System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing a user's utterance using natural language processing to extract relevant keywords; means for displaying the extracted relevant keywords on the user's screen; means for recording goals and key results set by the user and tracking progress; means for generating an efficient task list based on the recorded goals and key results; and means for displaying the task list on the user's screen.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] This invention relates to a system that supports the creation of new products and services through collaboration between AI and humans. Conventional collaboration tools have the problem of not being able to fully utilize users' free speech and efficiently extracting and organizing ideas. Furthermore, they are not sufficient in terms of goal progress management or streamlining work promotion, so there is a need for effective visualization of progress toward goals set by users and recommendation of tasks. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means. The system includes a means for analyzing user comments using natural language processing, extracting related keywords, and displaying the extracted related keywords on the user's screen, thereby efficiently extracting and sharing ideas from the user's free comments. The system also includes a means for recording user-set goals and key results and tracking progress, and a means for generating an efficient task list based on the recorded goals and key results and displaying the task list on the user's screen, thereby supporting the visualization of progress toward goals and effective business promotion. The system also includes a means for transmitting the user's comments as voice data to a server in real time, converting the voice data into text, and a means for updating the server's progress status against the recorded goals and key results and transmitting the progress data to the user's terminal. This provides a system that enables a consistent cycle of idea extraction, goal management, and business promotion.

[0006] "Natural language processing" is the technology that gives computers the ability to understand, interpret, and generate human language.

[0007] "User utterances" are verbal statements made by people using the system.

[0008] "Related keywords" are important words and phrases extracted from the user's comments.

[0009] A "goal" is a specific objective that a user aims to achieve.

[0010] "Key results" are the specific deliverables or progress items required to achieve the set goals.

[0011] A "task list" is a list of specific work items that are generated based on set goals and key results.

[0012] A "server" is a remote computer system that processes data over a network.

[0013] A "terminal" is a device that is directly operated by a user and is an interface for communicating with a server.

[0014] "Progress" refers to the current level of achievement of set goals and key results.

[0015] "Voice data" refers to a recording of a user's speech in digital form.

[0016] "Convert to text" is a process of converting voice data into character data. [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] The present invention is a system that supports the creation of new products and services through collaboration between AI and humans. Specifically, it includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[0039] 1. AI-assisted brainstorming

[0040] System configuration:

[0041] When a user initiates a brainstorming session via their device, their speech is sent as voice data to the server, which converts the received voice data into text and uses natural language processing (NLP) technology to extract relevant keywords. These relevant keywords are then sent to the device and displayed on the user's screen.

[0042] Examples:

[0043] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[0044] 2. OKR Tools

[0045] System configuration:

[0046] The user enters the objectives and key results into the OKR setting form on the device. The device sends this input data to the server, which stores it in a database. The server periodically tracks the progress, visualizes the data, and sends it to the device.

[0047] Examples:

[0048] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user, clarifying the path to achieving the goal.

[0049] 3. Business promotion assistance

[0050] System configuration:

[0051] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status to the server from the device. The server receives the report, updates the progress status in the database, and sends the latest progress status to the device.

[0052] Examples:

[0053] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[0054] The system of the present invention allows users to easily understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing promotes collaboration among users and accelerates the generation of creative ideas.

[0055] The processing flow will be explained below.

[0056] 1. AI-assisted brainstorming

[0057] Processing Steps:

[0058] Step 1:

[0059] User: Clicks a button on their device to start a brainstorming session.

[0060] Step 2:

[0061] Terminal: Sends a session initiation request to the server.

[0062] Step 3:

[0063] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[0064] Step 4:

[0065] Terminal: Starts audio recording and captures audio data in real time.

[0066] Step 5:

[0067] Terminal: Sends recorded audio data to the server in real time.

[0068] Step 6:

[0069] Server: Converts the received voice data into text.

[0070] Step 7:

[0071] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[0072] Step 8:

[0073] Server: Sends the extracted related keywords to the terminal.

[0074] Step 9:

[0075] Terminal: Displays the received related keywords on the user's screen.

[0076] Step 10:

[0077] User: Submit a new idea based on the displayed keywords.

[0078] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[0079] 2. OKR Tools

[0080] Processing Steps:

[0081] Step 1:

[0082] User: Opens the OKR setting form on the device and enters the objective and key results.

[0083] Step 2:

[0084] Terminal: Sends the entered OKR data to the server.

[0085] Step 3:

[0086] Server: Stores the received OKR data in a database.

[0087] Step 4:

[0088] Server: Generates data to track progress based on saved goals and key results.

[0089] Step 5:

[0090] Server: Sends the generated progress data to the device.

[0091] Step 6:

[0092] Terminal: Visualizes and displays the received progress data to the user.

[0093] Step 7:

[0094] User: Enters actual progress into the terminal.

[0095] Step 8:

[0096] Device: Sends updated progress data to the server.

[0097] Step 9:

[0098] Server: Updates the progress information in the database.

[0099] 3. Business promotion assistance

[0100] Processing Steps:

[0101] Step 1:

[0102] Server: Automatically generates an efficient task list based on the set OKRs.

[0103] Step 2:

[0104] Server: Sends the generated task list to the terminal.

[0105] Step 3:

[0106] Terminal: Displays the received task list to the user.

[0107] Step 4:

[0108] User: Selects a task to perform from a presented task list.

[0109] Step 5:

[0110] User: Perform the selected task.

[0111] Step 6:

[0112] User: Enter task completion status into the terminal.

[0113] Step 7:

[0114] Terminal: Reports information about completed tasks to the server.

[0115] Step 8:

[0116] Server: Updates OKR progress information based on reports.

[0117] Step 9:

[0118] Server: Sends progress updates to the device.

[0119] Step 10:

[0120] Terminal: Shows the user a visual update on the progress.

[0121] ---

[0122] Through the above steps, this system will support efficient and effective business promotion and the creation of new products and services through cooperation between users and AI.

[0123] Example 1

[0124] 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."

[0125] Conventional brainstorming and business promotion systems lacked the functionality to efficiently analyze user comments and extract and present related keywords. This resulted in problems such as users being unable to generate ideas or promote business effectively. Furthermore, systems for managing goals and progress lacked the functionality to track, update, and visualize progress in real time, significantly limiting users' work efficiency.

[0126] 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.

[0127] In this invention, the server includes natural language processing means for analyzing user comments and extracting related keywords, presentation means for displaying the extracted related keywords on the user's screen, data storage means for recording goals and major results set by the user, progress visualization means for tracking progress and visualizing the data, task generation means for generating an efficient task list based on the recorded goals and major results, and task display means for displaying the task list on the user's screen. This makes it possible to efficiently analyze the content of user comments, track and update progress in real time, and improve work efficiency.

[0128] "Natural language processing means" is a technology for converting the content of a user's speech from voice data into text data, and analyzing the text data to extract related keywords.

[0129] The "presentation means" refers to a method or technique for displaying the extracted related keywords on the user's screen.

[0130] "Data storage" refers to the methods and techniques used to record user-defined goals and key results and to store such data over the long term.

[0131] "Progress visualization tools" are techniques for collecting progress on goals and key results and displaying that data in visual formats such as graphs and charts.

[0132] "Task generation means" is a technique for generating a list of specific tasks and procedures that are required based on recorded goals and key results.

[0133] The "task display means" refers to a method or technology for displaying the generated task list on the user's terminal so that the user can confirm and execute the tasks.

[0134] "Speech recognition means" refers to technology or software for converting voice data into text data.

[0135] The "progress update means" is a technique for updating the progress status for the recorded goals and major results with the latest data and transmitting the updated data to the user's terminal.

[0136] The present invention is a system that supports the creation of new products and services through collaboration between AI technology and humans. This system includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[0137] AI-assisted brainstorming

[0138] When a user starts a brainstorming session through a device, the device collects speech data from the user via a microphone and transmits the collected speech data to a server. The server converts the received speech data into text data using speech recognition software (e.g., Google® Cloud Speech-to-Text API). The server then extracts relevant keywords from the text data using natural language processing (NLP) techniques (e.g., Google Cloud Natural Language API). The relevant keywords are then transmitted from the server to the device and displayed on the user's screen.

[0139] Examples:

[0140] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snack." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[0141] Example prompt sentence:

[0142] Let's start a brainstorming session for a new healthy food. If User A says "snacks rich in vitamin C," extract related keywords.

[0143] OKR Tools

[0144] The user enters objectives and key results into the OKR setting form via the terminal. The terminal sends the entered data to the server, which stores the data in a database (e.g., MySQL (registered trademark)). The server periodically collects progress data, generates a graph using a tool for visualizing progress (e.g., Chart.js), and sends the graph to the terminal. The user can check the progress graph via the terminal.

[0145] Examples:

[0146] The user sets a goal of "Complete a prototype of a new product during Q1" and inputs three main results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user.

[0147] Example prompt sentence:

[0148] Please set the following as the key results required to complete the prototype of the new product during Q1: "Market research completed," "Design completed," and "Prototype created."

[0149] Business promotion assistance

[0150] The server generates an efficient task list based on the set OKRs. The server sends the generated task list to the device, which displays it on the user's screen. The user executes the displayed tasks one by one and reports their completion status to the device. The device sends the report to the server, which updates the progress status in a database and sends the latest progress status to the device. The user can always check the latest progress status.

[0151] Examples:

[0152] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[0153] Example prompt sentence:

[0154] Set market research as the achievement criteria, and generate tasks based on that, such as "analyzing competing products," "creating a consumer survey," and "listing sales channels."

[0155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0156] AI-assisted brainstorming

[0157] Step 1:

[0158] A user operates a terminal to start a brainstorming session.

[0159] Input: User action (e.g. clicking a button to start a brainstorming session)

[0160] Output: Session start (event trigger) is generated

[0161] Step 2:

[0162] The device collects what the user says through the microphone and sends it to the server as voice data.

[0163] Input: User utterance (audio)

[0164] Output: Audio data is sent to the server

[0165] Step 3:

[0166] The server converts the received voice data into text data using voice recognition software (Google Cloud Speech-to-Text API).

[0167] Input: Audio data

[0168] Output: Text data

[0169] Step 4:

[0170] The server uses natural language processing (NLP) technology (Google Cloud Natural Language API) to extract relevant keywords from the text data.

[0171] Input: Text data

[0172] Output: Extracted related keywords

[0173] Step 5:

[0174] The server transmits the extracted related keywords to the terminal, which then displays them on the user's screen.

[0175] Input: Extracted related keywords

[0176] Output: Related keywords are displayed on the user's screen.

[0177] OKR Tools

[0178] Step 1:

[0179] The user enters the objective and key results in the OKR setting form on the device.

[0180] Inputs: Objective and key result input data

[0181] Output: Input data is sent from the device to the server

[0182] Step 2:

[0183] The server saves the received data in a database (MySQL).

[0184] Inputs: Objective and key result input data

[0185] Output: Saved data

[0186] Step 3:

[0187] The server periodically collects progress data and uses a visualization tool (Chart.js) to graph the progress.

[0188] Input: Progress data from the database

[0189] Output: Visualized progress (graph)

[0190] Step 4:

[0191] The server sends the visualized progress to the terminal and displays it on the user's screen.

[0192] Input: Visualized progress (graph)

[0193] Output: A progress graph is displayed on the user's screen

[0194] Business promotion assistance

[0195] Step 1:

[0196] The server generates an efficient task list based on the configured OKRs.

[0197] Input: Data on set goals and key results

[0198] Output: Generated task list

[0199] Step 2:

[0200] The server transmits the generated task list to the terminal, which displays it on the user's screen.

[0201] Input: Generated task list

[0202] Output: The task list is displayed on the user's screen.

[0203] Step 3:

[0204] The user performs the displayed task and reports its completion status to the terminal.

[0205] Input: Execute tasks and report completion

[0206] Output: Completion status data is sent from the terminal to the server

[0207] Step 4:

[0208] The server updates the progress to a database and sends the latest progress to the device.

[0209] Input: Completion status data

[0210] Output: Latest progress data

[0211] Step 5:

[0212] The user checks the latest progress via the device.

[0213] Input: Latest progress data

[0214] Output: The user sees progress updates on their screen

[0215] The above are the specific processing steps of the program of this system.

[0216] (Application example 1)

[0217] 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."

[0218] In the industrial sector, there is a demand for improved operational efficiency and the promotion of brainstorming for new product development. Conventional systems have limited progress management and automatic task generation functions, and it is difficult to support real-time brainstorming based on voice. In particular, there is a problem of insufficient coordination between workers and machines in factories, which reduces overall productivity.

[0219] 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.

[0220] In this invention, the server includes means for collecting user voice data in real time using an industrial automated machine and transmitting it to the server, means for converting the voice data into text, and means for analyzing the voice data collected by the industrial automated machine and displaying related keywords to support brainstorming. This makes it possible to extract and display related keywords in real time using natural language processing technology based on what the user says, thereby realizing efficient and effective brainstorming and progress management with the cooperation of the industrial automated machine.

[0221] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0222] "Related keywords" are important words and phrases extracted by analyzing the content of a user's comments.

[0223] A "user display device" is a device used by a user to visually view information.

[0224] "Goals and Key Results" are specific goals set by the user and the important deliverables required to achieve them.

[0225] A "task list" is a list of specific work items that need to be done to achieve a goal.

[0226] "Industrial automation equipment" refers to industrial robots and automation devices used in factories, etc.

[0227] "Audio data" refers to data in which a user's speech is recorded in audio format.

[0228] A "server" is a computer system for processing, storing, and communicating data.

[0229] "Means for converting to text" refers to techniques or methods for converting audio data into text format.

[0230] Brainstorming is a meeting technique that allows teams to freely exchange ideas and seek creative solutions.

[0231] "Progress tracking measures" are methods and techniques for continuously recording and managing progress toward goals.

[0232] This invention is a system that uses industrial automation equipment, a server, and a user's display device, and utilizes natural language processing technology to analyze utterances, extract related keywords, set goals, manage progress, and manage tasks more efficiently.

[0233] 1. Configuration

[0234] The system includes the following components:

[0235] Industrial automation: Industrial robots and automated devices for collecting audio data.

[0236] Server: A computer system for processing and storing collected data.

[0237] User display device: The device used by a user to visually view information.

[0238] 2. Program Overview

[0239] Voice Recognition

[0240] The automated industrial machine collects user speech in real time and sends it as voice data to a server, where it uses a Python library called speech_recognition to convert the voice data into text.

[0241] Natural Language Processing

[0242] On the server, the voice data is converted into text and related keywords are extracted using NLP technology, such as libraries like OpenAI's GPT-3 and spaCy.

[0243] Brainstorming support

[0244] The automated industrial machine analyzes the collected voice data and displays related keywords on the user's display, which allows for efficient brainstorming among users.

[0245] Goal setting and progress management

[0246] The user sets objectives and key results through the display device, and the data is sent to the server and recorded in a database. The server periodically tracks the progress data and displays it visually on the user's display device.

[0247] Task Management

[0248] The server generates an efficient task list based on the set goals and key results, which is also displayed on the user's display device, allowing the user to report progress after completing the tasks.

[0249] 3. Processing Description

[0250] The server converts voice data collected from automated industrial machines into text and analyzes the text to extract relevant keywords. The hardware used is a Linux server, and the software used includes the speech_recognition library, GPT-3 API, or spaCy.

[0251] When a user sets goals and key results, the data is sent to the server and stored in a database in JSON format. The server periodically updates the progress status based on this data and displays it on the user's display device in real time.

[0252] As a concrete example, consider a scenario in which a prototype of a new product is being developed in a factory. An automated industrial machine collects the user's speech, "Snacks rich in vitamin C," and sends the speech data to a server. The server converts the speech data into text and extracts related keywords, such as "vitamin C" and "snacks." This information is displayed on the user's display device, making it easier for other users to come up with new ideas.

[0253] Example prompts to input to a generative AI model:

[0254] I'm thinking of a new idea for a health food product. Based on comments from other staff members, I would like you to specifically analyze the keywords "vitamin C" and "snacks" and come up with a new related proposal. In addition, I would like you to generate a specific task list to complete the prototype during Q1.

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] Input: Industrial automation collects user utterances in real time.

[0258] How it works: An automated industrial machine uses a microphone for voice recognition to record what a user says as audio data.

[0259] Output: The collected audio data is generated.

[0260] Step 2:

[0261] Input: An automated industrial machine transmits collected audio data to a server.

[0262] How it works: Audio data is transmitted from an automated industrial device to a server, where it is stored in a database.

[0263] Output: Audio data stored on the server.

[0264] Step 3:

[0265] Input: The server receives the stored audio data.

[0266] How it works: The server uses the speech_recognition library to convert the audio data into text.

[0267] Output: Text data converted from audio data.

[0268] Step 4:

[0269] Input: The server parses the text data.

[0270] How it works: The server uses NLP techniques to extract relevant keywords from text data, using the GPT-3 API and spaCy.

[0271] Output: Extracted related keywords.

[0272] Step 5:

[0273] Input: The server receives the extracted related keywords.

[0274] Behavior: The server converts the related keywords into JSON format and sends it to the user's display device.

[0275] Output: Related keywords data in JSON format.

[0276] Step 6:

[0277] Input: The user's display device receives the relevant keywords.

[0278] What it does: The user's display device visually displays related keywords, helping the user generate new ideas.

[0279] Output: A visual representation of related keywords for the user.

[0280] Step 7:

[0281] Input: The user sets the objective and key results.

[0282] How it works: The user enters the goals and key results into a form on the display device and sends it to the server.

[0283] Outputs: Set objectives and key outcome data.

[0284] Step 8:

[0285] Input: The server receives the set goals and key outcome data.

[0286] How it works: The server stores this data in a database and sets initial values ​​for tracking progress.

[0287] Output: A database record of the objectives and key results set.

[0288] Step 9:

[0289] Input: The server collects and updates progress data.

[0290] How it works: The server periodically checks the progress and updates the progress data based on the task completion information reported by the user.

[0291] Output: The updated progress data.

[0292] Step 10:

[0293] Input: The server sends progress data to the user's display device.

[0294] Behavior: The server sends updated progress data in JSON format to the user's display device.

[0295] Output: Progress data that can be visually confirmed by the user.

[0296] 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.

[0297] The present invention relates to a system that supports the creation of new products and services through collaboration between AI and humans, and by combining it with an emotion engine that recognizes user emotions, it achieves a higher level of interaction and effective collaboration. Detailed embodiments of each function are described below.

