system

Abstract

Provide a system. A means for receiving a message input by a user; means for transmitting said message to a server; A means for transmitting the message received by the server to a generative artificial intelligence to generate an appropriate response; A means for receiving a response generated by the generative artificial intelligence; means for displaying said response to a user; A system including:

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, daily schedule management and task prioritization are important, but doing so efficiently requires a great deal of time and effort. Furthermore, traditional calendar and task management apps require users to manually input and adjust information, often resulting in a lack of convenience. Therefore, a new system is needed to reduce the burden on users and enable more efficient schedule management and task prioritization. [Means for solving the problem]

[0005] The present invention provides a system that improves the efficiency of a user's schedule management and task prioritization by combining a calendar application, a task management application, and generative artificial intelligence.

[0006] The system according to the present invention comprises the following means:

[0007] The system comprises a means for receiving a message input by a user, a means for transmitting the message to a server, a means for the server to transmit the received message to a generative artificial intelligence and generate an appropriate response, a means for receiving the response generated by the generative artificial intelligence, and a means for displaying the response to the user. This allows the user to receive instructions and advice from the generative artificial intelligence in a conversational format, enabling effective schedule management and task prioritization while significantly reducing the amount of work required.

[0008] "User" refers to the person who uses the system to manage schedules and prioritize tasks.

[0009] A "message" refers to text data of instructions or inquiries that a user inputs to the system.

[0010] "Server" refers to a central management system for receiving messages, sending them to the generative artificial intelligence, and receiving responses and sending them back to the terminal.

[0011] "Generative AI" refers to an AI model that generates an appropriate response based on an input message.

[0012] "Response" refers to a reply generated by generative artificial intelligence, and refers to text data that includes instructions and advice for the user.

[0013] "Terminal" refers to a device (e.g., a smartphone, a personal computer, etc.) that a user uses to access the system, input messages, and receive responses.

[0014] "JSON format" refers to a data exchange format that structures and expresses data in text format, and is an abbreviation for JavaScript (registered trademark) Object Notation.

[0015] "Schedule management" refers to the act of planning and coordinating a user's daily schedule and events.

[0016] "Task prioritization" refers to the process of determining the order in which multiple tasks are performed based on their importance and urgency. [Brief explanation of the drawings]

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

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

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

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

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

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

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

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

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0038] This invention is a system that combines a calendar application, a task management application, and a generative artificial intelligence to improve the efficiency of schedule management and task prioritization. The system sends messages entered by the user to the generative artificial intelligence, generates an appropriate response, and presents that response to the user.

[0039] Explanation of system program processing

[0040] System Overview

[0041] The system consists of the following major components:

[0042] 1. Users: Use the system to manage schedules and tasks.

[0043] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0044] 3. Server: A central management system that receives messages from users, sends them to the generative AI, and sends the generated responses back to the terminal.

[0045] 4. Generative AI: An AI model that generates appropriate responses based on messages received from the server (e.g., GPT-3 (registered trademark)).

[0046] Example of user interaction

[0047] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[0048] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[0049] 3. The server analyzes the request received from the device and obtains the user's message. The server performs preprocessing and sends this message to the generative AI.

[0050] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "We've scheduled a project meeting for tomorrow at 10:00 AM. After that, we recommend scheduling a conference call with the client for one hour."

[0051] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0052] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0053] 7. The user decides on their next action based on the provided responses, such as asking more specific questions, adding other tasks, or reviewing or modifying the proposed schedule.

[0054] Specific operation example

[0055] 1. The user types into the terminal, "Add a new task at 2 PM."

[0056] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[0057] 3. The server analyzes the request received from the device and obtains the user's message. The server then performs preprocessing and sends the message to the generative AI.

[0058] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "A new task has been added for 2 PM. We recommend that you adjust your schedule so that the task can be completed in the morning."

[0059] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0060] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0061] 7. The user decides on the next action based on the presented response.

[0062] In this way, this system can improve the efficiency of users' schedule management and task prioritization, reducing the burden on users.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[0066] Step 2:

[0067] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[0068] Step 3:

[0069] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[0070] Step 4:

[0071] The server sends the acquired user message to the generative AI. Specifically, it creates a request to the OpenAI (registered trademark) API and sends the message to the API as a prompt.

[0072] Step 5:

[0073] The generative AI analyzes messages received from the server and generates appropriate responses, such as, "We've set up a project meeting for tomorrow at 10 a.m. After that, we recommend scheduling a conference call with your client for one hour later."

[0074] Step 6:

[0075] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the response from GPT-3, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[0076] Step 7:

[0077] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[0078] Step 8:

[0079] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] Conventional schedule management and task management systems have the problem that users have to manually input and manage a large number of tasks and schedules, which is time-consuming. In addition, the system has limited functionality for automatically prioritizing tasks and providing advice on required time, making efficient management difficult. There is a need for a schedule management and task management system that solves these problems and reduces the burden on users.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes means for the terminal to send user input in JSON format to the server, means for the server to analyze the received JSON data and extract the user message, means for preprocessing the message to be sent by the server to the generative AI, means for formatting a response received from the generative AI in JSON format and sending it to the terminal, and means for the terminal to analyze and display the JSON formatted response sent from the server. This allows the user to have their input processed quickly and accurately and receive a response including advice on task prioritization and required time.

[0085] A "user" is an entity that uses the system to manage schedules and tasks.

[0086] A "message" refers to information or instructions entered by a user.

[0087] "Terminal" refers to the device a user uses to enter messages and view received responses (e.g., a smartphone or PC).

[0088] "Server" refers to a central management system that receives messages sent from a terminal, works with generative artificial intelligence to generate a response, and sends it back to the terminal.

[0089] "Generative AI" refers to an AI model that generates appropriate responses based on messages received from a server.

[0090] "Response" refers to a reply that a generative artificial intelligence generates based on a user's message.

[0091] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a standard format for structuring and representing data.

[0092] "Preprocessing" refers to the process of formatting the messages that the server sends to the generative artificial intelligence into an appropriate format.

[0093] "Formatting" refers to converting data into a particular form.

[0094] This invention is a system that streamlines schedule and task management, integrating a calendar app, a task management app, and generative artificial intelligence. This system analyzes messages entered by the user and provides appropriate schedule and task prioritization.

[0095] Hardware and software used

[0096] User: The entity that uses the system to manage schedules and tasks.

[0097] Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0098] Server: A central management system that receives messages from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0099] Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages received from a server.

[0100] JSON format: A standard format for structuring and representing data.

[0101] Processing flow details

[0102] 1. A user enters schedule or task-related instructions into a device. For example, a user might enter, "Schedule a project meeting tomorrow at 10 AM."

[0103] 2. The device captures the user's input message, encodes it into JSON format, and sends it to the server as an HTTP POST request.

[0104] 3. The server analyzes the request received from the device and extracts the user's message from the JSON data.

[0105] 4. The server preprocesses the extracted message into a format suitable for the generative AI and sends this message to the generative AI as a prompt. An example of a prompt sentence: "Schedule a project meeting tomorrow at 10 AM."

[0106] 5. The generative AI generates a response based on the prompt received from the server, for example, "We've set up a project meeting for tomorrow at 10 AM. After that, we recommend scheduling a conference call with the client for one hour later."

[0107] 6. The server formats the generated response into JSON and sends it back to the device.

[0108] 7. The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[0109] Specific examples

[0110] A user types into a terminal, "Add a new task at 2 PM."

[0111] The device encodes the message in JSON format and sends it to the server as an HTTP POST request.

[0112] The server receives the request and parses the JSON data to extract the user's message.

[0113] The server preprocesses the extracted message and sends it to the generative AI as a prompt. An example of a prompt is: "Add a new task at 2 PM."

[0114] The generative AI generates a response like, "You've added a new task at 2 PM. We recommend that you adjust your schedule so that you can complete the task in the morning before that."

[0115] The server formats the response in JSON and sends it back to the device.

[0116] The terminal parses the response and displays it to the user.

[0117] In this way, the system can streamline user schedule management and task prioritization.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] The user enters a message into the terminal.

[0121] Specific behavior:

[0122] The user opens the calendar app on their smartphone or PC.

[0123] Type "Schedule a project meeting tomorrow at 10 AM" into the text box.

[0124] Input data:

[0125] Message entered by user: "Schedule a project meeting tomorrow at 10 AM"

[0126] Output data:

[0127] User messages captured on the device

[0128] Step 2:

[0129] The device encodes the user's input message into JSON format and sends it to the server as an HTTP POST request.

[0130] Specific behavior:

[0131] The terminal captures the user's input.

[0132] The captured message is encoded in JSON format as follows:

[0133] json

[0134] {

[0135] "message": "Schedule a project meeting tomorrow at 10 AM"

[0136] }

[0137] Send an HTTP POST request containing the encoded data to the server.

[0138] Input data:

[0139] Captured user messages

[0140] Output data:

[0141] JSON formatted HTTP POST request

[0142] Step 3:

[0143] The server analyzes the request received from the device and extracts the user's message from the JSON data.

[0144] Specific behavior:

[0145] The server receives an HTTP request.

[0146] Parse the request body and extract the "message" field from the JSON data.

[0147] Input data:

[0148] HTTP POST request in JSON format sent from the terminal

[0149] Output data:

[0150] Extracted user message: "Schedule a project meeting tomorrow at 10 AM"

[0151] Step 4:

[0152] The server preprocesses the extracted message into a format suitable for the generative artificial intelligence and sends this message to the generative artificial intelligence as a prompt.

[0153] Specific behavior:

[0154] The server preprocesses the extracted message, for example removing unnecessary whitespace and special characters.

[0155] Format the preprocessed message in a prompt format, such as "Schedule a project meeting tomorrow at 10 AM."

[0156] Send the formatted prompt to the generative artificial intelligence.

[0157] Input data:

[0158] Extracted user messages

[0159] Output data:

[0160] Preprocessed and prompt-formatted message

[0161] Step 5:

[0162] The generative artificial intelligence generates a response based on the message received from the server.

[0163] Specific behavior:

[0164] The generative artificial intelligence receives the prompt.

[0165] Parse the prompt and generate an appropriate response, such as "I've set up a project meeting for tomorrow at 10 AM. After that, I'd recommend scheduling a conference call with your client in an hour."

[0166] Input data:

[0167] The prompt sent by the server

[0168] Output data:

[0169] The generated response message

[0170] Step 6:

[0171] The server formats the response received from the generative AI into JSON format and sends it back to the device.

[0172] Specific behavior:

[0173] The server receives the response from the generative artificial intelligence.

[0174] The response message is formatted as JSON, as follows:

[0175] json

[0176] {

[0177] "response": "I've set up a project meeting for tomorrow at 10 AM. I'd like to schedule a conference call with the client afterwards in an hour."

[0178] }

[0179] The formatted JSON data is returned to the terminal as an HTTP response.

[0180] Input data:

[0181] Response message received from generative artificial intelligence

[0182] Output data:

[0183] JSON formatted HTTP response

[0184] Step 7:

[0185] The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[0186] Specific behavior:

[0187] The device receives the HTTP response.

[0188] Parse the JSON data from the response and extract the "response" field.

[0189] The extracted response text is displayed to the user.

[0190] Input data:

[0191] JSON formatted HTTP response sent from the server

[0192] Output data:

[0193] Parsed response text: "I've set up a project meeting for tomorrow at 10 AM. After that, I'd like to suggest scheduling a conference call with my client in an hour."

[0194] Step 8:

[0195] The user decides on the next action based on the provided response.

[0196] Specific behavior:

[0197] The user checks the response displayed on the terminal.

[0198] If necessary, enter the following instructions into the terminal:

[0199] Input data:

[0200] The parsed response text

[0201] Output data:

[0202] User's next action

[0203] (Application example 1)

[0204] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0205] Conventional factory management systems require users to manually manage machine operation status and maintenance plans, which is inefficient. Furthermore, they have problems with not being able to propose appropriate schedules when an emergency response is required or when adjusting task priorities. This can lead to work delays, increased costs, and reduced overall efficiency.

[0206] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0207] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence and generating an appropriate response, means for receiving the response generated by the generative artificial intelligence, means for integrated management of the operating status and maintenance plans of machines in a factory, means for the generative artificial intelligence to propose optimal schedules and task priorities for machines based on inquiries input by the user, and means for displaying the response of the generative artificial intelligence to the user. This allows users to easily optimize the operating status and maintenance plans of machines, enabling efficient factory operation.

[0208] A "user" is an entity that uses the system to manage machinery within a factory and optimize maintenance plans.

[0209] A "message" is an instruction or inquiry that a user inputs to the system.

[0210] The "server" is a central computer that processes messages received from users, sends them to the generative artificial intelligence, and generates appropriate responses.

[0211] "Generative AI" refers to an AI model that generates responses based on messages from users, and includes models such as GPT-3.

[0212] A "response" is an appropriate answer or suggestion to a user's message, generated by generative artificial intelligence.

[0213] "Operation status" is information that indicates the current operating status of machines in a factory.

[0214] A "maintenance plan" refers to a plan for the maintenance and inspection of machinery in a factory, and is a schedule for optimizing the operation of machinery over the long term.

[0215] "Integrated management" is a method for centrally managing the operating status and maintenance plans of multiple machines and systems to achieve overall optimization.

[0216] An "optimal schedule" refers to the time allocation and work order that are deemed most appropriate for efficient operation and maintenance of machines.

[0217] "Task priority" refers to the order in which tasks should be performed first among multiple jobs or operations, determined based on importance and urgency.

[0218] The present invention provides a system that allows users to efficiently manage machinery in factories and plan maintenance. This system includes a mechanism that receives inquiries and instructions from users, uses generative artificial intelligence to generate optimal responses based on those inquiries and instructions, and displays the responses to the users.

[0219] First, a user accesses the system using a device such as a smartphone or PC. For example, the user enters a message such as, "When is the next maintenance?" The device then sends this message to the server. The server then sends the received message to the generative artificial intelligence, which then generates an appropriate response.

[0220] The server uses a model such as GPT-3 as a generative AI. This generative AI generates a response based on the user's message. For example, a possible response might be, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd." The server receives the generated response and sends it to the terminal in JSON format.

[0221] Factory management requires a system that can integrate and manage the operating status and maintenance plans of machines within the factory. This system uses generative artificial intelligence to propose optimal machine schedules and task priorities based on inquiries entered by the user. For example, if a user enters, "What is tomorrow's shift schedule?", the system can generate a response such as, "Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow all safety precautions when working."

[0222] The main components required to implement this system are:

[0223] 1. User: The entity that uses a terminal to access the system and input messages and instructions.

[0224] 2. Terminal: A device that receives messages entered by the user, sends them to the server, and displays responses from the server to the user.

[0225] 3. Server: A central management system that analyzes messages received from users and sends them to the generative AI, and also receives generated responses and sends them back to the terminal.

[0226] 4. Generative AI: An AI model that generates responses based on messages from users. An example is GPT-3.

[0227] This allows users to automatically optimize the operating status and maintenance plans of machines within their factories, enabling efficient factory operations.

[0228] Example prompt sentence:

[0229] Message from factory manager: When is the next maintenance?

[0230] Please respond in the following format:

[0231] Response: The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd.

[0232] For example, if a user types "What is tomorrow's shift schedule?", the generative AI will generate the following response:

[0233] Response: Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow the safety precautions when working.

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] The user types a message into a device (such as a smartphone or PC). For example, they type, "When is the next maintenance?"

[0237] Input: A message from the user.

[0238] Output: The user's message displayed on the terminal.

[0239] Step 2:

[0240] The device encodes the message entered by the user as a JSON-formatted request and sends an HTTP POST request to the server.

[0241] Input: The message entered by the user.

[0242] Output: The HTTP POST request sent to the server.

[0243] Step 3:

[0244] The server analyzes the request received from the device, acquires the user's message, and then performs preprocessing to send the user's message to the generative AI.

[0245] Input: HTTP POST request.

