: Generating the prompt message

[0141] The server uses the selected generation AI model to generate prompt messages that are optimal for the user's industry and business operations. These generated prompt messages serve as input for the next step.

[0142] Input: Selected generative AI model, analyzed industry and business content

[0143] Output: Generated prompt message

[0144] Specific operation: The server applies a generation AI model to generate prompts based on the analysis results (industry and business content). For example, it might generate a prompt for "detailed method of recording patient symptoms."

[0145] Step 6: Formatting the prompt and generating the response

[0146] The server formats the generated prompt message into a structured data format (JSON) and prepares it as an HTTP response. This response is then sent to the terminal.

[0147] Input: Generated prompt message

[0148] Output: HTTP response sent to the terminal

[0149] Specific operation: The server converts the generated prompt message into JSON format, sets the HTTP header to "Content-Type: application / json", and generates a response.

[0150] Step 7: Receiving and analyzing the response by the terminal

[0151] The terminal receives an HTTP response from the server and parses its contents. It extracts prompt text from the JSON format and prepares it to be displayed to the user.

[0152] Input: HTTP response from the server

[0153] Output: Parsed prompt message

[0154] Specific operation: After receiving the response, the terminal uses a JSON parsing library to extract the prompt text. This content will be displayed in the next step.

[0155] Step 8: Display a prompt message to the user.

[0156] The terminal displays the parsed prompt message to the user. The user can then use this prompt message to efficiently carry out their tasks.

[0157] Input: Parsed prompt message

[0158] Output: Prompt displayed to the user

[0159] Specific operation: The terminal displays the parsed prompt text in a designated area of ​​the user interface. For example, it will display "Detailed recording method for patient symptoms: 1. Basic patient information 2. Chief complaint..." in the browser's text area.

[0160] The above outlines the specific processing steps of this system.

[0161] (Application Example 1)

[0162] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0163] Logistics centers face complex procedures for efficiently and effectively performing tasks such as inventory management, picking, and optimizing delivery routes. This results in decreased operational efficiency and an increased risk of errors. Furthermore, insufficient standardization of work procedures makes it difficult for new employees to quickly learn their jobs. To address these problems and improve operational consistency and efficiency, there is a need for a system that automatically generates and provides optimal prompts for each task.

[0164] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0165] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for sending a request to the server based on the industry and business content; means for identifying the target industry from the request content and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; means for automatically generating prompts corresponding to specific tasks such as inventory management, picking list creation, and delivery route optimization in order to improve the operational efficiency of the logistics center; and means for displaying the generated prompts on the user's smartphone. This makes it possible to perform operations at the logistics center efficiently, standardized, and with consistently high quality.

[0166] An "industry" refers to a group of companies or organizations that operate in a specific field or market as part of their business activities.

[0167] "Job description" refers to a list of specific tasks and duties performed within a particular industry.

[0168] A "request" refers to a request for information to be sent to the server based on user input.

[0169] A "server" is a computer system that performs specific calculations and data processing to provide services to a client (in this case, a terminal).

[0170] A "database" is a digital system that organizes and stores structured information so that it can be easily accessed, managed, and updated later.

[0171] A "prompt" is a phrase that provides guidance or instructions to a user when performing a specific task or operation.

[0172] "Inventory management" refers to operations in a logistics center that include receiving, storing, shipping, and tracking and managing inventory levels.

[0173] A "picking list" is a set of guidelines or lists used to select items from a warehouse based on an order.

[0174] "Delivery route optimization" is the process of calculating the most efficient route to minimize the time and cost of delivering goods.

[0175] A "smartphone" is a portable electronic device that has functions beyond those of a regular mobile phone, such as internet connectivity, application execution, and camera operation.

[0176] "JSON format" is a method of structurally representing data in text format, and it is an abbreviation for JavaScript Object Notation.

[0177] An "AJAX request" is an asynchronous communication method for retrieving data from a server without reloading a web page.

[0178] A "logistics center" is a facility that stores, manages, and ships goods, and constitutes part of the supply chain.

[0179] Overall system configuration

[0180] This invention is a system designed to improve user work efficiency and ensure consistency and quality in operations. The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for processing requests and managing the database, while the terminal receives input from the user and transmits it to the server. Users access the system using the terminal and select their industry and work content.

[0181] User actions

[0182] The user first uses an interface on their smartphone application to select their industry and job duties. For example, suppose the user works in a logistics center and selects "Create Picking List." Once the user confirms their selection, the device sends this information to the server as a request.

[0183] Terminal operation

[0184] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user. This process uses front-end technologies such as HTML, CSS, and JavaScript.

[0185] Server operation

[0186] When the server receives a request, it first parses the request to identify the industry and task. For example, it might obtain information about a "logistics center" and "creating a picking list" from the request. The server then consults a database to find the most suitable prompt for the relevant industry and task. The database contains prompts for various industries and tasks, which are retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response. This process uses Flask (a lightweight web framework for Python) and SQLite (a lightweight relational database).

[0187] Specific example

[0188] For example, if a user selects "Create Picking List" in the "Logistics Center" section, the server selects prompts from the database that include the "Optimal Picking Route" and a "Detailed List of Products." The selected prompts are returned in JSON format and displayed on the terminal.

[0189] Example of a prompt

[0190] Create a picking list:

[0191] 1. Please check the stock availability of each item.

[0192] 2. Set the optimal picking route as follows:

[0193] Aisle 5: Product A

[0194] Aisle 3: Product B

[0195] Aisle 7: Product C

[0196] 3. Once the list is complete, review it and proceed to the next step.

[0197] Usage scenarios and benefits

[0198] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency.

[0199] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0200] Step 1:

[0201] The user launches a smartphone application and selects an industry (e.g., "logistics center") and a task (e.g., "creating picking lists"). The input consists of the selected industry and task, which the terminal receives. In terms of specific actions, the user interacts with interfaces such as dropdown menus and checkboxes.

[0202] Step 2:

[0203] Based on the selected industry and job description, the terminal sends a request to the server in AJAX request format. The input is the user's selection information, and the output is the sending of the request to the server. The terminal uses JavaScript for asynchronous communication.

[0204] Step 3:

[0205] The server analyzes the received request and identifies the selected industry and business content. The input is the request data from the terminal, and the output is the analysis result, which is information about the industry and business content. The server uses Flask to analyze the request.

[0206] Step 4:

[0207] Based on the analysis results, the server uses SQL queries to retrieve the appropriate prompt from the database. The input is the industry and business content information from the analysis results, and the output is the prompt data retrieved from the database. The server queries the database (SQLite) using SQL.

[0208] Step 5:

[0209] The server formats the retrieved prompt data into JSON format and sends it back to the terminal as an HTTP response. The input is prompt data from the database, and the output is prompt data in JSON format. The server generates the JSON data using the Python standard library.

[0210] Step 6:

[0211] The terminal receives a response from the server and parses the prompt data. The input is prompt data in JSON format from the server, and the output is the prompt text as a result of the parsing. The terminal uses JavaScript to parse the JSON data.

[0212] Step 7:

[0213] The parsed prompt is displayed to the user. The input is the parsed prompt text, and the output is the prompt displayed to the user. The terminal visually presents the prompt to the user using HTML and CSS.

[0214] Step 8:

[0215] The user follows the prompts presented and performs specific tasks (e.g., tasks based on a picking list). The input is the displayed prompt text, and the output is the actual work performed. The user works while looking at their smartphone screen.

[0216] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0217] This invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes user emotions, and provides prompts accordingly. This system is designed to improve user work efficiency, ensure consistency and quality of operations, and provide flexible responses that respond to user emotions. Specific embodiments of this system are described below.

[0218] Overall system configuration

[0219] This system primarily consists of four main elements: "server," "terminal," "user," and "emotion engine." Users access the system via the terminal and select their industry and job content. During the selection process, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from its database based on the emotional state, and sends it back to the terminal.

[0220] User actions

[0221] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[0222] Terminal operation

[0223] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[0224] Server operation

[0225] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and task. For example, the request might reveal information such as "healthcare industry," "creating medical records," and "the user is stressed." The server then consults its database to find the most suitable prompt for the given industry, task, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response.

[0226] Specific example

[0227] For example, if a user selects "patient interview" in the medical field, and the emotion engine detects that the user is nervous, the server selects prompts from the database that include "a flow of interviews to help the patient relax" and "examples of questions in a gentle tone." The selected prompts are returned in JSON format and displayed on the terminal. Based on these prompts, the user can efficiently conduct the interview while easing their tension.

[0228] Usage scenarios and benefits

[0229] This system allows users to quickly obtain prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[0230] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, and to respond flexibly to emotions, thereby significantly improving the quality and efficiency of work in various industries.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[0234] Step 2:

[0235] The emotion engine analyzes the user's facial expressions, voice, and body movements to identify the user's current emotional state. For example, it can determine whether the user is feeling stressed or anxious.

[0236] Step 3:

[0237] The terminal retrieves the user's selections and identified emotional state, and creates an AJAX request. The request data includes the selected industry and job description, and the recognized emotional state.

[0238] Step 4:

[0239] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[0240] Step 5:

[0241] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[0242] Step 6:

[0243] The server analyzes the request content and emotional state to identify the industry and job content. For example, it extracts information such as "healthcare industry," "creating medical records," and "user is stressed" from the request data.

[0244] Step 7:

[0245] The server references the database based on the identified industry, job description, and emotional state. It then creates an SQL query to search the database for the corresponding prompt.

[0246] Step 8:

[0247] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[0248] Step 9:

[0249] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[0250] Step 10:

[0251] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[0252] Step 11:

[0253] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[0254] Step 12:

[0255] The terminal displays the analyzed prompt data on the user interface. For example, a "Create Medical Record" prompt that takes the user's stress level into consideration may be displayed.

[0256] Step 13:

[0257] Users perform tasks using the displayed prompts. For example, in the medical field, when creating a medical record, they refer to the provided prompts to create the appropriate record.

[0258] Step 14:

[0259] The system encourages users to follow on-screen prompts to relax and perform tasks efficiently. Tasks are adjusted according to the user's current emotional state, allowing them to work with reduced stress.

[0260] (Example 2)

[0261] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0262] Traditional prompt generation systems provided responses simply by the user selecting an industry and job content, without considering the user's emotional state. Therefore, depending on the user's emotional state, appropriate prompts could not be provided, leading to decreased work efficiency and quality. Furthermore, they lacked the flexibility to improve user comfort and satisfaction.

[0263] The identification processing performed by the identification 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 receiving the selection of industry and work content from the user; means for sending a request to the server based on the industry and work content; means for identifying the target industry from the request content and selecting the appropriate prompt by referring to the database; means for responding to the user with the selected prompt; and means for recognizing the user's emotions and providing prompts corresponding to those emotions. This makes it possible to provide flexible and appropriate prompts according to the user's emotional state, thereby improving work efficiency and work quality. It is also possible to improve the ease of work and satisfaction of the user.

[0264] "Industry" refers to the type of business field or industry to which the user belongs.

[0265] "Job description" refers to the specific tasks or types of work that the user performs within the industry.

[0266] A "request" is a request for information sent to the server based on the user's selected industry, job content, and emotional state.

[0267] A "server" is a central computer system that receives requests, parses them, retrieves prompts from a database, and provides responses.

[0268] A "database" is a system that stores prompts tailored to the user's industry, job responsibilities, and emotional state, and allows users to search for them as needed.

[0269] A "prompt" is a specific instruction or guideline that a user can refer to when performing a task.

[0270] "Selection" is the process of choosing the most suitable prompt from the database based on industry, job content, and emotional state.

[0271] A "response" is the act of providing the user with a selected prompt.

[0272] "Emotions" refer to the user's current psychological or emotional state.

[0273] "Emotional state" refers to a specific psychological or emotional state of the user as recognized by the emotion engine.

[0274] An "emotion engine" is a technical means of recognizing emotions by analyzing a user's facial expressions and voice data.

[0275] An "AJAX request" is a technology that uses asynchronous communication to send user selections to a server.

[0276] "JSON format" is a lightweight data exchange format for structuring data in text form.

[0277] "Analysis" is the process by which the server understands the content of the received request and extracts the data necessary to execute an appropriate process.

[0278] "SQL query" is an instruction in the Structured Query Language used to retrieve specific information from a database.

[0279] The present invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes the emotions of users, and provides prompts accordingly. This system is configured not only to improve the work efficiency of users, ensure the consistency and quality of work, but also to realize flexible responses according to the emotions of users. The components of this system and their operations will be specifically described below.

[0280] Overall Configuration of the System

[0281] This system is composed of five main elements: "server", "terminal", "user", "emotion engine", and "database".

[0282] User Operations

[0283] First, the user selects the industry and the content of the operation through the interface on the terminal. For example, the user selects "medical industry" and "medical record creation". At this time, the emotion engine recognizes the emotional state from the user's expression and voice. When the user confirms the selection, the terminal sends this information and the emotional state to the server as a request.

[0284] Terminal Operations

[0285] After the user selects an industry and business content on the terminal, the emotion engine analyzes the user's emotion and sends the status to the system. The terminal obtains the user's selection content and emotion state and sends them to the server in the form of an AJAX request. The request contains information on the industry, business content, and recognized emotion state. Specific examples of requests are in the following formats:

[0286] json

[0287] {

[0288] "Industry": "Medical industry",

[0289] "Business content": "Medical record creation",

[0290] "Emotion state": "Nervous"

[0291] }

[0292] Upon receiving the response from the server, the terminal analyzes the content and displays the generated prompt to the user.

[0293] Server operations

[0294] When the server receives a request, it first analyzes the request content and emotion state to identify the industry and business content. For example, it obtains information such as "Medical industry", "Medical record creation", and "The user is in a nervous state" from the request. Then, the server refers to the database and searches for the most suitable prompt for the corresponding industry, business content, and emotion state. This information is obtained using an SQL query. For example:

[0295] sql

[0296] SELECT prompt FROM prompts WHERE industry='Medical industry' AND task='Medical record creation' AND emotion='Nervous';

[0297] The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response. An example of a returned prompt may include the following instructions:

[0298] "Please create the medical record following these steps: 1. Enter the patient's basic information. 2. Record the chief complaint. 3. Enter the examination results."

[0299] Specific example

[0300] For example, if a user selects "Patient Interview" in the medical field, and the emotion engine detects that the user is nervous, the server will select prompts from the database that include "a patient interview flow to help the patient relax" and "examples of questions in a gentle tone." The selected prompts will be displayed on the terminal in the following format:

[0301] "To help the patient relax, greet them with a smile and ask the following questions in a gentle tone."

[0302] This allows users to quickly receive prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Moreover, the introduction of an emotion engine enables more personalized support based on the user's state, which is expected to improve user satisfaction.

[0303] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0304] Step 1:

[0305] The user selects the industry and job description.

[0306] Input: Selected data for industry and job description

[0307] Specific operations: The user accesses the terminal interface and selects an industry (e.g., "medical industry") and a business content (e.g., "medical record creation"). Once the selection is confirmed, the data is saved as it will be used in the next step.

[0308] Step 2:

[0309] The emotion engine recognizes the user's emotional state.

[0310] Input: User's facial expression and voice data

[0311] Specific operations: The emotion engine analyzes the user's facial expression and voice to recognize the emotional state. For example, based on the data collected through the camera and microphone, it is determined that the user is "nervous". The determination result is sent to the next step.