[0298] 1. AI-assisted brainstorming

[0299] System configuration:

[0300] When a user starts a brainstorming session via their device, their speech is sent as voice data to the server. The server converts the received voice data into text and uses natural language processing (NLP) technology to extract related keywords. These related keywords are sent to the device and displayed on the user's screen. The emotion engine also recognizes emotions from the user's speech and adjusts the presentation of related keywords accordingly.

[0301] Examples:

[0302] For example, consider a scenario in which users are brainstorming ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords; if the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[0303] 2. OKR Tools

[0304] System configuration:

[0305] Users enter their objectives and key results in an OKR setting form on their device. The device sends this input data to the server, which stores it in a database. The server periodically tracks progress, visualizes the data, and sends it to the device. An emotion engine can also record the user's emotional state and display it along with the progress data.

[0306] Examples:

[0307] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user. The emotion engine adjusts progress based on the user's emotional data, and when positive emotions are observed, it highlights the degree of achievement at that point.

[0308] 3. Business promotion assistance

[0309] System configuration:

[0310] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status from the device to the server. The server receives the report, updates the progress status in a database, and sends the latest progress status to the device. In addition, the emotion engine can take the user's emotional data into account and adjust the priority and content of tasks.

[0311] Examples:

[0312] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the device, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status. The emotion engine monitors the user's emotional state while performing the task, and if motivation drops, it will take action such as recommending a lighter task.

[0313] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[0314] The processing flow will be explained below.

[0315] MODE FOR CARRYING OUT THE INVENTION

[0316] 1. AI-assisted brainstorming

[0317] Processing Steps:

[0318] Step 1:

[0319] User: Clicks a button on their device to start a brainstorming session.

[0320] Step 2:

[0321] Terminal: Sends a session initiation request to the server.

[0322] Step 3:

[0323] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[0324] Step 4:

[0325] Terminal: Starts audio recording and captures audio data in real time.

[0326] Step 5:

[0327] Terminal: Sends recorded audio data to the server in real time.

[0328] Step 6:

[0329] Server: Converts the received voice data into text.

[0330] Step 7:

[0331] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[0332] Step 8:

[0333] Server: Analyzes emotions from user comments using an emotion engine.

[0334] Step 9:

[0335] Server: Adjusts the content of related keywords presented based on the extracted related keywords and analyzed emotion data.

[0336] Step 10:

[0337] Server: Sends the adjusted related keywords to the terminal.

[0338] Step 11:

[0339] Terminal: Displays the received related keywords on the user's screen.

[0340] Step 12:

[0341] User: Submit a new idea based on the displayed keywords.

[0342] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[0343] 2. OKR Tools

[0344] Processing Steps:

[0345] Step 1:

[0346] User: Opens the OKR setting form on the device and enters the objective and key results.

[0347] Step 2:

[0348] Terminal: Sends the entered OKR data to the server.

[0349] Step 3:

[0350] Server: Stores the received OKR data in a database.

[0351] Step 4:

[0352] Server: Generates data to track progress based on saved goals and key results.

[0353] Step 5:

[0354] Server: Analyzes the user's emotional state using an emotion engine.

[0355] Step 6:

[0356] Server: Combines emotion data with data that tracks progress and generates data to display along with the progress.

[0357] Step 7:

[0358] Server: Sends the generated progress data to the device.

[0359] Step 8:

[0360] Terminal: Visualizes and displays the received progress data to the user.

[0361] Step 9:

[0362] User: Enters actual progress into the terminal.

[0363] Step 10:

[0364] Device: Sends updated progress data to the server.

[0365] Step 11:

[0366] Server: Updates the progress information in the database.

[0367] 3. Business promotion assistance

[0368] Processing Steps:

[0369] Step 1:

[0370] Server: Automatically generates an efficient task list based on the set OKRs.

[0371] Step 2:

[0372] Server: Analyzes the user's emotional state using an emotion engine and adjusts the priority and content of tasks.

[0373] Step 3:

[0374] Server: Sends the generated task list to the terminal.

[0375] Step 4:

[0376] Terminal: Displays the received task list to the user.

[0377] Step 5:

[0378] User: Selects a task to perform from a presented task list.

[0379] Step 6:

[0380] User: Perform the selected task.

[0381] Step 7:

[0382] User: Enter task completion status into the terminal.

[0383] Step 8:

[0384] Terminal: Reports information about completed tasks to the server.

[0385] Step 9:

[0386] Server: Updates OKR progress information based on reports.

[0387] Step 10:

[0388] Server: Sends progress updates to the device.

[0389] Step 11:

[0390] Terminal: Shows the user a visual update on the progress.

[0391] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[0392] Example 2

[0393] 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."

[0394] Current business promotion support systems lack functionality that takes into account the user's emotional state, making them unable to flexibly respond to the user's motivation and emotional state. Furthermore, there is a lack of systems that can properly analyze the content and emotions of users' comments during brainstorming sessions and use this information to provide effective interactions. It is necessary to solve these issues.

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

[0396] In this invention, the server includes: means for analyzing a user's utterances using natural language processing and extracting related keywords; means for displaying the extracted related keywords on the user's screen; means for recording goals and key results set by the user and tracking progress; means for generating an efficient task list based on the recorded goals and key results; means for transmitting voice data to the server in real time and converting the voice data to text at the server; means for converting and analyzing the received voice data and recognizing the user's emotions using an emotion engine; means for adjusting the display of related keywords based on the recognized emotions; and means for recording the user's emotional state and displaying it together with progress data. This enables flexible and effective support for business promotion and brainstorming sessions that take the user's emotional state into consideration.

[0397] "Natural language processing" is the technology that enables computers to understand, interpret, and generate human language.

[0398] "Related keywords" are important words and phrases extracted from user statements and documents.

[0399] "Voice data" refers to data that is a digital recording of a user's speech or voice communication.

[0400] "Text conversion" is the process of converting audio data into written information.

[0401] An "emotion engine" is a technology or system that analyzes and recognizes a user's emotional state from their words and actions.

[0402] A "goal" is a specific outcome or result that a user aims to achieve.

[0403] "Key results" are the specific actions or performance indicators required to achieve a goal.

[0404] A "task list" is a list of work items that a user needs to accomplish.

[0405] A "timestamp" is data that digitally records the date and time that a particular event occurred.

[0406] "Progress data" is data that indicates the progress of a project or task.

[0407] This invention is a system that supports the creation of new products and services through collaboration between AI and humans, and realizes more effective collaboration by combining it with an emotion engine that recognizes user emotions. Detailed embodiments of each function are described below.

[0408] 1. AI-assisted brainstorming

[0409] System configuration:

[0410] When a user starts a brainstorming session via their device, their speech is sent to the server as audio data. The server then converts the speech data into text using the Google Cloud Speech-to-Text API. NLP techniques are then applied to the text data to extract relevant keywords. NLP libraries such as spaCy and BERT are used for this natural language processing. The extracted keywords are sent to the user's device and displayed on the screen. An emotion engine (e.g., IBM Watson® Tone Analyzer) analyzes the user's speech to recognize their emotions and adjusts the display of relevant keywords accordingly.

[0411] Examples:

[0412] For example, consider a scenario in which users are discussing ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords. If the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[0413] Example prompt sentence:

[0414] "When discussing new healthy food ideas, please comment on vitamin C-rich snacks."

[0415] 2. OKR Tools

[0416] System configuration:

[0417] When a user enters objectives and key results into the OKR setting form on their device, the device sends this data to the server. The server stores the received data in a MySQL or PostgreSQL database. The server periodically retrieves the contents of the database, tracks and scans the progress, and visualizes the retrieved progress data in graph form using Chart.js or similar tools and displays it on the user's device. The emotion engine can also record the user's emotional state during this process and display it along with the progress data.

[0418] Examples:

[0419] Let's say a user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records this in a database and periodically retrieves the data to track progress. Progress is visualized and displayed in graph form on the user's device. The emotion engine analyzes the user's emotions when reporting progress, and if positive emotions are observed, it takes measures such as highlighting the degree of achievement.

[0420] Example prompt sentence:

[0421] "What efforts did you make to complete prototypes of new products during Q1?"

[0422] 3. Business promotion assistance

[0423] System configuration:

[0424] The server generates an efficient task list based on the set OKRs, determines the priority of each task, and provides it to the user. The user performs tasks based on the task list displayed on their device and reports the completion status to the server. The server updates the received progress data to a database and reflects the progress status on the device in real time. In addition, the emotion engine monitors the user's emotional state and can adjust the priority and content of tasks if motivation drops.

[0425] Examples:

[0426] For example, if a user sets the goal of "completing market research," the server will generate specific tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels," and display them on the device. When the user performs these tasks and reports their completion, the server updates the database and displays the latest progress on the device in real time. The emotion engine monitors the user's emotional state while performing the tasks, and if their motivation drops, it will take measures such as suggesting lighter tasks.

[0427] Example prompt sentence:

[0428] "What market research tasks are you currently performing?"

[0429] As a result, this system realizes flexible and effective support for business promotion and brainstorming sessions that take into account the user's emotional state.

[0430] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0431] AI-assisted brainstorming process steps

[0432] Step 1:

[0433] A user initiates a brainstorming session via a terminal.

[0434] Input: User clicks the session start button.

[0435] Output: Session initiation request to the server.

[0436] How it works: When you click the "Start Session" button on the device, the device sends a session start request to the server.

[0437] Step 2:

[0438] When a user speaks, the terminal captures the voice and transmits the voice data to the server.

[0439] Input: User speech.

[0440] Output: The audio data sent to the server.

[0441] How it works: The device captures what the user says with a microphone and transmits it to the server in real time.

[0442] Step 3:

[0443] The server uses a speech recognition API to convert the received voice data into text.

[0444] Input: Received audio data.

[0445] Output: Text data.

[0446] How it works: The server calls the Google Cloud Speech-to-Text API to convert the audio data into text and temporarily stores the text.

[0447] Step 4:

[0448] The server analyzes the text data using natural language processing (NLP) technology and extracts relevant keywords.

[0449] Input: Text data.

[0450] Output: Related keywords.

[0451] How it works: The server uses NLP libraries such as spaCy or BERT to extract relevant keywords from text data.

[0452] Step 5:

[0453] The emotion engine analyzes the text data and recognizes the user's emotions.

[0454] Input: Text data and extracted related keywords.

[0455] Output: The user's emotional state.

[0456] How it works: The server uses an emotion analysis engine such as IBM Watson Tone Analyzer to analyze the user's emotional state and saves the results.

[0457] Step 6:

[0458] Based on the emotion recognition results, the display content of related keywords is adjusted and sent to the terminal.

[0459] Input: Emotion recognition results and related keywords.

[0460] Output: The adjusted related keywords displayed on the user's device.

[0461] How it works: Based on the emotion recognition results, if the emotion is strong positive, the number of related keywords displayed is increased, and if the emotion is strong negative, the content displayed is adjusted to encourage other users to respond. The adjusted related keywords are sent to the device and displayed on the user's screen.

[0462] OKR tool processing steps

[0463] Step 1:

[0464] The user enters the objective and key results into the OKR setting form on the device.

[0465] Inputs: Form data with objectives and key results.

[0466] Output: Data sent to the server.

[0467] How it works: The user enters goals and key results into the OKR form on their device and presses the "Submit" button, sending the data to the server.

[0468] Step 2:

[0469] The server saves the received form data in the database.

[0470] Input: Form data.

[0471] Output: Objectives and key results stored in a database.

[0472] Operation: The server saves the received data in a MySQL or PostgreSQL database and sends a confirmation message to the terminal that the data has been saved.

[0473] Step 3:

[0474] The server regularly tracks progress against goals and key results.

[0475] Inputs: Objectives and key results stored in the database.

[0476] Output: Tracked progress data.

[0477] How it works: The server periodically retrieves progress data from a database and tracks progress. This process uses a cron job or task scheduler.

[0478] Step 4:

[0479] The server visualizes the progress data and sends it to the terminal.

[0480] Input: Tracked progress data.

[0481] Output: Visualized progress data (in the form of graphs and charts).

[0482] How it works: The server converts the acquired progress data into a graph using a tool such as Chart.js, and sends the visualized data to the terminal for display.

[0483] Step 5:

[0484] An emotion engine records the user's emotional state and displays it along with progress data.

[0485] Input: Progress data and the user's emotional state.

[0486] Output: A screen showing progress data and emotional state.

[0487] How it works: The emotion engine analyzes the user's emotions when reporting progress, records the results, and displays them on the device along with the progress data.

[0488] Processing steps for business promotion assistance

[0489] Step 1:

[0490] The server generates an efficient task list based on the configured OKRs.

[0491] Inputs: Objectives and key results stored in the database.

[0492] Output: The generated task list.

[0493] How it works: The server generates an efficient task list based on the recorded OKRs and determines the priority of each task.

[0494] Step 2:

[0495] The terminal displays the generated task list to the user.

[0496] Input: The generated task list.

[0497] Output: The task list displayed on the user's terminal.

[0498] Operation: The terminal displays the task list sent from the server to the user.

[0499] Step 3:

[0500] The user selects and executes a task.

[0501] Input: The task selected by the user.

[0502] Output: The task being executed.

[0503] Action: The user selects a task to perform from the task list displayed on the terminal, and the selected task is executed.

[0504] Step 4:

[0505] The user reports the task completion status to the server from the terminal.

[0506] Input: The task you reported as finished.

[0507] Output: Task completion data reported to the server.

[0508] How it works: After a user completes a task, they report their completion status to the server via their device, which records the report with a timestamp.

[0509] Step 5:

[0510] The server updates the progress to a database, and the latest progress is reflected on the device in real time.

[0511] Input: Reported task completion data.

[0512] Output: Updated progress.

[0513] How it works: The server receives the report, updates the database with the progress, and reflects the latest status on the device in real time.

[0514] Step 6:

[0515] An emotion engine monitors the user's emotional state and adjusts the priority and content of the task list as needed.

[0516] Input: The user's emotional state.

[0517] Output: The adjusted task list.

[0518] How it works: The emotion engine monitors the user's emotional state in real time, and if motivation drops, it adjusts the priority and content of the task list to appropriately suggest the next task to be done.

[0519] (Application example 2)

[0520] 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."

[0521] Conventional factory robots simply perform tasks based on set instructions, making it difficult for them to effectively collaborate with humans. Furthermore, tasks are assigned without considering the emotional state of workers, which can lead to a decline in motivation and work efficiency. Therefore, the goal of this invention is to provide a factory robot collaboration support system incorporating an emotion engine that recognizes the emotional state of workers and adjusts tasks accordingly, thereby improving work efficiency and increasing worker satisfaction.

[0522] 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.

[0523] In this invention, the server includes means for analyzing user utterances using natural language processing and extracting related keywords, means for displaying the extracted related keywords on the user's screen, means for recording goals and major results set by the user and tracking progress, means for generating an efficient task list based on the recorded goals and major results, means for displaying the task list on the user's screen, and means for recognizing the user's emotional state using an emotion engine and adjusting task allocation based on the emotional state, thereby enabling factory robots to dynamically adjust tasks according to the emotional states of workers and enabling cooperative work.

[0524] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0525] "Related keywords" are key words and phrases extracted based on the content of a user's comments.

[0526] A "goal" is a specific result or purpose that a user is trying to achieve.

[0527] "Key results" are the specific accomplishments required to achieve the set goals.

[0528] "Progress" refers to the current achievement of goals and key results.

[0529] A "worklist" is a list of efficient tasks generated based on recorded goals and key results.

[0530] An "emotion engine" is a technology that analyzes a user's voice and facial expressions to recognize their emotional state.

[0531] "Task allocation" means determining appropriate tasks and instructing users on them based on each user's emotional state and progress.

[0532] The "user screen" refers to the display device used by the user to check the list of tasks and progress.

[0533] A "server" is a computer system that receives and analyzes data sent from user terminals and executes various functions.

[0534] The present invention provides a factory robot collaboration support system that combines natural language processing and an emotion engine, and efficiently assigns tasks based on the user's emotional state. The system includes the following means.

[0535] First, a high-performance microphone is used to capture the user's speech as audio data. The audio data uttered by the user is sent to a server in real time. The server then converts the received audio data into text data using speech recognition technology. This text data is then analyzed using natural language processing (NLP) technology to extract relevant keywords. The natural language processing technology used is based on the Transformer model (for example, the Transformers library in Hugging Face).

[0536] Next, an emotion engine analyzes the extracted text data and recognizes the user's emotional state. Machine learning libraries such as TENSORFLOW (registered trademark) are used to implement the emotion engine. This engine analyzes the user's speech and facial expression data and assigns emotion labels such as "positive" or "negative."

[0537] Based on the analysis results, the server generates an efficient task list. This task list is organized based on pre-defined objectives and key results. The server dynamically assigns appropriate tasks taking into account the user's emotional state. For example, if the emotion engine identifies a positive emotion, a high-priority task is assigned. On the other hand, if a negative emotion is identified, a relatively easy task is assigned.

[0538] The generated task list is then sent to the user's terminal and displayed on the user's screen. This terminal is a device with a high-resolution display and provides an interface that the user can operate intuitively. The user can check the progress of the tasks on the screen in real time and report the completion status of the tasks as needed. The server receives reports from the user and updates the database with the progress status against the goals and key results.

[0539] Specific examples

[0540] 1. A user says, "I'm very tired from working together, but I'll do my best to finish it."

[0541] 2. The server converts this comment into text data, and the emotion engine assigns a "negative" emotion label.

[0542] 3. Based on this emotional state, the server assigns the user the relatively easy task of "inspecting the finished product."

[0543] 4. The results of the task assignment are displayed on the user's device, and the user can check the contents and proceed with the work.

[0544] Prompt Sentence Examples

[0545] "We are very tired from working together, but we will do our best to finish it."

[0546] Desired output:

[0547] Since positive emotions are not strong, we assigned them the easy task of "Inspecting the finished product."

[0548] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0549] Step 1: User speaks

[0550] The user inputs voice data through a high-performance microphone, for example, saying, "I'm very tired from working together, but I'll try my best to finish it."

[0551] Step 2: Sending audio data and converting it to text

[0552] The voice data is sent to the server in real time. The server uses voice recognition technology to convert the voice data into text data. The input is voice data and the output is text data.

[0553] Step 3: Keyword extraction using natural language processing

[0554] The server analyzes the converted text data using natural language processing (NLP) technology and extracts key related keywords. The input is the text data, and the output is the extracted related keywords.