[0246] Output: The message sent to the generative artificial intelligence.

[0247] Step 4:

[0248] The generative AI generates a response based on the message received from the server. For example, it generates a response such as, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd."

[0249] Input: The message sent by the server.

[0250] Output: The generated response.

[0251] Step 5:

[0252] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0253] Input: A response from a generative artificial intelligence.

[0254] Output: The response in JSON format.

[0255] Step 6:

[0256] The terminal parses the JSON format response sent from the server, extracts the response text, and displays it to the user.

[0257] Input: JSON response from the server.

[0258] Output: The response text that is displayed to the user.

[0259] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0260] This invention is a system that combines a calendar app and a task management app with generative artificial intelligence and an emotion engine to improve the efficiency of schedule management and task prioritization, and also provides responses that take the user's emotions into consideration. The system's purpose is to use generative artificial intelligence to generate appropriate responses from messages entered by the user, and then add an emotion engine to generate more personalized responses based on the user's emotions.

[0261] Explanation of system program processing

[0262] System Overview

[0263] The system consists of the following major components:

[0264] 1. Users: Use the system to manage schedules and tasks.

[0265] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0266] 3. Emotion engine: A system that analyzes messages entered by users and recognizes their emotions.

[0267] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0268] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[0269] Example of user interaction

[0270] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[0271] 2. The device receives this message and sends it to the server. The device encodes the message as a JSON-formatted request and sends it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[0272] 3. The emotion engine analyzes the user's messages and recognizes their emotions, for example, detecting stress or tension from the messages.

[0273] 4. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[0274] 5. The generative AI generates a response based on the message and emotional information received from the server. For example, if the system determines that the user is nervous, the response may include gentle advice such as "Please relax and enjoy the meeting."

[0275] 6. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the GPT-3 response, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[0276] 7. The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[0277] 8. The user decides on the next action based on the generative AI's response presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[0278] Specific operation example

[0279] 1. A user types into their device, "Add a new task at 2 PM." The emotion engine recognizes the emotion of impatience from the user's message.

[0280] 2. The device receives this message and sends it to the server, encoding it as a JSON request and sending it to the server as an HTTP POST request.

[0281] 3. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[0282] 4. The generative AI generates a response based on the message and emotional information. An example of a generated response might be, "I added a new task at 2 PM. Before that, I recommend you take a break to get through your busy day."

[0283] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0284] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0285] 7. The user decides on the next action based on the presented response.

[0286] In this way, by combining emotion engines, personalized responses tailored to the user's emotions can be provided, further reducing the burden on the user.

[0287] The processing flow will be explained below.

[0288] Step 1:

[0289] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[0290] Step 2:

[0291] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[0292] Step 3:

[0293] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[0294] Step 4:

[0295] The server sends the acquired user message to the emotion engine, which recognizes the user's emotion. The emotion engine analyzes the message and generates emotion information.

[0296] Step 5:

[0297] The emotion engine recognizes emotions from the user's input message and returns the emotion information to the server. For example, if the user is nervous, the degree of nervousness is returned as numerical information.

[0298] Step 6:

[0299] The server receives the emotion information from the emotion engine and sends it along with the user's message to the generative AI. Specifically, it includes the emotion information in the OpenAI API request.

[0300] Step 7:

[0301] The generative AI generates an appropriate response based on the message and emotional information received from the server. For example, it generates a response that takes emotions into account, such as, "We've scheduled a project meeting for tomorrow at 10:00 AM. If you attend in a relaxed manner, you'll likely see results."

[0302] Step 8:

[0303] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it encodes the generated response text in JSON format and sends it to the device as an HTTP response.

[0304] Step 9:

[0305] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[0306] Step 10:

[0307] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[0308] In this way, the system allows users to receive personalized responses that take their emotions into account, allowing them to manage their schedules and prioritize tasks more efficiently.

[0309] Example 2

[0310] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0311] Conventional schedule management systems and task management systems only provide mechanical responses to user input and have the problem of not being able to respond in a way that takes into account the user's emotional state. This makes it difficult to provide appropriate advice and support to users who are feeling stressed or tense.

[0312] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing a message input by a user and recognizing the user's emotion using an emotion engine, means for transmitting the message and emotion information to the generative AI, and means for receiving a response generated by the generative AI. This enables a personalized response based on the user's emotion.

[0313] "User" refers to the person who uses the system to manage schedules and tasks.

[0314] A "terminal" is a device that receives a message entered by a user, sends it to a server, and displays a response from the server to the user, and specifically includes a smartphone, a personal computer, etc.

[0315] "Server" refers to a central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0316] An "emotion engine" refers to a system that analyzes messages entered by users and recognizes their emotions.

[0317] "Generative AI" refers to an AI model that generates appropriate responses based on messages and emotional information received from a server, and a specific example is GPT-3.

[0318] "Message" refers to text information about schedules and tasks that a user inputs into a terminal.

[0319] "Response" refers to a reply that the generative artificial intelligence generates based on the user's message and emotional information.

[0320] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format that is widely used as a data exchange format.

[0321] This invention is a response generation system that combines generative artificial intelligence and an emotion engine based on a message entered by a user. The system aims to provide personalized responses by taking into account the user's emotions. The system mainly consists of the following components:

[0322] 1. User: A user who uses the system to manage schedules and tasks. The user enters text messages into the terminal.

[0323] 2. Terminal: A device that receives messages entered by users and sends them to the server. The terminal can be a smartphone or a PC, and encodes the messages entered by users into JSON format and sends them to the server as an HTTP POST request.

[0324] 3. Server: A central management system that receives messages and emotional information from users, analyzes them, and sends them to the generative AI. The server uses an emotion engine to recognize the user's emotions and passes that information to the generative AI.

[0325] 4. Emotion Engine: A system that analyzes messages entered by users and recognizes their emotions. The emotion engine understands the context of the message and can detect emotions such as stress or tension.

[0326] 5. Generative AI: An AI model that generates appropriate responses based on messages and emotional information received from the server. A specific example is a generative AI model such as GPT-3.

[0327] As an example of how the system works, consider the case where a user types into a terminal, "Schedule a project meeting tomorrow at 10 AM." This message is processed as follows:

[0328] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[0329] 2. The device receives this message, encodes it into a JSON-formatted request, and sends it to the server as an HTTP POST request.

[0330] 3. After the server receives the request, it uses the emotion engine to analyze the user's emotion. For example, it may determine that the user is nervous.

[0331] 4. The server sends the analyzed message and emotional information to the generative artificial intelligence.

[0332] 5. The generative AI generates a response based on the information it receives. For example, it might say, "We've scheduled a project meeting for tomorrow at 10:00 AM. Please relax and enjoy the meeting."

[0333] 6. The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[0334] 7. The device receives the response from the server, parses it, and displays it to the user.

[0335] An example prompt might have the following format:

[0336] "Schedule a project meeting tomorrow at 10 AM"

[0337] Through the above process, it is possible to provide appropriate responses that take the user's emotions into consideration. This system provides advice according to the user's emotional state, reducing stress and enabling efficient schedule management.

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Step 1:

[0340] The user enters a message into the terminal.

[0341] How it works: A user types into a device (such as a smartphone or computer) the following: "Schedule a project meeting tomorrow at 10 AM." This message triggers the system's processing.

[0342] Input: A user-entered text message: "Schedule a project meeting tomorrow at 10 AM."

[0343] Output: A text message is sent to the terminal.

[0344] Step 2:

[0345] The terminal receives the message and sends it to the server.

[0346] How it works: The device encodes the received message into JSON format and sends it as an HTTP POST request to a specific endpoint on the server (e.g., / schedule).

[0347] Input: The text message entered by the user.

[0348] Output: The message is encoded as JSON data and sent to the server.

[0349] json

[0350] {

[0351] "message": "Schedule a project meeting tomorrow at 10 AM"

[0352] }

[0353] Step 3:

[0354] The server receives the message and analyzes it using the emotion engine.

[0355] How it works: The server analyzes the received message and uses an emotion engine to recognize the user's emotions. For example, tension or stress may be detected.

[0356] Input: JSON formatted message sent from the device.

[0357] Output: Perceived emotional information (e.g., tension).

[0358] json

[0359] {

[0360] "message": "Schedule a project meeting tomorrow at 10 AM",

[0361] "emotion": "nervous"

[0362] }

[0363] Step 4:

[0364] The server sends the message and emotion information to the generative artificial intelligence.

[0365] How it works: Sends the user's message and emotion information to a generative artificial intelligence model (e.g., GPT-3).

[0366] Input: User's message and parsed sentiment information.

[0367] Output: Message and emotion information forwarded to the generative AI.

[0368] Step 5:

[0369] Generative artificial intelligence generates responses.

[0370] How it works: The generative AI model generates an appropriate response based on the received message and emotional information. For example, if the user is nervous, it will generate a response that includes advice such as "Relax and approach the meeting."

[0371] Input: Message and emotion information received from the server.

[0372] Output: The generated response.

[0373] json

[0374] {

[0375] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[0376] }

[0377] Step 6:

[0378] The server sends the generated response to the terminal.

[0379] Behavior: The generated response is encoded in JSON format and sent to the device as an HTTP response.

[0380] Input: Response received from the generative artificial intelligence.

[0381] Output: Response data in JSON format is sent to the terminal.

[0382] Step 7:

[0383] The terminal receives the response and displays it to the user.

[0384] How it works: The device receives the JSON response sent by the server, parses it to extract the textual response, and displays it to the user.

[0385] Input: JSON formatted response data sent from the server.

[0386] Output: A text response that is displayed to the user.

[0387] json

[0388] {

[0389] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[0390] }

[0391] In this way, by clarifying the specific operations and inputs and outputs at each step, it becomes easier to understand the processing flow of the entire system.

[0392] (Application example 2)

[0393] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0394] In modern factories, workers must efficiently manage a wide variety of complex tasks, which often leads to stress and impatience. Conventional systems struggle to provide feedback and support that takes into account workers' emotional states, resulting in problems such as reduced work efficiency and increased errors. Furthermore, task scheduling and prioritization are not centrally managed, placing a heavy burden on workers.

[0395] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0396] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence (AI) to generate an appropriate response, means for receiving the response generated by the generative artificial intelligence (AI), means for displaying the response to the user, means for managing the user's tasks and schedule using smart glasses, and means for recognizing the user's emotional state using an emotion engine and generating a response based on the emotion. This enables personalized feedback and support that takes into account the worker's emotional state, enabling efficient task management and reducing the worker's mental burden.

[0397] "Users" are factory workers who use the system to manage tasks and schedules.

[0398] A "message" is any information or instruction entered by a user that is processed and interpreted by the system.

[0399] The "server" is a central management system that receives messages from users, sends them to the generative artificial intelligence, and generates, receives, and sends responses.

[0400] "Generative AI" is an AI model that generates appropriate responses based on the user's message and emotional information.

[0401] A "response" is a reply generated by the generative artificial intelligence to a message entered by a user.

[0402] "Smart glasses" are wearable devices that users wear to manage tasks and schedules and receive notifications in real time.

[0403] An "emotion engine" is a system that analyzes emotions from the user's input message and recognizes those emotions.

[0404] A "task" is a job or operation that a user performs in a factory and is managed by the system.

[0405] A "schedule" is a plan that organizes and arranges a user's tasks in terms of time.

[0406] "Personalized feedback" refers to individualized feedback or advice that takes into account the user's emotional state.

[0407] This invention is a system for improving the efficiency of factory work. It uses smart glasses worn by workers to manage tasks and schedules, and also combines an emotion engine to recognize the emotional state of the worker and provide personalized feedback and advice based on that emotion. The main function of this system is to generate appropriate responses using generative artificial intelligence.

[0408] Hardware and software used

[0409] The system consists of the following main components:

[0410] 1. Smart glasses: A wearable device worn by the user to manage tasks and schedules in real time.

[0411] 2. Terminal: A device that acts as part of the smart glasses, receives user input messages, sends them to the server, and displays responses from the server to the user.

[0412] 3. Emotion engine: A system that analyzes emotions from the user's input message and recognizes those emotions.

[0413] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0414] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[0415] System Operation

[0416] 1. User input: A voice message entered by the worker through the smart glasses, such as "Start a new task at 2pm."

[0417] 2. Sentiment Analysis: The emotion engine analyzes the user's message and recognizes their emotional state (e.g., impatience, stress, joy, etc.). This analysis is done using natural language processing techniques.

[0418] 3. Send message: The device encodes the user's message in JSON format and sends it to the server as an HTTP POST request.

[0419] 4. Response generation: The server sends the received message and emotional information to the generative AI, which then generates an appropriate response. For example, if the user is feeling impatient, the system might generate a response such as, "I'll start a new task at 2 p.m. Please proceed without rushing."

[0420] 5. Sending response: The server receives the response from the generative AI, formats it again in JSON format, and sends it back to the device.

[0421] 6. Response display: The terminal parses the JSON response sent from the server, extracts the response text, and displays it to the user. The response is displayed on the smart glasses display, allowing the worker to check it in real time.

[0422] Specific examples

[0423] When a user voice-inputs "I'll start a new task at 2 PM" into the smart glasses, the emotion engine recognizes the emotion of impatience. Then, a generative AI (e.g., GPT-3) generates a response such as "I'll start a new task at 2 PM. Please proceed calmly," which is displayed on the smart glasses. This process allows workers to efficiently manage changes to work plans and additional tasks.

[0424] Prompt Sentence Examples

[0425] The text prompt is as follows:

[0426] User says: "I'll start a new task at 2 PM."

[0427] Emotional state: Impatience

[0428] Generate a response.

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1:

[0431] The user uses the smart glasses to input voice messages related to tasks and schedules, such as "Start a new task at 2 p.m." The smart glasses convert the voice messages into text format and send them to the device as data.

[0432] Step 2:

[0433] The terminal receives a user input message, which is a text message such as "Start a new task at 2 PM." The terminal encodes this message into JSON format and sends it to the server using an HTTP POST request.

[0434] Step 3:

[0435] The server receives a JSON-formatted message sent from the device. The server analyzes the received data and extracts the user's message. In this case, the message is text data such as "Start new work at 2 PM," and the server sends this to the emotion engine.

[0436] Step 4:

[0437] The emotion engine analyzes messages sent from the server and recognizes the user's emotional state. The input is text data such as "I'll start a new task at 2 PM," and the analysis results in emotional data such as "impatient." This emotional information is then sent back to the server.

[0438] Step 5:

[0439] The server receives emotional information from the emotion engine. The server combines this emotional information with the message and sends it to the generative AI. The input is the text message "Start new work at 2 PM" and the emotional information "impatient," and an appropriate response is generated based on these.

[0440] Step 6:

[0441] The generative AI generates a response based on the message and emotional information from the server. The input is the text message "Start new work at 2 PM" and the emotional information "impatience," and the output is a response such as "Start new work at 2 PM. Please proceed without rushing." This response is sent to the server.

[0442] Step 7:

[0443] The server receives the response from the generative AI and encodes it into JSON format. The input is the response text "A new task will start at 2 PM. Please proceed slowly." The response data converted into JSON format is output. The server sends this data to the terminal.

[0444] Step 8:

[0445] The device receives the JSON-formatted response sent from the server. The device parses this data and extracts the response text. The input is the JSON-formatted response data, and the output is text data that reads, "A new task will start at 2 p.m. Please proceed without rushing." The device displays this response on the smart glasses.

[0446] Step 9:

[0447] The user checks the response displayed on the smart glasses. The response is "You will start a new task at 2 PM. Please take your time." Based on this, the user decides on the next action. "Specific actions include starting the task, taking a break, etc."

[0448] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0449] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0450] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0451] [Second embodiment]

[0452] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0453] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0454] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0455] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0456] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0458] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0459] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0460] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0461] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0462] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0463] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0464] This invention is a system that combines a calendar application, a task management application, and a generative artificial intelligence to improve the efficiency of schedule management and task prioritization. The system sends messages entered by the user to the generative artificial intelligence, generates an appropriate response, and presents that response to the user.