[0312] Step 3:

[0313] The terminal generates a request and sends it to the server.

[0314] Input: Industry, business content, emotional state

[0315] Output: AJAX request to the server

[0316] Specific operations: The terminal generates a request containing information on the user's industry selection, business content selection, and recognized emotional state. The request is composed in JSON format using AJAX and sent to the server.

[0317] Step 4:

[0318] The server receives and analyzes the request.

[0319] Input: AJAX request

[0320] Output: Analysis result (industry, business content, emotional state)

[0321] Specific operation: When the server receives a request, it analyzes the request content and identifies the industry (e.g., "healthcare industry"), the task (e.g., "creating medical records"), and the emotional state (e.g., "stressed").

[0322] Step 5:

[0323] The server retrieves the corresponding prompt from the database.

[0324] Input: Analysis results

[0325] Output: Prompt data

[0326] Specific operation: The server looks up a database to find a prompt appropriate for the relevant industry, job content, and emotional state. For example, it generates an SQL query and searches the database.

[0327] Example SQL query: SELECT prompt FROM prompts WHERE industry='healthcare industry' AND task='create medical records' AND emotion='nervous';

[0328] Step 6:

[0329] The server formats the prompt in JSON format and sends it back to the terminal.

[0330] Input: Prompt data

[0331] Output: HTTP response (prompt in JSON format)

[0332] Specific operation: The server formats the prompt obtained as a search result into JSON format and sends it as an HTTP response.

[0333] Step 7:

[0334] The terminal receives and analyzes the response from the server.

[0335] Input: HTTP response (JSON format prompt)

[0336] Output: Analyzed prompt data

[0337] Specific operation: The terminal receives a response from the server, analyzes its contents, and extracts the necessary information.

[0338] Step 8:

[0339] The terminal displays a prompt to the user.

[0340] Input: Analyzed prompt data

[0341] Output: Displayed to the user (text-based prompt)

[0342] Specific operation: The terminal displays the analyzed prompt data in a format that is easy for the user to understand. For example, instructions such as "Please create the medical record following the steps below" will be displayed on the terminal screen.

[0343] In this way, this system can improve user work efficiency and satisfaction by consistently handling everything from industry and job content selection to emotional state recognition and the provision of appropriate prompts.

[0344] (Application Example 2)

[0345] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0346] Conventional prompt generation systems only have the function of providing appropriate prompts based on the user's industry and job content, and are unable to provide prompts that take into account the user's emotional state. As a result, even when a user is feeling stressed or anxious, only a uniform prompt is provided, which can lead to a decrease in work efficiency and quality. The present invention aims to improve the user's work efficiency and enhance the quality of work by providing a system that recognizes the user's emotional state in real time and automatically generates appropriate prompts accordingly.

[0347] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0348] In this invention, the server includes means for receiving a selection of industry and work content from a user; means for sending a request to the server based on the industry, work content, and emotional state; means for identifying the target industry from the request content and emotional state, and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; and means including an emotion engine for recognizing the user's emotional state in real time. This enables flexible responses according to the user's emotional state, improving work efficiency and quality.

[0349] A "user" refers to a person who uses the system to select an industry and job content and receives prompts.

[0350] "Industry" refers to a category that indicates a specific economic field or occupational area.

[0351] "Job description" refers to the specific tasks and types of work that the user will be engaged in.

[0352] "Options" refers to the multiple possible choices presented to a user when selecting an industry and job.

[0353] "Emotional state" refers to the user's psychological and emotional state, including, for example, tension, anxiety, and relaxation.

[0354] An "emotion engine" refers to a software module that recognizes and analyzes a user's emotional state in real time.

[0355] A "prompt" refers to the text of specific instructions or guidance generated to support the user's work.

[0356] A "request" refers to the information a user submits regarding their industry, job responsibilities, and emotional state.

[0357] A "server" is a central computer system within a system that receives requests, analyzes them, retrieves the results from a database, and sends them back.

[0358] A "database" refers to a data storage system used to store and manage prompt information based on industry and business content.

[0359] A "prompt generator" refers to a software module that generates appropriate prompts based on the user's emotional state.

[0360] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text format for exchanging data.

[0361] An "AJAX request" refers to a technology that uses asynchronous communication to send requests from users to a server.

[0362] Overall system configuration

[0363] The system that realizes this invention mainly consists of four main elements: a server, a terminal, a user, and an emotion engine. The user accesses the system via the terminal and selects an industry and job content. When selecting, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from the database according to the emotional state, and sends it back to the terminal.

[0364] User actions

[0365] The user first uses an interface on their device to select their industry and job duties. For example, suppose a user works in the security industry and selects "nighttime building patrols." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[0366] Terminal operation

[0367] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[0368] Server operation

[0369] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and job content. For example, the request might reveal information such as "security industry," "nighttime building patrol," and "user is anxious." The server then consults its database to find the most appropriate prompt for the relevant industry, job content, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[0370] Specific example

[0371] For example, if a user selects "nighttime building patrol" in the security industry, and the emotion engine recognizes that the user is anxious, the server will select a prompt from the database such as, "An anomaly has been detected in the stairwell at your current location. Please first assess the situation and thoroughly check for any danger. If you are feeling anxious, take a deep breath and calm down." The selected prompt will be returned in JSON format and displayed on the device. Based on this prompt, the user can proceed with the patrol safely while alleviating their anxiety.

[0372] Usage scenarios and benefits

[0373] This system allows users to quickly obtain optimal and emotionally appropriate prompts tailored to their work content, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The user selects their industry and job content using a terminal. The terminal retrieves the user's selections, while simultaneously an emotion engine analyzes the user's facial expressions and voice data to recognize their emotional state. Inputs include the user's selected industry and job content, as well as their facial expressions and voice data. Outputs include the selected industry and job content information, and the recognized emotional state.

[0377] Step 2:

[0378] The terminal sends information about its industry, job duties, and emotional state to the server as a request. Specifically, this information is sent to the server in the form of an AJAX request. The inputs are the user's industry, job duties, and emotional state. The output is the request sent to the server.

[0379] Step 3:

[0380] The server analyzes received requests to identify the industry, job content, and emotional state. Specifically, it parses the request content and extracts information on the industry, job content, and emotional state. The input is the request sent from the terminal. The output is the analyzed information on the industry, job content, and emotional state.

[0381] Step 4:

[0382] The server searches the database based on the analysis results and selects the most appropriate prompt for the industry, job content, and emotional state. Specifically, it uses SQL queries to retrieve the corresponding prompt from the database. The input is the analyzed information on the industry, job content, and emotional state. The output is the selected prompt.

[0383] Step 5:

[0384] The server formats the selected prompt into JSON format and sends it back to the terminal as an HTTP response. Specifically, it converts the received prompt into JSON format and sends it as an HTTP response. The input is the selected prompt. The output is an HTTP response containing the prompt in JSON format.

[0385] Step 6:

[0386] The terminal parses the prompt received from the server and displays it to the user. Specifically, it parses the received JSON-formatted prompt and displays it in the user interface. The input is a JSON-formatted prompt sent from the server. The output is the prompt that will be displayed to the user.

[0387] Step 7:

[0388] The user performs tasks based on prompts displayed on the terminal. Specifically, they follow the displayed prompts and select the industry and task content again as needed to receive further instructions. The input is the prompts displayed on the terminal. The output is the efficient execution of tasks.

[0389] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0390] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0391] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0392] [Second Embodiment]

[0393] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0394] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0395] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0396] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0397] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0398] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0399] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0400] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0401] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0402] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0403] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0404] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0405] This invention relates to a system that automatically generates and provides users with prompts tailored to the specific tasks of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[0406] Overall system configuration

[0407] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via a terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, selects an appropriate prompt from its database, and sends it back to the terminal.

[0408] User actions

[0409] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[0410] Terminal operation

[0411] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user.

[0412] Server operation

[0413] When the server receives a request, it first parses the request content to identify the industry and the nature of the work. For example, the request might provide information about the "healthcare industry" and "creating medical records." The server then consults its database to find the most suitable prompt for the relevant industry and work content. This information is obtained using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[0414] Specific example

[0415] For example, if a user selects "patient interview" in the medical field, the server selects prompts from the database that include "detailed methods for recording patient symptoms" and "examples of important questions to ask during the interview." The selected prompts are returned in JSON format and displayed on the terminal. The user can then use these prompts to efficiently conduct the actual interview.

[0416] Usage scenarios and benefits

[0417] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This results in significant time and effort savings, and a substantial improvement in work efficiency.

[0418] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, thereby significantly improving the quality and efficiency of work in various industries.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[0422] Step 2:

[0423] The terminal retrieves the user's selections and creates an AJAX request. The request data includes the selected industry and job description.

[0424] Step 3:

[0425] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[0426] Step 4:

[0427] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[0428] Step 5:

[0429] The server analyzes the request content to identify the industry and type of work. For example, it might extract information about the "healthcare industry" and "creating medical records" from the request data.

[0430] Step 6:

[0431] The server references the database based on the identified industry and business content. It creates an SQL query and searches the database for the corresponding prompt.

[0432] Step 7:

[0433] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[0434] Step 8:

[0435] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[0436] Step 9:

[0437] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[0438] Step 10:

[0439] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[0440] Step 11:

[0441] The terminal displays the analyzed prompt data in the user interface. Users can then review the prompts and use them for their work.

[0442] Step 12:

[0443] Users perform tasks using the displayed prompts. For example, when creating a medical record in the healthcare industry, they refer to the provided prompts to create the appropriate record.

[0444] (Example 1)

[0445] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0446] Traditional systems made it difficult for users to quickly obtain appropriate prompts to improve work efficiency. Furthermore, the consistency and quality of prompts were not guaranteed, leading to inconsistencies in the quality of work. Additionally, the lack of functionality to automatically generate custom prompts for different industries and tasks meant users had to handle them manually, resulting in significant time and effort.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for transmitting a request to an information processing device based on the industry and business content; means for identifying the target industry from the request content, referring to a data storage device to select a corresponding generating AI model; and means for generating an optimal prompt statement from the selected generating AI model and responding to the user. This enables the user to quickly and consistently obtain the optimal prompt to improve business efficiency.

[0449] A "user" is an individual or group that operates the system and makes selections regarding industry and business content.

[0450] "Industry" refers to a specific business field in which certain occupations or job roles exist.

[0451] "Job description" refers to the specific tasks or work that a user is required to perform within the industry to which they belong.

[0452] An "information processing device" refers to a device that has the function of receiving requests from users, analyzing them, and making necessary queries to data storage devices.

[0453] A "data storage device" is a storage means that stores necessary data such as prompt statements and generated AI models, and provides it to an information processing device in response to a request.

[0454] A "generative AI model" is an artificial intelligence algorithm that automatically generates prompt messages that are optimal for a specific industry or job function.

[0455] A "prompt message" is a document containing specific instructions or guidelines that users should refer to in order to perform their tasks efficiently.

[0456] JSON format is a lightweight data exchange format for structuring and representing data.

[0457] A "structured query language" is a query language used for database management and data retrieval, and primarily refers to SQL (Structured Query Language).

[0458] "Asynchronous JavaScript and XML" refers to a technology for asynchronously exchanging data between a server and a client, specifically AJAX (Asynchronous JavaScript and XML).

[0459] "Selection" is the process of choosing the generation AI model and prompt sentences that best suit the user's requirements from the data storage device.

[0460] This invention relates to a system that automatically generates and provides prompt messages suitable for the operations of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[0461] Overall system configuration

[0462] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via the terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, generates a prompt using an appropriate generative AI model, and sends it back to the terminal.

[0463] User actions

[0464] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[0465] Terminal operation

[0466] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an asynchronous JavaScript and XML (AJAX) request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt message to the user.

[0467] Server operation

[0468] When the server receives a request, it first analyzes the request content to identify the industry and business content. For example, it might obtain information about the "healthcare industry" and "creation of medical records" from the request. The server then refers to the data storage device to search for the corresponding generative AI model and uses this model to generate the optimal prompt message. This information is obtained using a Structured Query Language (SQL) query, the generated prompt message is formatted into Structured Data Format (JSON), and sent back to the terminal as an HTTP response.

[0469] Specific example

[0470] For example, if the user selects "Patient Interview" in the medical field, the server uses a generative AI model to generate a prompt message like the following:

[0471] "Methods for recording detailed patient symptoms":

[0472] 1. Basic patient information (name, age, gender)

[0473] 2. Chief complaint (current symptoms and their progression)

[0474] 3. Present illness (chronology and background of symptoms)

[0475] 4. Family history (health information related to family members)

[0476] 5. Medical history (past illnesses and surgeries)

[0477] 6. Current treatment methods (medications and treatments currently being used)

[0478] Examples of important questions to ask during a medical interview:

[0479] 1. How many days ago did the symptoms first appear?

[0480] 2. Are there any specific factors that cause symptoms to worsen or improve?

[0481] 3. Are there any other symptoms you are experiencing?

[0482] 4. How is this affecting your daily life?

[0483] Users can use these prompts to efficiently conduct the actual medical interview.

[0484] Usage scenarios and benefits

[0485] This system allows users to quickly obtain prompts optimized for their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from data storage devices using an AI model, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. This system has the potential to dramatically improve the quality and efficiency of work across various industries.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1: User selects industry and job content.

[0488] The user interacts with an interface on their device to select their industry and job duties. For example, if the user works in the medical industry, they might select "Create patient records." This selection then becomes the input for the next step.

[0489] Input: User's selected industry and job duties (e.g., healthcare industry, creating medical records)

[0490] Output: Selections sent to the terminal

[0491] Specific operation: The user opens a browser and uses a UI to select their industry and job description. Once the selection is confirmed, the input data is sent to the terminal.

[0492] Step 2: The terminal generates and sends the request.

[0493] The terminal retrieves the user's selections and sends them to the server as asynchronous JavaScript and XML (AJAX) requests. These requests include the user's industry and job description.

[0494] Input: User's selected industry and job description

[0495] Output: AJAX request sent to the server

[0496] Specific operation: The terminal uses JavaScript to generate an AJAX request containing the user's selections. This request is sent to the server using the HTTP POST method.

[0497] Step 3: Server receives and parses the request

[0498] The server receives the AJAX request sent from the terminal and parses the request content. It extracts the industry and business details and prepares to query the data storage device.

[0499] Input: AJAX request sent from the terminal

[0500] Output: Analyzed industries and business operations

[0501] Specific operation: After receiving a request, the server uses an analysis module to extract the industry and business content. For example, it might extract "healthcare industry" and "creation of medical records."

[0502] Step 4: Reference to data storage device and selection of generated AI model

[0503] Based on the analysis results, the server consults the data storage device and searches for the corresponding generation AI model. It then prepares to generate the optimal prompt sentence using this model.

[0504] Input: Analyzed industry and business content

[0505] Output: Selected generative AI models

[0506] Specific operation: The server generates an SQL query to search for generated AI models from the data storage device. For example, to retrieve models for "healthcare industry" and "medical record creation," it executes "SELECT FROM models WHERE industry='healthcare' AND task='medical record creation';".

[0507] Step 5: Generating the prompt message

[0508] The server uses the selected generation AI model to generate prompt messages that are optimal for the user's industry and business operations. These generated prompt messages serve as input for the next step.