[0555] Step 4: Emotion Recognition with the Emotion Engine

[0556] The server uses an emotion engine to recognize the user's emotional state from the text data. This engine uses machine learning libraries such as TensorFlow. The input is the text data, and the output is an emotion label (e.g., positive, negative).

[0557] Step 5: Assign tasks

[0558] The server selects appropriate tasks based on the user's emotional state and generates a task list. If the emotion is positive, it assigns important tasks, and if it is negative, it assigns easy tasks. The input is the emotion label and pre-defined goals and key results, and the output is the adjusted task list.

[0559] Step 6: Send and view the work list

[0560] The generated task list is sent to the user's terminal and displayed on the user's screen. The user checks the assigned tasks on the terminal. The input is the adjusted task list, and the output is the task list displayed on the user's screen.

[0561] Step 7: Task progress reports and updates

[0562] The user performs a task on the device and reports its completion status. The device sends this progress status back to the server, which updates the database with progress data for goals and key results. The input is the user's task completion report, and the output is the updated progress data.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] [Second embodiment]

[0567] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0578] 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."

[0579] The present invention is a system that supports the creation of new products and services through collaboration between AI and humans. Specifically, it includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[0580] 1. AI-assisted brainstorming

[0581] System configuration:

[0582] When a user initiates a brainstorming session via their device, their speech is sent as voice data to the server, which converts the received voice data into text and uses natural language processing (NLP) technology to extract relevant keywords. These relevant keywords are then sent to the device and displayed on the user's screen.

[0583] Examples:

[0584] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[0585] 2. OKR Tools

[0586] System configuration:

[0587] The user enters the objectives and key results into the OKR setting form on the device. The device sends this input data to the server, which stores it in a database. The server periodically tracks the progress, visualizes the data, and sends it to the device.

[0588] Examples:

[0589] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user, clarifying the path to achieving the goal.

[0590] 3. Business promotion assistance

[0591] System configuration:

[0592] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status to the server from the device. The server receives the report, updates the progress status in the database, and sends the latest progress status to the device.

[0593] Examples:

[0594] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[0595] The system of the present invention allows users to easily understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing promotes collaboration among users and accelerates the generation of creative ideas.

[0596] The processing flow will be explained below.

[0597] 1. AI-assisted brainstorming

[0598] Processing Steps:

[0599] Step 1:

[0600] User: Clicks a button on their device to start a brainstorming session.

[0601] Step 2:

[0602] Terminal: Sends a session initiation request to the server.

[0603] Step 3:

[0604] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[0605] Step 4:

[0606] Terminal: Starts audio recording and captures audio data in real time.

[0607] Step 5:

[0608] Terminal: Sends recorded audio data to the server in real time.

[0609] Step 6:

[0610] Server: Converts the received voice data into text.

[0611] Step 7:

[0612] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[0613] Step 8:

[0614] Server: Sends the extracted related keywords to the terminal.

[0615] Step 9:

[0616] Terminal: Displays the received related keywords on the user's screen.

[0617] Step 10:

[0618] User: Submit a new idea based on the displayed keywords.

[0619] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[0620] 2. OKR Tools

[0621] Processing Steps:

[0622] Step 1:

[0623] User: Opens the OKR setting form on the device and enters the objective and key results.

[0624] Step 2:

[0625] Terminal: Sends the entered OKR data to the server.

[0626] Step 3:

[0627] Server: Stores the received OKR data in a database.

[0628] Step 4:

[0629] Server: Generates data to track progress based on saved goals and key results.

[0630] Step 5:

[0631] Server: Sends the generated progress data to the device.

[0632] Step 6:

[0633] Terminal: Visualizes and displays the received progress data to the user.

[0634] Step 7:

[0635] User: Enters actual progress into the terminal.

[0636] Step 8:

[0637] Device: Sends updated progress data to the server.

[0638] Step 9:

[0639] Server: Updates the progress information in the database.

[0640] 3. Business promotion assistance

[0641] Processing Steps:

[0642] Step 1:

[0643] Server: Automatically generates an efficient task list based on the set OKRs.

[0644] Step 2:

[0645] Server: Sends the generated task list to the terminal.

[0646] Step 3:

[0647] Terminal: Displays the received task list to the user.

[0648] Step 4:

[0649] User: Selects a task to perform from a presented task list.

[0650] Step 5:

[0651] User: Perform the selected task.

[0652] Step 6:

[0653] User: Enter task completion status into the terminal.

[0654] Step 7:

[0655] Terminal: Reports information about completed tasks to the server.

[0656] Step 8:

[0657] Server: Updates OKR progress information based on reports.

[0658] Step 9:

[0659] Server: Sends progress updates to the device.

[0660] Step 10:

[0661] Terminal: Shows the user a visual update on the progress.

[0662] ---

[0663] Through the above steps, this system will support efficient and effective business promotion and the creation of new products and services through cooperation between users and AI.

[0664] Example 1

[0665] 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."

[0666] Conventional brainstorming and business promotion systems lacked the functionality to efficiently analyze user comments and extract and present related keywords. This resulted in problems such as users being unable to generate ideas or promote business effectively. Furthermore, systems for managing goals and progress lacked the functionality to track, update, and visualize progress in real time, significantly limiting users' work efficiency.

[0667] 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.

[0668] In this invention, the server includes natural language processing means for analyzing user comments and extracting related keywords, presentation means for displaying the extracted related keywords on the user's screen, data storage means for recording goals and major results set by the user, progress visualization means for tracking progress and visualizing the data, task generation means for generating an efficient task list based on the recorded goals and major results, and task display means for displaying the task list on the user's screen. This makes it possible to efficiently analyze the content of user comments, track and update progress in real time, and improve work efficiency.

[0669] "Natural language processing means" is a technology for converting the content of a user's speech from voice data into text data, and analyzing the text data to extract related keywords.

[0670] The "presentation means" refers to a method or technique for displaying the extracted related keywords on the user's screen.

[0671] "Data storage" refers to the methods and techniques used to record user-defined goals and key results and to store such data over the long term.

[0672] "Progress visualization tools" are techniques for collecting progress on goals and key results and displaying that data in visual formats such as graphs and charts.

[0673] "Task generation means" is a technique for generating a list of specific tasks and procedures that are required based on recorded goals and key results.

[0674] The "task display means" refers to a method or technology for displaying the generated task list on the user's terminal so that the user can confirm and execute the tasks.

[0675] "Speech recognition means" refers to technology or software for converting voice data into text data.

[0676] The "progress update means" is a technique for updating the progress status for the recorded goals and major results with the latest data and transmitting the updated data to the user's terminal.

[0677] The present invention is a system that supports the creation of new products and services through collaboration between AI technology and humans. This system includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[0678] AI-assisted brainstorming

[0679] When a user starts a brainstorming session through a device, the device collects the user's speech as audio data through the microphone and sends the audio data to a server. The server uses speech recognition software (e.g., Google Cloud Speech-to-Text API) to convert the received audio data into text data. The server then uses natural language processing (NLP) technology (e.g., Google Cloud Natural Language API) to extract relevant keywords from the text data. The relevant keywords are sent from the server to the device and displayed on the user's screen.

[0680] Examples:

[0681] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snack." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[0682] Example prompt sentence:

[0683] Let's start a brainstorming session for a new healthy food. If User A says "snacks rich in vitamin C," extract related keywords.

[0684] OKR Tools

[0685] The user enters objectives and key results into the OKR setting form via the terminal. The terminal sends the entered data to the server, which stores the data in a database (e.g., MySQL). The server periodically collects progress data, generates a graph using a tool for visualizing progress (e.g., Chart.js), and sends the graph to the terminal. The user can check the progress graph via the terminal.

[0686] Examples:

[0687] The user sets a goal of "Complete a prototype of a new product during Q1" and inputs three main results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user.

[0688] Example prompt sentence:

[0689] Please set the following as the key results required to complete the prototype of the new product during Q1: "Market research completed," "Design completed," and "Prototype created."

[0690] Business promotion assistance

[0691] The server generates an efficient task list based on the set OKRs. The server sends the generated task list to the device, which displays it on the user's screen. The user executes the displayed tasks one by one and reports their completion status to the device. The device sends the report to the server, which updates the progress status in a database and sends the latest progress status to the device. The user can always check the latest progress status.

[0692] Examples:

[0693] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[0694] Example prompt sentence:

[0695] Set market research as the achievement criteria, and generate tasks based on that, such as "analyzing competing products," "creating a consumer survey," and "listing sales channels."

[0696] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0697] AI-assisted brainstorming

[0698] Step 1:

[0699] A user operates a terminal to start a brainstorming session.

[0700] Input: User action (e.g. clicking a button to start a brainstorming session)

[0701] Output: Session start (event trigger) is generated

[0702] Step 2:

[0703] The device collects what the user says through the microphone and sends it to the server as voice data.

[0704] Input: User utterance (audio)

[0705] Output: Audio data is sent to the server

[0706] Step 3:

[0707] The server converts the received voice data into text data using voice recognition software (Google Cloud Speech-to-Text API).

[0708] Input: Audio data

[0709] Output: Text data

[0710] Step 4:

[0711] The server uses natural language processing (NLP) technology (Google Cloud Natural Language API) to extract relevant keywords from the text data.

[0712] Input: Text data

[0713] Output: Extracted related keywords

[0714] Step 5:

[0715] The server transmits the extracted related keywords to the terminal, which then displays them on the user's screen.

[0716] Input: Extracted related keywords

[0717] Output: Related keywords are displayed on the user's screen.

[0718] OKR Tools

[0719] Step 1:

[0720] The user enters the objective and key results in the OKR setting form on the device.

[0721] Inputs: Objective and key result input data

[0722] Output: Input data is sent from the device to the server

[0723] Step 2:

[0724] The server saves the received data in a database (MySQL).

[0725] Inputs: Objective and key result input data

[0726] Output: Saved data

[0727] Step 3:

[0728] The server periodically collects progress data and uses a visualization tool (Chart.js) to graph the progress.

[0729] Input: Progress data from the database

[0730] Output: Visualized progress (graph)

[0731] Step 4:

[0732] The server sends the visualized progress to the terminal and displays it on the user's screen.

[0733] Input: Visualized progress (graph)

[0734] Output: A progress graph is displayed on the user's screen

[0735] Business promotion assistance

[0736] Step 1:

[0737] The server generates an efficient task list based on the configured OKRs.

[0738] Input: Data on set goals and key results

[0739] Output: Generated task list

[0740] Step 2:

[0741] The server transmits the generated task list to the terminal, which displays it on the user's screen.

[0742] Input: Generated task list

[0743] Output: The task list is displayed on the user's screen.

[0744] Step 3:

[0745] The user performs the displayed task and reports its completion status to the terminal.

[0746] Input: Execute tasks and report completion

[0747] Output: Completion status data is sent from the terminal to the server

[0748] Step 4:

[0749] The server updates the progress to a database and sends the latest progress to the device.

[0750] Input: Completion status data

[0751] Output: Latest progress data

[0752] Step 5:

[0753] The user checks the latest progress via the device.

[0754] Input: Latest progress data

[0755] Output: The user sees progress updates on their screen

[0756] The above are the specific processing steps of the program of this system.

[0757] (Application example 1)

[0758] 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."

[0759] In the industrial sector, there is a demand for improved operational efficiency and the promotion of brainstorming for new product development. Conventional systems have limited progress management and automatic task generation functions, and it is difficult to support real-time brainstorming based on voice. In particular, there is a problem of insufficient coordination between workers and machines in factories, which reduces overall productivity.

[0760] 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.

[0761] In this invention, the server includes means for collecting user voice data in real time using an industrial automated machine and transmitting it to the server, means for converting the voice data into text, and means for analyzing the voice data collected by the industrial automated machine and displaying related keywords to support brainstorming. This makes it possible to extract and display related keywords in real time using natural language processing technology based on what the user says, thereby realizing efficient and effective brainstorming and progress management with the cooperation of the industrial automated machine.

[0762] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0763] "Related keywords" are important words and phrases extracted by analyzing the content of a user's comments.

[0764] A "user display device" is a device used by a user to visually view information.

[0765] "Goals and Key Results" are specific goals set by the user and the important deliverables required to achieve them.

[0766] A "task list" is a list of specific work items that need to be done to achieve a goal.

[0767] "Industrial automation equipment" refers to industrial robots and automation devices used in factories, etc.

[0768] "Audio data" refers to data in which a user's speech is recorded in audio format.

[0769] A "server" is a computer system for processing, storing, and communicating data.

[0770] "Means for converting to text" refers to techniques or methods for converting audio data into text format.

[0771] Brainstorming is a meeting technique that allows teams to freely exchange ideas and seek creative solutions.

[0772] "Progress tracking measures" are methods and techniques for continuously recording and managing progress toward goals.

[0773] This invention is a system that uses industrial automation equipment, a server, and a user's display device, and utilizes natural language processing technology to analyze utterances, extract related keywords, set goals, manage progress, and manage tasks more efficiently.

[0774] 1. Configuration

[0775] The system includes the following components:

[0776] Industrial automation: Industrial robots and automated devices for collecting audio data.

[0777] Server: A computer system for processing and storing collected data.

[0778] User display device: The device used by a user to visually view information.

[0779] 2. Program Overview

[0780] Voice Recognition

[0781] The automated industrial machine collects user speech in real time and sends it as voice data to a server, where it uses a Python library called speech_recognition to convert the voice data into text.

[0782] Natural Language Processing

[0783] On the server, the voice data is converted into text and related keywords are extracted using NLP techniques, such as OpenAI's GPT-3 and spaCy libraries.

[0784] Brainstorming support

[0785] The automated industrial machine analyzes the collected voice data and displays related keywords on the user's display, which allows for efficient brainstorming among users.

[0786] Goal setting and progress management

[0787] The user sets objectives and key results through the display device, and the data is sent to the server and recorded in a database. The server periodically tracks the progress data and displays it visually on the user's display device.

[0788] Task Management

[0789] The server generates an efficient task list based on the set goals and key results, which is also displayed on the user's display device, allowing the user to report progress after completing the tasks.

[0790] 3. Processing Description

[0791] The server converts voice data collected from automated industrial machines into text and analyzes the text to extract relevant keywords. The hardware used is a Linux server, and the software used includes the speech_recognition library, GPT-3 API, or spaCy.

[0792] When a user sets goals and key results, the data is sent to the server and stored in a database in JSON format. The server periodically updates the progress status based on this data and displays it on the user's display device in real time.

[0793] As a concrete example, consider a scenario in which a prototype of a new product is being developed in a factory. An automated industrial machine collects the user's speech, "Snacks rich in vitamin C," and sends the speech data to a server. The server converts the speech data into text and extracts related keywords, such as "vitamin C" and "snacks." This information is displayed on the user's display device, making it easier for other users to come up with new ideas.

[0794] Example prompts to input to a generative AI model:

[0795] I'm thinking of a new idea for a health food product. Based on comments from other staff members, I would like you to specifically analyze the keywords "vitamin C" and "snacks" and come up with a new related proposal. In addition, I would like you to generate a specific task list to complete the prototype during Q1.

[0796] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0797] Step 1:

[0798] Input: Industrial automation collects user utterances in real time.

[0799] How it works: An automated industrial machine uses a microphone for voice recognition to record what a user says as audio data.

[0800] Output: The collected audio data is generated.

[0801] Step 2:

[0802] Input: An automated industrial machine transmits collected audio data to a server.

[0803] How it works: Audio data is transmitted from an automated industrial device to a server, where it is stored in a database.

[0804] Output: Audio data stored on the server.

[0805] Step 3:

[0806] Input: The server receives the stored audio data.

[0807] How it works: The server uses the speech_recognition library to convert the audio data into text.

[0808] Output: Text data converted from audio data.

[0809] Step 4:

[0810] Input: The server parses the text data.

[0811] How it works: The server uses NLP techniques to extract relevant keywords from text data, using the GPT-3 API and spaCy.

[0812] Output: Extracted related keywords.

[0813] Step 5:

[0814] Input: The server receives the extracted related keywords.

[0815] Behavior: The server converts the related keywords into JSON format and sends it to the user's display device.

[0816] Output: Related keywords data in JSON format.

[0817] Step 6:

[0818] Input: The user's display device receives the relevant keywords.

[0819] What it does: The user's display device visually displays related keywords, helping the user generate new ideas.

[0820] Output: A visual representation of related keywords for the user.

[0821] Step 7:

[0822] Input: The user sets the objective and key results.

[0823] How it works: The user enters the goals and key results into a form on the display device and sends it to the server.

[0824] Outputs: Set objectives and key outcome data.

[0825] Step 8:

[0826] Input: The server receives the set goals and key outcome data.

[0827] How it works: The server stores this data in a database and sets initial values ​​for tracking progress.

[0828] Output: A database record of the objectives and key results set.

[0829] Step 9:

[0830] Input: The server collects and updates progress data.

[0831] How it works: The server periodically checks the progress and updates the progress data based on the task completion information reported by the user.

[0832] Output: The updated progress data.

[0833] Step 10:

[0834] Input: The server sends progress data to the user's display device.

[0835] Behavior: The server sends updated progress data in JSON format to the user's display device.

[0836] Output: Progress data that can be visually confirmed by the user.

[0837] 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.

[0838] The present invention relates to a system that supports the creation of new products and services through collaboration between AI and humans, and by combining it with an emotion engine that recognizes user emotions, it achieves a higher level of interaction and effective collaboration. Detailed embodiments of each function are described below.

[0839] 1. AI-assisted brainstorming

[0840] System configuration:

[0841] When a user starts a brainstorming session via their device, their speech is sent as voice data to the server. The server converts the received voice data into text and uses natural language processing (NLP) technology to extract related keywords. These related keywords are sent to the device and displayed on the user's screen. The emotion engine also recognizes emotions from the user's speech and adjusts the presentation of related keywords accordingly.

[0842] Examples:

[0843] For example, consider a scenario in which users are brainstorming ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords; if the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[0844] 2. OKR Tools

[0845] System configuration:

[0846] Users enter their objectives and key results in an OKR setting form on their device. The device sends this input data to the server, which stores it in a database. The server periodically tracks progress, visualizes the data, and sends it to the device. An emotion engine can also record the user's emotional state and display it along with the progress data.

[0847] Examples:

[0848] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user. The emotion engine adjusts progress based on the user's emotional data, and when positive emotions are observed, it highlights the degree of achievement at that point.