[0465] Explanation of system program processing

[0466] System Overview

[0467] The system consists of the following major components:

[0468] 1. Users: Use the system to manage schedules and tasks.

[0469] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0470] 3. Server: A central management system that receives messages from users, sends them to the generative AI, and sends the generated responses back to the terminal.

[0471] 4. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages received from the server.

[0472] Example of user interaction

[0473] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[0474] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[0475] 3. The server analyzes the request received from the device and obtains the user's message. The server performs preprocessing and sends this message to the generative AI.

[0476] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "We've scheduled a project meeting for tomorrow at 10:00 AM. After that, we recommend scheduling a conference call with the client for one hour."

[0477] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0478] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0479] 7. The user decides on their next action based on the provided responses, such as asking more specific questions, adding other tasks, or reviewing or modifying the proposed schedule.

[0480] Specific operation example

[0481] 1. The user types into the terminal, "Add a new task at 2 PM."

[0482] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[0483] 3. The server analyzes the request received from the device and obtains the user's message. The server then performs preprocessing and sends the message to the generative AI.

[0484] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "A new task has been added for 2 PM. We recommend that you adjust your schedule so that the task can be completed in the morning."

[0485] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0486] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0487] 7. The user decides on the next action based on the presented response.

[0488] In this way, this system can improve the efficiency of users' schedule management and task prioritization, reducing the burden on users.

[0489] The processing flow will be explained below.

[0490] Step 1:

[0491] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[0492] Step 2:

[0493] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[0494] Step 3:

[0495] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[0496] Step 4:

[0497] The server sends the acquired user message to the generative AI by creating a request to the OpenAI API and sending the message as a prompt to the API.

[0498] Step 5:

[0499] The generative AI analyzes messages received from the server and generates appropriate responses, such as, "We've set up a project meeting for tomorrow at 10 a.m. After that, we recommend scheduling a conference call with your client for one hour later."

[0500] Step 6:

[0501] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the response from GPT-3, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[0502] Step 7:

[0503] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[0504] Step 8:

[0505] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[0506] Example 1

[0507] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0508] Conventional schedule management and task management systems have the problem that users have to manually input and manage a large number of tasks and schedules, which is time-consuming. In addition, the system has limited functionality for automatically prioritizing tasks and providing advice on required time, making efficient management difficult. There is a need for a schedule management and task management system that solves these problems and reduces the burden on users.

[0509] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0510] In this invention, the server includes means for the terminal to send user input in JSON format to the server, means for the server to analyze the received JSON data and extract the user message, means for preprocessing the message to be sent by the server to the generative AI, means for formatting a response received from the generative AI in JSON format and sending it to the terminal, and means for the terminal to analyze and display the JSON formatted response sent from the server. This allows the user to have their input processed quickly and accurately and receive a response including advice on task prioritization and required time.

[0511] A "user" is an entity that uses the system to manage schedules and tasks.

[0512] A "message" refers to information or instructions entered by a user.

[0513] "Terminal" refers to the device a user uses to enter messages and view received responses (e.g., a smartphone or PC).

[0514] "Server" refers to a central management system that receives messages sent from a terminal, works with generative artificial intelligence to generate a response, and sends it back to the terminal.

[0515] "Generative AI" refers to an AI model that generates appropriate responses based on messages received from a server.

[0516] "Response" refers to a reply that a generative artificial intelligence generates based on a user's message.

[0517] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a standard format for structuring and representing data.

[0518] "Preprocessing" refers to the process of formatting the messages that the server sends to the generative artificial intelligence into an appropriate format.

[0519] "Formatting" refers to converting data into a particular form.

[0520] This invention is a system that streamlines schedule and task management, integrating a calendar app, a task management app, and generative artificial intelligence. This system analyzes messages entered by the user and provides appropriate schedule and task prioritization.

[0521] Hardware and software used

[0522] User: The entity that uses the system to manage schedules and tasks.

[0523] Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0524] Server: A central management system that receives messages from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0525] Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages received from a server.

[0526] JSON format: A standard format for structuring and representing data.

[0527] Processing flow details

[0528] 1. A user enters schedule or task-related instructions into a device. For example, a user might enter, "Schedule a project meeting tomorrow at 10 AM."

[0529] 2. The device captures the user's input message, encodes it into JSON format, and sends it to the server as an HTTP POST request.

[0530] 3. The server analyzes the request received from the device and extracts the user's message from the JSON data.

[0531] 4. The server preprocesses the extracted message into a format suitable for the generative AI and sends this message to the generative AI as a prompt. An example of a prompt sentence: "Schedule a project meeting tomorrow at 10 AM."

[0532] 5. The generative AI generates a response based on the prompt received from the server, for example, "We've set up a project meeting for tomorrow at 10 AM. After that, we recommend scheduling a conference call with the client for one hour later."

[0533] 6. The server formats the generated response into JSON and sends it back to the device.

[0534] 7. The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[0535] Specific examples

[0536] A user types into a terminal, "Add a new task at 2 PM."

[0537] The device encodes the message in JSON format and sends it to the server as an HTTP POST request.

[0538] The server receives the request and parses the JSON data to extract the user's message.

[0539] The server preprocesses the extracted message and sends it to the generative AI as a prompt. An example of a prompt is: "Add a new task at 2 PM."

[0540] The generative AI generates a response like, "You've added a new task at 2 PM. We recommend that you adjust your schedule so that you can complete the task in the morning before that."

[0541] The server formats the response in JSON and sends it back to the device.

[0542] The terminal parses the response and displays it to the user.

[0543] In this way, the system can streamline user schedule management and task prioritization.

[0544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0545] Step 1:

[0546] The user enters a message into the terminal.

[0547] Specific behavior:

[0548] The user opens the calendar app on their smartphone or PC.

[0549] Type "Schedule a project meeting tomorrow at 10 AM" into the text box.

[0550] Input data:

[0551] Message entered by user: "Schedule a project meeting tomorrow at 10 AM"

[0552] Output data:

[0553] User messages captured on the device

[0554] Step 2:

[0555] The device encodes the user's input message into JSON format and sends it to the server as an HTTP POST request.

[0556] Specific behavior:

[0557] The terminal captures the user's input.

[0558] The captured message is encoded in JSON format as follows:

[0559] json

[0560] {

[0561] "message": "Schedule a project meeting tomorrow at 10 AM"

[0562] }

[0563] Send an HTTP POST request containing the encoded data to the server.

[0564] Input data:

[0565] Captured user messages

[0566] Output data:

[0567] JSON formatted HTTP POST request

[0568] Step 3:

[0569] The server analyzes the request received from the device and extracts the user's message from the JSON data.

[0570] Specific behavior:

[0571] The server receives an HTTP request.

[0572] Parse the request body and extract the "message" field from the JSON data.

[0573] Input data:

[0574] HTTP POST request in JSON format sent from the terminal

[0575] Output data:

[0576] Extracted user message: "Schedule a project meeting tomorrow at 10 AM"

[0577] Step 4:

[0578] The server preprocesses the extracted message into a format suitable for the generative artificial intelligence and sends this message to the generative artificial intelligence as a prompt.

[0579] Specific behavior:

[0580] The server preprocesses the extracted message, for example removing unnecessary whitespace and special characters.

[0581] Format the preprocessed message in a prompt format, such as "Schedule a project meeting tomorrow at 10 AM."

[0582] Send the formatted prompt to the generative artificial intelligence.

[0583] Input data:

[0584] Extracted user messages

[0585] Output data:

[0586] Preprocessed and prompt-formatted message

[0587] Step 5:

[0588] The generative artificial intelligence generates a response based on the message received from the server.

[0589] Specific behavior:

[0590] The generative artificial intelligence receives the prompt.

[0591] Parse the prompt and generate an appropriate response, such as "I've set up a project meeting for tomorrow at 10 AM. After that, I'd recommend scheduling a conference call with your client in an hour."

[0592] Input data:

[0593] The prompt sent by the server

[0594] Output data:

[0595] The generated response message

[0596] Step 6:

[0597] The server formats the response received from the generative AI into JSON format and sends it back to the device.

[0598] Specific behavior:

[0599] The server receives the response from the generative artificial intelligence.

[0600] The response message is formatted as JSON, as follows:

[0601] json

[0602] {

[0603] "response": "I've set up a project meeting for tomorrow at 10 AM. I'd like to schedule a conference call with the client afterwards in an hour."

[0604] }

[0605] The formatted JSON data is returned to the terminal as an HTTP response.

[0606] Input data:

[0607] Response message received from generative artificial intelligence

[0608] Output data:

[0609] JSON formatted HTTP response

[0610] Step 7:

[0611] The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[0612] Specific behavior:

[0613] The device receives the HTTP response.

[0614] Parse the JSON data from the response and extract the "response" field.

[0615] The extracted response text is displayed to the user.

[0616] Input data:

[0617] JSON formatted HTTP response sent from the server

[0618] Output data:

[0619] Parsed response text: "I've set up a project meeting for tomorrow at 10 AM. After that, I'd like to suggest scheduling a conference call with my client in an hour."

[0620] Step 8:

[0621] The user decides on the next action based on the provided response.

[0622] Specific behavior:

[0623] The user checks the response displayed on the terminal.

[0624] If necessary, enter the following instructions into the terminal:

[0625] Input data:

[0626] The parsed response text

[0627] Output data:

[0628] User's next action

[0629] (Application example 1)

[0630] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0631] Conventional factory management systems require users to manually manage machine operation status and maintenance plans, which is inefficient. Furthermore, they have problems with not being able to propose appropriate schedules when an emergency response is required or when adjusting task priorities. This can lead to work delays, increased costs, and reduced overall efficiency.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0633] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence and generating an appropriate response, means for receiving the response generated by the generative artificial intelligence, means for integrated management of the operating status and maintenance plans of machines in a factory, means for the generative artificial intelligence to propose optimal schedules and task priorities for machines based on inquiries input by the user, and means for displaying the response of the generative artificial intelligence to the user. This allows users to easily optimize the operating status and maintenance plans of machines, enabling efficient factory operation.

[0634] A "user" is an entity that uses the system to manage machinery within a factory and optimize maintenance plans.

[0635] A "message" is an instruction or inquiry that a user inputs to the system.

[0636] The "server" is a central computer that processes messages received from users, sends them to the generative artificial intelligence, and generates appropriate responses.

[0637] "Generative AI" refers to an AI model that generates responses based on messages from users, and includes models such as GPT-3.

[0638] A "response" is an appropriate answer or suggestion to a user's message, generated by generative artificial intelligence.

[0639] "Operation status" is information that indicates the current operating status of machines in a factory.

[0640] A "maintenance plan" refers to a plan for the maintenance and inspection of machinery in a factory, and is a schedule for optimizing the operation of machinery over the long term.

[0641] "Integrated management" is a method for centrally managing the operating status and maintenance plans of multiple machines and systems to achieve overall optimization.

[0642] An "optimal schedule" refers to the time allocation and work order that are deemed most appropriate for efficient operation and maintenance of machines.

[0643] "Task priority" refers to the order in which tasks should be performed first among multiple jobs or operations, determined based on importance and urgency.

[0644] The present invention provides a system that allows users to efficiently manage machinery in factories and plan maintenance. This system includes a mechanism that receives inquiries and instructions from users, uses generative artificial intelligence to generate optimal responses based on those inquiries and instructions, and displays the responses to the users.

[0645] First, a user accesses the system using a device such as a smartphone or PC. For example, the user enters a message such as, "When is the next maintenance?" The device then sends this message to the server. The server then sends the received message to the generative artificial intelligence, which then generates an appropriate response.

[0646] The server uses a model such as GPT-3 as a generative AI. This generative AI generates a response based on the user's message. For example, a possible response might be, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd." The server receives the generated response and sends it to the terminal in JSON format.

[0647] Factory management requires a system that can integrate and manage the operating status and maintenance plans of machines within the factory. This system uses generative artificial intelligence to propose optimal machine schedules and task priorities based on inquiries entered by the user. For example, if a user enters, "What is tomorrow's shift schedule?", the system can generate a response such as, "Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow all safety precautions when working."

[0648] The main components required to implement this system are:

[0649] 1. User: The entity that uses a terminal to access the system and input messages and instructions.

[0650] 2. Terminal: A device that receives messages entered by the user, sends them to the server, and displays responses from the server to the user.

[0651] 3. Server: A central management system that analyzes messages received from users and sends them to the generative AI, and also receives generated responses and sends them back to the terminal.

[0652] 4. Generative AI: An AI model that generates responses based on messages from users. An example is GPT-3.

[0653] This allows users to automatically optimize the operating status and maintenance plans of machines within their factories, enabling efficient factory operations.

[0654] Example prompt sentence:

[0655] Message from factory manager: When is the next maintenance?

[0656] Please respond in the following format:

[0657] Response: The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd.

[0658] For example, if a user types "What is tomorrow's shift schedule?", the generative AI will generate the following response:

[0659] Response: Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow the safety precautions when working.

[0660] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0661] Step 1:

[0662] The user types a message into a device (such as a smartphone or PC). For example, they type, "When is the next maintenance?"

[0663] Input: A message from the user.

[0664] Output: The user's message displayed on the terminal.

[0665] Step 2:

[0666] The device encodes the message entered by the user as a JSON-formatted request and sends an HTTP POST request to the server.

[0667] Input: The message entered by the user.

[0668] Output: The HTTP POST request sent to the server.

[0669] Step 3:

[0670] The server analyzes the request received from the device, acquires the user's message, and then performs preprocessing to send the user's message to the generative AI.

[0671] Input: HTTP POST request.

[0672] Output: The message sent to the generative artificial intelligence.

[0673] Step 4:

[0674] The generative AI generates a response based on the message received from the server. For example, it generates a response such as, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd."

[0675] Input: The message sent by the server.

[0676] Output: The generated response.

[0677] Step 5:

[0678] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0679] Input: A response from a generative artificial intelligence.

[0680] Output: The response in JSON format.

[0681] Step 6:

[0682] The terminal parses the JSON format response sent from the server, extracts the response text, and displays it to the user.

[0683] Input: JSON response from the server.

[0684] Output: The response text that is displayed to the user.

[0685] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0686] This invention is a system that combines a calendar app and a task management app with generative artificial intelligence and an emotion engine to improve the efficiency of schedule management and task prioritization, and also provides responses that take the user's emotions into consideration. The system's purpose is to use generative artificial intelligence to generate appropriate responses from messages entered by the user, and then add an emotion engine to generate more personalized responses based on the user's emotions.

[0687] Explanation of system program processing

[0688] System Overview

[0689] The system consists of the following major components:

[0690] 1. Users: Use the system to manage schedules and tasks.

[0691] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0692] 3. Emotion engine: A system that analyzes messages entered by users and recognizes their emotions.

[0693] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0694] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[0695] Example of user interaction

[0696] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[0697] 2. The device receives this message and sends it to the server. The device encodes the message as a JSON-formatted request and sends it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[0698] 3. The emotion engine analyzes the user's messages and recognizes their emotions, for example, detecting stress or tension from the messages.

[0699] 4. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[0700] 5. The generative AI generates a response based on the message and emotional information received from the server. For example, if the system determines that the user is nervous, the response may include gentle advice such as "Please relax and enjoy the meeting."

[0701] 6. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the GPT-3 response, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[0702] 7. The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[0703] 8. The user decides on the next action based on the generative AI's response presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[0704] Specific operation example

[0705] 1. A user types into their device, "Add a new task at 2 PM." The emotion engine recognizes the emotion of impatience from the user's message.

[0706] 2. The device receives this message and sends it to the server, encoding it as a JSON request and sending it to the server as an HTTP POST request.

[0707] 3. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[0708] 4. The generative AI generates a response based on the message and emotional information. An example of a generated response might be, "I added a new task at 2 PM. Before that, I recommend you take a break to get through your busy day."

[0709] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0710] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0711] 7. The user decides on the next action based on the presented response.