[0509] Input: Selected generative AI model, analyzed industry and business content

[0510] Output: Generated prompt message

[0511] Specific operation: The server applies a generation AI model to generate prompts based on the analysis results (industry and business content). For example, it might generate a prompt for "detailed method of recording patient symptoms."

[0512] Step 6: Formatting the prompt and generating the response

[0513] The server formats the generated prompt message into a structured data format (JSON) and prepares it as an HTTP response. This response is then sent to the terminal.

[0514] Input: Generated prompt message

[0515] Output: HTTP response sent to the terminal

[0516] Specific operation: The server converts the generated prompt message into JSON format, sets the HTTP header to "Content-Type: application / json", and generates a response.

[0517] Step 7: Receiving and analyzing the response by the terminal

[0518] The terminal receives an HTTP response from the server and parses its contents. It extracts prompt text from the JSON format and prepares it to be displayed to the user.

[0519] Input: HTTP response from the server

[0520] Output: Parsed prompt message

[0521] Specific operation: After receiving the response, the terminal uses a JSON parsing library to extract the prompt text. This content will be displayed in the next step.

[0522] Step 8: Display a prompt message to the user.

[0523] The terminal displays the parsed prompt message to the user. The user can then use this prompt message to efficiently carry out their tasks.

[0524] Input: Parsed prompt message

[0525] Output: Prompt displayed to the user

[0526] Specific operation: The terminal displays the parsed prompt text in a designated area of ​​the user interface. For example, it will display "Detailed recording method for patient symptoms: 1. Basic patient information 2. Chief complaint..." in the browser's text area.

[0527] The above outlines the specific processing steps of this system.

[0528] (Application Example 1)

[0529] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0530] Logistics centers face complex procedures for efficiently and effectively performing tasks such as inventory management, picking, and optimizing delivery routes. This results in decreased operational efficiency and an increased risk of errors. Furthermore, insufficient standardization of work procedures makes it difficult for new employees to quickly learn their jobs. To address these problems and improve operational consistency and efficiency, there is a need for a system that automatically generates and provides optimal prompts for each task.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0532] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for sending a request to the server based on the industry and business content; means for identifying the target industry from the request content and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; means for automatically generating prompts corresponding to specific tasks such as inventory management, picking list creation, and delivery route optimization in order to improve the operational efficiency of the logistics center; and means for displaying the generated prompts on the user's smartphone. This makes it possible to perform operations at the logistics center efficiently, standardized, and with consistently high quality.

[0533] An "industry" refers to a group of companies or organizations that operate in a specific field or market as part of their business activities.

[0534] "Job description" refers to a list of specific tasks and duties performed within a particular industry.

[0535] A "request" refers to a request for information to be sent to the server based on user input.

[0536] A "server" is a computer system that performs specific calculations and data processing to provide services to a client (in this case, a terminal).

[0537] A "database" is a digital system that organizes and stores structured information so that it can be easily accessed, managed, and updated later.

[0538] A "prompt" is a phrase that provides guidance or instructions to a user when performing a specific task or operation.

[0539] "Inventory management" refers to operations in a logistics center that include receiving, storing, shipping, and tracking and managing inventory levels.

[0540] A "picking list" is a set of guidelines or lists used to select items from a warehouse based on an order.

[0541] "Delivery route optimization" is the process of calculating the most efficient route to minimize the time and cost of delivering goods.

[0542] A "smartphone" is a portable electronic device that has functions beyond those of a regular mobile phone, such as internet connectivity, application execution, and camera operation.

[0543] "JSON format" is a method of structurally representing data in text format, and it is an abbreviation for JavaScript Object Notation.

[0544] An "AJAX request" is an asynchronous communication method for retrieving data from a server without reloading a web page.

[0545] A "logistics center" is a facility that stores, manages, and ships goods, and constitutes part of the supply chain.

[0546] Overall system configuration

[0547] This invention is a system designed to improve user work efficiency and ensure consistency and quality in operations. The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for processing requests and managing the database, while the terminal receives input from the user and transmits it to the server. Users access the system using the terminal and select their industry and work content.

[0548] User actions

[0549] The user first uses an interface on their smartphone application to select their industry and job duties. For example, suppose the user works in a logistics center and selects "Create Picking List." Once the user confirms their selection, the device sends this information to the server as a request.

[0550] Terminal operation

[0551] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user. This process uses front-end technologies such as HTML, CSS, and JavaScript.

[0552] Server operation

[0553] When the server receives a request, it first parses the request to identify the industry and task. For example, it might obtain information about a "logistics center" and "creating a picking list" from the request. The server then consults a database to find the most suitable prompt for the relevant industry and task. The database contains prompts for various industries and tasks, which are retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response. This process uses Flask (a lightweight web framework for Python) and SQLite (a lightweight relational database).

[0554] Specific example

[0555] For example, if a user selects "Create Picking List" in the "Logistics Center" section, the server selects prompts from the database that include the "Optimal Picking Route" and a "Detailed List of Products." The selected prompts are returned in JSON format and displayed on the terminal.

[0556] Example of a prompt

[0557] Create a picking list:

[0558] 1. Please check the stock availability of each item.

[0559] 2. Set the optimal picking route as follows:

[0560] Aisle 5: Product A

[0561] Aisle 3: Product B

[0562] Aisle 7: Product C

[0563] 3. Once the list is complete, review it and proceed to the next step.

[0564] Usage scenarios and benefits

[0565] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency.

[0566] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0567] Step 1:

[0568] The user launches a smartphone application and selects an industry (e.g., "logistics center") and a task (e.g., "creating picking lists"). The input consists of the selected industry and task, which the terminal receives. In terms of specific actions, the user interacts with interfaces such as dropdown menus and checkboxes.

[0569] Step 2:

[0570] Based on the selected industry and job description, the terminal sends a request to the server in AJAX request format. The input is the user's selection information, and the output is the sending of the request to the server. The terminal uses JavaScript for asynchronous communication.

[0571] Step 3:

[0572] The server analyzes the received request and identifies the selected industry and business content. The input is the request data from the terminal, and the output is the analysis result, which is information about the industry and business content. The server uses Flask to analyze the request.

[0573] Step 4:

[0574] Based on the analysis results, the server uses SQL queries to retrieve the appropriate prompt from the database. The input is the industry and business content information from the analysis results, and the output is the prompt data retrieved from the database. The server queries the database (SQLite) using SQL.

[0575] Step 5:

[0576] The server formats the retrieved prompt data into JSON format and sends it back to the terminal as an HTTP response. The input is prompt data from the database, and the output is prompt data in JSON format. The server generates the JSON data using the Python standard library.

[0577] Step 6:

[0578] The terminal receives a response from the server and parses the prompt data. The input is prompt data in JSON format from the server, and the output is the prompt text as a result of the parsing. The terminal uses JavaScript to parse the JSON data.

[0579] Step 7:

[0580] The parsed prompt is displayed to the user. The input is the parsed prompt text, and the output is the prompt displayed to the user. The terminal visually presents the prompt to the user using HTML and CSS.

[0581] Step 8:

[0582] The user follows the prompts presented and performs specific tasks (e.g., tasks based on a picking list). The input is the displayed prompt text, and the output is the actual work performed. The user works while looking at their smartphone screen.

[0583] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0584] This invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes user emotions, and provides prompts accordingly. This system is designed to improve user work efficiency, ensure consistency and quality of operations, and provide flexible responses that respond to user emotions. Specific embodiments of this system are described below.

[0585] Overall system configuration

[0586] This system primarily consists of four main elements: "server," "terminal," "user," and "emotion engine." Users access the system via the terminal and select their industry and job content. During the selection process, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from its database based on the emotional state, and sends it back to the terminal.

[0587] User actions

[0588] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[0589] Terminal operation

[0590] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[0591] Server operation

[0592] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and task. For example, the request might reveal information such as "healthcare industry," "creating medical records," and "the user is stressed." The server then consults its database to find the most suitable prompt for the given industry, task, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response.

[0593] Specific example

[0594] For example, if a user selects "patient interview" in the medical field, and the emotion engine detects that the user is nervous, the server selects prompts from the database that include "a flow of interviews to help the patient relax" and "examples of questions in a gentle tone." The selected prompts are returned in JSON format and displayed on the terminal. Based on these prompts, the user can efficiently conduct the interview while easing their tension.

[0595] Usage scenarios and benefits

[0596] This system allows users to quickly obtain prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[0597] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, and to respond flexibly to emotions, thereby significantly improving the quality and efficiency of work in various industries.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[0601] Step 2:

[0602] The emotion engine analyzes the user's facial expressions, voice, and body movements to identify the user's current emotional state. For example, it can determine whether the user is feeling stressed or anxious.

[0603] Step 3:

[0604] The terminal retrieves the user's selections and identified emotional state, and creates an AJAX request. The request data includes the selected industry and job description, and the recognized emotional state.

[0605] Step 4:

[0606] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[0607] Step 5:

[0608] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[0609] Step 6:

[0610] The server analyzes the request content and emotional state to identify the industry and job content. For example, it extracts information such as "healthcare industry," "creating medical records," and "user is stressed" from the request data.

[0611] Step 7:

[0612] The server references the database based on the identified industry, job description, and emotional state. It then creates an SQL query to search the database for the corresponding prompt.

[0613] Step 8:

[0614] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[0615] Step 9:

[0616] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[0617] Step 10:

[0618] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[0619] Step 11:

[0620] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[0621] Step 12:

[0622] The terminal displays the analyzed prompt data on the user interface. For example, a "Create Medical Record" prompt that takes the user's stress level into consideration may be displayed.

[0623] Step 13:

[0624] Users perform tasks using the displayed prompts. For example, in the medical field, when creating a medical record, they refer to the provided prompts to create the appropriate record.

[0625] Step 14:

[0626] The system encourages users to follow on-screen prompts to relax and perform tasks efficiently. Tasks are adjusted according to the user's current emotional state, allowing them to work with reduced stress.

[0627] (Example 2)

[0628] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0629] Traditional prompt generation systems provided responses simply by the user selecting an industry and job content, without considering the user's emotional state. Therefore, depending on the user's emotional state, appropriate prompts could not be provided, leading to decreased work efficiency and quality. Furthermore, they lacked the flexibility to improve user comfort and satisfaction.

[0630] The identification processing performed by the identification 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 receiving the selection of industry and work content from the user; means for sending a request to the server based on the industry and work content; means for identifying the target industry from the request content and selecting the appropriate prompt by referring to the database; means for responding to the user with the selected prompt; and means for recognizing the user's emotions and providing prompts corresponding to those emotions. This makes it possible to provide flexible and appropriate prompts according to the user's emotional state, thereby improving work efficiency and work quality. It is also possible to improve the ease of work and satisfaction of the user.

[0631] "Industry" refers to the type of business field or industry to which the user belongs.

[0632] "Job description" refers to the specific tasks or types of work that the user performs within the industry.

[0633] A "request" is a request for information sent to the server based on the user's selected industry, job content, and emotional state.

[0634] A "server" is a central computer system that receives requests, parses them, retrieves prompts from a database, and provides responses.

[0635] A "database" is a system that stores prompts tailored to the user's industry, job responsibilities, and emotional state, and allows users to search for them as needed.

[0636] A "prompt" is a specific instruction or guideline that a user can refer to when performing a task.

[0637] "Selection" is the process of choosing the most suitable prompt from the database based on industry, job content, and emotional state.

[0638] A "response" is the act of providing the user with a selected prompt.

[0639] "Emotions" refer to the user's current psychological or emotional state.

[0640] "Emotional state" refers to a specific psychological or emotional state of the user as recognized by the emotion engine.

[0641] An "emotion engine" is a technical means of recognizing emotions by analyzing a user's facial expressions and voice data.

[0642] An "AJAX request" is a technology that uses asynchronous communication to send user selections to a server.

[0643] JSON format is a lightweight data exchange format for structuring data in text format.

[0644] "Analysis" is the process by which a server understands the content of a received request and extracts the data necessary to execute the appropriate process.

[0645] A "SQL query" is a set of instructions in a structured query language used to retrieve specific information from a database.

[0646] This invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes user emotions, and provides prompts accordingly. This system is designed not only to improve user work efficiency and ensure consistency and quality of operations, but also to provide flexible responses that respond to user emotions. The components of this system and their operation are described in detail below.

[0647] Overall system configuration

[0648] This system consists of five main elements: "server," "terminal," "user," "emotion engine," and "database."

[0649] User actions

[0650] The user first selects their industry and job description on the terminal interface. For example, the user might select "Medical Industry" and then "Creating Medical Records." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the terminal sends this information and their emotional state as a request to the server.

[0651] Terminal operation

[0652] After the user selects their industry and job responsibilities, the terminal's emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request includes information about the industry, job responsibilities, and recognized emotional state. A specific example of a request is as follows:

[0653] json

[0654] {

[0655] "Industry": "Healthcare industry",

[0656] "Job Description": "Creating medical records",

[0657] "Emotional state": "Nervous"

[0658] }

[0659] Upon receiving a response from the server, the terminal parses its contents and displays the generated prompt to the user.

[0660] Server operation

[0661] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and task. For example, the request might reveal information such as "healthcare industry," "creating medical records," and "user is stressed." The server then consults its database to find the most appropriate prompt for the given industry, task, and emotional state. This information is retrieved using SQL queries. For example:

[0662] SQL

[0663] SELECT prompt FROM prompts WHERE industry='medical industry' AND task='creating medical records' AND emotion='nervous';

[0664] The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response. An example of a returned prompt may include the following instructions:

[0665] "Please create the medical record following these steps: 1. Enter the patient's basic information. 2. Record the chief complaint. 3. Enter the examination results."

[0666] Specific example

[0667] For example, if a user selects "Patient Interview" in the medical field, and the emotion engine detects that the user is nervous, the server will select prompts from the database that include "a patient interview flow to help the patient relax" and "examples of questions in a gentle tone." The selected prompts will be displayed on the terminal in the following format:

[0668] "To help the patient relax, greet them with a smile and ask the following questions in a gentle tone."

[0669] This allows users to quickly receive prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Moreover, the introduction of an emotion engine enables more personalized support based on the user's state, which is expected to improve user satisfaction.

[0670] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0671] Step 1:

[0672] The user selects the industry and job description.

[0673] Input: Selected data for industry and job description

[0674] Specific operation: The user accesses the terminal interface and selects an industry (e.g., "healthcare industry") and a job function (e.g., "creating medical records"). Once the selection is confirmed, the data is saved for use in the next step.

[0675] Step 2:

[0676] The emotion engine recognizes the user's emotional state.

[0677] Input: User's facial expressions and voice data

[0678] Specific operation: The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. For example, based on data collected through the camera and microphone, it determines that the user is "nervous." The determination result is then sent to the next step.

[0679] Step 3:

[0680] The terminal generates a request and sends it to the server.

[0681] Input: Industry, job description, emotional state

[0682] Output: AJAX request to the server

[0683] Specific operation: The terminal generates a request containing information about the user's industry selection, job content selection, and perceived emotional state. The request is structured in JSON format using AJAX and sent to the server.

[0684] Step 4:

[0685] The server receives and parses the request.

[0686] Input: AJAX request

[0687] Output: Analysis results (industry, job content, emotional state)

[0688] Specific operation: When the server receives a request, it analyzes the request content and identifies the industry (e.g., "healthcare industry"), the task (e.g., "creating medical records"), and the emotional state (e.g., "stressed").