[0849] 3. Business promotion assistance

[0850] System configuration:

[0851] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status from the device to the server. The server receives the report, updates the progress status in a database, and sends the latest progress status to the device. In addition, the emotion engine can take the user's emotional data into account and adjust the priority and content of tasks.

[0852] Examples:

[0853] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the device, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status. The emotion engine monitors the user's emotional state while performing the task, and if motivation drops, it will take action such as recommending a lighter task.

[0854] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[0855] The processing flow will be explained below.

[0856] MODE FOR CARRYING OUT THE INVENTION

[0857] 1. AI-assisted brainstorming

[0858] Processing Steps:

[0859] Step 1:

[0860] User: Clicks a button on their device to start a brainstorming session.

[0861] Step 2:

[0862] Terminal: Sends a session initiation request to the server.

[0863] Step 3:

[0864] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[0865] Step 4:

[0866] Terminal: Starts audio recording and captures audio data in real time.

[0867] Step 5:

[0868] Terminal: Sends recorded audio data to the server in real time.

[0869] Step 6:

[0870] Server: Converts the received voice data into text.

[0871] Step 7:

[0872] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[0873] Step 8:

[0874] Server: Analyzes emotions from user comments using an emotion engine.

[0875] Step 9:

[0876] Server: Adjusts the content of related keywords presented based on the extracted related keywords and analyzed emotion data.

[0877] Step 10:

[0878] Server: Sends the adjusted related keywords to the terminal.

[0879] Step 11:

[0880] Terminal: Displays the received related keywords on the user's screen.

[0881] Step 12:

[0882] User: Submit a new idea based on the displayed keywords.

[0883] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[0884] 2. OKR Tools

[0885] Processing Steps:

[0886] Step 1:

[0887] User: Opens the OKR setting form on the device and enters the objective and key results.

[0888] Step 2:

[0889] Terminal: Sends the entered OKR data to the server.

[0890] Step 3:

[0891] Server: Stores the received OKR data in a database.

[0892] Step 4:

[0893] Server: Generates data to track progress based on saved goals and key results.

[0894] Step 5:

[0895] Server: Analyzes the user's emotional state using an emotion engine.

[0896] Step 6:

[0897] Server: Combines emotion data with data that tracks progress and generates data to display along with the progress.

[0898] Step 7:

[0899] Server: Sends the generated progress data to the device.

[0900] Step 8:

[0901] Terminal: Visualizes and displays the received progress data to the user.

[0902] Step 9:

[0903] User: Enters actual progress into the terminal.

[0904] Step 10:

[0905] Device: Sends updated progress data to the server.

[0906] Step 11:

[0907] Server: Updates the progress information in the database.

[0908] 3. Business promotion assistance

[0909] Processing Steps:

[0910] Step 1:

[0911] Server: Automatically generates an efficient task list based on the set OKRs.

[0912] Step 2:

[0913] Server: Analyzes the user's emotional state using an emotion engine and adjusts the priority and content of tasks.

[0914] Step 3:

[0915] Server: Sends the generated task list to the terminal.

[0916] Step 4:

[0917] Terminal: Displays the received task list to the user.

[0918] Step 5:

[0919] User: Selects a task to perform from a presented task list.

[0920] Step 6:

[0921] User: Perform the selected task.

[0922] Step 7:

[0923] User: Enter task completion status into the terminal.

[0924] Step 8:

[0925] Terminal: Reports information about completed tasks to the server.

[0926] Step 9:

[0927] Server: Updates OKR progress information based on reports.

[0928] Step 10:

[0929] Server: Sends progress updates to the device.

[0930] Step 11:

[0931] Terminal: Shows the user a visual update on the progress.

[0932] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[0933] Example 2

[0934] 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."

[0935] Current business promotion support systems lack functionality that takes into account the user's emotional state, making them unable to flexibly respond to the user's motivation and emotional state. Furthermore, there is a lack of systems that can properly analyze the content and emotions of users' comments during brainstorming sessions and use this information to provide effective interactions. It is necessary to solve these issues.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0937] In this invention, the server includes: means for analyzing a user's utterances using natural language processing and extracting related keywords; means for displaying the extracted related keywords on the user's screen; means for recording goals and key results set by the user and tracking progress; means for generating an efficient task list based on the recorded goals and key results; means for transmitting voice data to the server in real time and converting the voice data to text at the server; means for converting and analyzing the received voice data and recognizing the user's emotions using an emotion engine; means for adjusting the display of related keywords based on the recognized emotions; and means for recording the user's emotional state and displaying it together with progress data. This enables flexible and effective support for business promotion and brainstorming sessions that take the user's emotional state into consideration.

[0938] "Natural language processing" is the technology that enables computers to understand, interpret, and generate human language.

[0939] "Related keywords" are important words and phrases extracted from user statements and documents.

[0940] "Voice data" refers to data that is a digital recording of a user's speech or voice communication.

[0941] "Text conversion" is the process of converting audio data into written information.

[0942] An "emotion engine" is a technology or system that analyzes and recognizes a user's emotional state from their words and actions.

[0943] A "goal" is a specific outcome or result that a user aims to achieve.

[0944] "Key results" are the specific actions or performance indicators required to achieve a goal.

[0945] A "task list" is a list of work items that a user needs to accomplish.

[0946] A "timestamp" is data that digitally records the date and time that a particular event occurred.

[0947] "Progress data" is data that indicates the progress of a project or task.

[0948] This invention is a system that supports the creation of new products and services through collaboration between AI and humans, and realizes more effective collaboration by combining it with an emotion engine that recognizes user emotions. Detailed embodiments of each function are described below.

[0949] 1. AI-assisted brainstorming

[0950] System configuration:

[0951] When a user starts a brainstorming session via their device, what they say is sent to the server as audio data. The server then converts the audio data into text using the Google Cloud Speech-to-Text API. NLP techniques are then applied to the text data to extract relevant keywords. NLP libraries such as spaCy and BERT are used for this natural language processing. The extracted keywords are sent to the user's device and displayed on the screen. An emotion engine (for example, IBM Watson Tone Analyzer) analyzes what the user says to recognize their emotions and adjusts the display of related keywords accordingly.

[0952] Examples:

[0953] For example, consider a scenario in which users are discussing ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords. If the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[0954] Example prompt sentence:

[0955] "When discussing new healthy food ideas, please comment on vitamin C-rich snacks."

[0956] 2. OKR Tools

[0957] System configuration:

[0958] When a user enters objectives and key results into the OKR setting form on their device, the device sends this data to the server. The server stores the received data in a MySQL or PostgreSQL database. The server periodically retrieves the contents of the database, tracks and scans the progress, and visualizes the retrieved progress data in graph form using Chart.js or similar tools and displays it on the user's device. The emotion engine can also record the user's emotional state during this process and display it along with the progress data.

[0959] Examples:

[0960] Let's say a user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records this in a database and periodically retrieves the data to track progress. Progress is visualized and displayed in graph form on the user's device. The emotion engine analyzes the user's emotions when reporting progress, and if positive emotions are observed, it takes measures such as highlighting the degree of achievement.

[0961] Example prompt sentence:

[0962] "What efforts did you make to complete prototypes of new products during Q1?"

[0963] 3. Business promotion assistance

[0964] System configuration:

[0965] The server generates an efficient task list based on the set OKRs, determines the priority of each task, and provides it to the user. The user performs tasks based on the task list displayed on their device and reports the completion status to the server. The server updates the received progress data to a database and reflects the progress status on the device in real time. In addition, the emotion engine monitors the user's emotional state and can adjust the priority and content of tasks if motivation drops.

[0966] Examples:

[0967] For example, if a user sets the goal of "completing market research," the server will generate specific tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels," and display them on the device. When the user performs these tasks and reports their completion, the server updates the database and displays the latest progress on the device in real time. The emotion engine monitors the user's emotional state while performing the tasks, and if their motivation drops, it will take measures such as suggesting lighter tasks.

[0968] Example prompt sentence:

[0969] "What market research tasks are you currently performing?"

[0970] As a result, this system realizes flexible and effective support for business promotion and brainstorming sessions that take into account the user's emotional state.

[0971] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0972] AI-assisted brainstorming process steps

[0973] Step 1:

[0974] A user initiates a brainstorming session via a terminal.

[0975] Input: User clicks the session start button.

[0976] Output: Session initiation request to the server.

[0977] How it works: When you click the "Start Session" button on the device, the device sends a session start request to the server.

[0978] Step 2:

[0979] When a user speaks, the terminal captures the voice and transmits the voice data to the server.

[0980] Input: User speech.

[0981] Output: The audio data sent to the server.

[0982] How it works: The device captures what the user says with a microphone and transmits it to the server in real time.

[0983] Step 3:

[0984] The server uses a speech recognition API to convert the received voice data into text.

[0985] Input: Received audio data.

[0986] Output: Text data.

[0987] How it works: The server calls the Google Cloud Speech-to-Text API to convert the audio data into text and temporarily stores the text.

[0988] Step 4:

[0989] The server analyzes the text data using natural language processing (NLP) technology and extracts relevant keywords.

[0990] Input: Text data.

[0991] Output: Related keywords.

[0992] How it works: The server uses NLP libraries such as spaCy or BERT to extract relevant keywords from text data.

[0993] Step 5:

[0994] The emotion engine analyzes the text data and recognizes the user's emotions.

[0995] Input: Text data and extracted related keywords.

[0996] Output: The user's emotional state.

[0997] How it works: The server uses an emotion analysis engine such as IBM Watson Tone Analyzer to analyze the user's emotional state and saves the results.

[0998] Step 6:

[0999] Based on the emotion recognition results, the display content of related keywords is adjusted and sent to the terminal.

[1000] Input: Emotion recognition results and related keywords.

[1001] Output: The adjusted related keywords displayed on the user's device.

[1002] How it works: Based on the emotion recognition results, if the emotion is strong positive, the number of related keywords displayed is increased, and if the emotion is strong negative, the content displayed is adjusted to encourage other users to respond. The adjusted related keywords are sent to the device and displayed on the user's screen.

[1003] OKR tool processing steps

[1004] Step 1:

[1005] The user enters the objective and key results into the OKR setting form on the device.

[1006] Inputs: Form data with objectives and key results.

[1007] Output: Data sent to the server.

[1008] How it works: The user enters goals and key results into the OKR form on their device and presses the "Submit" button, sending the data to the server.

[1009] Step 2:

[1010] The server saves the received form data in the database.

[1011] Input: Form data.

[1012] Output: Objectives and key results stored in a database.

[1013] Operation: The server saves the received data in a MySQL or PostgreSQL database and sends a confirmation message to the terminal that the data has been saved.

[1014] Step 3:

[1015] The server regularly tracks progress against goals and key results.

[1016] Inputs: Objectives and key results stored in the database.

[1017] Output: Tracked progress data.

[1018] How it works: The server periodically retrieves progress data from a database and tracks progress. This process uses a cron job or task scheduler.

[1019] Step 4:

[1020] The server visualizes the progress data and sends it to the terminal.

[1021] Input: Tracked progress data.

[1022] Output: Visualized progress data (in the form of graphs and charts).

[1023] How it works: The server converts the acquired progress data into a graph using a tool such as Chart.js, and sends the visualized data to the terminal for display.

[1024] Step 5:

[1025] An emotion engine records the user's emotional state and displays it along with progress data.

[1026] Input: Progress data and the user's emotional state.

[1027] Output: A screen showing progress data and emotional state.

[1028] How it works: The emotion engine analyzes the user's emotions when reporting progress, records the results, and displays them on the device along with the progress data.

[1029] Processing steps for business promotion assistance

[1030] Step 1:

[1031] The server generates an efficient task list based on the configured OKRs.

[1032] Inputs: Objectives and key results stored in the database.

[1033] Output: The generated task list.

[1034] How it works: The server generates an efficient task list based on the recorded OKRs and determines the priority of each task.

[1035] Step 2:

[1036] The terminal displays the generated task list to the user.

[1037] Input: The generated task list.

[1038] Output: The task list displayed on the user's terminal.

[1039] Operation: The terminal displays the task list sent from the server to the user.

[1040] Step 3:

[1041] The user selects and executes a task.

[1042] Input: The task selected by the user.

[1043] Output: The task being executed.

[1044] Action: The user selects a task to perform from the task list displayed on the terminal, and the selected task is executed.

[1045] Step 4:

[1046] The user reports the task completion status to the server from the terminal.

[1047] Input: The task you reported as finished.

[1048] Output: Task completion data reported to the server.

[1049] How it works: After a user completes a task, they report their completion status to the server via their device, which records the report with a timestamp.

[1050] Step 5:

[1051] The server updates the progress to a database, and the latest progress is reflected on the device in real time.

[1052] Input: Reported task completion data.

[1053] Output: Updated progress.

[1054] How it works: The server receives the report, updates the database with the progress, and reflects the latest status on the device in real time.

[1055] Step 6:

[1056] An emotion engine monitors the user's emotional state and adjusts the priority and content of the task list as needed.

[1057] Input: The user's emotional state.

[1058] Output: The adjusted task list.

[1059] How it works: The emotion engine monitors the user's emotional state in real time, and if motivation drops, it adjusts the priority and content of the task list to appropriately suggest the next task to be done.

[1060] (Application example 2)

[1061] 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."

[1062] Conventional factory robots simply perform tasks based on set instructions, making it difficult for them to effectively collaborate with humans. Furthermore, tasks are assigned without considering the emotional state of workers, which can lead to a decline in motivation and work efficiency. Therefore, the goal of this invention is to provide a factory robot collaboration support system incorporating an emotion engine that recognizes the emotional state of workers and adjusts tasks accordingly, thereby improving work efficiency and increasing worker satisfaction.

[1063] 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.

[1064] In this invention, the server includes means for analyzing user utterances using natural language processing and extracting related keywords, means for displaying the extracted related keywords on the user's screen, means for recording goals and major results set by the user and tracking progress, means for generating an efficient task list based on the recorded goals and major results, means for displaying the task list on the user's screen, and means for recognizing the user's emotional state using an emotion engine and adjusting task allocation based on the emotional state, thereby enabling factory robots to dynamically adjust tasks according to the emotional states of workers and enabling cooperative work.

[1065] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[1066] "Related keywords" are key words and phrases extracted based on the content of a user's comments.

[1067] A "goal" is a specific result or purpose that a user is trying to achieve.

[1068] "Key results" are the specific accomplishments required to achieve the set goals.

[1069] "Progress" refers to the current achievement of goals and key results.

[1070] A "worklist" is a list of efficient tasks generated based on recorded goals and key results.

[1071] An "emotion engine" is a technology that analyzes a user's voice and facial expressions to recognize their emotional state.

[1072] "Task allocation" means determining appropriate tasks and instructing users on them based on each user's emotional state and progress.

[1073] The "user screen" refers to the display device used by the user to check the list of tasks and progress.

[1074] A "server" is a computer system that receives and analyzes data sent from user terminals and executes various functions.

[1075] The present invention provides a factory robot collaboration support system that combines natural language processing and an emotion engine, and efficiently assigns tasks based on the user's emotional state. The system includes the following means.

[1076] First, a high-performance microphone is used to capture the user's speech as audio data. The audio data uttered by the user is sent to a server in real time. The server then converts the received audio data into text data using speech recognition technology. This text data is then analyzed using natural language processing (NLP) technology to extract relevant keywords. The natural language processing technology used is based on the Transformer model (for example, the Transformers library in Hugging Face).

[1077] Next, the emotion engine analyzes the extracted text data and recognizes the user's emotional state. Machine learning libraries such as TensorFlow are used to implement the emotion engine. This engine analyzes the user's speech and facial expression data and assigns emotion labels such as "positive" or "negative."

[1078] Based on the analysis results, the server generates an efficient task list. This task list is organized based on pre-defined objectives and key results. The server dynamically assigns appropriate tasks taking into account the user's emotional state. For example, if the emotion engine identifies a positive emotion, a high-priority task is assigned. On the other hand, if a negative emotion is identified, a relatively easy task is assigned.

[1079] The generated task list is then sent to the user's terminal and displayed on the user's screen. This terminal is a device with a high-resolution display and provides an interface that the user can operate intuitively. The user can check the progress of the tasks on the screen in real time and report the completion status of the tasks as needed. The server receives reports from the user and updates the database with the progress status against the goals and key results.

[1080] Specific examples

[1081] 1. A user says, "I'm very tired from working together, but I'll do my best to finish it."

[1082] 2. The server converts this comment into text data, and the emotion engine assigns a "negative" emotion label.

[1083] 3. Based on this emotional state, the server assigns the user the relatively easy task of "inspecting the finished product."

[1084] 4. The results of the task assignment are displayed on the user's device, and the user can check the contents and proceed with the work.

[1085] Prompt Sentence Examples

[1086] "We are very tired from working together, but we will do our best to finish it."

[1087] Desired output:

[1088] Since positive emotions are not strong, we assigned them the easy task of "Inspecting the finished product."

[1089] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1090] Step 1: User speaks

[1091] The user inputs voice data through a high-performance microphone, for example, saying, "I'm very tired from working together, but I'll try my best to finish it."

[1092] Step 2: Sending audio data and converting it to text

[1093] The voice data is sent to the server in real time. The server uses voice recognition technology to convert the voice data into text data. The input is voice data and the output is text data.

[1094] Step 3: Keyword extraction using natural language processing

[1095] The server analyzes the converted text data using natural language processing (NLP) technology and extracts key related keywords. The input is the text data, and the output is the extracted related keywords.

[1096] Step 4: Emotion Recognition with the Emotion Engine

[1097] The server uses an emotion engine to recognize the user's emotional state from the text data. This engine uses machine learning libraries such as TensorFlow. The input is the text data, and the output is an emotion label (e.g., positive, negative).

[1098] Step 5: Assign tasks

[1099] The server selects appropriate tasks based on the user's emotional state and generates a task list. If the emotion is positive, it assigns important tasks, and if it is negative, it assigns easy tasks. The input is the emotion label and pre-defined goals and key results, and the output is the adjusted task list.

[1100] Step 6: Send and view the work list

[1101] The generated task list is sent to the user's terminal and displayed on the user's screen. The user checks the assigned tasks on the terminal. The input is the adjusted task list, and the output is the task list displayed on the user's screen.