[0712] In this way, by combining emotion engines, personalized responses tailored to the user's emotions can be provided, further reducing the burden on the user.

[0713] The processing flow will be explained below.

[0714] Step 1:

[0715] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[0716] Step 2:

[0717] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[0718] Step 3:

[0719] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[0720] Step 4:

[0721] The server sends the acquired user message to the emotion engine, which recognizes the user's emotion. The emotion engine analyzes the message and generates emotion information.

[0722] Step 5:

[0723] The emotion engine recognizes emotions from the user's input message and returns the emotion information to the server. For example, if the user is nervous, the degree of nervousness is returned as numerical information.

[0724] Step 6:

[0725] The server receives the emotion information from the emotion engine and sends it along with the user's message to the generative AI. Specifically, it includes the emotion information in the OpenAI API request.

[0726] Step 7:

[0727] The generative AI generates an appropriate response based on the message and emotional information received from the server. For example, it generates a response that takes emotions into account, such as, "We've scheduled a project meeting for tomorrow at 10 a.m. If you attend in a relaxed manner, you'll likely see results."

[0728] Step 8:

[0729] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it encodes the generated response text in JSON format and sends it to the device as an HTTP response.

[0730] Step 9:

[0731] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[0732] Step 10:

[0733] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[0734] In this way, the system allows users to receive personalized responses that take their emotions into account, allowing them to manage their schedules and prioritize tasks more efficiently.

[0735] Example 2

[0736] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0737] Conventional schedule management systems and task management systems only provide mechanical responses to user input and have the problem of not being able to respond in a way that takes into account the user's emotional state. This makes it difficult to provide appropriate advice and support to users who are feeling stressed or tense.

[0738] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing a message input by a user and recognizing the user's emotion using an emotion engine, means for transmitting the message and emotion information to the generative AI, and means for receiving a response generated by the generative AI. This enables a personalized response based on the user's emotion.

[0739] "User" refers to the person who uses the system to manage schedules and tasks.

[0740] A "terminal" is a device that receives a message entered by a user, sends it to a server, and displays a response from the server to the user, and specifically includes a smartphone, a personal computer, etc.

[0741] "Server" refers to a central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0742] An "emotion engine" refers to a system that analyzes messages entered by users and recognizes their emotions.

[0743] "Generative AI" refers to an AI model that generates appropriate responses based on messages and emotional information received from a server, and a specific example is GPT-3.

[0744] "Message" refers to text information about schedules and tasks that a user inputs into a terminal.

[0745] "Response" refers to a reply that the generative artificial intelligence generates based on the user's message and emotional information.

[0746] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format that is widely used as a data exchange format.

[0747] This invention is a response generation system that combines generative artificial intelligence and an emotion engine based on a message entered by a user. The system aims to provide personalized responses by taking into account the user's emotions. The system mainly consists of the following components:

[0748] 1. User: A user who uses the system to manage schedules and tasks. The user enters text messages into the terminal.

[0749] 2. Terminal: A device that receives messages entered by users and sends them to the server. The terminal can be a smartphone or a PC, and encodes the messages entered by users into JSON format and sends them to the server as an HTTP POST request.

[0750] 3. Server: A central management system that receives messages and emotional information from users, analyzes them, and sends them to the generative AI. The server uses an emotion engine to recognize the user's emotions and passes that information to the generative AI.

[0751] 4. Emotion Engine: A system that analyzes messages entered by users and recognizes their emotions. The emotion engine understands the context of the message and can detect emotions such as stress or tension.

[0752] 5. Generative AI: An AI model that generates appropriate responses based on messages and emotional information received from the server. A specific example is a generative AI model such as GPT-3.

[0753] As an example of how the system works, consider the case where a user types into a terminal, "Schedule a project meeting tomorrow at 10 AM." This message is processed as follows:

[0754] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[0755] 2. The device receives this message, encodes it into a JSON-formatted request, and sends it to the server as an HTTP POST request.

[0756] 3. After the server receives the request, it uses the emotion engine to analyze the user's emotion. For example, it may determine that the user is nervous.

[0757] 4. The server sends the analyzed message and emotional information to the generative artificial intelligence.

[0758] 5. The generative AI generates a response based on the information it receives. For example, it might say, "We've scheduled a project meeting for tomorrow at 10:00 AM. Please relax and enjoy the meeting."

[0759] 6. The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[0760] 7. The device receives the response from the server, parses it, and displays it to the user.

[0761] An example prompt might have the following format:

[0762] "Schedule a project meeting tomorrow at 10 AM"

[0763] Through the above process, it is possible to provide appropriate responses that take the user's emotions into consideration. This system provides advice according to the user's emotional state, reducing stress and enabling efficient schedule management.

[0764] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0765] Step 1:

[0766] The user enters a message into the terminal.

[0767] How it works: A user types into a device (such as a smartphone or computer) the following: "Schedule a project meeting tomorrow at 10 AM." This message triggers the system's processing.

[0768] Input: A user-entered text message: "Schedule a project meeting tomorrow at 10 AM."

[0769] Output: A text message is sent to the terminal.

[0770] Step 2:

[0771] The terminal receives the message and sends it to the server.

[0772] How it works: The device encodes the received message into JSON format and sends it as an HTTP POST request to a specific endpoint on the server (e.g., / schedule).

[0773] Input: The text message entered by the user.

[0774] Output: The message is encoded as JSON data and sent to the server.

[0775] json

[0776] {

[0777] "message": "Schedule a project meeting tomorrow at 10 AM"

[0778] }

[0779] Step 3:

[0780] The server receives the message and analyzes it using the emotion engine.

[0781] How it works: The server analyzes the received message and uses an emotion engine to recognize the user's emotions. For example, tension or stress may be detected.

[0782] Input: JSON formatted message sent from the device.

[0783] Output: Perceived emotional information (e.g., tension).

[0784] json

[0785] {

[0786] "message": "Schedule a project meeting tomorrow at 10 AM",

[0787] "emotion": "nervous"

[0788] }

[0789] Step 4:

[0790] The server sends the message and emotion information to the generative artificial intelligence.

[0791] How it works: Sends the user's message and emotion information to a generative artificial intelligence model (e.g., GPT-3).

[0792] Input: User's message and parsed sentiment information.

[0793] Output: Message and emotion information forwarded to the generative AI.

[0794] Step 5:

[0795] Generative artificial intelligence generates responses.

[0796] How it works: The generative AI model generates an appropriate response based on the received message and emotional information. For example, if the user is nervous, it will generate a response that includes advice such as "Relax and approach the meeting."

[0797] Input: Message and emotion information received from the server.

[0798] Output: The generated response.

[0799] json

[0800] {

[0801] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[0802] }

[0803] Step 6:

[0804] The server sends the generated response to the terminal.

[0805] Behavior: The generated response is encoded in JSON format and sent to the device as an HTTP response.

[0806] Input: Response received from the generative artificial intelligence.

[0807] Output: Response data in JSON format is sent to the terminal.

[0808] Step 7:

[0809] The terminal receives the response and displays it to the user.

[0810] How it works: The device receives the JSON response sent by the server, parses it to extract the textual response, and displays it to the user.

[0811] Input: JSON formatted response data sent from the server.

[0812] Output: A text response that is displayed to the user.

[0813] json

[0814] {

[0815] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[0816] }

[0817] In this way, by clarifying the specific operations and inputs and outputs at each step, it becomes easier to understand the processing flow of the entire system.

[0818] (Application example 2)

[0819] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0820] In modern factories, workers must efficiently manage a wide variety of complex tasks, which often leads to stress and impatience. Conventional systems struggle to provide feedback and support that takes into account workers' emotional states, resulting in problems such as reduced work efficiency and increased errors. Furthermore, task scheduling and prioritization are not centrally managed, placing a heavy burden on workers.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0822] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence (AI) to generate an appropriate response, means for receiving the response generated by the generative artificial intelligence (AI), means for displaying the response to the user, means for managing the user's tasks and schedule using smart glasses, and means for recognizing the user's emotional state using an emotion engine and generating a response based on the emotion. This enables personalized feedback and support that takes into account the worker's emotional state, enabling efficient task management and reducing the worker's mental burden.

[0823] "Users" are factory workers who use the system to manage tasks and schedules.

[0824] A "message" is any information or instruction entered by a user that is processed and interpreted by the system.

[0825] The "server" is a central management system that receives messages from users, sends them to the generative artificial intelligence, and generates, receives, and sends responses.

[0826] "Generative AI" is an AI model that generates appropriate responses based on the user's message and emotional information.

[0827] A "response" is a reply generated by the generative artificial intelligence to a message entered by a user.

[0828] "Smart glasses" are wearable devices that users wear to manage tasks and schedules and receive notifications in real time.

[0829] An "emotion engine" is a system that analyzes emotions from the user's input message and recognizes those emotions.

[0830] A "task" is a job or operation that a user performs in a factory and is managed by the system.

[0831] A "schedule" is a plan that organizes and arranges a user's tasks in terms of time.

[0832] "Personalized feedback" refers to individualized feedback or advice that takes into account the user's emotional state.

[0833] This invention is a system for improving the efficiency of factory work. It uses smart glasses worn by workers to manage tasks and schedules, and also combines an emotion engine to recognize the emotional state of the worker and provide personalized feedback and advice based on that emotion. The main function of this system is to generate appropriate responses using generative artificial intelligence.

[0834] Hardware and software used

[0835] The system consists of the following main components:

[0836] 1. Smart glasses: A wearable device worn by the user to manage tasks and schedules in real time.

[0837] 2. Terminal: A device that acts as part of the smart glasses, receives user input messages, sends them to the server, and displays responses from the server to the user.

[0838] 3. Emotion engine: A system that analyzes emotions from the user's input message and recognizes those emotions.

[0839] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0840] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[0841] System Operation

[0842] 1. User input: A voice message entered by the worker through the smart glasses, such as "Start a new task at 2pm."

[0843] 2. Sentiment Analysis: The emotion engine analyzes the user's message and recognizes their emotional state (e.g., impatience, stress, joy, etc.). This analysis is done using natural language processing techniques.

[0844] 3. Send message: The device encodes the user's message in JSON format and sends it to the server as an HTTP POST request.

[0845] 4. Response generation: The server sends the received message and emotional information to the generative AI, which then generates an appropriate response. For example, if the user is feeling impatient, the system might generate a response such as, "I'll start a new task at 2 p.m. Please proceed without rushing."

[0846] 5. Sending response: The server receives the response from the generative AI, formats it again in JSON format, and sends it back to the device.

[0847] 6. Response display: The terminal parses the JSON response sent from the server, extracts the response text, and displays it to the user. The response is displayed on the smart glasses display, allowing the worker to check it in real time.

[0848] Specific examples

[0849] When a user voice-inputs "I'll start a new task at 2 PM" into the smart glasses, the emotion engine recognizes the emotion of impatience. Then, a generative AI (e.g., GPT-3) generates a response such as "I'll start a new task at 2 PM. Please proceed calmly," which is displayed on the smart glasses. This process allows workers to efficiently manage changes to work plans and additional tasks.

[0850] Prompt Sentence Examples

[0851] The text prompt is as follows:

[0852] User says: "I'll start a new task at 2 PM."

[0853] Emotional state: Impatience

[0854] Generate a response.

[0855] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0856] Step 1:

[0857] The user uses the smart glasses to input voice messages related to tasks and schedules, such as "Start a new task at 2 p.m." The smart glasses convert the voice messages into text format and send them to the device as data.

[0858] Step 2:

[0859] The terminal receives a user input message, which is a text message such as "Start a new task at 2 PM." The terminal encodes this message into JSON format and sends it to the server using an HTTP POST request.

[0860] Step 3:

[0861] The server receives a JSON-formatted message sent from the device. The server analyzes the received data and extracts the user's message. In this case, the message is text data such as "Start new work at 2 PM," and the server sends this to the emotion engine.

[0862] Step 4:

[0863] The emotion engine analyzes messages sent from the server and recognizes the user's emotional state. The input is text data such as "I'll start a new task at 2 PM," and the analysis results in emotional data such as "impatient." This emotional information is then sent back to the server.

[0864] Step 5:

[0865] The server receives emotional information from the emotion engine. The server combines this emotional information with the message and sends it to the generative AI. The input is the text message "Start new work at 2 PM" and the emotional information "impatient," and an appropriate response is generated based on these.

[0866] Step 6:

[0867] The generative AI generates a response based on the message and emotional information from the server. The input is the text message "Start new work at 2 PM" and the emotional information "impatience," and the output is a response such as "Start new work at 2 PM. Please proceed without rushing." This response is sent to the server.

[0868] Step 7:

[0869] The server receives the response from the generative AI and encodes it into JSON format. The input is the response text "A new task will start at 2 PM. Please proceed slowly." The response data converted into JSON format is output. The server sends this data to the terminal.

[0870] Step 8:

[0871] The device receives the JSON-formatted response sent from the server. The device parses this data and extracts the response text. The input is the JSON-formatted response data, and the output is text data that reads, "A new task will start at 2 p.m. Please proceed without rushing." The device displays this response on the smart glasses.

[0872] Step 9:

[0873] The user checks the response displayed on the smart glasses. The response is "You will start a new task at 2 PM. Please take your time." Based on this, the user decides on the next action. "Specific actions include starting the task, taking a break, etc."

[0874] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0875] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0876] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0877] [Third embodiment]

[0878] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0879] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0880] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0881] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0882] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0883] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0884] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0885] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0886] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0887] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0888] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0889] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0890] This invention is a system that combines a calendar application, a task management application, and a generative artificial intelligence to improve the efficiency of schedule management and task prioritization. The system sends messages entered by the user to the generative artificial intelligence, generates an appropriate response, and presents that response to the user.

[0891] Explanation of system program processing

[0892] System Overview

[0893] The system consists of the following major components:

[0894] 1. Users: Use the system to manage schedules and tasks.

[0895] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0896] 3. Server: A central management system that receives messages from users, sends them to the generative AI, and sends the generated responses back to the terminal.

[0897] 4. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages received from the server.

[0898] Example of user interaction

[0899] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[0900] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[0901] 3. The server analyzes the request received from the device and obtains the user's message. The server performs preprocessing and sends this message to the generative AI.

[0902] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "We've scheduled a project meeting for tomorrow at 10:00 AM. After that, we recommend scheduling a conference call with the client for one hour."

[0903] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0904] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0905] 7. The user decides on their next action based on the provided responses, such as asking more specific questions, adding other tasks, or reviewing or modifying the proposed schedule.

[0906] Specific operation example

[0907] 1. The user types into the terminal, "Add a new task at 2 PM."

[0908] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[0909] 3. The server analyzes the request received from the device and obtains the user's message. The server then performs preprocessing and sends the message to the generative AI.

[0910] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "A new task has been added for 2 PM. We recommend that you adjust your schedule so that the task can be completed in the morning."

[0911] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[0912] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[0913] 7. The user decides on the next action based on the presented response.

[0914] In this way, this system can improve the efficiency of users' schedule management and task prioritization, reducing the burden on users.

[0915] The processing flow will be explained below.

[0916] Step 1:

[0917] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[0918] Step 2:

[0919] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[0920] Step 3:

[0921] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[0922] Step 4:

[0923] The server sends the acquired user message to the generative AI by creating a request to the OpenAI API and sending the message as a prompt to the API.

[0924] Step 5:

[0925] The generative AI analyzes messages received from the server and generates appropriate responses, such as, "We've set up a project meeting for tomorrow at 10 a.m. After that, we recommend scheduling a conference call with your client for one hour later."

[0926] Step 6:

[0927] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the response from GPT-3, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[0928] Step 7:

[0929] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[0930] Step 8:

[0931] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[0932] Example 1

[0933] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0934] Conventional schedule management and task management systems have the problem that users have to manually input and manage a large number of tasks and schedules, which is time-consuming. In addition, the system has limited functionality for automatically prioritizing tasks and providing advice on required time, making efficient management difficult. There is a need for a schedule management and task management system that solves these problems and reduces the burden on users.