[0689] Step 5:

[0690] The server retrieves the corresponding prompt from the database.

[0691] Input: Analysis results

[0692] Output: Prompt data

[0693] Specific operation: The server looks up a database to find a prompt appropriate for the relevant industry, job content, and emotional state. For example, it generates an SQL query and searches the database.

[0694] Example SQL query: SELECT prompt FROM prompts WHERE industry='healthcare industry' AND task='create medical records' AND emotion='nervous';

[0695] Step 6:

[0696] The server formats the prompt in JSON format and sends it back to the terminal.

[0697] Input: Prompt data

[0698] Output: HTTP response (prompt in JSON format)

[0699] Specific operation: The server formats the prompt obtained as a search result into JSON format and sends it as an HTTP response.

[0700] Step 7:

[0701] The terminal receives and analyzes the response from the server.

[0702] Input: HTTP response (JSON format prompt)

[0703] Output: Analyzed prompt data

[0704] Specific operation: The terminal receives a response from the server, analyzes its contents, and extracts the necessary information.

[0705] Step 8:

[0706] The terminal displays a prompt to the user.

[0707] Input: Analyzed prompt data

[0708] Output: Displayed to the user (text-based prompt)

[0709] Specific operation: The terminal displays the analyzed prompt data in a format that is easy for the user to understand. For example, instructions such as "Please create the medical record following the steps below" will be displayed on the terminal screen.

[0710] In this way, this system can improve user work efficiency and satisfaction by consistently handling everything from industry and job content selection to emotional state recognition and the provision of appropriate prompts.

[0711] (Application Example 2)

[0712] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0713] Conventional prompt generation systems only have the function of providing appropriate prompts based on the user's industry and job content, and are unable to provide prompts that take into account the user's emotional state. As a result, even when a user is feeling stressed or anxious, only a uniform prompt is provided, which can lead to a decrease in work efficiency and quality. The present invention aims to improve the user's work efficiency and enhance the quality of work by providing a system that recognizes the user's emotional state in real time and automatically generates appropriate prompts accordingly.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0715] In this invention, the server includes means for receiving a selection of industry and work content from a user; means for sending a request to the server based on the industry, work content, and emotional state; means for identifying the target industry from the request content and emotional state, and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; and means including an emotion engine for recognizing the user's emotional state in real time. This enables flexible responses according to the user's emotional state, improving work efficiency and quality.

[0716] A "user" refers to a person who uses the system to select an industry and job content and receives prompts.

[0717] "Industry" refers to a category that indicates a specific economic field or occupational area.

[0718] "Job description" refers to the specific tasks and types of work that the user will be engaged in.

[0719] "Options" refers to the multiple possible choices presented to a user when selecting an industry and job.

[0720] "Emotional state" refers to the user's psychological and emotional state, including, for example, tension, anxiety, and relaxation.

[0721] An "emotion engine" refers to a software module that recognizes and analyzes a user's emotional state in real time.

[0722] A "prompt" refers to the text of specific instructions or guidance generated to support the user's work.

[0723] A "request" refers to the information a user submits regarding their industry, job responsibilities, and emotional state.

[0724] A "server" is a central computer system within a system that receives requests, analyzes them, retrieves the results from a database, and sends them back.

[0725] A "database" refers to a data storage system used to store and manage prompt information based on industry and business content.

[0726] A "prompt generator" refers to a software module that generates appropriate prompts based on the user's emotional state.

[0727] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text format for exchanging data.

[0728] An "AJAX request" refers to a technology that uses asynchronous communication to send requests from users to a server.

[0729] Overall system configuration

[0730] The system that realizes this invention mainly consists of four main elements: a server, a terminal, a user, and an emotion engine. The user accesses the system via the terminal and selects an industry and job content. When selecting, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from the database according to the emotional state, and sends it back to the terminal.

[0731] User actions

[0732] The user first uses an interface on their device to select their industry and job duties. For example, suppose a user works in the security industry and selects "nighttime building patrols." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[0733] Terminal operation

[0734] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[0735] Server operation

[0736] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and job content. For example, the request might reveal information such as "security industry," "nighttime building patrol," and "user is anxious." The server then consults its database to find the most appropriate prompt for the relevant industry, job content, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[0737] Specific example

[0738] For example, if a user selects "nighttime building patrol" in the security industry, and the emotion engine recognizes that the user is anxious, the server will select a prompt from the database such as, "An anomaly has been detected in the stairwell at your current location. Please first assess the situation and thoroughly check for any danger. If you are feeling anxious, take a deep breath and calm down." The selected prompt will be returned in JSON format and displayed on the device. Based on this prompt, the user can proceed with the patrol safely while alleviating their anxiety.

[0739] Usage scenarios and benefits

[0740] This system allows users to quickly obtain optimal and emotionally appropriate prompts tailored to their work content, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The user selects their industry and job content using a terminal. The terminal retrieves the user's selections, while simultaneously an emotion engine analyzes the user's facial expressions and voice data to recognize their emotional state. Inputs include the user's selected industry and job content, as well as their facial expressions and voice data. Outputs include the selected industry and job content information, and the recognized emotional state.

[0744] Step 2:

[0745] The terminal sends information about its industry, job duties, and emotional state to the server as a request. Specifically, this information is sent to the server in the form of an AJAX request. The inputs are the user's industry, job duties, and emotional state. The output is the request sent to the server.

[0746] Step 3:

[0747] The server analyzes received requests to identify the industry, job content, and emotional state. Specifically, it parses the request content and extracts information on the industry, job content, and emotional state. The input is the request sent from the terminal. The output is the analyzed information on the industry, job content, and emotional state.

[0748] Step 4:

[0749] The server searches the database based on the analysis results and selects the most appropriate prompt for the industry, job content, and emotional state. Specifically, it uses SQL queries to retrieve the corresponding prompt from the database. The input is the analyzed information on the industry, job content, and emotional state. The output is the selected prompt.

[0750] Step 5:

[0751] The server formats the selected prompt into JSON format and sends it back to the terminal as an HTTP response. Specifically, it converts the received prompt into JSON format and sends it as an HTTP response. The input is the selected prompt. The output is an HTTP response containing the prompt in JSON format.

[0752] Step 6:

[0753] The terminal parses the prompt received from the server and displays it to the user. Specifically, it parses the received JSON-formatted prompt and displays it in the user interface. The input is a JSON-formatted prompt sent from the server. The output is the prompt that will be displayed to the user.

[0754] Step 7:

[0755] The user performs tasks based on prompts displayed on the terminal. Specifically, they follow the displayed prompts and select the industry and task content again as needed to receive further instructions. The input is the prompts displayed on the terminal. The output is the efficient execution of tasks.

[0756] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0757] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0758] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0759] [Third Embodiment]

[0760] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0761] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0762] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0763] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0764] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0765] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0766] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0767] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0768] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0769] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0770] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0771] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0772] This invention relates to a system that automatically generates and provides users with prompts tailored to the specific tasks of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[0773] Overall system configuration

[0774] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via a terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, selects an appropriate prompt from its database, and sends it back to the terminal.

[0775] User actions

[0776] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[0777] Terminal operation

[0778] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user.

[0779] Server operation

[0780] When the server receives a request, it first parses the request content to identify the industry and the nature of the work. For example, the request might provide information about the "healthcare industry" and "creating medical records." The server then consults its database to find the most suitable prompt for the relevant industry and work content. This information is obtained using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[0781] Specific example

[0782] For example, if a user selects "patient interview" in the medical field, the server selects prompts from the database that include "detailed methods for recording patient symptoms" and "examples of important questions to ask during the interview." The selected prompts are returned in JSON format and displayed on the terminal. The user can then use these prompts to efficiently conduct the actual interview.

[0783] Usage scenarios and benefits

[0784] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This results in significant time and effort savings, and a substantial improvement in work efficiency.

[0785] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, thereby significantly improving the quality and efficiency of work in various industries.

[0786] The following describes the processing flow.

[0787] Step 1:

[0788] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[0789] Step 2:

[0790] The terminal retrieves the user's selections and creates an AJAX request. The request data includes the selected industry and job description.

[0791] Step 3:

[0792] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[0793] Step 4:

[0794] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[0795] Step 5:

[0796] The server analyzes the request content to identify the industry and type of work. For example, it might extract information about the "healthcare industry" and "creating medical records" from the request data.

[0797] Step 6:

[0798] The server references the database based on the identified industry and business content. It creates an SQL query and searches the database for the corresponding prompt.

[0799] Step 7:

[0800] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[0801] Step 8:

[0802] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[0803] Step 9:

[0804] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[0805] Step 10:

[0806] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[0807] Step 11:

[0808] The terminal displays the analyzed prompt data in the user interface. Users can then review the prompts and use them for their work.

[0809] Step 12:

[0810] Users perform tasks using the displayed prompts. For example, when creating a medical record in the healthcare industry, they refer to the provided prompts to create the appropriate record.

[0811] (Example 1)

[0812] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0813] Traditional systems made it difficult for users to quickly obtain appropriate prompts to improve work efficiency. Furthermore, the consistency and quality of prompts were not guaranteed, leading to inconsistencies in the quality of work. Additionally, the lack of functionality to automatically generate custom prompts for different industries and tasks meant users had to handle them manually, resulting in significant time and effort.

[0814] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0815] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for transmitting a request to an information processing device based on the industry and business content; means for identifying the target industry from the request content, referring to a data storage device to select a corresponding generating AI model; and means for generating an optimal prompt statement from the selected generating AI model and responding to the user. This enables the user to quickly and consistently obtain the optimal prompt to improve business efficiency.

[0816] A "user" is an individual or group that operates the system and makes selections regarding industry and business content.

[0817] "Industry" refers to a specific business field in which certain occupations or job roles exist.

[0818] "Job description" refers to the specific tasks or work that a user is required to perform within the industry to which they belong.

[0819] An "information processing device" refers to a device that has the function of receiving requests from users, analyzing them, and making necessary queries to data storage devices.

[0820] A "data storage device" is a storage means that stores necessary data such as prompt statements and generated AI models, and provides it to an information processing device in response to a request.

[0821] A "generative AI model" is an artificial intelligence algorithm that automatically generates prompt messages that are optimal for a specific industry or job function.

[0822] A "prompt message" is a document containing specific instructions or guidelines that users should refer to in order to perform their tasks efficiently.

[0823] JSON format is a lightweight data exchange format for structuring and representing data.

[0824] A "structured query language" is a query language used for database management and data retrieval, and primarily refers to SQL (Structured Query Language).

[0825] "Asynchronous JavaScript and XML" refers to a technology for asynchronously exchanging data between a server and a client, specifically AJAX (Asynchronous JavaScript and XML).

[0826] "Selection" is the process of choosing the generation AI model and prompt sentences that best suit the user's requirements from the data storage device.

[0827] This invention relates to a system that automatically generates and provides prompt messages suitable for the operations of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[0828] Overall system configuration

[0829] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via the terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, generates a prompt using an appropriate generative AI model, and sends it back to the terminal.

[0830] User actions

[0831] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[0832] Terminal operation

[0833] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an asynchronous JavaScript and XML (AJAX) request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt message to the user.

[0834] Server operation

[0835] When the server receives a request, it first analyzes the request content to identify the industry and business content. For example, it might obtain information about the "healthcare industry" and "creation of medical records" from the request. The server then refers to the data storage device to search for the corresponding generative AI model and uses this model to generate the optimal prompt message. This information is obtained using a Structured Query Language (SQL) query, the generated prompt message is formatted into Structured Data Format (JSON), and sent back to the terminal as an HTTP response.

[0836] Specific example

[0837] For example, if the user selects "Patient Interview" in the medical field, the server uses a generative AI model to generate a prompt message like the following:

[0838] "Methods for recording detailed patient symptoms":

[0839] 1. Basic patient information (name, age, gender)

[0840] 2. Chief complaint (current symptoms and their progression)

[0841] 3. Present illness (chronology and background of symptoms)

[0842] 4. Family history (health information related to family members)

[0843] 5. Medical history (past illnesses and surgeries)

[0844] 6. Current treatment methods (medications and treatments currently being used)

[0845] Examples of important questions to ask during a medical interview:

[0846] 1. How many days ago did the symptoms first appear?

[0847] 2. Are there any specific factors that cause symptoms to worsen or improve?

[0848] 3. Are there any other symptoms you are experiencing?

[0849] 4. How is this affecting your daily life?

[0850] Users can use these prompts to efficiently conduct the actual medical interview.

[0851] Usage scenarios and benefits

[0852] This system allows users to quickly obtain prompts optimized for their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from data storage devices using an AI model, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. This system has the potential to dramatically improve the quality and efficiency of work across various industries.

[0853] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0854] Step 1: User selects industry and job content.

[0855] The user interacts with an interface on their device to select their industry and job duties. For example, if the user works in the medical industry, they might select "Create patient records." This selection then becomes the input for the next step.

[0856] Input: User's selected industry and job duties (e.g., healthcare industry, creating medical records)

[0857] Output: Selections sent to the terminal

[0858] Specific operation: The user opens a browser and uses a UI to select their industry and job description. Once the selection is confirmed, the input data is sent to the terminal.

[0859] Step 2: The terminal generates and sends the request.

[0860] The terminal retrieves the user's selections and sends them to the server as asynchronous JavaScript and XML (AJAX) requests. These requests include the user's industry and job description.

[0861] Input: User's selected industry and job description

[0862] Output: AJAX request sent to the server

[0863] Specific operation: The terminal uses JavaScript to generate an AJAX request containing the user's selections. This request is sent to the server using the HTTP POST method.

[0864] Step 3: Server receives and parses the request

[0865] The server receives the AJAX request sent from the terminal and parses the request content. It extracts the industry and business details and prepares to query the data storage device.

[0866] Input: AJAX request sent from the terminal

[0867] Output: Analyzed industries and business operations

[0868] Specific operation: After receiving a request, the server uses an analysis module to extract the industry and business content. For example, it might extract "healthcare industry" and "creation of medical records."

[0869] Step 4: Reference to data storage device and selection of generated AI model

[0870] Based on the analysis results, the server consults the data storage device and searches for the corresponding generation AI model. It then prepares to generate the optimal prompt sentence using this model.

[0871] Input: Analyzed industry and business content

[0872] Output: Selected generative AI models

[0873] Specific operation: The server generates an SQL query to search for generated AI models from the data storage device. For example, to retrieve models for "healthcare industry" and "medical record creation," it executes "SELECT FROM models WHERE industry='healthcare' AND task='medical record creation';".

[0874] Step 5: Generating the prompt message

[0875] The server uses the selected generation AI model to generate prompt messages that are optimal for the user's industry and business operations. These generated prompt messages serve as input for the next step.

[0876] Input: Selected generative AI model, analyzed industry and business content

[0877] Output: Generated prompt message

[0878] Specific operation: The server applies a generation AI model to generate prompts based on the analysis results (industry and business content). For example, it might generate a prompt for "detailed method of recording patient symptoms."

[0879] Step 6: Formatting the prompt and generating the response

[0880] The server formats the generated prompt message into a structured data format (JSON) and prepares it as an HTTP response. This response is then sent to the terminal.

[0881] Input: Generated prompt message

[0882] Output: HTTP response sent to the terminal

[0883] Specific operation: The server converts the generated prompt message into JSON format, sets the HTTP header to "Content-Type: application / json", and generates a response.