[1102] Step 7: Task progress reports and updates

[1103] The user performs a task on the device and reports its completion status. The device sends this progress status back to the server, which updates the database with progress data for goals and key results. The input is the user's task completion report, and the output is the updated progress data.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] [Third embodiment]

[1108] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1109] 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.

[1110] 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).

[1111] 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.

[1112] 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.

[1113] 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).

[1114] 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.

[1115] 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.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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."

[1120] The present invention is a system that supports the creation of new products and services through collaboration between AI and humans. Specifically, it includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[1121] 1. AI-assisted brainstorming

[1122] System configuration:

[1123] When a user initiates a brainstorming session via their device, their speech is sent as voice data to the server, which converts the received voice data into text and uses natural language processing (NLP) technology to extract relevant keywords. These relevant keywords are then sent to the device and displayed on the user's screen.

[1124] Examples:

[1125] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[1126] 2. OKR Tools

[1127] System configuration:

[1128] The user enters the objectives and key results into the OKR setting form on the device. The device sends this input data to the server, which stores it in a database. The server periodically tracks the progress, visualizes the data, and sends it to the device.

[1129] Examples:

[1130] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user, clarifying the path to achieving the goal.

[1131] 3. Business promotion assistance

[1132] System configuration:

[1133] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status to the server from the device. The server receives the report, updates the progress status in the database, and sends the latest progress status to the device.

[1134] Examples:

[1135] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[1136] The system of the present invention allows users to easily understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing promotes collaboration among users and accelerates the generation of creative ideas.

[1137] The processing flow will be explained below.

[1138] 1. AI-assisted brainstorming

[1139] Processing Steps:

[1140] Step 1:

[1141] User: Clicks a button on their device to start a brainstorming session.

[1142] Step 2:

[1143] Terminal: Sends a session initiation request to the server.

[1144] Step 3:

[1145] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[1146] Step 4:

[1147] Terminal: Starts audio recording and captures audio data in real time.

[1148] Step 5:

[1149] Terminal: Sends recorded audio data to the server in real time.

[1150] Step 6:

[1151] Server: Converts the received voice data into text.

[1152] Step 7:

[1153] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[1154] Step 8:

[1155] Server: Sends the extracted related keywords to the terminal.

[1156] Step 9:

[1157] Terminal: Displays the received related keywords on the user's screen.

[1158] Step 10:

[1159] User: Submit a new idea based on the displayed keywords.

[1160] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[1161] 2. OKR Tools

[1162] Processing Steps:

[1163] Step 1:

[1164] User: Opens the OKR setting form on the device and enters the objective and key results.

[1165] Step 2:

[1166] Terminal: Sends the entered OKR data to the server.

[1167] Step 3:

[1168] Server: Stores the received OKR data in a database.

[1169] Step 4:

[1170] Server: Generates data to track progress based on saved goals and key results.

[1171] Step 5:

[1172] Server: Sends the generated progress data to the device.

[1173] Step 6:

[1174] Terminal: Visualizes and displays the received progress data to the user.

[1175] Step 7:

[1176] User: Enters actual progress into the terminal.

[1177] Step 8:

[1178] Device: Sends updated progress data to the server.

[1179] Step 9:

[1180] Server: Updates the progress information in the database.

[1181] 3. Business promotion assistance

[1182] Processing Steps:

[1183] Step 1:

[1184] Server: Automatically generates an efficient task list based on the set OKRs.

[1185] Step 2:

[1186] Server: Sends the generated task list to the terminal.

[1187] Step 3:

[1188] Terminal: Displays the received task list to the user.

[1189] Step 4:

[1190] User: Selects a task to perform from a presented task list.

[1191] Step 5:

[1192] User: Perform the selected task.

[1193] Step 6:

[1194] User: Enter task completion status into the terminal.

[1195] Step 7:

[1196] Terminal: Reports information about completed tasks to the server.

[1197] Step 8:

[1198] Server: Updates OKR progress information based on reports.

[1199] Step 9:

[1200] Server: Sends progress updates to the device.

[1201] Step 10:

[1202] Terminal: Shows the user a visual update on the progress.

[1203] ---

[1204] Through the above steps, this system will support efficient and effective business promotion and the creation of new products and services through cooperation between users and AI.

[1205] Example 1

[1206] 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."

[1207] Conventional brainstorming and business promotion systems lacked the functionality to efficiently analyze user comments and extract and present related keywords. This resulted in problems such as users being unable to generate ideas or promote business effectively. Furthermore, systems for managing goals and progress lacked the functionality to track, update, and visualize progress in real time, significantly limiting users' work efficiency.

[1208] 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.

[1209] In this invention, the server includes natural language processing means for analyzing user comments and extracting related keywords, presentation means for displaying the extracted related keywords on the user's screen, data storage means for recording goals and major results set by the user, progress visualization means for tracking progress and visualizing the data, task generation means for generating an efficient task list based on the recorded goals and major results, and task display means for displaying the task list on the user's screen. This makes it possible to efficiently analyze the content of user comments, track and update progress in real time, and improve work efficiency.

[1210] "Natural language processing means" is a technology for converting the content of a user's speech from voice data into text data, and analyzing the text data to extract related keywords.

[1211] The "presentation means" refers to a method or technique for displaying the extracted related keywords on the user's screen.

[1212] "Data storage" refers to the methods and techniques used to record user-defined goals and key results and to store such data over the long term.

[1213] "Progress visualization tools" are techniques for collecting progress on goals and key results and displaying that data in visual formats such as graphs and charts.

[1214] "Task generation means" is a technique for generating a list of specific tasks and procedures that are required based on recorded goals and key results.

[1215] The "task display means" refers to a method or technology for displaying the generated task list on the user's terminal so that the user can confirm and execute the tasks.

[1216] "Speech recognition means" refers to technology or software for converting voice data into text data.

[1217] The "progress update means" is a technique for updating the progress status for the recorded goals and major results with the latest data and transmitting the updated data to the user's terminal.

[1218] The present invention is a system that supports the creation of new products and services through collaboration between AI technology and humans. This system includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[1219] AI-assisted brainstorming

[1220] When a user starts a brainstorming session through a device, the device collects the user's speech as audio data through the microphone and sends the audio data to a server. The server uses speech recognition software (e.g., Google Cloud Speech-to-Text API) to convert the received audio data into text data. The server then uses natural language processing (NLP) technology (e.g., Google Cloud Natural Language API) to extract relevant keywords from the text data. The relevant keywords are sent from the server to the device and displayed on the user's screen.

[1221] Examples:

[1222] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snack." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[1223] Example prompt sentence:

[1224] Let's start a brainstorming session for a new healthy food. If User A says "snacks rich in vitamin C," extract related keywords.

[1225] OKR Tools

[1226] The user enters objectives and key results into the OKR setting form via the terminal. The terminal sends the entered data to the server, which stores the data in a database (e.g., MySQL). The server periodically collects progress data, generates a graph using a tool for visualizing progress (e.g., Chart.js), and sends the graph to the terminal. The user can check the progress graph via the terminal.

[1227] Examples:

[1228] The user sets a goal of "Complete a prototype of a new product during Q1" and inputs three main results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user.

[1229] Example prompt sentence:

[1230] Please set the following as the key results required to complete the prototype of the new product during Q1: "Market research completed," "Design completed," and "Prototype created."

[1231] Business promotion assistance

[1232] The server generates an efficient task list based on the set OKRs. The server sends the generated task list to the device, which displays it on the user's screen. The user executes the displayed tasks one by one and reports their completion status to the device. The device sends the report to the server, which updates the progress status in a database and sends the latest progress status to the device. The user can always check the latest progress status.

[1233] Examples:

[1234] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[1235] Example prompt sentence:

[1236] Set market research as the achievement criteria, and generate tasks based on that, such as "analyzing competing products," "creating a consumer survey," and "listing sales channels."

[1237] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1238] AI-assisted brainstorming

[1239] Step 1:

[1240] A user operates a terminal to start a brainstorming session.

[1241] Input: User action (e.g. clicking a button to start a brainstorming session)

[1242] Output: Session start (event trigger) is generated

[1243] Step 2:

[1244] The device collects what the user says through the microphone and sends it to the server as voice data.

[1245] Input: User utterance (audio)

[1246] Output: Audio data is sent to the server

[1247] Step 3:

[1248] The server converts the received voice data into text data using voice recognition software (Google Cloud Speech-to-Text API).

[1249] Input: Audio data

[1250] Output: Text data

[1251] Step 4:

[1252] The server uses natural language processing (NLP) technology (Google Cloud Natural Language API) to extract relevant keywords from the text data.

[1253] Input: Text data

[1254] Output: Extracted related keywords

[1255] Step 5:

[1256] The server transmits the extracted related keywords to the terminal, which then displays them on the user's screen.

[1257] Input: Extracted related keywords

[1258] Output: Related keywords are displayed on the user's screen.

[1259] OKR Tools

[1260] Step 1:

[1261] The user enters the objective and key results in the OKR setting form on the device.

[1262] Inputs: Objective and key result input data

[1263] Output: Input data is sent from the device to the server

[1264] Step 2:

[1265] The server saves the received data in a database (MySQL).

[1266] Inputs: Objective and key result input data

[1267] Output: Saved data

[1268] Step 3:

[1269] The server periodically collects progress data and uses a visualization tool (Chart.js) to graph the progress.

[1270] Input: Progress data from the database

[1271] Output: Visualized progress (graph)

[1272] Step 4:

[1273] The server sends the visualized progress to the terminal and displays it on the user's screen.

[1274] Input: Visualized progress (graph)

[1275] Output: A progress graph is displayed on the user's screen

[1276] Business promotion assistance

[1277] Step 1:

[1278] The server generates an efficient task list based on the configured OKRs.

[1279] Input: Data on set goals and key results

[1280] Output: Generated task list

[1281] Step 2:

[1282] The server transmits the generated task list to the terminal, which displays it on the user's screen.

[1283] Input: Generated task list

[1284] Output: The task list is displayed on the user's screen.

[1285] Step 3:

[1286] The user performs the displayed task and reports its completion status to the terminal.

[1287] Input: Execute tasks and report completion

[1288] Output: Completion status data is sent from the terminal to the server

[1289] Step 4:

[1290] The server updates the progress to a database and sends the latest progress to the device.

[1291] Input: Completion status data

[1292] Output: Latest progress data

[1293] Step 5:

[1294] The user checks the latest progress via the device.

[1295] Input: Latest progress data

[1296] Output: The user sees progress updates on their screen

[1297] The above are the specific processing steps of the program of this system.

[1298] (Application example 1)

[1299] 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."

[1300] In the industrial sector, there is a demand for improved operational efficiency and the promotion of brainstorming for new product development. Conventional systems have limited progress management and automatic task generation functions, and it is difficult to support real-time brainstorming based on voice. In particular, there is a problem of insufficient coordination between workers and machines in factories, which reduces overall productivity.

[1301] 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.

[1302] In this invention, the server includes means for collecting user voice data in real time using an industrial automated machine and transmitting it to the server, means for converting the voice data into text, and means for analyzing the voice data collected by the industrial automated machine and displaying related keywords to support brainstorming. This makes it possible to extract and display related keywords in real time using natural language processing technology based on what the user says, thereby realizing efficient and effective brainstorming and progress management with the cooperation of the industrial automated machine.

[1303] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[1304] "Related keywords" are important words and phrases extracted by analyzing the content of a user's comments.

[1305] A "user display device" is a device used by a user to visually view information.

[1306] "Goals and Key Results" are specific goals set by the user and the important deliverables required to achieve them.

[1307] A "task list" is a list of specific work items that need to be done to achieve a goal.

[1308] "Industrial automation equipment" refers to industrial robots and automation devices used in factories, etc.

[1309] "Audio data" refers to data in which a user's speech is recorded in audio format.

[1310] A "server" is a computer system for processing, storing, and communicating data.

[1311] "Means for converting to text" refers to techniques or methods for converting audio data into text format.

[1312] Brainstorming is a meeting technique that allows teams to freely exchange ideas and seek creative solutions.

[1313] "Progress tracking measures" are methods and techniques for continuously recording and managing progress toward goals.

[1314] This invention is a system that uses industrial automation equipment, a server, and a user's display device, and utilizes natural language processing technology to analyze utterances, extract related keywords, set goals, manage progress, and manage tasks more efficiently.

[1315] 1. Configuration

[1316] The system includes the following components:

[1317] Industrial automation: Industrial robots and automated devices for collecting audio data.

[1318] Server: A computer system for processing and storing collected data.

[1319] User display device: The device used by a user to visually view information.

[1320] 2. Program Overview

[1321] Voice Recognition

[1322] The automated industrial machine collects user speech in real time and sends it as voice data to a server, where it uses a Python library called speech_recognition to convert the voice data into text.

[1323] Natural Language Processing

[1324] On the server, the voice data is converted into text and related keywords are extracted using NLP techniques, such as OpenAI's GPT-3 and spaCy libraries.

[1325] Brainstorming support

[1326] The automated industrial machine analyzes the collected voice data and displays related keywords on the user's display, which allows for efficient brainstorming among users.

[1327] Goal setting and progress management

[1328] The user sets objectives and key results through the display device, and the data is sent to the server and recorded in a database. The server periodically tracks the progress data and displays it visually on the user's display device.

[1329] Task Management

[1330] The server generates an efficient task list based on the set goals and key results, which is also displayed on the user's display device, allowing the user to report progress after completing the tasks.

[1331] 3. Processing Description

[1332] The server converts voice data collected from automated industrial machines into text and analyzes the text to extract relevant keywords. The hardware used is a Linux server, and the software used includes the speech_recognition library, GPT-3 API, or spaCy.

[1333] When a user sets goals and key results, the data is sent to the server and stored in a database in JSON format. The server periodically updates the progress status based on this data and displays it on the user's display device in real time.

[1334] As a concrete example, consider a scenario in which a prototype of a new product is being developed in a factory. An automated industrial machine collects the user's speech, "Snacks rich in vitamin C," and sends the speech data to a server. The server converts the speech data into text and extracts related keywords, such as "vitamin C" and "snacks." This information is displayed on the user's display device, making it easier for other users to come up with new ideas.

[1335] Example prompts to input to a generative AI model:

[1336] I'm thinking of a new idea for a health food product. Based on comments from other staff members, I would like you to specifically analyze the keywords "vitamin C" and "snacks" and come up with a new related proposal. In addition, I would like you to generate a specific task list to complete the prototype during Q1.

[1337] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1338] Step 1:

[1339] Input: Industrial automation collects user utterances in real time.

[1340] How it works: An automated industrial machine uses a microphone for voice recognition to record what a user says as audio data.

[1341] Output: The collected audio data is generated.

[1342] Step 2:

[1343] Input: An automated industrial machine transmits collected audio data to a server.

[1344] How it works: Audio data is transmitted from an automated industrial device to a server, where it is stored in a database.

[1345] Output: Audio data stored on the server.

[1346] Step 3:

[1347] Input: The server receives the stored audio data.

[1348] How it works: The server uses the speech_recognition library to convert the audio data into text.

[1349] Output: Text data converted from audio data.

[1350] Step 4:

[1351] Input: The server parses the text data.

[1352] How it works: The server uses NLP techniques to extract relevant keywords from text data, using the GPT-3 API and spaCy.

[1353] Output: Extracted related keywords.

[1354] Step 5:

[1355] Input: The server receives the extracted related keywords.

[1356] Behavior: The server converts the related keywords into JSON format and sends it to the user's display device.

[1357] Output: Related keywords data in JSON format.

[1358] Step 6:

[1359] Input: The user's display device receives the relevant keywords.

[1360] What it does: The user's display device visually displays related keywords, helping the user generate new ideas.

[1361] Output: A visual representation of related keywords for the user.

[1362] Step 7:

[1363] Input: The user sets the objective and key results.

[1364] How it works: The user enters the goals and key results into a form on the display device and sends it to the server.

[1365] Outputs: Set objectives and key outcome data.

[1366] Step 8:

[1367] Input: The server receives the set goals and key outcome data.

[1368] How it works: The server stores this data in a database and sets initial values ​​for tracking progress.

[1369] Output: A database record of the objectives and key results set.

[1370] Step 9:

[1371] Input: The server collects and updates progress data.

[1372] How it works: The server periodically checks the progress and updates the progress data based on the task completion information reported by the user.

[1373] Output: The updated progress data.

[1374] Step 10:

[1375] Input: The server sends progress data to the user's display device.

[1376] Behavior: The server sends updated progress data in JSON format to the user's display device.

[1377] Output: Progress data that can be visually confirmed by the user.

[1378] 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.

[1379] The present invention relates to a system that supports the creation of new products and services through collaboration between AI and humans, and by combining it with an emotion engine that recognizes user emotions, it achieves a higher level of interaction and effective collaboration. Detailed embodiments of each function are described below.

[1380] 1. AI-assisted brainstorming

[1381] System configuration:

[1382] When a user starts a brainstorming session via their device, their speech is sent as voice data to the server. The server converts the received voice data into text and uses natural language processing (NLP) technology to extract related keywords. These related keywords are sent to the device and displayed on the user's screen. The emotion engine also recognizes emotions from the user's speech and adjusts the presentation of related keywords accordingly.

[1383] Examples:

[1384] For example, consider a scenario in which users are brainstorming ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords; if the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[1385] 2. OKR Tools

[1386] System configuration:

[1387] Users enter their objectives and key results in an OKR setting form on their device. The device sends this input data to the server, which stores it in a database. The server periodically tracks progress, visualizes the data, and sends it to the device. An emotion engine can also record the user's emotional state and display it along with the progress data.

[1388] Examples:

[1389] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user. The emotion engine adjusts progress based on the user's emotional data, and when positive emotions are observed, it highlights the degree of achievement at that point.

[1390] 3. Business promotion assistance

[1391] System configuration:

[1392] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status from the device to the server. The server receives the report, updates the progress status in a database, and sends the latest progress status to the device. In addition, the emotion engine can take the user's emotional data into account and adjust the priority and content of tasks.

[1393] Examples:

[1394] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the device, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status. The emotion engine monitors the user's emotional state while performing the task, and if motivation drops, it will take action such as recommending a lighter task.

[1395] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[1396] The processing flow will be explained below.

[1397] MODE FOR CARRYING OUT THE INVENTION

[1398] 1. AI-assisted brainstorming

[1399] Processing Steps:

[1400] Step 1:

[1401] User: Clicks a button on their device to start a brainstorming session.

[1402] Step 2:

[1403] Terminal: Sends a session initiation request to the server.