[0935] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0936] In this invention, the server includes means for the terminal to send user input in JSON format to the server, means for the server to analyze the received JSON data and extract the user message, means for preprocessing the message to be sent by the server to the generative AI, means for formatting a response received from the generative AI in JSON format and sending it to the terminal, and means for the terminal to analyze and display the JSON formatted response sent from the server. This allows the user to have their input processed quickly and accurately and receive a response including advice on task prioritization and required time.

[0937] A "user" is an entity that uses the system to manage schedules and tasks.

[0938] A "message" refers to information or instructions entered by a user.

[0939] "Terminal" refers to the device a user uses to enter messages and view received responses (e.g., a smartphone or PC).

[0940] "Server" refers to a central management system that receives messages sent from a terminal, works with generative artificial intelligence to generate a response, and sends it back to the terminal.

[0941] "Generative AI" refers to an AI model that generates appropriate responses based on messages received from a server.

[0942] "Response" refers to a reply that a generative artificial intelligence generates based on a user's message.

[0943] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a standard format for structuring and representing data.

[0944] "Preprocessing" refers to the process of formatting the messages that the server sends to the generative artificial intelligence into an appropriate format.

[0945] "Formatting" refers to converting data into a particular form.

[0946] This invention is a system that streamlines schedule and task management, integrating a calendar app, a task management app, and generative artificial intelligence. This system analyzes messages entered by the user and provides appropriate schedule and task prioritization.

[0947] Hardware and software used

[0948] User: The entity that uses the system to manage schedules and tasks.

[0949] Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[0950] Server: A central management system that receives messages from users, sends them to the generative AI, and returns the generated responses to the terminal.

[0951] Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages received from a server.

[0952] JSON format: A standard format for structuring and representing data.

[0953] Processing flow details

[0954] 1. A user enters schedule or task-related instructions into a device. For example, a user might enter, "Schedule a project meeting tomorrow at 10 AM."

[0955] 2. The device captures the user's input message, encodes it into JSON format, and sends it to the server as an HTTP POST request.

[0956] 3. The server analyzes the request received from the device and extracts the user's message from the JSON data.

[0957] 4. The server preprocesses the extracted message into a format suitable for the generative AI and sends this message to the generative AI as a prompt. An example of a prompt sentence: "Schedule a project meeting tomorrow at 10 AM."

[0958] 5. The generative AI generates a response based on the prompt received from the server, for example, "We've set up a project meeting for tomorrow at 10 AM. After that, we recommend scheduling a conference call with the client for one hour later."

[0959] 6. The server formats the generated response into JSON and sends it back to the device.

[0960] 7. The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[0961] Specific examples

[0962] A user types into a terminal, "Add a new task at 2 PM."

[0963] The device encodes the message in JSON format and sends it to the server as an HTTP POST request.

[0964] The server receives the request and parses the JSON data to extract the user's message.

[0965] The server preprocesses the extracted message and sends it to the generative AI as a prompt. An example of a prompt is: "Add a new task at 2 PM."

[0966] The generative AI generates a response like, "You've added a new task at 2 PM. We recommend that you adjust your schedule so that you can complete the task in the morning before that."

[0967] The server formats the response in JSON and sends it back to the device.

[0968] The terminal parses the response and displays it to the user.

[0969] In this way, the system can streamline user schedule management and task prioritization.

[0970] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0971] Step 1:

[0972] The user enters a message into the terminal.

[0973] Specific behavior:

[0974] The user opens the calendar app on their smartphone or PC.

[0975] Type "Schedule a project meeting tomorrow at 10 AM" into the text box.

[0976] Input data:

[0977] Message entered by user: "Schedule a project meeting tomorrow at 10 AM"

[0978] Output data:

[0979] User messages captured on the device

[0980] Step 2:

[0981] The device encodes the user's input message into JSON format and sends it to the server as an HTTP POST request.

[0982] Specific behavior:

[0983] The terminal captures the user's input.

[0984] The captured message is encoded in JSON format as follows:

[0985] json

[0986] {

[0987] "message": "Schedule a project meeting tomorrow at 10 AM"

[0988] }

[0989] Send an HTTP POST request containing the encoded data to the server.

[0990] Input data:

[0991] Captured user messages

[0992] Output data:

[0993] JSON formatted HTTP POST request

[0994] Step 3:

[0995] The server analyzes the request received from the device and extracts the user's message from the JSON data.

[0996] Specific behavior:

[0997] The server receives an HTTP request.

[0998] Parse the request body and extract the "message" field from the JSON data.

[0999] Input data:

[1000] HTTP POST request in JSON format sent from the terminal

[1001] Output data:

[1002] Extracted user message: "Schedule a project meeting tomorrow at 10 AM"

[1003] Step 4:

[1004] The server preprocesses the extracted message into a format suitable for the generative artificial intelligence and sends this message to the generative artificial intelligence as a prompt.

[1005] Specific behavior:

[1006] The server preprocesses the extracted message, for example removing unnecessary whitespace and special characters.

[1007] Format the preprocessed message in a prompt format, such as "Schedule a project meeting tomorrow at 10 AM."

[1008] Send the formatted prompt to the generative artificial intelligence.

[1009] Input data:

[1010] Extracted user messages

[1011] Output data:

[1012] Preprocessed and prompt-formatted message

[1013] Step 5:

[1014] The generative artificial intelligence generates a response based on the message received from the server.

[1015] Specific behavior:

[1016] The generative artificial intelligence receives the prompt.

[1017] Parse the prompt and generate an appropriate response, such as "I've set up a project meeting for tomorrow at 10 AM. After that, I'd recommend scheduling a conference call with your client in an hour."

[1018] Input data:

[1019] The prompt sent by the server

[1020] Output data:

[1021] The generated response message

[1022] Step 6:

[1023] The server formats the response received from the generative AI into JSON format and sends it back to the device.

[1024] Specific behavior:

[1025] The server receives the response from the generative artificial intelligence.

[1026] The response message is formatted as JSON, as follows:

[1027] json

[1028] {

[1029] "response": "I've set up a project meeting for tomorrow at 10 AM. I'd like to schedule a conference call with the client afterwards in an hour."

[1030] }

[1031] The formatted JSON data is returned to the terminal as an HTTP response.

[1032] Input data:

[1033] Response message received from generative artificial intelligence

[1034] Output data:

[1035] JSON formatted HTTP response

[1036] Step 7:

[1037] The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[1038] Specific behavior:

[1039] The device receives the HTTP response.

[1040] Parse the JSON data from the response and extract the "response" field.

[1041] The extracted response text is displayed to the user.

[1042] Input data:

[1043] JSON formatted HTTP response sent from the server

[1044] Output data:

[1045] Parsed response text: "I've set up a project meeting for tomorrow at 10 AM. After that, I'd like to suggest scheduling a conference call with my client in an hour."

[1046] Step 8:

[1047] The user decides on the next action based on the provided response.

[1048] Specific behavior:

[1049] The user checks the response displayed on the terminal.

[1050] If necessary, enter the following instructions into the terminal:

[1051] Input data:

[1052] The parsed response text

[1053] Output data:

[1054] User's next action

[1055] (Application example 1)

[1056] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1057] Conventional factory management systems require users to manually manage machine operation status and maintenance plans, which is inefficient. Furthermore, they have problems with not being able to propose appropriate schedules when an emergency response is required or when adjusting task priorities. This can lead to work delays, increased costs, and reduced overall efficiency.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1059] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence and generating an appropriate response, means for receiving the response generated by the generative artificial intelligence, means for integrated management of the operating status and maintenance plans of machines in a factory, means for the generative artificial intelligence to propose optimal schedules and task priorities for machines based on inquiries input by the user, and means for displaying the response of the generative artificial intelligence to the user. This allows users to easily optimize the operating status and maintenance plans of machines, enabling efficient factory operation.

[1060] A "user" is an entity that uses the system to manage machinery within a factory and optimize maintenance plans.

[1061] A "message" is an instruction or inquiry that a user inputs to the system.

[1062] The "server" is a central computer that processes messages received from users, sends them to the generative artificial intelligence, and generates appropriate responses.

[1063] "Generative AI" refers to an AI model that generates responses based on messages from users, and includes models such as GPT-3.

[1064] A "response" is an appropriate answer or suggestion to a user's message, generated by generative artificial intelligence.

[1065] "Operation status" is information that indicates the current operating status of machines in a factory.

[1066] A "maintenance plan" refers to a plan for the maintenance and inspection of machinery in a factory, and is a schedule for optimizing the operation of machinery over the long term.

[1067] "Integrated management" is a method for centrally managing the operating status and maintenance plans of multiple machines and systems to achieve overall optimization.

[1068] An "optimal schedule" refers to the time allocation and work order that are deemed most appropriate for efficient operation and maintenance of machines.

[1069] "Task priority" refers to the order in which tasks should be performed first among multiple jobs or operations, determined based on importance and urgency.

[1070] The present invention provides a system that allows users to efficiently manage machinery in factories and plan maintenance. This system includes a mechanism that receives inquiries and instructions from users, uses generative artificial intelligence to generate optimal responses based on those inquiries and instructions, and displays the responses to the users.

[1071] First, a user accesses the system using a device such as a smartphone or PC. For example, the user enters a message such as, "When is the next maintenance?" The device then sends this message to the server. The server then sends the received message to the generative artificial intelligence, which then generates an appropriate response.

[1072] The server uses a model such as GPT-3 as a generative AI. This generative AI generates a response based on the user's message. For example, a possible response might be, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd." The server receives the generated response and sends it to the terminal in JSON format.

[1073] Factory management requires a system that can integrate and manage the operating status and maintenance plans of machines within the factory. This system uses generative artificial intelligence to propose optimal machine schedules and task priorities based on inquiries entered by the user. For example, if a user enters, "What is tomorrow's shift schedule?", the system can generate a response such as, "Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow all safety precautions when working."

[1074] The main components required to implement this system are:

[1075] 1. User: The entity that uses a terminal to access the system and input messages and instructions.

[1076] 2. Terminal: A device that receives messages entered by the user, sends them to the server, and displays responses from the server to the user.

[1077] 3. Server: A central management system that analyzes messages received from users and sends them to the generative AI, and also receives generated responses and sends them back to the terminal.

[1078] 4. Generative AI: An AI model that generates responses based on messages from users. An example is GPT-3.

[1079] This allows users to automatically optimize the operating status and maintenance plans of machines within their factories, enabling efficient factory operations.

[1080] Example prompt sentence:

[1081] Message from factory manager: When is the next maintenance?

[1082] Please respond in the following format:

[1083] Response: The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd.

[1084] For example, if a user types "What is tomorrow's shift schedule?", the generative AI will generate the following response:

[1085] Response: Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow the safety precautions when working.

[1086] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1087] Step 1:

[1088] The user types a message into a device (such as a smartphone or PC). For example, they type, "When is the next maintenance?"

[1089] Input: A message from the user.

[1090] Output: The user's message displayed on the terminal.

[1091] Step 2:

[1092] The device encodes the message entered by the user as a JSON-formatted request and sends an HTTP POST request to the server.

[1093] Input: The message entered by the user.

[1094] Output: The HTTP POST request sent to the server.

[1095] Step 3:

[1096] The server analyzes the request received from the device, acquires the user's message, and then performs preprocessing to send the user's message to the generative AI.

[1097] Input: HTTP POST request.

[1098] Output: The message sent to the generative artificial intelligence.

[1099] Step 4:

[1100] The generative AI generates a response based on the message received from the server. For example, it generates a response such as, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd."

[1101] Input: The message sent by the server.

[1102] Output: The generated response.

[1103] Step 5:

[1104] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[1105] Input: A response from a generative artificial intelligence.

[1106] Output: The response in JSON format.

[1107] Step 6:

[1108] The terminal parses the JSON format response sent from the server, extracts the response text, and displays it to the user.

[1109] Input: JSON response from the server.

[1110] Output: The response text that is displayed to the user.

[1111] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1112] This invention is a system that combines a calendar app and a task management app with generative artificial intelligence and an emotion engine to improve the efficiency of schedule management and task prioritization, and also provides responses that take the user's emotions into consideration. The system's purpose is to use generative artificial intelligence to generate appropriate responses from messages entered by the user, and then add an emotion engine to generate more personalized responses based on the user's emotions.

[1113] Explanation of system program processing

[1114] System Overview

[1115] The system consists of the following major components:

[1116] 1. Users: Use the system to manage schedules and tasks.

[1117] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[1118] 3. Emotion engine: A system that analyzes messages entered by users and recognizes their emotions.

[1119] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[1120] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[1121] Example of user interaction

[1122] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[1123] 2. The device receives this message and sends it to the server. The device encodes the message as a JSON-formatted request and sends it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[1124] 3. The emotion engine analyzes the user's messages and recognizes their emotions, for example, detecting stress or tension from the messages.

[1125] 4. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[1126] 5. The generative AI generates a response based on the message and emotional information received from the server. For example, if the system determines that the user is nervous, the response may include gentle advice such as "Please relax and enjoy the meeting."

[1127] 6. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the GPT-3 response, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[1128] 7. The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[1129] 8. The user decides on the next action based on the generative AI's response presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[1130] Specific operation example

[1131] 1. A user types into their device, "Add a new task at 2 PM." The emotion engine recognizes the emotion of impatience from the user's message.

[1132] 2. The device receives this message and sends it to the server, encoding it as a JSON request and sending it to the server as an HTTP POST request.

[1133] 3. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[1134] 4. The generative AI generates a response based on the message and emotional information. An example of a generated response might be, "I added a new task at 2 PM. Before that, I recommend you take a break to get through your busy day."

[1135] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[1136] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[1137] 7. The user decides on the next action based on the presented response.

[1138] In this way, by combining emotion engines, personalized responses tailored to the user's emotions can be provided, further reducing the burden on the user.

[1139] The processing flow will be explained below.

[1140] Step 1:

[1141] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[1142] Step 2:

[1143] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[1144] Step 3:

[1145] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[1146] Step 4:

[1147] The server sends the acquired user message to the emotion engine, which recognizes the user's emotion. The emotion engine analyzes the message and generates emotion information.

[1148] Step 5:

[1149] The emotion engine recognizes emotions from the user's input message and returns the emotion information to the server. For example, if the user is nervous, the degree of nervousness is returned as numerical information.

[1150] Step 6:

[1151] The server receives the emotion information from the emotion engine and sends it along with the user's message to the generative AI. Specifically, it includes the emotion information in the OpenAI API request.

[1152] Step 7:

[1153] The generative AI generates an appropriate response based on the message and emotional information received from the server. For example, it generates a response that takes emotions into account, such as, "We've scheduled a project meeting for tomorrow at 10:00 AM. If you attend in a relaxed manner, you'll likely see results."

[1154] Step 8:

[1155] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it encodes the generated response text in JSON format and sends it to the device as an HTTP response.

[1156] Step 9:

[1157] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[1158] Step 10:

[1159] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[1160] In this way, the system allows users to receive personalized responses that take their emotions into account, allowing them to manage their schedules and prioritize tasks more efficiently.

[1161] Example 2

[1162] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1163] Conventional schedule management systems and task management systems only provide mechanical responses to user input and have the problem of not being able to respond in a way that takes into account the user's emotional state. This makes it difficult to provide appropriate advice and support to users who are feeling stressed or tense.

[1164] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing a message input by a user and recognizing the user's emotion using an emotion engine, means for transmitting the message and emotion information to the generative AI, and means for receiving a response generated by the generative AI. This enables a personalized response based on the user's emotion.

[1165] "User" refers to the person who uses the system to manage schedules and tasks.

[1166] A "terminal" is a device that receives a message entered by a user, sends it to a server, and displays a response from the server to the user, and specifically includes a smartphone, a personal computer, etc.

[1167] "Server" refers to a central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[1168] An "emotion engine" refers to a system that analyzes messages entered by users and recognizes their emotions.