[0884] Step 7: Receiving and analyzing the response by the terminal

[0885] The terminal receives an HTTP response from the server and parses its contents. It extracts prompt text from the JSON format and prepares it to be displayed to the user.

[0886] Input: HTTP response from the server

[0887] Output: Parsed prompt message

[0888] Specific operation: After receiving the response, the terminal uses a JSON parsing library to extract the prompt text. This content will be displayed in the next step.

[0889] Step 8: Display a prompt message to the user.

[0890] The terminal displays the parsed prompt message to the user. The user can then use this prompt message to efficiently carry out their tasks.

[0891] Input: Parsed prompt message

[0892] Output: Prompt displayed to the user

[0893] Specific operation: The terminal displays the parsed prompt text in a designated area of ​​the user interface. For example, it will display "Detailed recording method for patient symptoms: 1. Basic patient information 2. Chief complaint..." in the browser's text area.

[0894] The above outlines the specific processing steps of this system.

[0895] (Application Example 1)

[0896] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0897] Logistics centers face complex procedures for efficiently and effectively performing tasks such as inventory management, picking, and optimizing delivery routes. This results in decreased operational efficiency and an increased risk of errors. Furthermore, insufficient standardization of work procedures makes it difficult for new employees to quickly learn their jobs. To address these problems and improve operational consistency and efficiency, there is a need for a system that automatically generates and provides optimal prompts for each task.

[0898] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0899] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for sending a request to the server based on the industry and business content; means for identifying the target industry from the request content and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; means for automatically generating prompts corresponding to specific tasks such as inventory management, picking list creation, and delivery route optimization in order to improve the operational efficiency of the logistics center; and means for displaying the generated prompts on the user's smartphone. This makes it possible to perform operations at the logistics center efficiently, standardized, and with consistently high quality.

[0900] An "industry" refers to a group of companies or organizations that operate in a specific field or market as part of their business activities.

[0901] "Job description" refers to a list of specific tasks and duties performed within a particular industry.

[0902] A "request" refers to a request for information to be sent to the server based on user input.

[0903] A "server" is a computer system that performs specific calculations and data processing to provide services to a client (in this case, a terminal).

[0904] A "database" is a digital system that organizes and stores structured information so that it can be easily accessed, managed, and updated later.

[0905] A "prompt" is a phrase that provides guidance or instructions to a user when performing a specific task or operation.

[0906] "Inventory management" refers to operations in a logistics center that include receiving, storing, shipping, and tracking and managing inventory levels.

[0907] A "picking list" is a set of guidelines or lists used to select items from a warehouse based on an order.

[0908] "Delivery route optimization" is the process of calculating the most efficient route to minimize the time and cost of delivering goods.

[0909] A "smartphone" is a portable electronic device that has functions beyond those of a regular mobile phone, such as internet connectivity, application execution, and camera operation.

[0910] "JSON format" is a method of structurally representing data in text format, and it is an abbreviation for JavaScript Object Notation.

[0911] An "AJAX request" is an asynchronous communication method for retrieving data from a server without reloading a web page.

[0912] A "logistics center" is a facility that stores, manages, and ships goods, and constitutes part of the supply chain.

[0913] Overall system configuration

[0914] This invention is a system designed to improve user work efficiency and ensure consistency and quality in operations. The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for processing requests and managing the database, while the terminal receives input from the user and transmits it to the server. Users access the system using the terminal and select their industry and work content.

[0915] User actions

[0916] The user first uses an interface on their smartphone application to select their industry and job duties. For example, suppose the user works in a logistics center and selects "Create Picking List." Once the user confirms their selection, the device sends this information to the server as a request.

[0917] Terminal operation

[0918] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user. This process uses front-end technologies such as HTML, CSS, and JavaScript.

[0919] Server operation

[0920] When the server receives a request, it first parses the request to identify the industry and task. For example, it might obtain information about a "logistics center" and "creating a picking list" from the request. The server then consults a database to find the most suitable prompt for the relevant industry and task. The database contains prompts for various industries and tasks, which are retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response. This process uses Flask (a lightweight web framework for Python) and SQLite (a lightweight relational database).

[0921] Specific example

[0922] For example, if a user selects "Create Picking List" in the "Logistics Center" section, the server selects prompts from the database that include the "Optimal Picking Route" and a "Detailed List of Products." The selected prompts are returned in JSON format and displayed on the terminal.

[0923] Example of a prompt

[0924] Create a picking list:

[0925] 1. Please check the stock availability of each item.

[0926] 2. Set the optimal picking route as follows:

[0927] Aisle 5: Product A

[0928] Aisle 3: Product B

[0929] Aisle 7: Product C

[0930] 3. Once the list is complete, review it and proceed to the next step.

[0931] Usage scenarios and benefits

[0932] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency.

[0933] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0934] Step 1:

[0935] The user launches a smartphone application and selects an industry (e.g., "logistics center") and a task (e.g., "creating picking lists"). The input consists of the selected industry and task, which the terminal receives. In terms of specific actions, the user interacts with interfaces such as dropdown menus and checkboxes.

[0936] Step 2:

[0937] Based on the selected industry and job description, the terminal sends a request to the server in AJAX request format. The input is the user's selection information, and the output is the sending of the request to the server. The terminal uses JavaScript for asynchronous communication.

[0938] Step 3:

[0939] The server analyzes the received request and identifies the selected industry and business content. The input is the request data from the terminal, and the output is the analysis result, which is information about the industry and business content. The server uses Flask to analyze the request.

[0940] Step 4:

[0941] Based on the analysis results, the server uses SQL queries to retrieve the appropriate prompt from the database. The input is the industry and business content information from the analysis results, and the output is the prompt data retrieved from the database. The server queries the database (SQLite) using SQL.

[0942] Step 5:

[0943] The server formats the retrieved prompt data into JSON format and sends it back to the terminal as an HTTP response. The input is prompt data from the database, and the output is prompt data in JSON format. The server generates the JSON data using the Python standard library.

[0944] Step 6:

[0945] The terminal receives a response from the server and parses the prompt data. The input is prompt data in JSON format from the server, and the output is the prompt text as a result of the parsing. The terminal uses JavaScript to parse the JSON data.

[0946] Step 7:

[0947] The parsed prompt is displayed to the user. The input is the parsed prompt text, and the output is the prompt displayed to the user. The terminal visually presents the prompt to the user using HTML and CSS.

[0948] Step 8:

[0949] The user follows the prompts presented and performs specific tasks (e.g., tasks based on a picking list). The input is the displayed prompt text, and the output is the actual work performed. The user works while looking at their smartphone screen.

[0950] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0951] This invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes user emotions, and provides prompts accordingly. This system is designed to improve user work efficiency, ensure consistency and quality of operations, and provide flexible responses that respond to user emotions. Specific embodiments of this system are described below.

[0952] Overall system configuration

[0953] This system primarily consists of four main elements: "server," "terminal," "user," and "emotion engine." Users access the system via the terminal and select their industry and job content. During the selection process, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from its database based on the emotional state, and sends it back to the terminal.

[0954] User actions

[0955] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[0956] Terminal operation

[0957] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[0958] Server operation

[0959] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and task. For example, the request might reveal information such as "healthcare industry," "creating medical records," and "the user is stressed." The server then consults its database to find the most suitable prompt for the given industry, task, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response.

[0960] Specific example

[0961] For example, if a user selects "patient interview" in the medical field, and the emotion engine detects that the user is nervous, the server selects prompts from the database that include "a flow of interviews to help the patient relax" and "examples of questions in a gentle tone." The selected prompts are returned in JSON format and displayed on the terminal. Based on these prompts, the user can efficiently conduct the interview while easing their tension.

[0962] Usage scenarios and benefits

[0963] This system allows users to quickly obtain prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[0964] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, and to respond flexibly to emotions, thereby significantly improving the quality and efficiency of work in various industries.

[0965] The following describes the processing flow.

[0966] Step 1:

[0967] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[0968] Step 2:

[0969] The emotion engine analyzes the user's facial expressions, voice, and body movements to identify the user's current emotional state. For example, it can determine whether the user is feeling stressed or anxious.

[0970] Step 3:

[0971] The terminal retrieves the user's selections and identified emotional state, and creates an AJAX request. The request data includes the selected industry and job description, and the recognized emotional state.

[0972] Step 4:

[0973] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[0974] Step 5:

[0975] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[0976] Step 6:

[0977] The server analyzes the request content and emotional state to identify the industry and job content. For example, it extracts information such as "healthcare industry," "creating medical records," and "user is stressed" from the request data.

[0978] Step 7:

[0979] The server references the database based on the identified industry, job description, and emotional state. It then creates an SQL query to search the database for the corresponding prompt.

[0980] Step 8:

[0981] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[0982] Step 9:

[0983] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[0984] Step 10:

[0985] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[0986] Step 11:

[0987] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[0988] Step 12:

[0989] The terminal displays the analyzed prompt data on the user interface. For example, a "Create Medical Record" prompt that takes the user's stress level into consideration may be displayed.

[0990] Step 13:

[0991] Users perform tasks using the displayed prompts. For example, in the medical field, when creating a medical record, they refer to the provided prompts to create the appropriate record.

[0992] Step 14:

[0993] The system encourages users to follow on-screen prompts to relax and perform tasks efficiently. Tasks are adjusted according to the user's current emotional state, allowing them to work with reduced stress.

[0994] (Example 2)

[0995] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0996] Traditional prompt generation systems provided responses simply by the user selecting an industry and job content, without considering the user's emotional state. Therefore, depending on the user's emotional state, appropriate prompts could not be provided, leading to decreased work efficiency and quality. Furthermore, they lacked the flexibility to improve user comfort and satisfaction.

[0997] The identification processing performed by the identification 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 receiving the selection of industry and work content from the user; means for sending a request to the server based on the industry and work content; means for identifying the target industry from the request content and selecting the appropriate prompt by referring to the database; means for responding to the user with the selected prompt; and means for recognizing the user's emotions and providing prompts corresponding to those emotions. This makes it possible to provide flexible and appropriate prompts according to the user's emotional state, thereby improving work efficiency and work quality. It is also possible to improve the ease of work and satisfaction of the user.

[0998] "Industry" refers to the type of business field or industry to which the user belongs.

[0999] "Job description" refers to the specific tasks or types of work that the user performs within the industry.

[1000] A "request" is a request for information sent to the server based on the user's selected industry, job content, and emotional state.

[1001] A "server" is a central computer system that receives requests, parses them, retrieves prompts from a database, and provides responses.

[1002] A "database" is a system that stores prompts tailored to the user's industry, job responsibilities, and emotional state, and allows users to search for them as needed.

[1003] A "prompt" is a specific instruction or guideline that a user can refer to when performing a task.

[1004] "Selection" is the process of choosing the most suitable prompt from the database based on industry, job content, and emotional state.

[1005] A "response" is the act of providing the user with a selected prompt.

[1006] "Emotions" refer to the user's current psychological or emotional state.

[1007] "Emotional state" refers to a specific psychological or emotional state of the user as recognized by the emotion engine.

[1008] An "emotion engine" is a technical means of recognizing emotions by analyzing a user's facial expressions and voice data.

[1009] An "AJAX request" is a technology that uses asynchronous communication to send user selections to a server.

[1010] JSON format is a lightweight data exchange format for structuring data in text format.

[1011] "Analysis" is the process by which a server understands the content of a received request and extracts the data necessary to execute the appropriate process.

[1012] A "SQL query" is a set of instructions in a structured query language used to retrieve specific information from a database.

[1013] This invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes user emotions, and provides prompts accordingly. This system is designed not only to improve user work efficiency and ensure consistency and quality of operations, but also to provide flexible responses that respond to user emotions. The components of this system and their operation are described in detail below.

[1014] Overall system configuration

[1015] This system consists of five main elements: "server," "terminal," "user," "emotion engine," and "database."

[1016] User actions

[1017] The user first selects their industry and job description on the terminal interface. For example, the user might select "Medical Industry" and then "Creating Medical Records." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the terminal sends this information and their emotional state as a request to the server.

[1018] Terminal operation

[1019] After the user selects their industry and job responsibilities, the terminal's emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request includes information about the industry, job responsibilities, and recognized emotional state. A specific example of a request is as follows:

[1020] json

[1021] {

[1022] "Industry": "Healthcare industry",

[1023] "Job Description": "Creating medical records",

[1024] "Emotional state": "Nervous"

[1025] }

[1026] Upon receiving a response from the server, the terminal parses its contents and displays the generated prompt to the user.

[1027] Server operation

[1028] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and task. For example, the request might reveal information such as "healthcare industry," "creating medical records," and "user is stressed." The server then consults its database to find the most appropriate prompt for the given industry, task, and emotional state. This information is retrieved using SQL queries. For example:

[1029] SQL

[1030] SELECT prompt FROM prompts WHERE industry='medical industry' AND task='creating medical records' AND emotion='nervous';

[1031] The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response. An example of a returned prompt may include the following instructions:

[1032] "Please create the medical record following these steps: 1. Enter the patient's basic information. 2. Record the chief complaint. 3. Enter the examination results."

[1033] Specific example

[1034] For example, if a user selects "Patient Interview" in the medical field, and the emotion engine detects that the user is nervous, the server will select prompts from the database that include "a patient interview flow to help the patient relax" and "examples of questions in a gentle tone." The selected prompts will be displayed on the terminal in the following format:

[1035] "To help the patient relax, greet them with a smile and ask the following questions in a gentle tone."

[1036] This allows users to quickly receive prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Moreover, the introduction of an emotion engine enables more personalized support based on the user's state, which is expected to improve user satisfaction.

[1037] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1038] Step 1:

[1039] The user selects the industry and job description.

[1040] Input: Selected data for industry and job description

[1041] Specific operation: The user accesses the terminal interface and selects an industry (e.g., "healthcare industry") and a job function (e.g., "creating medical records"). Once the selection is confirmed, the data is saved for use in the next step.

[1042] Step 2:

[1043] The emotion engine recognizes the user's emotional state.

[1044] Input: User's facial expressions and voice data

[1045] Specific operation: The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. For example, based on data collected through the camera and microphone, it determines that the user is "nervous." The determination result is then sent to the next step.

[1046] Step 3:

[1047] The terminal generates a request and sends it to the server.

[1048] Input: Industry, job description, emotional state

[1049] Output: AJAX request to the server

[1050] Specific operation: The terminal generates a request containing information about the user's industry selection, job content selection, and perceived emotional state. The request is structured in JSON format using AJAX and sent to the server.

[1051] Step 4:

[1052] The server receives and parses the request.

[1053] Input: AJAX request

[1054] Output: Analysis results (industry, job content, emotional state)

[1055] Specific operation: When the server receives a request, it analyzes the request content and identifies the industry (e.g., "healthcare industry"), the task (e.g., "creating medical records"), and the emotional state (e.g., "stressed").

[1056] Step 5:

[1057] The server retrieves the corresponding prompt from the database.

[1058] Input: Analysis results

[1059] Output: Prompt data

[1060] Specific operation: The server looks up a database to find a prompt appropriate for the relevant industry, job content, and emotional state. For example, it generates an SQL query and searches the database.

[1061] Example SQL query: SELECT prompt FROM prompts WHERE industry='healthcare industry' AND task='create medical records' AND emotion='nervous';

[1062] Step 6:

[1063] The server formats the prompt in JSON format and sends it back to the terminal.