[1404] Step 3:

[1405] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[1406] Step 4:

[1407] Terminal: Starts audio recording and captures audio data in real time.

[1408] Step 5:

[1409] Terminal: Sends recorded audio data to the server in real time.

[1410] Step 6:

[1411] Server: Converts the received voice data into text.

[1412] Step 7:

[1413] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[1414] Step 8:

[1415] Server: Analyzes emotions from user comments using an emotion engine.

[1416] Step 9:

[1417] Server: Adjusts the content of related keywords presented based on the extracted related keywords and analyzed emotion data.

[1418] Step 10:

[1419] Server: Sends the adjusted related keywords to the terminal.

[1420] Step 11:

[1421] Terminal: Displays the received related keywords on the user's screen.

[1422] Step 12:

[1423] User: Submit a new idea based on the displayed keywords.

[1424] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[1425] 2. OKR Tools

[1426] Processing Steps:

[1427] Step 1:

[1428] User: Opens the OKR setting form on the device and enters the objective and key results.

[1429] Step 2:

[1430] Terminal: Sends the entered OKR data to the server.

[1431] Step 3:

[1432] Server: Stores the received OKR data in a database.

[1433] Step 4:

[1434] Server: Generates data to track progress based on saved goals and key results.

[1435] Step 5:

[1436] Server: Analyzes the user's emotional state using an emotion engine.

[1437] Step 6:

[1438] Server: Combines emotion data with data that tracks progress and generates data to display along with the progress.

[1439] Step 7:

[1440] Server: Sends the generated progress data to the device.

[1441] Step 8:

[1442] Terminal: Visualizes and displays the received progress data to the user.

[1443] Step 9:

[1444] User: Enters actual progress into the terminal.

[1445] Step 10:

[1446] Device: Sends updated progress data to the server.

[1447] Step 11:

[1448] Server: Updates the progress information in the database.

[1449] 3. Business promotion assistance

[1450] Processing Steps:

[1451] Step 1:

[1452] Server: Automatically generates an efficient task list based on the set OKRs.

[1453] Step 2:

[1454] Server: Analyzes the user's emotional state using an emotion engine and adjusts the priority and content of tasks.

[1455] Step 3:

[1456] Server: Sends the generated task list to the terminal.

[1457] Step 4:

[1458] Terminal: Displays the received task list to the user.

[1459] Step 5:

[1460] User: Selects a task to perform from a presented task list.

[1461] Step 6:

[1462] User: Perform the selected task.

[1463] Step 7:

[1464] User: Enter task completion status into the terminal.

[1465] Step 8:

[1466] Terminal: Reports information about completed tasks to the server.

[1467] Step 9:

[1468] Server: Updates OKR progress information based on reports.

[1469] Step 10:

[1470] Server: Sends progress updates to the device.

[1471] Step 11:

[1472] Terminal: Shows the user a visual update on the progress.

[1473] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[1474] Example 2

[1475] 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."

[1476] Current business promotion support systems lack functionality that takes into account the user's emotional state, making them unable to flexibly respond to the user's motivation and emotional state. Furthermore, there is a lack of systems that can properly analyze the content and emotions of users' comments during brainstorming sessions and use this information to provide effective interactions. It is necessary to solve these issues.

[1477] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1478] In this invention, the server includes: means for analyzing a user's utterances using natural language processing and extracting related keywords; means for displaying the extracted related keywords on the user's screen; means for recording goals and key results set by the user and tracking progress; means for generating an efficient task list based on the recorded goals and key results; means for transmitting voice data to the server in real time and converting the voice data to text at the server; means for converting and analyzing the received voice data and recognizing the user's emotions using an emotion engine; means for adjusting the display of related keywords based on the recognized emotions; and means for recording the user's emotional state and displaying it together with progress data. This enables flexible and effective support for business promotion and brainstorming sessions that take the user's emotional state into consideration.

[1479] "Natural language processing" is the technology that enables computers to understand, interpret, and generate human language.

[1480] "Related keywords" are important words and phrases extracted from user statements and documents.

[1481] "Voice data" refers to data that is a digital recording of a user's speech or voice communication.

[1482] "Text conversion" is the process of converting audio data into written information.

[1483] An "emotion engine" is a technology or system that analyzes and recognizes a user's emotional state from their words and actions.

[1484] A "goal" is a specific outcome or result that a user aims to achieve.

[1485] "Key results" are the specific actions or performance indicators required to achieve a goal.

[1486] A "task list" is a list of work items that a user needs to accomplish.

[1487] A "timestamp" is data that digitally records the date and time that a particular event occurred.

[1488] "Progress data" is data that indicates the progress of a project or task.

[1489] This invention is a system that supports the creation of new products and services through collaboration between AI and humans, and realizes more effective collaboration by combining it with an emotion engine that recognizes user emotions. Detailed embodiments of each function are described below.

[1490] 1. AI-assisted brainstorming

[1491] System configuration:

[1492] When a user starts a brainstorming session via their device, what they say is sent to the server as audio data. The server then converts the audio data into text using the Google Cloud Speech-to-Text API. NLP techniques are then applied to the text data to extract relevant keywords. NLP libraries such as spaCy and BERT are used for this natural language processing. The extracted keywords are sent to the user's device and displayed on the screen. An emotion engine (for example, IBM Watson Tone Analyzer) analyzes what the user says to recognize their emotions and adjusts the display of related keywords accordingly.

[1493] Examples:

[1494] For example, consider a scenario in which users are discussing ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords. If the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[1495] Example prompt sentence:

[1496] "When discussing new healthy food ideas, please comment on vitamin C-rich snacks."

[1497] 2. OKR Tools

[1498] System configuration:

[1499] When a user enters objectives and key results into the OKR setting form on their device, the device sends this data to the server. The server stores the received data in a MySQL or PostgreSQL database. The server periodically retrieves the contents of the database, tracks and scans the progress, and visualizes the retrieved progress data in graph form using Chart.js or similar tools and displays it on the user's device. The emotion engine can also record the user's emotional state during this process and display it along with the progress data.

[1500] Examples:

[1501] Let's say a user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records this in a database and periodically retrieves the data to track progress. Progress is visualized and displayed in graph form on the user's device. The emotion engine analyzes the user's emotions when reporting progress, and if positive emotions are observed, it takes measures such as highlighting the degree of achievement.

[1502] Example prompt sentence:

[1503] "What efforts did you make to complete prototypes of new products during Q1?"

[1504] 3. Business promotion assistance

[1505] System configuration:

[1506] The server generates an efficient task list based on the set OKRs, determines the priority of each task, and provides it to the user. The user performs tasks based on the task list displayed on their device and reports the completion status to the server. The server updates the received progress data to a database and reflects the progress status on the device in real time. In addition, the emotion engine monitors the user's emotional state and can adjust the priority and content of tasks if motivation drops.

[1507] Examples:

[1508] For example, if a user sets the goal of "completing market research," the server will generate specific tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels," and display them on the device. When the user performs these tasks and reports their completion, the server updates the database and displays the latest progress on the device in real time. The emotion engine monitors the user's emotional state while performing the tasks, and if their motivation drops, it will take measures such as suggesting lighter tasks.

[1509] Example prompt sentence:

[1510] "What market research tasks are you currently performing?"

[1511] As a result, this system realizes flexible and effective support for business promotion and brainstorming sessions that take into account the user's emotional state.

[1512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1513] AI-assisted brainstorming process steps

[1514] Step 1:

[1515] A user initiates a brainstorming session via a terminal.

[1516] Input: User clicks the session start button.

[1517] Output: Session initiation request to the server.

[1518] How it works: When you click the "Start Session" button on the device, the device sends a session start request to the server.

[1519] Step 2:

[1520] When a user speaks, the terminal captures the voice and transmits the voice data to the server.

[1521] Input: User speech.

[1522] Output: The audio data sent to the server.

[1523] How it works: The device captures what the user says with a microphone and transmits it to the server in real time.

[1524] Step 3:

[1525] The server uses a speech recognition API to convert the received voice data into text.

[1526] Input: Received audio data.

[1527] Output: Text data.

[1528] How it works: The server calls the Google Cloud Speech-to-Text API to convert the audio data into text and temporarily stores the text.

[1529] Step 4:

[1530] The server analyzes the text data using natural language processing (NLP) technology and extracts relevant keywords.

[1531] Input: Text data.

[1532] Output: Related keywords.

[1533] How it works: The server uses NLP libraries such as spaCy or BERT to extract relevant keywords from text data.

[1534] Step 5:

[1535] The emotion engine analyzes the text data and recognizes the user's emotions.

[1536] Input: Text data and extracted related keywords.

[1537] Output: The user's emotional state.

[1538] How it works: The server uses an emotion analysis engine such as IBM Watson Tone Analyzer to analyze the user's emotional state and saves the results.

[1539] Step 6:

[1540] Based on the emotion recognition results, the display content of related keywords is adjusted and sent to the terminal.

[1541] Input: Emotion recognition results and related keywords.

[1542] Output: The adjusted related keywords displayed on the user's device.

[1543] How it works: Based on the emotion recognition results, if the emotion is strong positive, the number of related keywords displayed is increased, and if the emotion is strong negative, the content displayed is adjusted to encourage other users to respond. The adjusted related keywords are sent to the device and displayed on the user's screen.

[1544] OKR tool processing steps

[1545] Step 1:

[1546] The user enters the objective and key results into the OKR setting form on the device.

[1547] Inputs: Form data with objectives and key results.

[1548] Output: Data sent to the server.

[1549] How it works: The user enters goals and key results into the OKR form on their device and presses the "Submit" button, sending the data to the server.

[1550] Step 2:

[1551] The server saves the received form data in the database.

[1552] Input: Form data.

[1553] Output: Objectives and key results stored in a database.

[1554] Operation: The server saves the received data in a MySQL or PostgreSQL database and sends a confirmation message to the terminal that the data has been saved.

[1555] Step 3:

[1556] The server regularly tracks progress against goals and key results.

[1557] Inputs: Objectives and key results stored in the database.

[1558] Output: Tracked progress data.

[1559] How it works: The server periodically retrieves progress data from a database and tracks progress. This process uses a cron job or task scheduler.

[1560] Step 4:

[1561] The server visualizes the progress data and sends it to the terminal.

[1562] Input: Tracked progress data.

[1563] Output: Visualized progress data (in the form of graphs and charts).

[1564] How it works: The server converts the acquired progress data into a graph using a tool such as Chart.js, and sends the visualized data to the terminal for display.

[1565] Step 5:

[1566] An emotion engine records the user's emotional state and displays it along with progress data.

[1567] Input: Progress data and the user's emotional state.

[1568] Output: A screen showing progress data and emotional state.

[1569] How it works: The emotion engine analyzes the user's emotions when reporting progress, records the results, and displays them on the device along with the progress data.

[1570] Processing steps for business promotion assistance

[1571] Step 1:

[1572] The server generates an efficient task list based on the configured OKRs.

[1573] Inputs: Objectives and key results stored in the database.

[1574] Output: The generated task list.

[1575] How it works: The server generates an efficient task list based on the recorded OKRs and determines the priority of each task.

[1576] Step 2:

[1577] The terminal displays the generated task list to the user.

[1578] Input: The generated task list.

[1579] Output: The task list displayed on the user's terminal.

[1580] Operation: The terminal displays the task list sent from the server to the user.

[1581] Step 3:

[1582] The user selects and executes a task.

[1583] Input: The task selected by the user.

[1584] Output: The task being executed.

[1585] Action: The user selects a task to perform from the task list displayed on the terminal, and the selected task is executed.

[1586] Step 4:

[1587] The user reports the task completion status to the server from the terminal.

[1588] Input: The task you reported as finished.

[1589] Output: Task completion data reported to the server.

[1590] How it works: After a user completes a task, they report their completion status to the server via their device, which records the report with a timestamp.

[1591] Step 5:

[1592] The server updates the progress to a database, and the latest progress is reflected on the device in real time.

[1593] Input: Reported task completion data.

[1594] Output: Updated progress.

[1595] How it works: The server receives the report, updates the database with the progress, and reflects the latest status on the device in real time.

[1596] Step 6:

[1597] An emotion engine monitors the user's emotional state and adjusts the priority and content of the task list as needed.

[1598] Input: The user's emotional state.

[1599] Output: The adjusted task list.

[1600] How it works: The emotion engine monitors the user's emotional state in real time, and if motivation drops, it adjusts the priority and content of the task list to appropriately suggest the next task to be done.

[1601] (Application example 2)

[1602] 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."

[1603] Conventional factory robots simply perform tasks based on set instructions, making it difficult for them to effectively collaborate with humans. Furthermore, tasks are assigned without considering the emotional state of workers, which can lead to a decline in motivation and work efficiency. Therefore, the goal of this invention is to provide a factory robot collaboration support system incorporating an emotion engine that recognizes the emotional state of workers and adjusts tasks accordingly, thereby improving work efficiency and increasing worker satisfaction.

[1604] 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.

[1605] In this invention, the server includes means for analyzing user utterances using natural language processing and extracting related keywords, means for displaying the extracted related keywords on the user's screen, means for recording goals and major results set by the user and tracking progress, means for generating an efficient task list based on the recorded goals and major results, means for displaying the task list on the user's screen, and means for recognizing the user's emotional state using an emotion engine and adjusting task allocation based on the emotional state, thereby enabling factory robots to dynamically adjust tasks according to the emotional states of workers and enabling cooperative work.

[1606] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[1607] "Related keywords" are key words and phrases extracted based on the content of a user's comments.

[1608] A "goal" is a specific result or purpose that a user is trying to achieve.

[1609] "Key results" are the specific accomplishments required to achieve the set goals.

[1610] "Progress" refers to the current achievement of goals and key results.

[1611] A "worklist" is a list of efficient tasks generated based on recorded goals and key results.

[1612] An "emotion engine" is a technology that analyzes a user's voice and facial expressions to recognize their emotional state.

[1613] "Task allocation" means determining appropriate tasks and instructing users on them based on each user's emotional state and progress.

[1614] The "user screen" refers to the display device used by the user to check the list of tasks and progress.

[1615] A "server" is a computer system that receives and analyzes data sent from user terminals and executes various functions.

[1616] The present invention provides a factory robot collaboration support system that combines natural language processing and an emotion engine, and efficiently assigns tasks based on the user's emotional state. The system includes the following means.

[1617] First, a high-performance microphone is used to capture the user's speech as audio data. The audio data uttered by the user is sent to a server in real time. The server then converts the received audio data into text data using speech recognition technology. This text data is then analyzed using natural language processing (NLP) technology to extract relevant keywords. The natural language processing technology used is based on the Transformer model (for example, the Transformers library in Hugging Face).

[1618] Next, the emotion engine analyzes the extracted text data and recognizes the user's emotional state. Machine learning libraries such as TensorFlow are used to implement the emotion engine. This engine analyzes the user's speech and facial expression data and assigns emotion labels such as "positive" or "negative."

[1619] Based on the analysis results, the server generates an efficient task list. This task list is organized based on pre-defined objectives and key results. The server dynamically assigns appropriate tasks taking into account the user's emotional state. For example, if the emotion engine identifies a positive emotion, a high-priority task is assigned. On the other hand, if a negative emotion is identified, a relatively easy task is assigned.

[1620] The generated task list is then sent to the user's terminal and displayed on the user's screen. This terminal is a device with a high-resolution display and provides an interface that the user can operate intuitively. The user can check the progress of the tasks on the screen in real time and report the completion status of the tasks as needed. The server receives reports from the user and updates the database with the progress status against the goals and key results.

[1621] Specific examples

[1622] 1. A user says, "I'm very tired from working together, but I'll do my best to finish it."

[1623] 2. The server converts this comment into text data, and the emotion engine assigns a "negative" emotion label.

[1624] 3. Based on this emotional state, the server assigns the user the relatively easy task of "inspecting the finished product."

[1625] 4. The results of the task assignment are displayed on the user's device, and the user can check the contents and proceed with the work.

[1626] Prompt Sentence Examples

[1627] "We are very tired from working together, but we will do our best to finish it."

[1628] Desired output:

[1629] Since positive emotions are not strong, we assigned them the easy task of "Inspecting the finished product."

[1630] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1631] Step 1: User speaks

[1632] The user inputs voice data through a high-performance microphone, for example, saying, "I'm very tired from working together, but I'll try my best to finish it."

[1633] Step 2: Sending audio data and converting it to text

[1634] The voice data is sent to the server in real time. The server uses voice recognition technology to convert the voice data into text data. The input is voice data and the output is text data.

[1635] Step 3: Keyword extraction using natural language processing

[1636] The server analyzes the converted text data using natural language processing (NLP) technology and extracts key related keywords. The input is the text data, and the output is the extracted related keywords.

[1637] Step 4: Emotion Recognition with the Emotion Engine

[1638] The server uses an emotion engine to recognize the user's emotional state from the text data. This engine uses machine learning libraries such as TensorFlow. The input is the text data, and the output is an emotion label (e.g., positive, negative).

[1639] Step 5: Assign tasks

[1640] The server selects appropriate tasks based on the user's emotional state and generates a task list. If the emotion is positive, it assigns important tasks, and if it is negative, it assigns easy tasks. The input is the emotion label and pre-defined goals and key results, and the output is the adjusted task list.

[1641] Step 6: Send and view the work list

[1642] The generated task list is sent to the user's terminal and displayed on the user's screen. The user checks the assigned tasks on the terminal. The input is the adjusted task list, and the output is the task list displayed on the user's screen.

[1643] Step 7: Task progress reports and updates

[1644] The user performs a task on the device and reports its completion status. The device sends this progress status back to the server, which updates the database with progress data for goals and key results. The input is the user's task completion report, and the output is the updated progress data.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] [Fourth embodiment]

[1649] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1650] 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.

[1651] 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).

[1652] 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.

[1653] 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.

[1654] 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).

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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."

[1662] The present invention is a system that supports the creation of new products and services through collaboration between AI and humans. Specifically, it includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[1663] 1. AI-assisted brainstorming

[1664] System configuration:

[1665] When a user initiates a brainstorming session via their device, their speech is sent as voice data to the server, which converts the received voice data into text and uses natural language processing (NLP) technology to extract relevant keywords. These relevant keywords are then sent to the device and displayed on the user's screen.

[1666] Examples:

[1667] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[1668] 2. OKR Tools

[1669] System configuration:

[1670] The user enters the objectives and key results into the OKR setting form on the device. The device sends this input data to the server, which stores it in a database. The server periodically tracks the progress, visualizes the data, and sends it to the device.