[1169] "Generative AI" refers to an AI model that generates appropriate responses based on messages and emotional information received from a server, and a specific example is GPT-3.

[1170] "Message" refers to text information about schedules and tasks that a user inputs into a terminal.

[1171] "Response" refers to a reply that the generative artificial intelligence generates based on the user's message and emotional information.

[1172] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format that is widely used as a data exchange format.

[1173] This invention is a response generation system that combines generative artificial intelligence and an emotion engine based on a message entered by a user. The system aims to provide personalized responses by taking into account the user's emotions. The system mainly consists of the following components:

[1174] 1. User: A user who uses the system to manage schedules and tasks. The user enters text messages into the terminal.

[1175] 2. Terminal: A device that receives messages entered by users and sends them to the server. The terminal can be a smartphone or a PC, and encodes the messages entered by users into JSON format and sends them to the server as an HTTP POST request.

[1176] 3. Server: A central management system that receives messages and emotional information from users, analyzes them, and sends them to the generative AI. The server uses an emotion engine to recognize the user's emotions and passes that information to the generative AI.

[1177] 4. Emotion Engine: A system that analyzes messages entered by users and recognizes their emotions. The emotion engine understands the context of the message and can detect emotions such as stress or tension.

[1178] 5. Generative AI: An AI model that generates appropriate responses based on messages and emotional information received from the server. A specific example is a generative AI model such as GPT-3.

[1179] As an example of how the system works, consider the case where a user types into a terminal, "Schedule a project meeting tomorrow at 10 AM." This message is processed as follows:

[1180] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[1181] 2. The device receives this message, encodes it into a JSON-formatted request, and sends it to the server as an HTTP POST request.

[1182] 3. After the server receives the request, it uses the emotion engine to analyze the user's emotion. For example, it may determine that the user is nervous.

[1183] 4. The server sends the analyzed message and emotional information to the generative artificial intelligence.

[1184] 5. The generative AI generates a response based on the information it receives. For example, it might say, "We've scheduled a project meeting for tomorrow at 10:00 AM. Please relax and enjoy the meeting."

[1185] 6. The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[1186] 7. The device receives the response from the server, parses it, and displays it to the user.

[1187] An example prompt might have the following format:

[1188] "Schedule a project meeting tomorrow at 10 AM"

[1189] Through the above process, it is possible to provide appropriate responses that take the user's emotions into consideration. This system provides advice according to the user's emotional state, reducing stress and enabling efficient schedule management.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] The user enters a message into the terminal.

[1193] How it works: A user types into a device (such as a smartphone or computer) the following: "Schedule a project meeting tomorrow at 10 AM." This message triggers the system's processing.

[1194] Input: A user-entered text message: "Schedule a project meeting tomorrow at 10 AM."

[1195] Output: A text message is sent to the terminal.

[1196] Step 2:

[1197] The terminal receives the message and sends it to the server.

[1198] How it works: The device encodes the received message into JSON format and sends it as an HTTP POST request to a specific endpoint on the server (e.g., / schedule).

[1199] Input: The text message entered by the user.

[1200] Output: The message is encoded as JSON data and sent to the server.

[1201] json

[1202] {

[1203] "message": "Schedule a project meeting tomorrow at 10 AM"

[1204] }

[1205] Step 3:

[1206] The server receives the message and analyzes it using the emotion engine.

[1207] How it works: The server analyzes the received message and uses an emotion engine to recognize the user's emotions. For example, tension or stress may be detected.

[1208] Input: JSON formatted message sent from the device.

[1209] Output: Perceived emotional information (e.g., tension).

[1210] json

[1211] {

[1212] "message": "Schedule a project meeting tomorrow at 10 AM",

[1213] "emotion": "nervous"

[1214] }

[1215] Step 4:

[1216] The server sends the message and emotion information to the generative artificial intelligence.

[1217] How it works: Sends the user's message and emotion information to a generative artificial intelligence model (e.g., GPT-3).

[1218] Input: User's message and parsed sentiment information.

[1219] Output: Message and emotion information forwarded to the generative AI.

[1220] Step 5:

[1221] Generative artificial intelligence generates responses.

[1222] How it works: The generative AI model generates an appropriate response based on the received message and emotional information. For example, if the user is nervous, it will generate a response that includes advice such as "Relax and approach the meeting."

[1223] Input: Message and emotion information received from the server.

[1224] Output: The generated response.

[1225] json

[1226] {

[1227] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[1228] }

[1229] Step 6:

[1230] The server sends the generated response to the terminal.

[1231] Behavior: The generated response is encoded in JSON format and sent to the device as an HTTP response.

[1232] Input: Response received from the generative artificial intelligence.

[1233] Output: Response data in JSON format is sent to the terminal.

[1234] Step 7:

[1235] The terminal receives the response and displays it to the user.

[1236] How it works: The device receives the JSON response sent by the server, parses it to extract the textual response, and displays it to the user.

[1237] Input: JSON formatted response data sent from the server.

[1238] Output: A text response that is displayed to the user.

[1239] json

[1240] {

[1241] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[1242] }

[1243] In this way, by clarifying the specific operations and inputs and outputs at each step, it becomes easier to understand the processing flow of the entire system.

[1244] (Application example 2)

[1245] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1246] In modern factories, workers must efficiently manage a wide variety of complex tasks, which often leads to stress and impatience. Conventional systems struggle to provide feedback and support that takes into account workers' emotional states, resulting in problems such as reduced work efficiency and increased errors. Furthermore, task scheduling and prioritization are not centrally managed, placing a heavy burden on workers.

[1247] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1248] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence (AI) to generate an appropriate response, means for receiving the response generated by the generative artificial intelligence (AI), means for displaying the response to the user, means for managing the user's tasks and schedule using smart glasses, and means for recognizing the user's emotional state using an emotion engine and generating a response based on the emotion. This enables personalized feedback and support that takes into account the worker's emotional state, enabling efficient task management and reducing the worker's mental burden.

[1249] "Users" are factory workers who use the system to manage tasks and schedules.

[1250] A "message" is any information or instruction entered by a user that is processed and interpreted by the system.

[1251] The "server" is a central management system that receives messages from users, sends them to the generative artificial intelligence, and generates, receives, and sends responses.

[1252] "Generative AI" is an AI model that generates appropriate responses based on the user's message and emotional information.

[1253] A "response" is a reply generated by the generative artificial intelligence to a message entered by a user.

[1254] "Smart glasses" are wearable devices that users wear to manage tasks and schedules and receive notifications in real time.

[1255] An "emotion engine" is a system that analyzes emotions from the user's input message and recognizes those emotions.

[1256] A "task" is a job or operation that a user performs in a factory and is managed by the system.

[1257] A "schedule" is a plan that organizes and arranges a user's tasks in terms of time.

[1258] "Personalized feedback" refers to individualized feedback or advice that takes into account the user's emotional state.

[1259] This invention is a system for improving the efficiency of factory work. It uses smart glasses worn by workers to manage tasks and schedules, and also combines an emotion engine to recognize the emotional state of the worker and provide personalized feedback and advice based on that emotion. The main function of this system is to generate appropriate responses using generative artificial intelligence.

[1260] Hardware and software used

[1261] The system consists of the following main components:

[1262] 1. Smart glasses: A wearable device worn by the user to manage tasks and schedules in real time.

[1263] 2. Terminal: A device that acts as part of the smart glasses, receives user input messages, sends them to the server, and displays responses from the server to the user.

[1264] 3. Emotion engine: A system that analyzes emotions from the user's input message and recognizes those emotions.

[1265] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[1266] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[1267] System Operation

[1268] 1. User input: A voice message entered by the worker through the smart glasses, such as "Start a new task at 2pm."

[1269] 2. Sentiment Analysis: The emotion engine analyzes the user's message and recognizes their emotional state (e.g., impatience, stress, joy, etc.). This analysis is done using natural language processing techniques.

[1270] 3. Send message: The device encodes the user's message in JSON format and sends it to the server as an HTTP POST request.

[1271] 4. Response generation: The server sends the received message and emotional information to the generative AI, which then generates an appropriate response. For example, if the user is feeling impatient, the system might generate a response such as, "I'll start a new task at 2 p.m. Please proceed without rushing."

[1272] 5. Sending response: The server receives the response from the generative AI, formats it again in JSON format, and sends it back to the device.

[1273] 6. Response display: The terminal parses the JSON response sent from the server, extracts the response text, and displays it to the user. The response is displayed on the smart glasses display, allowing the worker to check it in real time.

[1274] Specific examples

[1275] When a user voice-inputs "I'll start a new task at 2 PM" into the smart glasses, the emotion engine recognizes the emotion of impatience. Then, a generative AI (e.g., GPT-3) generates a response such as "I'll start a new task at 2 PM. Please proceed calmly," which is displayed on the smart glasses. This process allows workers to efficiently manage changes to work plans and additional tasks.

[1276] Prompt Sentence Examples

[1277] The text prompt is as follows:

[1278] User says: "I'll start a new task at 2 PM."

[1279] Emotional state: Impatience

[1280] Generate a response.

[1281] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1282] Step 1:

[1283] The user uses the smart glasses to input voice messages related to tasks and schedules, such as "Start a new task at 2 p.m." The smart glasses convert the voice messages into text format and send them to the device as data.

[1284] Step 2:

[1285] The terminal receives a user input message, which is a text message such as "Start a new task at 2 PM." The terminal encodes this message into JSON format and sends it to the server using an HTTP POST request.

[1286] Step 3:

[1287] The server receives a JSON-formatted message sent from the device. The server analyzes the received data and extracts the user's message. In this case, the message is text data such as "Start new work at 2 PM," and the server sends this to the emotion engine.

[1288] Step 4:

[1289] The emotion engine analyzes messages sent from the server and recognizes the user's emotional state. The input is text data such as "I'll start a new task at 2 PM," and the analysis results in emotional data such as "impatient." This emotional information is then sent back to the server.

[1290] Step 5:

[1291] The server receives emotional information from the emotion engine. The server combines this emotional information with the message and sends it to the generative AI. The input is the text message "Start new work at 2 PM" and the emotional information "impatient," and an appropriate response is generated based on these.

[1292] Step 6:

[1293] The generative AI generates a response based on the message and emotional information from the server. The input is the text message "Start new work at 2 PM" and the emotional information "impatience," and the output is a response such as "Start new work at 2 PM. Please proceed without rushing." This response is sent to the server.

[1294] Step 7:

[1295] The server receives the response from the generative AI and encodes it into JSON format. The input is the response text "A new task will start at 2 PM. Please proceed slowly." The response data converted into JSON format is output. The server sends this data to the terminal.

[1296] Step 8:

[1297] The device receives the JSON-formatted response sent from the server. The device parses this data and extracts the response text. The input is the JSON-formatted response data, and the output is text data that reads, "A new task will start at 2 p.m. Please proceed without rushing." The device displays this response on the smart glasses.

[1298] Step 9:

[1299] The user checks the response displayed on the smart glasses. The response is "You will start a new task at 2 PM. Please take your time." Based on this, the user decides on the next action. "Specific actions include starting the task, taking a break, etc."

[1300] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1301] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1302] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1303] [Fourth embodiment]

[1304] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1305] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1306] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1307] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1308] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1309] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1310] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1311] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1312] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1313] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1314] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1315] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1316] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1317] This invention is a system that combines a calendar application, a task management application, and a generative artificial intelligence to improve the efficiency of schedule management and task prioritization. The system sends messages entered by the user to the generative artificial intelligence, generates an appropriate response, and presents that response to the user.

[1318] Explanation of system program processing

[1319] System Overview

[1320] The system consists of the following major components:

[1321] 1. Users: Use the system to manage schedules and tasks.

[1322] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[1323] 3. Server: A central management system that receives messages from users, sends them to the generative AI, and sends the generated responses back to the terminal.

[1324] 4. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages received from the server.

[1325] Example of user interaction

[1326] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[1327] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[1328] 3. The server analyzes the request received from the device and obtains the user's message. The server performs preprocessing and sends this message to the generative AI.

[1329] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "We've scheduled a project meeting for tomorrow at 10:00 AM. After that, we recommend scheduling a conference call with the client for one hour."

[1330] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[1331] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[1332] 7. The user decides on their next action based on the provided responses, such as asking more specific questions, adding other tasks, or reviewing or modifying the proposed schedule.

[1333] Specific operation example

[1334] 1. The user types into the terminal, "Add a new task at 2 PM."

[1335] 2. The device sends this message to the server. The device encodes the message as a JSON-formatted request and sends an HTTP POST request to the server.

[1336] 3. The server analyzes the request received from the device and obtains the user's message. The server then performs preprocessing and sends the message to the generative AI.

[1337] 4. The generative AI generates a response based on the message received from the server. An example of a generated response might be, "A new task has been added for 2 PM. We recommend that you adjust your schedule so that the task can be completed in the morning."

[1338] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[1339] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[1340] 7. The user decides on the next action based on the presented response.

[1341] In this way, this system can improve the efficiency of users' schedule management and task prioritization, reducing the burden on users.

[1342] The processing flow will be explained below.

[1343] Step 1:

[1344] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[1345] Step 2:

[1346] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[1347] Step 3:

[1348] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[1349] Step 4:

[1350] The server sends the acquired user message to the generative AI by creating a request to the OpenAI API and sending the message as a prompt to the API.

[1351] Step 5:

[1352] The generative AI analyzes messages received from the server and generates appropriate responses, such as, "We've set up a project meeting for tomorrow at 10 a.m. After that, we recommend scheduling a conference call with your client for one hour later."

[1353] Step 6:

[1354] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the response from GPT-3, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[1355] Step 7:

[1356] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[1357] Step 8:

[1358] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[1359] Example 1

[1360] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1361] Conventional schedule management and task management systems have the problem that users have to manually input and manage a large number of tasks and schedules, which is time-consuming. In addition, the system has limited functionality for automatically prioritizing tasks and providing advice on required time, making efficient management difficult. There is a need for a schedule management and task management system that solves these problems and reduces the burden on users.

[1362] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1363] In this invention, the server includes means for the terminal to send user input in JSON format to the server, means for the server to analyze the received JSON data and extract the user message, means for preprocessing the message to be sent by the server to the generative AI, means for formatting a response received from the generative AI in JSON format and sending it to the terminal, and means for the terminal to analyze and display the JSON formatted response sent from the server. This allows the user to have their input processed quickly and accurately and receive a response including advice on task prioritization and required time.

[1364] A "user" is an entity that uses the system to manage schedules and tasks.

[1365] A "message" refers to information or instructions entered by a user.

[1366] "Terminal" refers to the device a user uses to enter messages and view received responses (e.g., a smartphone or PC).

[1367] "Server" refers to a central management system that receives messages sent from a terminal, works with generative artificial intelligence to generate a response, and sends it back to the terminal.

[1368] "Generative AI" refers to an AI model that generates appropriate responses based on messages received from a server.

[1369] "Response" refers to a reply that a generative artificial intelligence generates based on a user's message.

[1370] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a standard format for structuring and representing data.

[1371] "Preprocessing" refers to the process of formatting the messages that the server sends to the generative artificial intelligence into an appropriate format.

[1372] "Formatting" refers to converting data into a particular form.

[1373] This invention is a system that streamlines schedule and task management, integrating a calendar app, a task management app, and generative artificial intelligence. This system analyzes messages entered by the user and provides appropriate schedule and task prioritization.

[1374] Hardware and software used

[1375] User: The entity that uses the system to manage schedules and tasks.

[1376] Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[1377] Server: A central management system that receives messages from users, sends them to the generative AI, and returns the generated responses to the terminal.

[1378] Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages received from a server.

[1379] JSON format: A standard format for structuring and representing data.

[1380] Processing flow details

[1381] 1. A user enters schedule or task-related instructions into a device. For example, a user might enter, "Schedule a project meeting tomorrow at 10 AM."

[1382] 2. The device captures the user's input message, encodes it into JSON format, and sends it to the server as an HTTP POST request.