[1064] Input: Prompt data

[1065] Output: HTTP response (prompt in JSON format)

[1066] Specific operation: The server formats the prompt obtained as a search result into JSON format and sends it as an HTTP response.

[1067] Step 7:

[1068] The terminal receives and analyzes the response from the server.

[1069] Input: HTTP response (JSON format prompt)

[1070] Output: Analyzed prompt data

[1071] Specific operation: The terminal receives a response from the server, analyzes its contents, and extracts the necessary information.

[1072] Step 8:

[1073] The terminal displays a prompt to the user.

[1074] Input: Analyzed prompt data

[1075] Output: Displayed to the user (text-based prompt)

[1076] Specific operation: The terminal displays the analyzed prompt data in a format that is easy for the user to understand. For example, instructions such as "Please create the medical record following the steps below" will be displayed on the terminal screen.

[1077] In this way, this system can improve user work efficiency and satisfaction by consistently handling everything from industry and job content selection to emotional state recognition and the provision of appropriate prompts.

[1078] (Application Example 2)

[1079] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1080] Conventional prompt generation systems only have the function of providing appropriate prompts based on the user's industry and job content, and are unable to provide prompts that take into account the user's emotional state. As a result, even when a user is feeling stressed or anxious, only a uniform prompt is provided, which can lead to a decrease in work efficiency and quality. The present invention aims to improve the user's work efficiency and enhance the quality of work by providing a system that recognizes the user's emotional state in real time and automatically generates appropriate prompts accordingly.

[1081] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1082] In this invention, the server includes means for receiving a selection of industry and work content from a user; means for sending a request to the server based on the industry, work content, and emotional state; means for identifying the target industry from the request content and emotional state, and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; and means including an emotion engine for recognizing the user's emotional state in real time. This enables flexible responses according to the user's emotional state, improving work efficiency and quality.

[1083] A "user" refers to a person who uses the system to select an industry and job content and receives prompts.

[1084] "Industry" refers to a category that indicates a specific economic field or occupational area.

[1085] "Job description" refers to the specific tasks and types of work that the user will be engaged in.

[1086] "Options" refers to the multiple possible choices presented to a user when selecting an industry and job.

[1087] "Emotional state" refers to the user's psychological and emotional state, including, for example, tension, anxiety, and relaxation.

[1088] An "emotion engine" refers to a software module that recognizes and analyzes a user's emotional state in real time.

[1089] A "prompt" refers to the text of specific instructions or guidance generated to support the user's work.

[1090] A "request" refers to the information a user submits regarding their industry, job responsibilities, and emotional state.

[1091] A "server" is a central computer system within a system that receives requests, analyzes them, retrieves the results from a database, and sends them back.

[1092] A "database" refers to a data storage system used to store and manage prompt information based on industry and business content.

[1093] A "prompt generator" refers to a software module that generates appropriate prompts based on the user's emotional state.

[1094] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text format for exchanging data.

[1095] An "AJAX request" refers to a technology that uses asynchronous communication to send requests from users to a server.

[1096] Overall system configuration

[1097] The system that realizes this invention mainly consists of four main elements: a server, a terminal, a user, and an emotion engine. The user accesses the system via the terminal and selects an industry and job content. When selecting, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from the database according to the emotional state, and sends it back to the terminal.

[1098] User actions

[1099] The user first uses an interface on their device to select their industry and job duties. For example, suppose a user works in the security industry and selects "nighttime building patrols." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[1100] Terminal operation

[1101] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[1102] Server operation

[1103] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and job content. For example, the request might reveal information such as "security industry," "nighttime building patrol," and "user is anxious." The server then consults its database to find the most appropriate prompt for the relevant industry, job content, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[1104] Specific example

[1105] For example, if a user selects "nighttime building patrol" in the security industry, and the emotion engine recognizes that the user is anxious, the server will select a prompt from the database such as, "An anomaly has been detected in the stairwell at your current location. Please first assess the situation and thoroughly check for any danger. If you are feeling anxious, take a deep breath and calm down." The selected prompt will be returned in JSON format and displayed on the device. Based on this prompt, the user can proceed with the patrol safely while alleviating their anxiety.

[1106] Usage scenarios and benefits

[1107] This system allows users to quickly obtain optimal and emotionally appropriate prompts tailored to their work content, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[1108] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1109] Step 1:

[1110] The user selects their industry and job content using a terminal. The terminal retrieves the user's selections, while simultaneously an emotion engine analyzes the user's facial expressions and voice data to recognize their emotional state. Inputs include the user's selected industry and job content, as well as their facial expressions and voice data. Outputs include the selected industry and job content information, and the recognized emotional state.

[1111] Step 2:

[1112] The terminal sends information about its industry, job duties, and emotional state to the server as a request. Specifically, this information is sent to the server in the form of an AJAX request. The inputs are the user's industry, job duties, and emotional state. The output is the request sent to the server.

[1113] Step 3:

[1114] The server analyzes received requests to identify the industry, job content, and emotional state. Specifically, it parses the request content and extracts information on the industry, job content, and emotional state. The input is the request sent from the terminal. The output is the analyzed information on the industry, job content, and emotional state.

[1115] Step 4:

[1116] The server searches the database based on the analysis results and selects the most appropriate prompt for the industry, job content, and emotional state. Specifically, it uses SQL queries to retrieve the corresponding prompt from the database. The input is the analyzed information on the industry, job content, and emotional state. The output is the selected prompt.

[1117] Step 5:

[1118] The server formats the selected prompt into JSON format and sends it back to the terminal as an HTTP response. Specifically, it converts the received prompt into JSON format and sends it as an HTTP response. The input is the selected prompt. The output is an HTTP response containing the prompt in JSON format.

[1119] Step 6:

[1120] The terminal parses the prompt received from the server and displays it to the user. Specifically, it parses the received JSON-formatted prompt and displays it in the user interface. The input is a JSON-formatted prompt sent from the server. The output is the prompt that will be displayed to the user.

[1121] Step 7:

[1122] The user performs tasks based on prompts displayed on the terminal. Specifically, they follow the displayed prompts and select the industry and task content again as needed to receive further instructions. The input is the prompts displayed on the terminal. The output is the efficient execution of tasks.

[1123] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1124] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1125] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1126] [Fourth Embodiment]

[1127] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1128] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1131] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1134] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1135] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1136] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1137] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1138] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1139] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1140] This invention relates to a system that automatically generates and provides users with prompts tailored to the specific tasks of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[1141] Overall system configuration

[1142] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via a terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, selects an appropriate prompt from its database, and sends it back to the terminal.

[1143] User actions

[1144] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[1145] Terminal operation

[1146] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user.

[1147] Server operation

[1148] When the server receives a request, it first parses the request content to identify the industry and the nature of the work. For example, the request might provide information about the "healthcare industry" and "creating medical records." The server then consults its database to find the most suitable prompt for the relevant industry and work content. This information is obtained using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[1149] Specific example

[1150] For example, if a user selects "patient interview" in the medical field, the server selects prompts from the database that include "detailed methods for recording patient symptoms" and "examples of important questions to ask during the interview." The selected prompts are returned in JSON format and displayed on the terminal. The user can then use these prompts to efficiently conduct the actual interview.

[1151] Usage scenarios and benefits

[1152] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This results in significant time and effort savings, and a substantial improvement in work efficiency.

[1153] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, thereby significantly improving the quality and efficiency of work in various industries.

[1154] The following describes the processing flow.

[1155] Step 1:

[1156] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[1157] Step 2:

[1158] The terminal retrieves the user's selections and creates an AJAX request. The request data includes the selected industry and job description.

[1159] Step 3:

[1160] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[1161] Step 4:

[1162] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[1163] Step 5:

[1164] The server analyzes the request content to identify the industry and type of work. For example, it might extract information about the "healthcare industry" and "creating medical records" from the request data.

[1165] Step 6:

[1166] The server references the database based on the identified industry and business content. It creates an SQL query and searches the database for the corresponding prompt.

[1167] Step 7:

[1168] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[1169] Step 8:

[1170] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[1171] Step 9:

[1172] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[1173] Step 10:

[1174] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[1175] Step 11:

[1176] The terminal displays the analyzed prompt data in the user interface. Users can then review the prompts and use them for their work.

[1177] Step 12:

[1178] Users perform tasks using the displayed prompts. For example, when creating a medical record in the healthcare industry, they refer to the provided prompts to create the appropriate record.

[1179] (Example 1)

[1180] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1181] Traditional systems made it difficult for users to quickly obtain appropriate prompts to improve work efficiency. Furthermore, the consistency and quality of prompts were not guaranteed, leading to inconsistencies in the quality of work. Additionally, the lack of functionality to automatically generate custom prompts for different industries and tasks meant users had to handle them manually, resulting in significant time and effort.

[1182] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1183] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for transmitting a request to an information processing device based on the industry and business content; means for identifying the target industry from the request content, referring to a data storage device to select a corresponding generating AI model; and means for generating an optimal prompt statement from the selected generating AI model and responding to the user. This enables the user to quickly and consistently obtain the optimal prompt to improve business efficiency.

[1184] A "user" is an individual or group that operates the system and makes selections regarding industry and business content.

[1185] "Industry" refers to a specific business field in which certain occupations or job roles exist.

[1186] "Job description" refers to the specific tasks or work that a user is required to perform within the industry to which they belong.

[1187] An "information processing device" refers to a device that has the function of receiving requests from users, analyzing them, and making necessary queries to data storage devices.

[1188] A "data storage device" is a storage means that stores necessary data such as prompt statements and generated AI models, and provides it to an information processing device in response to a request.

[1189] A "generative AI model" is an artificial intelligence algorithm that automatically generates prompt messages that are optimal for a specific industry or job function.

[1190] A "prompt message" is a document containing specific instructions or guidelines that users should refer to in order to perform their tasks efficiently.

[1191] JSON format is a lightweight data exchange format for structuring and representing data.

[1192] A "structured query language" is a query language used for database management and data retrieval, and primarily refers to SQL (Structured Query Language).

[1193] "Asynchronous JavaScript and XML" refers to a technology for asynchronously exchanging data between a server and a client, specifically AJAX (Asynchronous JavaScript and XML).

[1194] "Selection" is the process of choosing the generation AI model and prompt sentences that best suit the user's requirements from the data storage device.

[1195] This invention relates to a system that automatically generates and provides prompt messages suitable for the operations of various industries. This system is designed to improve user work efficiency and ensure consistency and quality in operations. Specific embodiments of this system are described below.

[1196] Overall system configuration

[1197] This system primarily consists of three main elements: "server," "terminal," and "user." Users access the system via the terminal and select their industry and job content. The terminal sends this selection as a request to the server. The server receives the request, generates a prompt using an appropriate generative AI model, and sends it back to the terminal.

[1198] User actions

[1199] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." Once the user confirms their selection, the device sends this information to the server as a request.

[1200] Terminal operation

[1201] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an asynchronous JavaScript and XML (AJAX) request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt message to the user.

[1202] Server operation

[1203] When the server receives a request, it first analyzes the request content to identify the industry and business content. For example, it might obtain information about the "healthcare industry" and "creation of medical records" from the request. The server then refers to the data storage device to search for the corresponding generative AI model and uses this model to generate the optimal prompt message. This information is obtained using a Structured Query Language (SQL) query, the generated prompt message is formatted into Structured Data Format (JSON), and sent back to the terminal as an HTTP response.

[1204] Specific example

[1205] For example, if the user selects "Patient Interview" in the medical field, the server uses a generative AI model to generate a prompt message like the following:

[1206] "Methods for recording detailed patient symptoms":

[1207] 1. Basic patient information (name, age, gender)

[1208] 2. Chief complaint (current symptoms and their progression)

[1209] 3. Present illness (chronology and background of symptoms)

[1210] 4. Family history (health information related to family members)

[1211] 5. Medical history (past illnesses and surgeries)

[1212] 6. Current treatment methods (medications and treatments currently being used)

[1213] Examples of important questions to ask during a medical interview:

[1214] 1. How many days ago did the symptoms first appear?

[1215] 2. Are there any specific factors that cause symptoms to worsen or improve?

[1216] 3. Are there any other symptoms you are experiencing?

[1217] 4. How is this affecting your daily life?

[1218] Users can use these prompts to efficiently conduct the actual medical interview.

[1219] Usage scenarios and benefits

[1220] This system allows users to quickly obtain prompts optimized for their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from data storage devices using an AI model, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. This system has the potential to dramatically improve the quality and efficiency of work across various industries.

[1221] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1222] Step 1: User selects industry and job content.

[1223] The user interacts with an interface on their device to select their industry and job duties. For example, if the user works in the medical industry, they might select "Create patient records." This selection then becomes the input for the next step.

[1224] Input: User's selected industry and job duties (e.g., healthcare industry, creating medical records)

[1225] Output: Selections sent to the terminal

[1226] Specific operation: The user opens a browser and uses a UI to select their industry and job description. Once the selection is confirmed, the input data is sent to the terminal.

[1227] Step 2: The terminal generates and sends the request.

[1228] The terminal retrieves the user's selections and sends them to the server as asynchronous JavaScript and XML (AJAX) requests. These requests include the user's industry and job description.

[1229] Input: User's selected industry and job description

[1230] Output: AJAX request sent to the server

[1231] Specific operation: The terminal uses JavaScript to generate an AJAX request containing the user's selections. This request is sent to the server using the HTTP POST method.

[1232] Step 3: Server receives and parses the request

[1233] The server receives the AJAX request sent from the terminal and parses the request content. It extracts the industry and business details and prepares to query the data storage device.

[1234] Input: AJAX request sent from the terminal

[1235] Output: Analyzed industries and business operations

[1236] Specific operation: After receiving a request, the server uses an analysis module to extract the industry and business content. For example, it might extract "healthcare industry" and "creation of medical records."

[1237] Step 4: Reference to data storage device and selection of generated AI model

[1238] Based on the analysis results, the server consults the data storage device and searches for the corresponding generation AI model. It then prepares to generate the optimal prompt sentence using this model.

[1239] Input: Analyzed industry and business content

[1240] Output: Selected generative AI models

[1241] Specific operation: The server generates an SQL query to search for generated AI models from the data storage device. For example, to retrieve models for "healthcare industry" and "medical record creation," it executes "SELECT FROM models WHERE industry='healthcare' AND task='medical record creation';".

[1242] Step 5: Generating the prompt message

[1243] The server uses the selected generation AI model to generate prompt messages that are optimal for the user's industry and business operations. These generated prompt messages serve as input for the next step.

[1244] Input: Selected generative AI model, analyzed industry and business content

[1245] Output: Generated prompt message

[1246] Specific operation: The server applies a generation AI model to generate prompts based on the analysis results (industry and business content). For example, it might generate a prompt for "detailed method of recording patient symptoms."

[1247] Step 6: Formatting the prompt and generating the response

[1248] The server formats the generated prompt message into a structured data format (JSON) and prepares it as an HTTP response. This response is then sent to the terminal.

[1249] Input: Generated prompt message

[1250] Output: HTTP response sent to the terminal

[1251] Specific operation: The server converts the generated prompt message into JSON format, sets the HTTP header to "Content-Type: application / json", and generates a response.