[1671] Examples:

[1672] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user, clarifying the path to achieving the goal.

[1673] 3. Business promotion assistance

[1674] System configuration:

[1675] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status to the server from the device. The server receives the report, updates the progress status in the database, and sends the latest progress status to the device.

[1676] Examples:

[1677] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[1678] The system of the present invention allows users to easily understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing promotes collaboration among users and accelerates the generation of creative ideas.

[1679] The processing flow will be explained below.

[1680] 1. AI-assisted brainstorming

[1681] Processing Steps:

[1682] Step 1:

[1683] User: Clicks a button on their device to start a brainstorming session.

[1684] Step 2:

[1685] Terminal: Sends a session initiation request to the server.

[1686] Step 3:

[1687] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[1688] Step 4:

[1689] Terminal: Starts audio recording and captures audio data in real time.

[1690] Step 5:

[1691] Terminal: Sends recorded audio data to the server in real time.

[1692] Step 6:

[1693] Server: Converts the received voice data into text.

[1694] Step 7:

[1695] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[1696] Step 8:

[1697] Server: Sends the extracted related keywords to the terminal.

[1698] Step 9:

[1699] Terminal: Displays the received related keywords on the user's screen.

[1700] Step 10:

[1701] User: Submit a new idea based on the displayed keywords.

[1702] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[1703] 2. OKR Tools

[1704] Processing Steps:

[1705] Step 1:

[1706] User: Opens the OKR setting form on the device and enters the objective and key results.

[1707] Step 2:

[1708] Terminal: Sends the entered OKR data to the server.

[1709] Step 3:

[1710] Server: Stores the received OKR data in a database.

[1711] Step 4:

[1712] Server: Generates data to track progress based on saved goals and key results.

[1713] Step 5:

[1714] Server: Sends the generated progress data to the device.

[1715] Step 6:

[1716] Terminal: Visualizes and displays the received progress data to the user.

[1717] Step 7:

[1718] User: Enters actual progress into the terminal.

[1719] Step 8:

[1720] Device: Sends updated progress data to the server.

[1721] Step 9:

[1722] Server: Updates the progress information in the database.

[1723] 3. Business promotion assistance

[1724] Processing Steps:

[1725] Step 1:

[1726] Server: Automatically generates an efficient task list based on the set OKRs.

[1727] Step 2:

[1728] Server: Sends the generated task list to the terminal.

[1729] Step 3:

[1730] Terminal: Displays the received task list to the user.

[1731] Step 4:

[1732] User: Selects a task to perform from a presented task list.

[1733] Step 5:

[1734] User: Perform the selected task.

[1735] Step 6:

[1736] User: Enter task completion status into the terminal.

[1737] Step 7:

[1738] Terminal: Reports information about completed tasks to the server.

[1739] Step 8:

[1740] Server: Updates OKR progress information based on reports.

[1741] Step 9:

[1742] Server: Sends progress updates to the device.

[1743] Step 10:

[1744] Terminal: Shows the user a visual update on the progress.

[1745] ---

[1746] Through the above steps, this system will support efficient and effective business promotion and the creation of new products and services through cooperation between users and AI.

[1747] Example 1

[1748] 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."

[1749] Conventional brainstorming and business promotion systems lacked the functionality to efficiently analyze user comments and extract and present related keywords. This resulted in problems such as users being unable to generate ideas or promote business effectively. Furthermore, systems for managing goals and progress lacked the functionality to track, update, and visualize progress in real time, significantly limiting users' work efficiency.

[1750] 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.

[1751] In this invention, the server includes natural language processing means for analyzing user comments and extracting related keywords, presentation means for displaying the extracted related keywords on the user's screen, data storage means for recording goals and major results set by the user, progress visualization means for tracking progress and visualizing the data, task generation means for generating an efficient task list based on the recorded goals and major results, and task display means for displaying the task list on the user's screen. This makes it possible to efficiently analyze the content of user comments, track and update progress in real time, and improve work efficiency.

[1752] "Natural language processing means" is a technology for converting the content of a user's speech from voice data into text data, and analyzing the text data to extract related keywords.

[1753] The "presentation means" refers to a method or technique for displaying the extracted related keywords on the user's screen.

[1754] "Data storage" refers to the methods and techniques used to record user-defined goals and key results and to store such data over the long term.

[1755] "Progress visualization tools" are techniques for collecting progress on goals and key results and displaying that data in visual formats such as graphs and charts.

[1756] "Task generation means" is a technique for generating a list of specific tasks and procedures that are required based on recorded goals and key results.

[1757] The "task display means" refers to a method or technology for displaying the generated task list on the user's terminal so that the user can confirm and execute the tasks.

[1758] "Speech recognition means" refers to technology or software for converting voice data into text data.

[1759] The "progress update means" is a technique for updating the progress status for the recorded goals and major results with the latest data and transmitting the updated data to the user's terminal.

[1760] The present invention is a system that supports the creation of new products and services through collaboration between AI technology and humans. This system includes functions to analyze user comments, extract and present related keywords, manage goal progress, and support efficient business operations. Detailed embodiments of each function are described below.

[1761] AI-assisted brainstorming

[1762] When a user starts a brainstorming session through a device, the device collects the user's speech as audio data through the microphone and sends the audio data to a server. The server uses speech recognition software (e.g., Google Cloud Speech-to-Text API) to convert the received audio data into text data. The server then uses natural language processing (NLP) technology (e.g., Google Cloud Natural Language API) to extract relevant keywords from the text data. The relevant keywords are sent from the server to the device and displayed on the user's screen.

[1763] Examples:

[1764] For example, consider a scenario in which users are brainstorming ideas for new health foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snack." The device displays these keywords on the user's screen, making it easier for other users to come up with new ideas based on those keywords.

[1765] Example prompt sentence:

[1766] Let's start a brainstorming session for a new healthy food. If User A says "snacks rich in vitamin C," extract related keywords.

[1767] OKR Tools

[1768] The user enters objectives and key results into the OKR setting form via the terminal. The terminal sends the entered data to the server, which stores the data in a database (e.g., MySQL). The server periodically collects progress data, generates a graph using a tool for visualizing progress (e.g., Chart.js), and sends the graph to the terminal. The user can check the progress graph via the terminal.

[1769] Examples:

[1770] The user sets a goal of "Complete a prototype of a new product during Q1" and inputs three main results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user.

[1771] Example prompt sentence:

[1772] Please set the following as the key results required to complete the prototype of the new product during Q1: "Market research completed," "Design completed," and "Prototype created."

[1773] Business promotion assistance

[1774] The server generates an efficient task list based on the set OKRs. The server sends the generated task list to the device, which displays it on the user's screen. The user executes the displayed tasks one by one and reports their completion status to the device. The device sends the report to the server, which updates the progress status in a database and sends the latest progress status to the device. The user can always check the latest progress status.

[1775] Examples:

[1776] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the terminal, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status.

[1777] Example prompt sentence:

[1778] Set market research as the achievement criteria, and generate tasks based on that, such as "analyzing competing products," "creating a consumer survey," and "listing sales channels."

[1779] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1780] AI-assisted brainstorming

[1781] Step 1:

[1782] A user operates a terminal to start a brainstorming session.

[1783] Input: User action (e.g. clicking a button to start a brainstorming session)

[1784] Output: Session start (event trigger) is generated

[1785] Step 2:

[1786] The device collects what the user says through the microphone and sends it to the server as voice data.

[1787] Input: User utterance (audio)

[1788] Output: Audio data is sent to the server

[1789] Step 3:

[1790] The server converts the received voice data into text data using voice recognition software (Google Cloud Speech-to-Text API).

[1791] Input: Audio data

[1792] Output: Text data

[1793] Step 4:

[1794] The server uses natural language processing (NLP) technology (Google Cloud Natural Language API) to extract relevant keywords from the text data.

[1795] Input: Text data

[1796] Output: Extracted related keywords

[1797] Step 5:

[1798] The server transmits the extracted related keywords to the terminal, which then displays them on the user's screen.

[1799] Input: Extracted related keywords

[1800] Output: Related keywords are displayed on the user's screen.

[1801] OKR Tools

[1802] Step 1:

[1803] The user enters the objective and key results in the OKR setting form on the device.

[1804] Inputs: Objective and key result input data

[1805] Output: Input data is sent from the device to the server

[1806] Step 2:

[1807] The server saves the received data in a database (MySQL).

[1808] Inputs: Objective and key result input data

[1809] Output: Saved data

[1810] Step 3:

[1811] The server periodically collects progress data and uses a visualization tool (Chart.js) to graph the progress.

[1812] Input: Progress data from the database

[1813] Output: Visualized progress (graph)

[1814] Step 4:

[1815] The server sends the visualized progress to the terminal and displays it on the user's screen.

[1816] Input: Visualized progress (graph)

[1817] Output: A progress graph is displayed on the user's screen

[1818] Business promotion assistance

[1819] Step 1:

[1820] The server generates an efficient task list based on the configured OKRs.

[1821] Input: Data on set goals and key results

[1822] Output: Generated task list

[1823] Step 2:

[1824] The server transmits the generated task list to the terminal, which displays it on the user's screen.

[1825] Input: Generated task list

[1826] Output: The task list is displayed on the user's screen.

[1827] Step 3:

[1828] The user performs the displayed task and reports its completion status to the terminal.

[1829] Input: Execute tasks and report completion

[1830] Output: Completion status data is sent from the terminal to the server

[1831] Step 4:

[1832] The server updates the progress to a database and sends the latest progress to the device.

[1833] Input: Completion status data

[1834] Output: Latest progress data

[1835] Step 5:

[1836] The user checks the latest progress via the device.

[1837] Input: Latest progress data

[1838] Output: The user sees progress updates on their screen

[1839] The above are the specific processing steps of the program of this system.

[1840] (Application example 1)

[1841] 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."

[1842] In the industrial sector, there is a demand for improved operational efficiency and the promotion of brainstorming for new product development. Conventional systems have limited progress management and automatic task generation functions, and it is difficult to support real-time brainstorming based on voice. In particular, there is a problem of insufficient coordination between workers and machines in factories, which reduces overall productivity.

[1843] 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.

[1844] In this invention, the server includes means for collecting user voice data in real time using an industrial automated machine and transmitting it to the server, means for converting the voice data into text, and means for analyzing the voice data collected by the industrial automated machine and displaying related keywords to support brainstorming. This makes it possible to extract and display related keywords in real time using natural language processing technology based on what the user says, thereby realizing efficient and effective brainstorming and progress management with the cooperation of the industrial automated machine.

[1845] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[1846] "Related keywords" are important words and phrases extracted by analyzing the content of a user's comments.

[1847] A "user display device" is a device used by a user to visually view information.

[1848] "Goals and Key Results" are specific goals set by the user and the important deliverables required to achieve them.

[1849] A "task list" is a list of specific work items that need to be done to achieve a goal.

[1850] "Industrial automation equipment" refers to industrial robots and automation devices used in factories, etc.

[1851] "Audio data" refers to data in which a user's speech is recorded in audio format.

[1852] A "server" is a computer system for processing, storing, and communicating data.

[1853] "Means for converting to text" refers to techniques or methods for converting audio data into text format.

[1854] Brainstorming is a meeting technique that allows teams to freely exchange ideas and seek creative solutions.

[1855] "Progress tracking measures" are methods and techniques for continuously recording and managing progress toward goals.

[1856] This invention is a system that uses industrial automation equipment, a server, and a user's display device, and utilizes natural language processing technology to analyze utterances, extract related keywords, set goals, manage progress, and manage tasks more efficiently.

[1857] 1. Configuration

[1858] The system includes the following components:

[1859] Industrial automation: Industrial robots and automated devices for collecting audio data.

[1860] Server: A computer system for processing and storing collected data.

[1861] User display device: The device used by a user to visually view information.

[1862] 2. Program Overview

[1863] Voice Recognition

[1864] The automated industrial machine collects user speech in real time and sends it as voice data to a server, where it uses a Python library called speech_recognition to convert the voice data into text.

[1865] Natural Language Processing

[1866] On the server, the voice data is converted into text and related keywords are extracted using NLP techniques, such as OpenAI's GPT-3 and spaCy libraries.

[1867] Brainstorming support

[1868] The automated industrial machine analyzes the collected voice data and displays related keywords on the user's display, which allows for efficient brainstorming among users.

[1869] Goal setting and progress management

[1870] The user sets objectives and key results through the display device, and the data is sent to the server and recorded in a database. The server periodically tracks the progress data and displays it visually on the user's display device.

[1871] Task Management

[1872] The server generates an efficient task list based on the set goals and key results, which is also displayed on the user's display device, allowing the user to report progress after completing the tasks.

[1873] 3. Processing Description

[1874] The server converts voice data collected from automated industrial machines into text and analyzes the text to extract relevant keywords. The hardware used is a Linux server, and the software used includes the speech_recognition library, GPT-3 API, or spaCy.

[1875] When a user sets goals and key results, the data is sent to the server and stored in a database in JSON format. The server periodically updates the progress status based on this data and displays it on the user's display device in real time.

[1876] As a concrete example, consider a scenario in which a prototype of a new product is being developed in a factory. An automated industrial machine collects the user's speech, "Snacks rich in vitamin C," and sends the speech data to a server. The server converts the speech data into text and extracts related keywords, such as "vitamin C" and "snacks." This information is displayed on the user's display device, making it easier for other users to come up with new ideas.

[1877] Example prompts to input to a generative AI model:

[1878] I'm thinking of a new idea for a health food product. Based on comments from other staff members, I would like you to specifically analyze the keywords "vitamin C" and "snacks" and come up with a new related proposal. In addition, I would like you to generate a specific task list to complete the prototype during Q1.

[1879] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1880] Step 1:

[1881] Input: Industrial automation collects user utterances in real time.

[1882] How it works: An automated industrial machine uses a microphone for voice recognition to record what a user says as audio data.

[1883] Output: The collected audio data is generated.

[1884] Step 2:

[1885] Input: An automated industrial machine transmits collected audio data to a server.

[1886] How it works: Audio data is transmitted from an automated industrial device to a server, where it is stored in a database.

[1887] Output: Audio data stored on the server.

[1888] Step 3:

[1889] Input: The server receives the stored audio data.

[1890] How it works: The server uses the speech_recognition library to convert the audio data into text.

[1891] Output: Text data converted from audio data.

[1892] Step 4:

[1893] Input: The server parses the text data.

[1894] How it works: The server uses NLP techniques to extract relevant keywords from text data, using the GPT-3 API and spaCy.

[1895] Output: Extracted related keywords.

[1896] Step 5:

[1897] Input: The server receives the extracted related keywords.

[1898] Behavior: The server converts the related keywords into JSON format and sends it to the user's display device.

[1899] Output: Related keywords data in JSON format.

[1900] Step 6:

[1901] Input: The user's display device receives the relevant keywords.

[1902] What it does: The user's display device visually displays related keywords, helping the user generate new ideas.

[1903] Output: A visual representation of related keywords for the user.

[1904] Step 7:

[1905] Input: The user sets the objective and key results.

[1906] How it works: The user enters the goals and key results into a form on the display device and sends it to the server.

[1907] Outputs: Set objectives and key outcome data.

[1908] Step 8:

[1909] Input: The server receives the set goals and key outcome data.

[1910] How it works: The server stores this data in a database and sets initial values ​​for tracking progress.

[1911] Output: A database record of the objectives and key results set.

[1912] Step 9:

[1913] Input: The server collects and updates progress data.

[1914] How it works: The server periodically checks the progress and updates the progress data based on the task completion information reported by the user.

[1915] Output: The updated progress data.

[1916] Step 10:

[1917] Input: The server sends progress data to the user's display device.

[1918] Behavior: The server sends updated progress data in JSON format to the user's display device.

[1919] Output: Progress data that can be visually confirmed by the user.

[1920] 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.

[1921] The present invention relates to a system that supports the creation of new products and services through collaboration between AI and humans, and by combining it with an emotion engine that recognizes user emotions, it achieves a higher level of interaction and effective collaboration. Detailed embodiments of each function are described below.

[1922] 1. AI-assisted brainstorming

[1923] System configuration:

[1924] When a user starts a brainstorming session via their device, their speech is sent as voice data to the server. The server converts the received voice data into text and uses natural language processing (NLP) technology to extract related keywords. These related keywords are sent to the device and displayed on the user's screen. The emotion engine also recognizes emotions from the user's speech and adjusts the presentation of related keywords accordingly.

[1925] Examples:

[1926] For example, consider a scenario in which users are brainstorming ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords; if the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[1927] 2. OKR Tools

[1928] System configuration:

[1929] Users enter their objectives and key results in an OKR setting form on their device. The device sends this input data to the server, which stores it in a database. The server periodically tracks progress, visualizes the data, and sends it to the device. An emotion engine can also record the user's emotional state and display it along with the progress data.

[1930] Examples:

[1931] The user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records these results and visualizes and displays daily progress to the user. The emotion engine adjusts progress based on the user's emotional data, and when positive emotions are observed, it highlights the degree of achievement at that point.

[1932] 3. Business promotion assistance

[1933] System configuration:

[1934] The server generates an efficient task list based on the set OKRs and sends it to the device. The user performs the presented tasks and reports their completion status from the device to the server. The server receives the report, updates the progress status in a database, and sends the latest progress status to the device. In addition, the emotion engine can take the user's emotional data into account and adjust the priority and content of tasks.

[1935] Examples:

[1936] When a user sets market research as the achievement criterion, the server generates tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels." These are displayed on the device, and each time the user performs a task, the progress is reported to the server, allowing the user to always check the latest status. The emotion engine monitors the user's emotional state while performing the task, and if motivation drops, it will take action such as recommending a lighter task.

[1937] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[1938] The processing flow will be explained below.

[1939] MODE FOR CARRYING OUT THE INVENTION

[1940] 1. AI-assisted brainstorming

[1941] Processing Steps:

[1942] Step 1:

[1943] User: Clicks a button on their device to start a brainstorming session.

[1944] Step 2:

[1945] Terminal: Sends a session initiation request to the server.

[1946] Step 3:

[1947] Server: Receives the session initiation request and sends permission to the device to start audio recording.

[1948] Step 4:

[1949] Terminal: Starts audio recording and captures audio data in real time.

[1950] Step 5:

[1951] Terminal: Sends recorded audio data to the server in real time.