[1383] 3. The server analyzes the request received from the device and extracts the user's message from the JSON data.

[1384] 4. The server preprocesses the extracted message into a format suitable for the generative AI and sends this message to the generative AI as a prompt. An example of a prompt sentence: "Schedule a project meeting tomorrow at 10 AM."

[1385] 5. The generative AI generates a response based on the prompt received from the server, for example, "We've set up a project meeting for tomorrow at 10 AM. After that, we recommend scheduling a conference call with the client for one hour later."

[1386] 6. The server formats the generated response into JSON and sends it back to the device.

[1387] 7. The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[1388] Specific examples

[1389] A user types into a terminal, "Add a new task at 2 PM."

[1390] The device encodes the message in JSON format and sends it to the server as an HTTP POST request.

[1391] The server receives the request and parses the JSON data to extract the user's message.

[1392] The server preprocesses the extracted message and sends it to the generative AI as a prompt. An example of a prompt is: "Add a new task at 2 PM."

[1393] The generative AI generates a response like, "You've added a new task at 2 PM. We recommend that you adjust your schedule so that you can complete the task in the morning before that."

[1394] The server formats the response in JSON and sends it back to the device.

[1395] The terminal parses the response and displays it to the user.

[1396] In this way, the system can streamline user schedule management and task prioritization.

[1397] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1398] Step 1:

[1399] The user enters a message into the terminal.

[1400] Specific behavior:

[1401] The user opens the calendar app on their smartphone or PC.

[1402] Type "Schedule a project meeting tomorrow at 10 AM" into the text box.

[1403] Input data:

[1404] Message entered by user: "Schedule a project meeting tomorrow at 10 AM"

[1405] Output data:

[1406] User messages captured on the device

[1407] Step 2:

[1408] The device encodes the user's input message into JSON format and sends it to the server as an HTTP POST request.

[1409] Specific behavior:

[1410] The terminal captures the user's input.

[1411] The captured message is encoded in JSON format as follows:

[1412] json

[1413] {

[1414] "message": "Schedule a project meeting tomorrow at 10 AM"

[1415] }

[1416] Send an HTTP POST request containing the encoded data to the server.

[1417] Input data:

[1418] Captured user messages

[1419] Output data:

[1420] JSON formatted HTTP POST request

[1421] Step 3:

[1422] The server analyzes the request received from the device and extracts the user's message from the JSON data.

[1423] Specific behavior:

[1424] The server receives an HTTP request.

[1425] Parse the request body and extract the "message" field from the JSON data.

[1426] Input data:

[1427] HTTP POST request in JSON format sent from the terminal

[1428] Output data:

[1429] Extracted user message: "Schedule a project meeting tomorrow at 10 AM"

[1430] Step 4:

[1431] The server preprocesses the extracted message into a format suitable for the generative artificial intelligence and sends this message to the generative artificial intelligence as a prompt.

[1432] Specific behavior:

[1433] The server preprocesses the extracted message, for example removing unnecessary whitespace and special characters.

[1434] Format the preprocessed message in a prompt format, such as "Schedule a project meeting tomorrow at 10 AM."

[1435] Send the formatted prompt to the generative artificial intelligence.

[1436] Input data:

[1437] Extracted user messages

[1438] Output data:

[1439] Preprocessed and prompt-formatted message

[1440] Step 5:

[1441] The generative artificial intelligence generates a response based on the message received from the server.

[1442] Specific behavior:

[1443] The generative artificial intelligence receives the prompt.

[1444] Parse the prompt and generate an appropriate response, such as "I've set up a project meeting for tomorrow at 10 AM. After that, I'd recommend scheduling a conference call with your client in an hour."

[1445] Input data:

[1446] The prompt sent by the server

[1447] Output data:

[1448] The generated response message

[1449] Step 6:

[1450] The server formats the response received from the generative AI into JSON format and sends it back to the device.

[1451] Specific behavior:

[1452] The server receives the response from the generative artificial intelligence.

[1453] The response message is formatted as JSON, as follows:

[1454] json

[1455] {

[1456] "response": "I've set up a project meeting for tomorrow at 10 AM. I'd like to schedule a conference call with the client afterwards in an hour."

[1457] }

[1458] The formatted JSON data is returned to the terminal as an HTTP response.

[1459] Input data:

[1460] Response message received from generative artificial intelligence

[1461] Output data:

[1462] JSON formatted HTTP response

[1463] Step 7:

[1464] The device receives the JSON-formatted response sent from the server, parses it, and displays the response text to the user.

[1465] Specific behavior:

[1466] The device receives the HTTP response.

[1467] Parse the JSON data from the response and extract the "response" field.

[1468] The extracted response text is displayed to the user.

[1469] Input data:

[1470] JSON formatted HTTP response sent from the server

[1471] Output data:

[1472] Parsed response text: "I've set up a project meeting for tomorrow at 10 AM. After that, I'd like to suggest scheduling a conference call with my client in an hour."

[1473] Step 8:

[1474] The user decides on the next action based on the provided response.

[1475] Specific behavior:

[1476] The user checks the response displayed on the terminal.

[1477] If necessary, enter the following instructions into the terminal:

[1478] Input data:

[1479] The parsed response text

[1480] Output data:

[1481] User's next action

[1482] (Application example 1)

[1483] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1484] Conventional factory management systems require users to manually manage machine operation status and maintenance plans, which is inefficient. Furthermore, they have problems with not being able to propose appropriate schedules when an emergency response is required or when adjusting task priorities. This can lead to work delays, increased costs, and reduced overall efficiency.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1486] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence and generating an appropriate response, means for receiving the response generated by the generative artificial intelligence, means for integrated management of the operating status and maintenance plans of machines in a factory, means for the generative artificial intelligence to propose optimal schedules and task priorities for machines based on inquiries input by the user, and means for displaying the response of the generative artificial intelligence to the user. This allows users to easily optimize the operating status and maintenance plans of machines, enabling efficient factory operation.

[1487] A "user" is an entity that uses the system to manage machinery within a factory and optimize maintenance plans.

[1488] A "message" is an instruction or inquiry that a user inputs to the system.

[1489] The "server" is a central computer that processes messages received from users, sends them to the generative artificial intelligence, and generates appropriate responses.

[1490] "Generative AI" refers to an AI model that generates responses based on messages from users, and includes models such as GPT-3.

[1491] A "response" is an appropriate answer or suggestion to a user's message, generated by generative artificial intelligence.

[1492] "Operation status" is information that indicates the current operating status of machines in a factory.

[1493] A "maintenance plan" refers to a plan for the maintenance and inspection of machinery in a factory, and is a schedule for optimizing the operation of machinery over the long term.

[1494] "Integrated management" is a method for centrally managing the operating status and maintenance plans of multiple machines and systems to achieve overall optimization.

[1495] An "optimal schedule" refers to the time allocation and work order that are deemed most appropriate for efficient operation and maintenance of machines.

[1496] "Task priority" refers to the order in which tasks should be performed first among multiple jobs or operations, determined based on importance and urgency.

[1497] The present invention provides a system that allows users to efficiently manage machinery in factories and plan maintenance. This system includes a mechanism that receives inquiries and instructions from users, uses generative artificial intelligence to generate optimal responses based on those inquiries and instructions, and displays the responses to the users.

[1498] First, a user accesses the system using a device such as a smartphone or PC. For example, the user enters a message such as, "When is the next maintenance?" The device then sends this message to the server. The server then sends the received message to the generative artificial intelligence, which then generates an appropriate response.

[1499] The server uses a model such as GPT-3 as a generative AI. This generative AI generates a response based on the user's message. For example, a possible response might be, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd." The server receives the generated response and sends it to the terminal in JSON format.

[1500] Factory management requires a system that can integrate and manage the operating status and maintenance plans of machines within the factory. This system uses generative artificial intelligence to propose optimal machine schedules and task priorities based on inquiries entered by the user. For example, if a user enters, "What is tomorrow's shift schedule?", the system can generate a response such as, "Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow all safety precautions when working."

[1501] The main components required to implement this system are:

[1502] 1. User: The entity that uses a terminal to access the system and input messages and instructions.

[1503] 2. Terminal: A device that receives messages entered by the user, sends them to the server, and displays responses from the server to the user.

[1504] 3. Server: A central management system that analyzes messages received from users and sends them to the generative AI, and also receives generated responses and sends them back to the terminal.

[1505] 4. Generative AI: An AI model that generates responses based on messages from users. An example is GPT-3.

[1506] This allows users to automatically optimize the operating status and maintenance plans of machines within their factories, enabling efficient factory operations.

[1507] Example prompt sentence:

[1508] Message from factory manager: When is the next maintenance?

[1509] Please respond in the following format:

[1510] Response: The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd.

[1511] For example, if a user types "What is tomorrow's shift schedule?", the generative AI will generate the following response:

[1512] Response: Tomorrow's shift schedule is as follows: 8:00 - 12:00: Team A, 12:00 - 16:00: Team B, 16:00 - 20:00: Team C. Please follow the safety precautions when working.

[1513] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1514] Step 1:

[1515] The user types a message into a device (such as a smartphone or PC). For example, they type, "When is the next maintenance?"

[1516] Input: A message from the user.

[1517] Output: The user's message displayed on the terminal.

[1518] Step 2:

[1519] The device encodes the message entered by the user as a JSON-formatted request and sends an HTTP POST request to the server.

[1520] Input: The message entered by the user.

[1521] Output: The HTTP POST request sent to the server.

[1522] Step 3:

[1523] The server analyzes the request received from the device, acquires the user's message, and then performs preprocessing to send the user's message to the generative AI.

[1524] Input: HTTP POST request.

[1525] Output: The message sent to the generative artificial intelligence.

[1526] Step 4:

[1527] The generative AI generates a response based on the message received from the server. For example, it generates a response such as, "The next scheduled maintenance is on October 5th. Before that, we recommend that you perform preventive maintenance on October 3rd."

[1528] Input: The message sent by the server.

[1529] Output: The generated response.

[1530] Step 5:

[1531] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[1532] Input: A response from a generative artificial intelligence.

[1533] Output: The response in JSON format.

[1534] Step 6:

[1535] The terminal parses the JSON format response sent from the server, extracts the response text, and displays it to the user.

[1536] Input: JSON response from the server.

[1537] Output: The response text that is displayed to the user.

[1538] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1539] This invention is a system that combines a calendar app and a task management app with generative artificial intelligence and an emotion engine to improve the efficiency of schedule management and task prioritization, and also provides responses that take the user's emotions into consideration. The system's purpose is to use generative artificial intelligence to generate appropriate responses from messages entered by the user, and then add an emotion engine to generate more personalized responses based on the user's emotions.

[1540] Explanation of system program processing

[1541] System Overview

[1542] The system consists of the following major components:

[1543] 1. Users: Use the system to manage schedules and tasks.

[1544] 2. Terminal: A device (smartphone, PC, etc.) that receives messages entered by users, sends them to the server, and displays responses from the server to the user.

[1545] 3. Emotion engine: A system that analyzes messages entered by users and recognizes their emotions.

[1546] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[1547] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[1548] Example of user interaction

[1549] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[1550] 2. The device receives this message and sends it to the server. The device encodes the message as a JSON-formatted request and sends it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[1551] 3. The emotion engine analyzes the user's messages and recognizes their emotions, for example, detecting stress or tension from the messages.

[1552] 4. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[1553] 5. The generative AI generates a response based on the message and emotional information received from the server. For example, if the system determines that the user is nervous, the response may include gentle advice such as "Please relax and enjoy the meeting."

[1554] 6. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it parses the GPT-3 response, obtains it as text, encodes the response text in JSON format, and sends it to the device as an HTTP response.

[1555] 7. The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[1556] 8. The user decides on the next action based on the generative AI's response presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[1557] Specific operation example

[1558] 1. A user types into their device, "Add a new task at 2 PM." The emotion engine recognizes the emotion of impatience from the user's message.

[1559] 2. The device receives this message and sends it to the server, encoding it as a JSON request and sending it to the server as an HTTP POST request.

[1560] 3. The server analyzes the request received from the device and extracts the user's message and emotional information. The server then sends this information to the generative AI.

[1561] 4. The generative AI generates a response based on the message and emotional information. An example of a generated response might be, "I added a new task at 2 PM. Before that, I recommend you take a break to get through your busy day."

[1562] 5. The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device.

[1563] 6. The device receives the response sent from the server, parses the JSON data to extract the response text, and displays it to the user.

[1564] 7. The user decides on the next action based on the presented response.

[1565] In this way, by combining emotion engines, personalized responses tailored to the user's emotions can be provided, further reducing the burden on the user.

[1566] The processing flow will be explained below.

[1567] Step 1:

[1568] A user types a message into the device interface, for example, "Schedule a project meeting tomorrow at 10 AM."

[1569] Step 2:

[1570] The device receives the message entered by the user and sends it to the server by encoding it as a JSON-formatted request and sending it as an HTTP POST request to the specified endpoint (e.g., / schedule) on the server.

[1571] Step 3:

[1572] The server receives an HTTP POST request from the device. The Flask application listens for this request and parses the received JSON data to extract the user's message.

[1573] Step 4:

[1574] The server sends the acquired user message to the emotion engine, which recognizes the user's emotion. The emotion engine analyzes the message and generates emotion information.

[1575] Step 5:

[1576] The emotion engine recognizes emotions from the user's input message and returns the emotion information to the server. For example, if the user is nervous, the degree of nervousness is returned as numerical information.

[1577] Step 6:

[1578] The server receives the emotion information from the emotion engine and sends it along with the user's message to the generative AI. Specifically, it includes the emotion information in the OpenAI API request.

[1579] Step 7:

[1580] The generative AI generates an appropriate response based on the message and emotional information received from the server. For example, it generates a response that takes emotions into account, such as, "We've scheduled a project meeting for tomorrow at 10:00 AM. If you attend in a relaxed manner, you'll likely see results."

[1581] Step 8:

[1582] The server receives the response from the generative AI, formats it in JSON format, and sends it back to the device. Specifically, it encodes the generated response text in JSON format and sends it to the device as an HTTP response.

[1583] Step 9:

[1584] The device receives the JSON response sent from the server, parses this data, extracts the response text, and displays it to the user.

[1585] Step 10:

[1586] The user decides on the next action based on the generative AI's responses presented on the device, such as asking more specific questions, adding other tasks, or confirming or modifying the proposed schedule.

[1587] In this way, the system allows users to receive personalized responses that take their emotions into account, allowing them to manage their schedules and prioritize tasks more efficiently.

[1588] Example 2

[1589] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1590] Conventional schedule management systems and task management systems only provide mechanical responses to user input and have the problem of not being able to respond in a way that takes into account the user's emotional state. This makes it difficult to provide appropriate advice and support to users who are feeling stressed or tense.

[1591] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing a message input by a user and recognizing the user's emotion using an emotion engine, means for transmitting the message and emotion information to the generative AI, and means for receiving a response generated by the generative AI. This enables a personalized response based on the user's emotion.

[1592] "User" refers to the person who uses the system to manage schedules and tasks.

[1593] A "terminal" is a device that receives a message entered by a user, sends it to a server, and displays a response from the server to the user, and specifically includes a smartphone, a personal computer, etc.

[1594] "Server" refers to a central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[1595] An "emotion engine" refers to a system that analyzes messages entered by users and recognizes their emotions.

[1596] "Generative AI" refers to an AI model that generates appropriate responses based on messages and emotional information received from a server, and a specific example is GPT-3.

[1597] "Message" refers to text information about schedules and tasks that a user inputs into a terminal.

[1598] "Response" refers to a reply that the generative artificial intelligence generates based on the user's message and emotional information.

[1599] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format that is widely used as a data exchange format.

[1600] This invention is a response generation system that combines generative artificial intelligence and an emotion engine based on a message entered by a user. The system aims to provide personalized responses by taking into account the user's emotions. The system mainly consists of the following components:

[1601] 1. User: A user who uses the system to manage schedules and tasks. The user enters text messages into the terminal.