[1252] Step 7: Receiving and analyzing the response by the terminal

[1253] The terminal receives an HTTP response from the server and parses its contents. It extracts prompt text from the JSON format and prepares it to be displayed to the user.

[1254] Input: HTTP response from the server

[1255] Output: Parsed prompt message

[1256] Specific operation: After receiving the response, the terminal uses a JSON parsing library to extract the prompt text. This content will be displayed in the next step.

[1257] Step 8: Display a prompt message to the user.

[1258] The terminal displays the parsed prompt message to the user. The user can then use this prompt message to efficiently carry out their tasks.

[1259] Input: Parsed prompt message

[1260] Output: Prompt displayed to the user

[1261] Specific operation: The terminal displays the parsed prompt text in a designated area of ​​the user interface. For example, it will display "Detailed recording method for patient symptoms: 1. Basic patient information 2. Chief complaint..." in the browser's text area.

[1262] The above outlines the specific processing steps of this system.

[1263] (Application Example 1)

[1264] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1265] Logistics centers face complex procedures for efficiently and effectively performing tasks such as inventory management, picking, and optimizing delivery routes. This results in decreased operational efficiency and an increased risk of errors. Furthermore, insufficient standardization of work procedures makes it difficult for new employees to quickly learn their jobs. To address these problems and improve operational consistency and efficiency, there is a need for a system that automatically generates and provides optimal prompts for each task.

[1266] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1267] In this invention, the server includes means for receiving the selection of industry and business content from the user; means for sending a request to the server based on the industry and business content; means for identifying the target industry from the request content and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; means for automatically generating prompts corresponding to specific tasks such as inventory management, picking list creation, and delivery route optimization in order to improve the operational efficiency of the logistics center; and means for displaying the generated prompts on the user's smartphone. This makes it possible to perform operations at the logistics center efficiently, standardized, and with consistently high quality.

[1268] An "industry" refers to a group of companies or organizations that operate in a specific field or market as part of their business activities.

[1269] "Job description" refers to a list of specific tasks and duties performed within a particular industry.

[1270] A "request" refers to a request for information to be sent to the server based on user input.

[1271] A "server" is a computer system that performs specific calculations and data processing to provide services to a client (in this case, a terminal).

[1272] A "database" is a digital system that organizes and stores structured information so that it can be easily accessed, managed, and updated later.

[1273] A "prompt" is a phrase that provides guidance or instructions to a user when performing a specific task or operation.

[1274] "Inventory management" refers to operations in a logistics center that include receiving, storing, shipping, and tracking and managing inventory levels.

[1275] A "picking list" is a set of guidelines or lists used to select items from a warehouse based on an order.

[1276] "Delivery route optimization" is the process of calculating the most efficient route to minimize the time and cost of delivering goods.

[1277] A "smartphone" is a portable electronic device that has functions beyond those of a regular mobile phone, such as internet connectivity, application execution, and camera operation.

[1278] "JSON format" is a method of structurally representing data in text format, and it is an abbreviation for JavaScript Object Notation.

[1279] An "AJAX request" is an asynchronous communication method for retrieving data from a server without reloading a web page.

[1280] A "logistics center" is a facility that stores, manages, and ships goods, and constitutes part of the supply chain.

[1281] Overall system configuration

[1282] This invention is a system designed to improve user work efficiency and ensure consistency and quality in operations. The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for processing requests and managing the database, while the terminal receives input from the user and transmits it to the server. Users access the system using the terminal and select their industry and work content.

[1283] User actions

[1284] The user first uses an interface on their smartphone application to select their industry and job duties. For example, suppose the user works in a logistics center and selects "Create Picking List." Once the user confirms their selection, the device sends this information to the server as a request.

[1285] Terminal operation

[1286] The terminal operates as follows: After the user selects an industry and job description, the terminal retrieves these selections and sends them to the server in the form of an AJAX request. The request contains information about the industry and job description selected by the user. Upon receiving a response from the server, the terminal parses its contents and displays a generated prompt to the user. This process uses front-end technologies such as HTML, CSS, and JavaScript.

[1287] Server operation

[1288] When the server receives a request, it first parses the request to identify the industry and task. For example, it might obtain information about a "logistics center" and "creating a picking list" from the request. The server then consults a database to find the most suitable prompt for the relevant industry and task. The database contains prompts for various industries and tasks, which are retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response. This process uses Flask (a lightweight web framework for Python) and SQLite (a lightweight relational database).

[1289] Specific example

[1290] For example, if a user selects "Create Picking List" in the "Logistics Center" section, the server selects prompts from the database that include the "Optimal Picking Route" and a "Detailed List of Products." The selected prompts are returned in JSON format and displayed on the terminal.

[1291] Example of a prompt

[1292] Create a picking list:

[1293] 1. Please check the stock availability of each item.

[1294] 2. Set the optimal picking route as follows:

[1295] Aisle 5: Product A

[1296] Aisle 3: Product B

[1297] Aisle 7: Product C

[1298] 3. Once the list is complete, review it and proceed to the next step.

[1299] Usage scenarios and benefits

[1300] This system allows users to quickly obtain prompts best suited to their tasks, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from the database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency.

[1301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1302] Step 1:

[1303] The user launches a smartphone application and selects an industry (e.g., "logistics center") and a task (e.g., "creating picking lists"). The input consists of the selected industry and task, which the terminal receives. In terms of specific actions, the user interacts with interfaces such as dropdown menus and checkboxes.

[1304] Step 2:

[1305] Based on the selected industry and job description, the terminal sends a request to the server in AJAX request format. The input is the user's selection information, and the output is the sending of the request to the server. The terminal uses JavaScript for asynchronous communication.

[1306] Step 3:

[1307] The server analyzes the received request and identifies the selected industry and business content. The input is the request data from the terminal, and the output is the analysis result, which is information about the industry and business content. The server uses Flask to analyze the request.

[1308] Step 4:

[1309] Based on the analysis results, the server uses SQL queries to retrieve the appropriate prompt from the database. The input is the industry and business content information from the analysis results, and the output is the prompt data retrieved from the database. The server queries the database (SQLite) using SQL.

[1310] Step 5:

[1311] The server formats the retrieved prompt data into JSON format and sends it back to the terminal as an HTTP response. The input is prompt data from the database, and the output is prompt data in JSON format. The server generates the JSON data using the Python standard library.

[1312] Step 6:

[1313] The terminal receives a response from the server and parses the prompt data. The input is prompt data in JSON format from the server, and the output is the prompt text as a result of the parsing. The terminal uses JavaScript to parse the JSON data.

[1314] Step 7:

[1315] The parsed prompt is displayed to the user. The input is the parsed prompt text, and the output is the prompt displayed to the user. The terminal visually presents the prompt to the user using HTML and CSS.

[1316] Step 8:

[1317] The user follows the prompts presented and performs specific tasks (e.g., tasks based on a picking list). The input is the displayed prompt text, and the output is the actual work performed. The user works while looking at their smartphone screen.

[1318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1319] This invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes user emotions, and provides prompts accordingly. This system is designed to improve user work efficiency, ensure consistency and quality of operations, and provide flexible responses that respond to user emotions. Specific embodiments of this system are described below.

[1320] Overall system configuration

[1321] This system primarily consists of four main elements: "server," "terminal," "user," and "emotion engine." Users access the system via the terminal and select their industry and job content. During the selection process, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from its database based on the emotional state, and sends it back to the terminal.

[1322] User actions

[1323] The user first uses an interface on their device to select their industry and job duties. For example, suppose the user works in the medical industry and selects "Create patient records." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[1324] Terminal operation

[1325] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[1326] Server operation

[1327] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and task. For example, the request might reveal information such as "healthcare industry," "creating medical records," and "the user is stressed." The server then consults its database to find the most suitable prompt for the given industry, task, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON and sent back to the terminal as an HTTP response.

[1328] Specific example

[1329] For example, if a user selects "patient interview" in the medical field, and the emotion engine detects that the user is nervous, the server selects prompts from the database that include "a flow of interviews to help the patient relax" and "examples of questions in a gentle tone." The selected prompts are returned in JSON format and displayed on the terminal. Based on these prompts, the user can efficiently conduct the interview while easing their tension.

[1330] Usage scenarios and benefits

[1331] This system allows users to quickly obtain prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[1332] The above describes the embodiments of the present invention. This system enables users to perform tasks quickly and efficiently, and to respond flexibly to emotions, thereby significantly improving the quality and efficiency of work in various industries.

[1333] The following describes the processing flow.

[1334] Step 1:

[1335] Users log in to the system on their terminal and select their industry and job responsibilities. Specifically, they choose their industry and specific job responsibilities from dropdown menus and checkboxes displayed in the user interface.

[1336] Step 2:

[1337] The emotion engine analyzes the user's facial expressions, voice, and body movements to identify the user's current emotional state. For example, it can determine whether the user is feeling stressed or anxious.

[1338] Step 3:

[1339] The terminal retrieves the user's selections and identified emotional state, and creates an AJAX request. The request data includes the selected industry and job description, and the recognized emotional state.

[1340] Step 4:

[1341] The terminal sends the created AJAX request to the server. The request is sent using the HTTP POST method.

[1342] Step 5:

[1343] The server receives requests from the terminal. Specifically, the server's API endpoint receives the request and parses its contents.

[1344] Step 6:

[1345] The server analyzes the request content and emotional state to identify the industry and job content. For example, it extracts information such as "healthcare industry," "creating medical records," and "user is stressed" from the request data.

[1346] Step 7:

[1347] The server references the database based on the identified industry, job description, and emotional state. It then creates an SQL query to search the database for the corresponding prompt.

[1348] Step 8:

[1349] The database responds to the server's SQL queries and returns the corresponding prompt data. For example, a prompt related to "creating a medical record" in the medical industry might be returned.

[1350] Step 9:

[1351] The server formats the prompt data returned from the database into JSON format. This prepares the data for being sent back to the terminal.

[1352] Step 10:

[1353] The server returns the formatted JSON data to the terminal as an HTTP response. The HTTP response includes the prompts requested by the user.

[1354] Step 11:

[1355] The terminal receives a response from the server. Specifically, the response handler for the AJAX request receives the JSON data and parses it.

[1356] Step 12:

[1357] The terminal displays the analyzed prompt data on the user interface. For example, a "Create Medical Record" prompt that takes the user's stress level into consideration may be displayed.

[1358] Step 13:

[1359] Users perform tasks using the displayed prompts. For example, in the medical field, when creating a medical record, they refer to the provided prompts to create the appropriate record.

[1360] Step 14:

[1361] The system encourages users to follow on-screen prompts to relax and perform tasks efficiently. Tasks are adjusted according to the user's current emotional state, allowing them to work with reduced stress.

[1362] (Example 2)

[1363] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1364] Traditional prompt generation systems provided responses simply by the user selecting an industry and job content, without considering the user's emotional state. Therefore, depending on the user's emotional state, appropriate prompts could not be provided, leading to decreased work efficiency and quality. Furthermore, they lacked the flexibility to improve user comfort and satisfaction.

[1365] The identification processing performed by the identification 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 receiving the selection of industry and work content from the user; means for sending a request to the server based on the industry and work content; means for identifying the target industry from the request content and selecting the appropriate prompt by referring to the database; means for responding to the user with the selected prompt; and means for recognizing the user's emotions and providing prompts corresponding to those emotions. This makes it possible to provide flexible and appropriate prompts according to the user's emotional state, thereby improving work efficiency and work quality. It is also possible to improve the ease of work and satisfaction of the user.

[1366] "Industry" refers to the type of business field or industry to which the user belongs.

[1367] "Job description" refers to the specific tasks or types of work that the user performs within the industry.

[1368] A "request" is a request for information sent to the server based on the user's selected industry, job content, and emotional state.

[1369] A "server" is a central computer system that receives requests, parses them, retrieves prompts from a database, and provides responses.

[1370] A "database" is a system that stores prompts tailored to the user's industry, job responsibilities, and emotional state, and allows users to search for them as needed.

[1371] A "prompt" is a specific instruction or guideline that a user can refer to when performing a task.

[1372] "Selection" is the process of choosing the most suitable prompt from the database based on industry, job content, and emotional state.

[1373] A "response" is the act of providing the user with a selected prompt.

[1374] "Emotions" refer to the user's current psychological or emotional state.

[1375] "Emotional state" refers to a specific psychological or emotional state of the user as recognized by the emotion engine.

[1376] An "emotion engine" is a technical means of recognizing emotions by analyzing a user's facial expressions and voice data.

[1377] An "AJAX request" is a technology that uses asynchronous communication to send user selections to a server.

[1378] JSON format is a lightweight data exchange format for structuring data in text format.

[1379] "Analysis" is the process by which a server understands the content of a received request and extracts the data necessary to execute the appropriate process.

[1380] A "SQL query" is a set of instructions in a structured query language used to retrieve specific information from a database.

[1381] This invention relates to a system that automatically generates prompts suitable for the operations of various industries, recognizes user emotions, and provides prompts accordingly. This system is designed not only to improve user work efficiency and ensure consistency and quality of operations, but also to provide flexible responses that respond to user emotions. The components of this system and their operation are described in detail below.

[1382] Overall system configuration

[1383] This system consists of five main elements: "server," "terminal," "user," "emotion engine," and "database."

[1384] User actions

[1385] The user first selects their industry and job description on the terminal interface. For example, the user might select "Medical Industry" and then "Creating Medical Records." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the terminal sends this information and their emotional state as a request to the server.

[1386] Terminal operation

[1387] After the user selects their industry and job responsibilities, the terminal's emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request includes information about the industry, job responsibilities, and recognized emotional state. A specific example of a request is as follows:

[1388] json

[1389] {

[1390] "Industry": "Healthcare industry",

[1391] "Job Description": "Creating medical records",

[1392] "Emotional state": "Nervous"

[1393] }

[1394] Upon receiving a response from the server, the terminal parses its contents and displays the generated prompt to the user.

[1395] Server operation

[1396] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and task. For example, the request might reveal information such as "healthcare industry," "creating medical records," and "user is stressed." The server then consults its database to find the most appropriate prompt for the given industry, task, and emotional state. This information is retrieved using SQL queries. For example:

[1397] SQL

[1398] SELECT prompt FROM prompts WHERE industry='medical industry' AND task='creating medical records' AND emotion='nervous';

[1399] The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response. An example of a returned prompt may include the following instructions:

[1400] "Please create the medical record following these steps: 1. Enter the patient's basic information. 2. Record the chief complaint. 3. Enter the examination results."

[1401] Specific example

[1402] For example, if a user selects "Patient Interview" in the medical field, and the emotion engine detects that the user is nervous, the server will select prompts from the database that include "a patient interview flow to help the patient relax" and "examples of questions in a gentle tone." The selected prompts will be displayed on the terminal in the following format:

[1403] "To help the patient relax, greet them with a smile and ask the following questions in a gentle tone."

[1404] This allows users to quickly receive prompts that are optimal for their tasks and tailored to their emotional state, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users have access to consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Moreover, the introduction of an emotion engine enables more personalized support based on the user's state, which is expected to improve user satisfaction.

[1405] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1406] Step 1:

[1407] The user selects the industry and job description.

[1408] Input: Selected data for industry and job description

[1409] Specific operation: The user accesses the terminal interface and selects an industry (e.g., "healthcare industry") and a job function (e.g., "creating medical records"). Once the selection is confirmed, the data is saved for use in the next step.