[1602] 2. Terminal: A device that receives messages entered by users and sends them to the server. The terminal can be a smartphone or a PC, and encodes the messages entered by users into JSON format and sends them to the server as an HTTP POST request.

[1603] 3. Server: A central management system that receives messages and emotional information from users, analyzes them, and sends them to the generative AI. The server uses an emotion engine to recognize the user's emotions and passes that information to the generative AI.

[1604] 4. Emotion Engine: A system that analyzes messages entered by users and recognizes their emotions. The emotion engine understands the context of the message and can detect emotions such as stress or tension.

[1605] 5. Generative AI: An AI model that generates appropriate responses based on messages and emotional information received from the server. A specific example is a generative AI model such as GPT-3.

[1606] As an example of how the system works, consider the case where a user types into a terminal, "Schedule a project meeting tomorrow at 10 AM." This message is processed as follows:

[1607] 1. A user types into a terminal, "Schedule a project meeting tomorrow at 10 AM."

[1608] 2. The device receives this message, encodes it into a JSON-formatted request, and sends it to the server as an HTTP POST request.

[1609] 3. After the server receives the request, it uses the emotion engine to analyze the user's emotion. For example, it may determine that the user is nervous.

[1610] 4. The server sends the analyzed message and emotional information to the generative artificial intelligence.

[1611] 5. The generative AI generates a response based on the information it receives. For example, it might say, "We've scheduled a project meeting for tomorrow at 10:00 AM. Please relax and enjoy the meeting."

[1612] 6. The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[1613] 7. The device receives the response from the server, parses it, and displays it to the user.

[1614] An example prompt might have the following format:

[1615] "Schedule a project meeting tomorrow at 10 AM"

[1616] Through the above process, it is possible to provide appropriate responses that take the user's emotions into consideration. This system provides advice according to the user's emotional state, reducing stress and enabling efficient schedule management.

[1617] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1618] Step 1:

[1619] The user enters a message into the terminal.

[1620] How it works: A user types into a device (such as a smartphone or computer) the following: "Schedule a project meeting tomorrow at 10 AM." This message triggers the system's processing.

[1621] Input: A user-entered text message: "Schedule a project meeting tomorrow at 10 AM."

[1622] Output: A text message is sent to the terminal.

[1623] Step 2:

[1624] The terminal receives the message and sends it to the server.

[1625] How it works: The device encodes the received message into JSON format and sends it as an HTTP POST request to a specific endpoint on the server (e.g., / schedule).

[1626] Input: The text message entered by the user.

[1627] Output: The message is encoded as JSON data and sent to the server.

[1628] json

[1629] {

[1630] "message": "Schedule a project meeting tomorrow at 10 AM"

[1631] }

[1632] Step 3:

[1633] The server receives the message and analyzes it using the emotion engine.

[1634] How it works: The server analyzes the received message and uses an emotion engine to recognize the user's emotions. For example, tension or stress may be detected.

[1635] Input: JSON formatted message sent from the device.

[1636] Output: Perceived emotional information (e.g., tension).

[1637] json

[1638] {

[1639] "message": "Schedule a project meeting tomorrow at 10 AM",

[1640] "emotion": "nervous"

[1641] }

[1642] Step 4:

[1643] The server sends the message and emotion information to the generative artificial intelligence.

[1644] How it works: Sends the user's message and emotion information to a generative artificial intelligence model (e.g., GPT-3).

[1645] Input: User's message and parsed sentiment information.

[1646] Output: Message and emotion information forwarded to the generative AI.

[1647] Step 5:

[1648] Generative artificial intelligence generates responses.

[1649] How it works: The generative AI model generates an appropriate response based on the received message and emotional information. For example, if the user is nervous, it will generate a response that includes advice such as "Relax and approach the meeting."

[1650] Input: Message and emotion information received from the server.

[1651] Output: The generated response.

[1652] json

[1653] {

[1654] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[1655] }

[1656] Step 6:

[1657] The server sends the generated response to the terminal.

[1658] Behavior: The generated response is encoded in JSON format and sent to the device as an HTTP response.

[1659] Input: Response received from the generative artificial intelligence.

[1660] Output: Response data in JSON format is sent to the terminal.

[1661] Step 7:

[1662] The terminal receives the response and displays it to the user.

[1663] How it works: The device receives the JSON response sent by the server, parses it to extract the textual response, and displays it to the user.

[1664] Input: JSON formatted response data sent from the server.

[1665] Output: A text response that is displayed to the user.

[1666] json

[1667] {

[1668] "response": "We've scheduled a project meeting for tomorrow at 10 AM. Please relax and enjoy the meeting."

[1669] }

[1670] In this way, by clarifying the specific operations and inputs and outputs at each step, it becomes easier to understand the processing flow of the entire system.

[1671] (Application example 2)

[1672] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1673] In modern factories, workers must efficiently manage a wide variety of complex tasks, which often leads to stress and impatience. Conventional systems struggle to provide feedback and support that takes into account workers' emotional states, resulting in problems such as reduced work efficiency and increased errors. Furthermore, task scheduling and prioritization are not centrally managed, placing a heavy burden on workers.

[1674] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1675] In this invention, the server includes means for receiving a message input by a user, means for transmitting the message to the server, means for transmitting the message received by the server to a generative artificial intelligence (AI) to generate an appropriate response, means for receiving the response generated by the generative artificial intelligence (AI), means for displaying the response to the user, means for managing the user's tasks and schedule using smart glasses, and means for recognizing the user's emotional state using an emotion engine and generating a response based on the emotion. This enables personalized feedback and support that takes into account the worker's emotional state, enabling efficient task management and reducing the worker's mental burden.

[1676] "Users" are factory workers who use the system to manage tasks and schedules.

[1677] A "message" is any information or instruction entered by a user that is processed and interpreted by the system.

[1678] The "server" is a central management system that receives messages from users, sends them to the generative artificial intelligence, and generates, receives, and sends responses.

[1679] "Generative AI" is an AI model that generates appropriate responses based on the user's message and emotional information.

[1680] A "response" is a reply generated by the generative artificial intelligence to a message entered by a user.

[1681] "Smart glasses" are wearable devices that users wear to manage tasks and schedules and receive notifications in real time.

[1682] An "emotion engine" is a system that analyzes emotions from the user's input message and recognizes those emotions.

[1683] A "task" is a job or operation that a user performs in a factory and is managed by the system.

[1684] A "schedule" is a plan that organizes and arranges a user's tasks in terms of time.

[1685] "Personalized feedback" refers to individualized feedback or advice that takes into account the user's emotional state.

[1686] This invention is a system for improving the efficiency of factory work. It uses smart glasses worn by workers to manage tasks and schedules, and also combines an emotion engine to recognize the emotional state of the worker and provide personalized feedback and advice based on that emotion. The main function of this system is to generate appropriate responses using generative artificial intelligence.

[1687] Hardware and software used

[1688] The system consists of the following main components:

[1689] 1. Smart glasses: A wearable device worn by the user to manage tasks and schedules in real time.

[1690] 2. Terminal: A device that acts as part of the smart glasses, receives user input messages, sends them to the server, and displays responses from the server to the user.

[1691] 3. Emotion engine: A system that analyzes emotions from the user's input message and recognizes those emotions.

[1692] 4. Server: A central management system that receives messages and emotional information from users, sends them to the generative AI, and returns the generated responses to the terminal.

[1693] 5. Generative AI: An AI model (e.g., GPT-3) that generates appropriate responses based on messages and emotional information received from the server.

[1694] System Operation

[1695] 1. User input: A voice message entered by the worker through the smart glasses, such as "Start a new task at 2pm."

[1696] 2. Sentiment Analysis: The emotion engine analyzes the user's message and recognizes their emotional state (e.g., impatience, stress, joy, etc.). This analysis is done using natural language processing techniques.

[1697] 3. Send message: The device encodes the user's message in JSON format and sends it to the server as an HTTP POST request.

[1698] 4. Response generation: The server sends the received message and emotional information to the generative AI, which then generates an appropriate response. For example, if the user is feeling impatient, the system might generate a response such as, "I'll start a new task at 2 p.m. Please proceed without rushing."

[1699] 5. Sending response: The server receives the response from the generative AI, formats it again in JSON format, and sends it back to the device.

[1700] 6. Response display: The terminal parses the JSON response sent from the server, extracts the response text, and displays it to the user. The response is displayed on the smart glasses display, allowing the worker to check it in real time.

[1701] Specific examples

[1702] When a user voice-inputs "I'll start a new task at 2 PM" into the smart glasses, the emotion engine recognizes the emotion of impatience. Then, a generative AI (e.g., GPT-3) generates a response such as "I'll start a new task at 2 PM. Please proceed calmly," which is displayed on the smart glasses. This process allows workers to efficiently manage changes to work plans and additional tasks.

[1703] Prompt Sentence Examples

[1704] The text prompt is as follows:

[1705] User says: "I'll start a new task at 2 PM."

[1706] Emotional state: Impatience

[1707] Generate a response.

[1708] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1709] Step 1:

[1710] The user uses the smart glasses to input voice messages related to tasks and schedules, such as "Start a new task at 2 p.m." The smart glasses convert the voice messages into text format and send them to the device as data.

[1711] Step 2:

[1712] The terminal receives a user input message, which is a text message such as "Start a new task at 2 PM." The terminal encodes this message into JSON format and sends it to the server using an HTTP POST request.

[1713] Step 3:

[1714] The server receives a JSON-formatted message sent from the device. The server analyzes the received data and extracts the user's message. In this case, the message is text data such as "Start new work at 2 PM," and the server sends this to the emotion engine.

[1715] Step 4:

[1716] The emotion engine analyzes messages sent from the server and recognizes the user's emotional state. The input is text data such as "I'll start a new task at 2 PM," and the analysis results in emotional data such as "impatient." This emotional information is then sent back to the server.

[1717] Step 5:

[1718] The server receives emotional information from the emotion engine. The server combines this emotional information with the message and sends it to the generative AI. The input is the text message "Start new work at 2 PM" and the emotional information "impatient," and an appropriate response is generated based on these.

[1719] Step 6:

[1720] The generative AI generates a response based on the message and emotional information from the server. The input is the text message "Start new work at 2 PM" and the emotional information "impatience," and the output is a response such as "Start new work at 2 PM. Please proceed without rushing." This response is sent to the server.

[1721] Step 7:

[1722] The server receives the response from the generative AI and encodes it into JSON format. The input is the response text "A new task will start at 2 PM. Please proceed slowly." The response data converted into JSON format is output. The server sends this data to the terminal.

[1723] Step 8:

[1724] The device receives the JSON-formatted response sent from the server. The device parses this data and extracts the response text. The input is the JSON-formatted response data, and the output is text data that reads, "A new task will start at 2 p.m. Please proceed without rushing." The device displays this response on the smart glasses.

[1725] Step 9:

[1726] The user checks the response displayed on the smart glasses. The response is "You will start a new task at 2 PM. Please take your time." Based on this, the user decides on the next action. "Specific actions include starting the task, taking a break, etc."

[1727] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1728] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1729] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1730] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1731] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1732] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1733] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1734] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1735] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1736] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1737] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1738] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1739] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1740] 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.

[1741] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1742] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1743] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1744] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1745] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1746] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1747] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1748] The following is further disclosed regarding the above embodiment.

[1749] (Claim 1)

[1750] means for receiving a message entered by a user;

[1751] means for transmitting said message to a server;

[1752] A means for transmitting the message received by the server to a generative artificial intelligence to generate an appropriate response;

[1753] A means for receiving a response generated by the generative artificial intelligence;

[1754] means for displaying said response to a user;

[1755] A system including:

[1756] (Claim 2)

[1757] The system according to claim 1, wherein the generative artificial intelligence generates advice on task priority and required time when generating a response.

[1758] (Claim 3)

[1759] The system according to claim 1, wherein the server receives a response from the generative artificial intelligence in JSON format and transmits it to the terminal.

[1760] "Example 1"

[1761] (Claim 1)

[1762] means for receiving a message entered by a user;

[1763] means for transmitting said message to a server;

[1764] A means for transmitting the message received by the server to a generative artificial intelligence to generate an appropriate response;

[1765] A means for receiving a response generated by the generative artificial intelligence;

[1766] means for displaying said response to a user;

[1767] A means for the device to send user input in JSON format to the server;

[1768] A means for the server to parse the received JSON data and extract the user message;

[1769] A means for the server to format the response received from the generative AI into JSON format and send it to the terminal;

[1770] A means for the terminal to parse and display the JSON format response sent from the server;

[1771] A system including:

[1772] (Claim 2)

[1773] The system according to claim 1, wherein the generative artificial intelligence generates advice on task priority and required time when generating a response.

[1774] (Claim 3)

[1775] The system of claim 1, further comprising means for preprocessing messages sent by the server to the generative artificial intelligence.

[1776] "Application Example 1"

[1777] (Claim 1)

[1778] means for receiving a message entered by a user;

[1779] means for transmitting said message to a server;

[1780] A means for transmitting the message received by the server to a generative artificial intelligence to generate an appropriate response;

[1781] A means for receiving a response generated by the generative artificial intelligence;

[1782] A means of integrated management of the operating status and maintenance plans of machines in the factory,

[1783] A means for the generative artificial intelligence to propose optimal schedules and task priorities for the machine based on inquiries input by a user;

[1784] a means for displaying a response from the generative artificial intelligence to a user;

[1785] A system including:

[1786] (Claim 2)

[1787] The system of claim 1, wherein the generative artificial intelligence generates advice on task priorities and required times by taking into account factory work schedules and maintenance schedules when generating a response.

[1788] (Claim 3)

[1789] The system according to claim 1, wherein the server receives a response from the generative artificial intelligence in JSON format and transmits it to the terminal.

[1790] "Example 2: Combining Emotion Engines"

[1791] (Claim 1)

[1792] means for receiving a message entered by a user;

[1793] means for transmitting said message to a server;

[1794] means for analyzing the message received by the server and recognizing the user's emotion using an emotion engine;

[1795] a means for the server to transmit the message and emotion information to a generative artificial intelligence;

[1796] A means for receiving a response generated by the generative artificial intelligence;

[1797] means for transmitting the response to a terminal and displaying the response to a user;

[1798] A system including:

[1799] (Claim 2)

[1800] The system according to claim 1, wherein the generative artificial intelligence generates advice on task priority and required time when generating a response.

[1801] (Claim 3)

[1802] The system according to claim 1, wherein the server receives a response from the generative artificial intelligence in JSON format and transmits it to the terminal.

[1803] "Application example 2 when combining emotion engines"

[1804] (Claim 1)

[1805] means for receiving a message entered by a user;

[1806] means for transmitting said message to a server;

[1807] A means for transmitting the message received by the server to a generative artificial intelligence to generate an appropriate response;

[1808] A means for receiving a response generated by the generative artificial intelligence;

[1809] means for displaying said response to a user;

[1810] A means for managing a user's tasks and schedules using the smart glasses;

[1811] means for recognizing an emotional state of a user using an emotion engine and generating a response based on the emotion;

[1812] A system including:

[1813] (Claim 2)

[1814] The system according to claim 1, wherein the generative artificial intelligence generates advice on task priority and required time when generating a response.

[1815] (Claim 3)

[1816] The system according to claim 1, wherein the server receives a response from the generative artificial intelligence in JSON format and transmits it to the terminal. [Explanation of symbols]

[1817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a message entered by a user; means for transmitting said message to a server; A means for transmitting the message received by the server to a generative artificial intelligence to generate an appropriate response; A means for receiving a response generated by the generative artificial intelligence; means for displaying said response to a user; A system including:

2. The system according to claim 1 , wherein the generative artificial intelligence generates advice on task priority and required time when generating a response.

3. The system according to claim 1 , wherein the server receives a response from the generative artificial intelligence in JSON format and transmits it to the terminal.

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