[1410] Step 2:

[1411] The emotion engine recognizes the user's emotional state.

[1412] Input: User's facial expressions and voice data

[1413] Specific operation: The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. For example, based on data collected through the camera and microphone, it determines that the user is "nervous." The determination result is then sent to the next step.

[1414] Step 3:

[1415] The terminal generates a request and sends it to the server.

[1416] Input: Industry, job description, emotional state

[1417] Output: AJAX request to the server

[1418] Specific operation: The terminal generates a request containing information about the user's industry selection, job content selection, and perceived emotional state. The request is structured in JSON format using AJAX and sent to the server.

[1419] Step 4:

[1420] The server receives and parses the request.

[1421] Input: AJAX request

[1422] Output: Analysis results (industry, job content, emotional state)

[1423] Specific operation: When the server receives a request, it analyzes the request content and identifies the industry (e.g., "healthcare industry"), the task (e.g., "creating medical records"), and the emotional state (e.g., "stressed").

[1424] Step 5:

[1425] The server retrieves the corresponding prompt from the database.

[1426] Input: Analysis results

[1427] Output: Prompt data

[1428] Specific operation: The server looks up a database to find a prompt appropriate for the relevant industry, job content, and emotional state. For example, it generates an SQL query and searches the database.

[1429] Example SQL query: SELECT prompt FROM prompts WHERE industry='healthcare industry' AND task='create medical records' AND emotion='nervous';

[1430] Step 6:

[1431] The server formats the prompt in JSON format and sends it back to the terminal.

[1432] Input: Prompt data

[1433] Output: HTTP response (prompt in JSON format)

[1434] Specific operation: The server formats the prompt obtained as a search result into JSON format and sends it as an HTTP response.

[1435] Step 7:

[1436] The terminal receives and analyzes the response from the server.

[1437] Input: HTTP response (JSON format prompt)

[1438] Output: Analyzed prompt data

[1439] Specific operation: The terminal receives a response from the server, analyzes its contents, and extracts the necessary information.

[1440] Step 8:

[1441] The terminal displays a prompt to the user.

[1442] Input: Analyzed prompt data

[1443] Output: Displayed to the user (text-based prompt)

[1444] Specific operation: The terminal displays the analyzed prompt data in a format that is easy for the user to understand. For example, instructions such as "Please create the medical record following the steps below" will be displayed on the terminal screen.

[1445] In this way, this system can improve user work efficiency and satisfaction by consistently handling everything from industry and job content selection to emotional state recognition and the provision of appropriate prompts.

[1446] (Application Example 2)

[1447] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1448] Conventional prompt generation systems only have the function of providing appropriate prompts based on the user's industry and job content, and are unable to provide prompts that take into account the user's emotional state. As a result, even when a user is feeling stressed or anxious, only a uniform prompt is provided, which can lead to a decrease in work efficiency and quality. The present invention aims to improve the user's work efficiency and enhance the quality of work by providing a system that recognizes the user's emotional state in real time and automatically generates appropriate prompts accordingly.

[1449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1450] In this invention, the server includes means for receiving a selection of industry and work content from a user; means for sending a request to the server based on the industry, work content, and emotional state; means for identifying the target industry from the request content and emotional state, and selecting a corresponding prompt by referring to a database; means for responding to the user with the selected prompt; and means including an emotion engine for recognizing the user's emotional state in real time. This enables flexible responses according to the user's emotional state, improving work efficiency and quality.

[1451] A "user" refers to a person who uses the system to select an industry and job content and receives prompts.

[1452] "Industry" refers to a category that indicates a specific economic field or occupational area.

[1453] "Job description" refers to the specific tasks and types of work that the user will be engaged in.

[1454] "Options" refers to the multiple possible choices presented to a user when selecting an industry and job.

[1455] "Emotional state" refers to the user's psychological and emotional state, including, for example, tension, anxiety, and relaxation.

[1456] An "emotion engine" refers to a software module that recognizes and analyzes a user's emotional state in real time.

[1457] A "prompt" refers to the text of specific instructions or guidance generated to support the user's work.

[1458] A "request" refers to the information a user submits regarding their industry, job responsibilities, and emotional state.

[1459] A "server" is a central computer system within a system that receives requests, analyzes them, retrieves the results from a database, and sends them back.

[1460] A "database" refers to a data storage system used to store and manage prompt information based on industry and business content.

[1461] A "prompt generator" refers to a software module that generates appropriate prompts based on the user's emotional state.

[1462] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text format for exchanging data.

[1463] An "AJAX request" refers to a technology that uses asynchronous communication to send requests from users to a server.

[1464] Overall system configuration

[1465] The system that realizes this invention mainly consists of four main elements: a server, a terminal, a user, and an emotion engine. The user accesses the system via the terminal and selects an industry and job content. When selecting, the emotion engine recognizes the user's emotional state. The terminal sends this selection and emotional state as a request to the server. The server receives the request, selects an appropriate prompt from the database according to the emotional state, and sends it back to the terminal.

[1466] User actions

[1467] The user first uses an interface on their device to select their industry and job duties. For example, suppose a user works in the security industry and selects "nighttime building patrols." At this point, the emotion engine recognizes the user's emotional state from their facial expressions and voice. Once the user confirms their selection, the device sends this information and their emotional state as a request to the server.

[1468] Terminal operation

[1469] The terminal operates as follows: After the user selects an industry and job, the emotion engine analyzes the user's emotions and sends that state to the system. The terminal retrieves the user's selections and emotional state and sends them to the server in the form of an AJAX request. The request contains information about the industry, job, and recognized emotional state selected by the user. Upon receiving a response from the server, the terminal analyzes its contents and displays a generated prompt to the user.

[1470] Server operation

[1471] When the server receives a request, it first analyzes the request content and emotional state to identify the industry and job content. For example, the request might reveal information such as "security industry," "nighttime building patrol," and "user is anxious." The server then consults its database to find the most appropriate prompt for the relevant industry, job content, and emotional state. This information is retrieved using SQL queries. The selected prompt is formatted in JSON format and sent back to the terminal as an HTTP response.

[1472] Specific example

[1473] For example, if a user selects "nighttime building patrol" in the security industry, and the emotion engine recognizes that the user is anxious, the server will select a prompt from the database such as, "An anomaly has been detected in the stairwell at your current location. Please first assess the situation and thoroughly check for any danger. If you are feeling anxious, take a deep breath and calm down." The selected prompt will be returned in JSON format and displayed on the device. Based on this prompt, the user can proceed with the patrol safely while alleviating their anxiety.

[1474] Usage scenarios and benefits

[1475] This system allows users to quickly obtain optimal and emotionally appropriate prompts tailored to their work content, thereby improving the quality of their work. Furthermore, since prompts are automatically generated from a database, all users can access consistent, high-quality information. This saves time and effort, significantly improving work efficiency. Additionally, the introduction of an emotion engine enables more personalized support based on the user's state, leading to improved user satisfaction.

[1476] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1477] Step 1:

[1478] The user selects their industry and job content using a terminal. The terminal retrieves the user's selections, while simultaneously an emotion engine analyzes the user's facial expressions and voice data to recognize their emotional state. Inputs include the user's selected industry and job content, as well as their facial expressions and voice data. Outputs include the selected industry and job content information, and the recognized emotional state.

[1479] Step 2:

[1480] The terminal sends information about its industry, job duties, and emotional state to the server as a request. Specifically, this information is sent to the server in the form of an AJAX request. The inputs are the user's industry, job duties, and emotional state. The output is the request sent to the server.

[1481] Step 3:

[1482] The server analyzes received requests to identify the industry, job content, and emotional state. Specifically, it parses the request content and extracts information on the industry, job content, and emotional state. The input is the request sent from the terminal. The output is the analyzed information on the industry, job content, and emotional state.

[1483] Step 4:

[1484] The server searches the database based on the analysis results and selects the most appropriate prompt for the industry, job content, and emotional state. Specifically, it uses SQL queries to retrieve the corresponding prompt from the database. The input is the analyzed information on the industry, job content, and emotional state. The output is the selected prompt.

[1485] Step 5:

[1486] The server formats the selected prompt into JSON format and sends it back to the terminal as an HTTP response. Specifically, it converts the received prompt into JSON format and sends it as an HTTP response. The input is the selected prompt. The output is an HTTP response containing the prompt in JSON format.

[1487] Step 6:

[1488] The terminal parses the prompt received from the server and displays it to the user. Specifically, it parses the received JSON-formatted prompt and displays it in the user interface. The input is a JSON-formatted prompt sent from the server. The output is the prompt that will be displayed to the user.

[1489] Step 7:

[1490] The user performs tasks based on prompts displayed on the terminal. Specifically, they follow the displayed prompts and select the industry and task content again as needed to receive further instructions. The input is the prompts displayed on the terminal. The output is the efficient execution of tasks.

[1491] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1492] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1493] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1494] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1495] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1496] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1497] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1498] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1499] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1500] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1501] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1502] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1503] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1504] 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.

[1505] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1506] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1507] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1508] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1509] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1510] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1511] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1512] The following is further disclosed regarding the embodiments described above.

[1513] (Claim 1)

[1514] A means of receiving industry and job content selections from users,

[1515] Based on the aforementioned industry and business content, a means for sending a request to the server,

[1516] A means for identifying the target industry from the aforementioned request content and selecting the appropriate prompt by referring to a database,

[1517] A system including means for responding to the user with a selected prompt.

[1518] (Claim 2)

[1519] A server receives the request and has means for analyzing the contents of the request,

[1520] Based on the analysis results, a means of retrieving the prompt from the database using an SQL query,

[1521] The system according to claim 1, further comprising means for formatting the prompt in JSON format and returning it to the user.

[1522] (Claim 3)

[1523] The terminal provides users with options for their industry and work content,

[1524] A means by which the terminal obtains the user's selections and sends them to the server using an AJAX request,

[1525] The system according to claim 1, further comprising means for receiving a response from a server and displaying the received prompt to a user.

[1526] "Example 1"

[1527] (Claim 1)

[1528] A means of receiving industry and job content selections from users,

[1529] Based on the aforementioned industry and business content, means for transmitting a request to an information processing device,

[1530] A means for identifying the target industry from the aforementioned request content and selecting the appropriate generation AI model by referring to a data storage device,

[1531] A system that includes a means for generating the optimal prompt sentence from a selected generative AI model and responding to the user.

[1532] (Claim 2)

[1533] The information processing device receives the request and has means for analyzing the content of the request,

[1534] Based on the analysis results, a means for retrieving prompt statements from a data storage device using a structured query language,

[1535] The system according to claim 1, further comprising means for formatting the prompt statement in a structured data format and returning it to the user.

[1536] (Claim 3)

[1537] Data entry devices provide users with a means of offering choices in their industry and work content.

[1538] A means by which a data input device acquires user selections and transmits them to an information processing device using asynchronous JavaScript and XML,

[1539] The system according to claim 1, further comprising means for receiving a response from an information processing device and displaying the received prompt message to the user.

[1540] "Application Example 1"

[1541] (Claim 1)

[1542] A means of receiving industry and job content selections from users,

[1543] Based on the aforementioned industry and business content, a means for sending a request to the server,

[1544] A means for identifying the target industry from the aforementioned request content and selecting the appropriate prompt by referring to a database,

[1545] A means of responding to the user with the selected prompt,

[1546] To improve the operational efficiency of logistics centers, a means of automatically generating prompts tailored to specific tasks such as inventory management, picking list creation, and delivery route optimization is provided.

[1547] A means of displaying the generated prompt on the user's smartphone and

[1548] A system that includes this.

[1549] (Claim 2)

[1550] A server receives the request and has means for analyzing the contents of the request,

[1551] Based on the analysis results, a means of retrieving the prompt from the database using an SQL query,

[1552] The system according to claim 1, further comprising means for formatting the prompt in JSON format and returning it to the user.

[1553] (Claim 3)

[1554] The terminal provides users with options for their industry and work content,

[1555] A means by which the terminal obtains the user's selections and sends them to the server using an AJAX request,

[1556] A means of receiving a response from the server and displaying the received prompt to the user.

[1557] A means to provide detailed instructions for optimizing inventory management, picking, and delivery routes in a logistics center based on the generated prompts.

[1558] The system according to claim 1, including the following:

[1559] "Example 2 of combining an emotion engine"

[1560] (Claim 1)

[1561] A means of receiving industry and job content selections from users,

[1562] Based on the aforementioned industry and business content, a means for sending a request to the server,

[1563] A means for identifying the target industry from the aforementioned request content and selecting the appropriate prompt by referring to a database,

[1564] A means of responding to the user with the selected prompt,

[1565] A system that includes means for recognizing a user's emotions and providing prompts corresponding to those emotions.

[1566] (Claim 2)

[1567] A server receives the request and has means for analyzing the contents of the request,

[1568] Based on the analysis results, a means of retrieving the prompt from the database using an SQL query,

[1569] A means for formatting the aforementioned prompt in JSON format and returning it to the user,

[1570] The system according to claim 1, wherein the analysis means includes means for identifying the user's emotional state and selecting the most appropriate prompt for that emotional state.

[1571] (Claim 3)

[1572] The terminal provides users with options for their industry and work content,

[1573] A means by which the terminal obtains the user's selections and sends them to the server using an AJAX request,

[1574] A means of receiving a response from the server and displaying the received prompt to the user,

[1575] The system according to claim 1, further comprising means for acquiring the user's emotional state and providing a prompt best suited to that emotional state.

[1576] "Application example 2 when combining with an emotional engine"

[1577] (Claim 1)

[1578] A means of receiving industry and job content selections from users,

[1579] A means for sending a request to a server based on the aforementioned industry, job description, and emotional state,

[1580] A means for identifying the target industry from the aforementioned request content and emotional state, and selecting the appropriate prompt by referring to a database,

[1581] A means of responding to the user with the selected prompt,

[1582] A means including an emotion engine for recognizing the user's emotional state in real time,

[1583] A system that includes this.

[1584] (Claim 2)

[1585] The server receives the request and has means for analyzing the request content and emotional state,

[1586] Based on the analysis results, a means of retrieving the prompt from the database using an SQL query,

[1587] A means for formatting the aforementioned prompt in JSON format and returning it to the user,

[1588] A means including a prompt generator for generating prompts corresponding to the user's emotional state,

[1589] The system according to claim 1, including the following:

[1590] (Claim 3)

[1591] The terminal provides users with options for their industry and work content,

[1592] A means by which the terminal obtains the user's selections and emotional state and sends them to the server using an AJAX request,

[1593] A means of receiving a response from the server and displaying the received prompt to the user,

[1594] The system according to claim 1, comprising means for providing a response based on the user's emotional state. [Explanation of symbols]

[1595] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving industry and job content selections from users, Based on the aforementioned industry and business content, a means for sending a request to the server, A means for identifying the target industry from the aforementioned request content and selecting the appropriate prompt by referring to a database, A system including means for responding to the user with a selected prompt.

2. A server receives the request and has means for analyzing the contents of the request, Based on the analysis results, a means of retrieving the prompt from the database using an SQL query, The system according to claim 1, further comprising means for formatting the prompt in JSON format and returning it to the user.

3. The terminal provides users with options for their industry and work content, A means by which the terminal obtains the user's selections and sends them to the server using an AJAX request, The system according to claim 1, further comprising means for receiving a response from a server and displaying the received prompt to the user.

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