[1952] Step 6:

[1953] Server: Converts the received voice data into text.

[1954] Step 7:

[1955] Server: Analyzes text data using natural language processing (NLP) and extracts relevant keywords.

[1956] Step 8:

[1957] Server: Analyzes emotions from user comments using an emotion engine.

[1958] Step 9:

[1959] Server: Adjusts the content of related keywords presented based on the extracted related keywords and analyzed emotion data.

[1960] Step 10:

[1961] Server: Sends the adjusted related keywords to the terminal.

[1962] Step 11:

[1963] Terminal: Displays the received related keywords on the user's screen.

[1964] Step 12:

[1965] User: Submit a new idea based on the displayed keywords.

[1966] Terminal: Send the new message to the server again and repeat steps 5 and onwards.

[1967] 2. OKR Tools

[1968] Processing Steps:

[1969] Step 1:

[1970] User: Opens the OKR setting form on the device and enters the objective and key results.

[1971] Step 2:

[1972] Terminal: Sends the entered OKR data to the server.

[1973] Step 3:

[1974] Server: Stores the received OKR data in a database.

[1975] Step 4:

[1976] Server: Generates data to track progress based on saved goals and key results.

[1977] Step 5:

[1978] Server: Analyzes the user's emotional state using an emotion engine.

[1979] Step 6:

[1980] Server: Combines emotion data with data that tracks progress and generates data to display along with the progress.

[1981] Step 7:

[1982] Server: Sends the generated progress data to the device.

[1983] Step 8:

[1984] Terminal: Visualizes and displays the received progress data to the user.

[1985] Step 9:

[1986] User: Enters actual progress into the terminal.

[1987] Step 10:

[1988] Device: Sends updated progress data to the server.

[1989] Step 11:

[1990] Server: Updates the progress information in the database.

[1991] 3. Business promotion assistance

[1992] Processing Steps:

[1993] Step 1:

[1994] Server: Automatically generates an efficient task list based on the set OKRs.

[1995] Step 2:

[1996] Server: Analyzes the user's emotional state using an emotion engine and adjusts the priority and content of tasks.

[1997] Step 3:

[1998] Server: Sends the generated task list to the terminal.

[1999] Step 4:

[2000] Terminal: Displays the received task list to the user.

[2001] Step 5:

[2002] User: Selects a task to perform from a presented task list.

[2003] Step 6:

[2004] User: Perform the selected task.

[2005] Step 7:

[2006] User: Enter task completion status into the terminal.

[2007] Step 8:

[2008] Terminal: Reports information about completed tasks to the server.

[2009] Step 9:

[2010] Server: Updates OKR progress information based on reports.

[2011] Step 10:

[2012] Server: Sends progress updates to the device.

[2013] Step 11:

[2014] Terminal: Shows the user a visual update on the progress.

[2015] The system of the present invention makes it easier for users to understand how each task contributes to the overall goal, enabling efficient and effective business operations. In addition, the brainstorming support function using natural language processing and an emotion engine promotes collaboration among users and accelerates the generation of creative ideas.

[2016] Example 2

[2017] 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."

[2018] Current business promotion support systems lack functionality that takes into account the user's emotional state, making them unable to flexibly respond to the user's motivation and emotional state. Furthermore, there is a lack of systems that can properly analyze the content and emotions of users' comments during brainstorming sessions and use this information to provide effective interactions. It is necessary to solve these issues.

[2019] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2020] In this invention, the server includes: means for analyzing a user's utterances using natural language processing and extracting related keywords; means for displaying the extracted related keywords on the user's screen; means for recording goals and key results set by the user and tracking progress; means for generating an efficient task list based on the recorded goals and key results; means for transmitting voice data to the server in real time and converting the voice data to text at the server; means for converting and analyzing the received voice data and recognizing the user's emotions using an emotion engine; means for adjusting the display of related keywords based on the recognized emotions; and means for recording the user's emotional state and displaying it together with progress data. This enables flexible and effective support for business promotion and brainstorming sessions that take the user's emotional state into consideration.

[2021] "Natural language processing" is the technology that enables computers to understand, interpret, and generate human language.

[2022] "Related keywords" are important words and phrases extracted from user statements and documents.

[2023] "Voice data" refers to data that is a digital recording of a user's speech or voice communication.

[2024] "Text conversion" is the process of converting audio data into written information.

[2025] An "emotion engine" is a technology or system that analyzes and recognizes a user's emotional state from their words and actions.

[2026] A "goal" is a specific outcome or result that a user aims to achieve.

[2027] "Key results" are the specific actions or performance indicators required to achieve a goal.

[2028] A "task list" is a list of work items that a user needs to accomplish.

[2029] A "timestamp" is data that digitally records the date and time that a particular event occurred.

[2030] "Progress data" is data that indicates the progress of a project or task.

[2031] This invention is a system that supports the creation of new products and services through collaboration between AI and humans, and realizes more effective collaboration by combining it with an emotion engine that recognizes user emotions. Detailed embodiments of each function are described below.

[2032] 1. AI-assisted brainstorming

[2033] System configuration:

[2034] When a user starts a brainstorming session via their device, what they say is sent to the server as audio data. The server then converts the audio data into text using the Google Cloud Speech-to-Text API. NLP techniques are then applied to the text data to extract relevant keywords. NLP libraries such as spaCy and BERT are used for this natural language processing. The extracted keywords are sent to the user's device and displayed on the screen. An emotion engine (for example, IBM Watson Tone Analyzer) analyzes what the user says to recognize their emotions and adjusts the display of related keywords accordingly.

[2035] Examples:

[2036] For example, consider a scenario in which users are discussing ideas for new healthy foods. If User A says, "Snacks rich in vitamin C," the server converts this statement into text and extracts the keywords "vitamin C" and "snacks." The emotion engine analyzes the tone of User A's statement, and if the emotion is strong, it increases the display of related keywords. If the emotion is strong, it adjusts the display of related keywords to encourage other users to respond.

[2037] Example prompt sentence:

[2038] "When discussing new healthy food ideas, please comment on vitamin C-rich snacks."

[2039] 2. OKR Tools

[2040] System configuration:

[2041] When a user enters objectives and key results into the OKR setting form on their device, the device sends this data to the server. The server stores the received data in a MySQL or PostgreSQL database. The server periodically retrieves the contents of the database, tracks and scans the progress, and visualizes the retrieved progress data in graph form using Chart.js or similar tools and displays it on the user's device. The emotion engine can also record the user's emotional state during this process and display it along with the progress data.

[2042] Examples:

[2043] Let's say a user sets a goal of "Complete a prototype of a new product during Q1" and enters three key results: "Market research completed," "Design completed," and "Prototype created." The server records this in a database and periodically retrieves the data to track progress. Progress is visualized and displayed in graph form on the user's device. The emotion engine analyzes the user's emotions when reporting progress, and if positive emotions are observed, it takes measures such as highlighting the degree of achievement.

[2044] Example prompt sentence:

[2045] "What efforts did you make to complete prototypes of new products during Q1?"

[2046] 3. Business promotion assistance

[2047] System configuration:

[2048] The server generates an efficient task list based on the set OKRs, determines the priority of each task, and provides it to the user. The user performs tasks based on the task list displayed on their device and reports the completion status to the server. The server updates the received progress data to a database and reflects the progress status on the device in real time. In addition, the emotion engine monitors the user's emotional state and can adjust the priority and content of tasks if motivation drops.

[2049] Examples:

[2050] For example, if a user sets the goal of "completing market research," the server will generate specific tasks such as "analyzing competing products," "creating a consumer survey," and "listing sales channels," and display them on the device. When the user performs these tasks and reports their completion, the server updates the database and displays the latest progress on the device in real time. The emotion engine monitors the user's emotional state while performing the tasks, and if their motivation drops, it will take measures such as suggesting lighter tasks.

[2051] Example prompt sentence:

[2052] "What market research tasks are you currently performing?"

[2053] As a result, this system realizes flexible and effective support for business promotion and brainstorming sessions that take into account the user's emotional state.

[2054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2055] AI-assisted brainstorming process steps

[2056] Step 1:

[2057] A user initiates a brainstorming session via a terminal.

[2058] Input: User clicks the session start button.

[2059] Output: Session initiation request to the server.

[2060] How it works: When you click the "Start Session" button on the device, the device sends a session start request to the server.

[2061] Step 2:

[2062] When a user speaks, the terminal captures the voice and transmits the voice data to the server.

[2063] Input: User speech.

[2064] Output: The audio data sent to the server.

[2065] How it works: The device captures what the user says with a microphone and transmits it to the server in real time.

[2066] Step 3:

[2067] The server uses a speech recognition API to convert the received voice data into text.

[2068] Input: Received audio data.

[2069] Output: Text data.

[2070] How it works: The server calls the Google Cloud Speech-to-Text API to convert the audio data into text and temporarily stores the text.

[2071] Step 4:

[2072] The server analyzes the text data using natural language processing (NLP) technology and extracts relevant keywords.

[2073] Input: Text data.

[2074] Output: Related keywords.

[2075] How it works: The server uses NLP libraries such as spaCy or BERT to extract relevant keywords from text data.

[2076] Step 5:

[2077] The emotion engine analyzes the text data and recognizes the user's emotions.

[2078] Input: Text data and extracted related keywords.

[2079] Output: The user's emotional state.

[2080] How it works: The server uses an emotion analysis engine such as IBM Watson Tone Analyzer to analyze the user's emotional state and saves the results.

[2081] Step 6:

[2082] Based on the emotion recognition results, the display content of related keywords is adjusted and sent to the terminal.

[2083] Input: Emotion recognition results and related keywords.

[2084] Output: The adjusted related keywords displayed on the user's device.

[2085] How it works: Based on the emotion recognition results, if the emotion is strong positive, the number of related keywords displayed is increased, and if the emotion is strong negative, the content displayed is adjusted to encourage other users to respond. The adjusted related keywords are sent to the device and displayed on the user's screen.

[2086] OKR tool processing steps

[2087] Step 1:

[2088] The user enters the objective and key results into the OKR setting form on the device.

[2089] Inputs: Form data with objectives and key results.

[2090] Output: Data sent to the server.

[2091] How it works: The user enters goals and key results into the OKR form on their device and presses the "Submit" button, sending the data to the server.

[2092] Step 2:

[2093] The server saves the received form data in the database.

[2094] Input: Form data.

[2095] Output: Objectives and key results stored in a database.

[2096] Operation: The server saves the received data in a MySQL or PostgreSQL database and sends a confirmation message to the terminal that the data has been saved.

[2097] Step 3:

[2098] The server regularly tracks progress against goals and key results.

[2099] Inputs: Objectives and key results stored in the database.

[2100] Output: Tracked progress data.

[2101] How it works: The server periodically retrieves progress data from a database and tracks progress. This process uses a cron job or task scheduler.

[2102] Step 4:

[2103] The server visualizes the progress data and sends it to the terminal.

[2104] Input: Tracked progress data.

[2105] Output: Visualized progress data (in the form of graphs and charts).

[2106] How it works: The server converts the acquired progress data into a graph using a tool such as Chart.js, and sends the visualized data to the terminal for display.

[2107] Step 5:

[2108] An emotion engine records the user's emotional state and displays it along with progress data.

[2109] Input: Progress data and the user's emotional state.

[2110] Output: A screen showing progress data and emotional state.

[2111] How it works: The emotion engine analyzes the user's emotions when reporting progress, records the results, and displays them on the device along with the progress data.

[2112] Processing steps for business promotion assistance

[2113] Step 1:

[2114] The server generates an efficient task list based on the configured OKRs.

[2115] Inputs: Objectives and key results stored in the database.

[2116] Output: The generated task list.

[2117] How it works: The server generates an efficient task list based on the recorded OKRs and determines the priority of each task.

[2118] Step 2:

[2119] The terminal displays the generated task list to the user.

[2120] Input: The generated task list.

[2121] Output: The task list displayed on the user's terminal.

[2122] Operation: The terminal displays the task list sent from the server to the user.

[2123] Step 3:

[2124] The user selects and executes a task.

[2125] Input: The task selected by the user.

[2126] Output: The task being executed.

[2127] Action: The user selects a task to perform from the task list displayed on the terminal, and the selected task is executed.

[2128] Step 4:

[2129] The user reports the task completion status to the server from the terminal.

[2130] Input: The task you reported as finished.

[2131] Output: Task completion data reported to the server.

[2132] How it works: After a user completes a task, they report their completion status to the server via their device, which records the report with a timestamp.

[2133] Step 5:

[2134] The server updates the progress to a database, and the latest progress is reflected on the device in real time.

[2135] Input: Reported task completion data.

[2136] Output: Updated progress.

[2137] How it works: The server receives the report, updates the database with the progress, and reflects the latest status on the device in real time.

[2138] Step 6:

[2139] An emotion engine monitors the user's emotional state and adjusts the priority and content of the task list as needed.

[2140] Input: The user's emotional state.

[2141] Output: The adjusted task list.

[2142] How it works: The emotion engine monitors the user's emotional state in real time, and if motivation drops, it adjusts the priority and content of the task list to appropriately suggest the next task to be done.

[2143] (Application example 2)

[2144] 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."

[2145] Conventional factory robots simply perform tasks based on set instructions, making it difficult for them to effectively collaborate with humans. Furthermore, tasks are assigned without considering the emotional state of workers, which can lead to a decline in motivation and work efficiency. Therefore, the goal of this invention is to provide a factory robot collaboration support system incorporating an emotion engine that recognizes the emotional state of workers and adjusts tasks accordingly, thereby improving work efficiency and increasing worker satisfaction.

[2146] 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.

[2147] In this invention, the server includes means for analyzing user utterances using natural language processing and extracting related keywords, means for displaying the extracted related keywords on the user's screen, means for recording goals and major results set by the user and tracking progress, means for generating an efficient task list based on the recorded goals and major results, means for displaying the task list on the user's screen, and means for recognizing the user's emotional state using an emotion engine and adjusting task allocation based on the emotional state, thereby enabling factory robots to dynamically adjust tasks according to the emotional states of workers and enabling cooperative work.

[2148] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[2149] "Related keywords" are key words and phrases extracted based on the content of a user's comments.

[2150] A "goal" is a specific result or purpose that a user is trying to achieve.

[2151] "Key results" are the specific accomplishments required to achieve the set goals.

[2152] "Progress" refers to the current achievement of goals and key results.

[2153] A "worklist" is a list of efficient tasks generated based on recorded goals and key results.

[2154] An "emotion engine" is a technology that analyzes a user's voice and facial expressions to recognize their emotional state.

[2155] "Task allocation" means determining appropriate tasks and instructing users on them based on each user's emotional state and progress.

[2156] The "user screen" refers to the display device used by the user to check the list of tasks and progress.

[2157] A "server" is a computer system that receives and analyzes data sent from user terminals and executes various functions.

[2158] The present invention provides a factory robot collaboration support system that combines natural language processing and an emotion engine, and efficiently assigns tasks based on the user's emotional state. The system includes the following means.

[2159] First, a high-performance microphone is used to capture the user's speech as audio data. The audio data uttered by the user is sent to a server in real time. The server then converts the received audio data into text data using speech recognition technology. This text data is then analyzed using natural language processing (NLP) technology to extract relevant keywords. The natural language processing technology used is based on the Transformer model (for example, the Transformers library in Hugging Face).

[2160] Next, the emotion engine analyzes the extracted text data and recognizes the user's emotional state. Machine learning libraries such as TensorFlow are used to implement the emotion engine. This engine analyzes the user's speech and facial expression data and assigns emotion labels such as "positive" or "negative."

[2161] Based on the analysis results, the server generates an efficient task list. This task list is organized based on pre-defined objectives and key results. The server dynamically assigns appropriate tasks taking into account the user's emotional state. For example, if the emotion engine identifies a positive emotion, a high-priority task is assigned. On the other hand, if a negative emotion is identified, a relatively easy task is assigned.

[2162] The generated task list is then sent to the user's terminal and displayed on the user's screen. This terminal is a device with a high-resolution display and provides an interface that the user can operate intuitively. The user can check the progress of the tasks on the screen in real time and report the completion status of the tasks as needed. The server receives reports from the user and updates the database with the progress status against the goals and key results.

[2163] Specific examples

[2164] 1. A user says, "I'm very tired from working together, but I'll do my best to finish it."

[2165] 2. The server converts this comment into text data, and the emotion engine assigns a "negative" emotion label.

[2166] 3. Based on this emotional state, the server assigns the user the relatively easy task of "inspecting the finished product."

[2167] 4. The results of the task assignment are displayed on the user's device, and the user can check the contents and proceed with the work.

[2168] Prompt Sentence Examples

[2169] "We are very tired from working together, but we will do our best to finish it."

[2170] Desired output:

[2171] Since positive emotions are not strong, we assigned them the easy task of "Inspecting the finished product."

[2172] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2173] Step 1: User speaks

[2174] The user inputs voice data through a high-performance microphone, for example, saying, "I'm very tired from working together, but I'll try my best to finish it."

[2175] Step 2: Sending audio data and converting it to text

[2176] The voice data is sent to the server in real time. The server uses voice recognition technology to convert the voice data into text data. The input is voice data and the output is text data.

[2177] Step 3: Keyword extraction using natural language processing

[2178] The server analyzes the converted text data using natural language processing (NLP) technology and extracts key related keywords. The input is the text data, and the output is the extracted related keywords.

[2179] Step 4: Emotion Recognition with the Emotion Engine

[2180] The server uses an emotion engine to recognize the user's emotional state from the text data. This engine uses machine learning libraries such as TensorFlow. The input is the text data, and the output is an emotion label (e.g., positive, negative).

[2181] Step 5: Assign tasks

[2182] The server selects appropriate tasks based on the user's emotional state and generates a task list. If the emotion is positive, it assigns important tasks, and if it is negative, it assigns easy tasks. The input is the emotion label and pre-defined goals and key results, and the output is the adjusted task list.

[2183] Step 6: Send and view the work list

[2184] The generated task list is sent to the user's terminal and displayed on the user's screen. The user checks the assigned tasks on the terminal. The input is the adjusted task list, and the output is the task list displayed on the user's screen.

[2185] Step 7: Task progress reports and updates

[2186] The user performs a task on the device and reports its completion status. The device sends this progress status back to the server, which updates the database with progress data for goals and key results. The input is the user's task completion report, and the output is the updated progress data.

[2187] 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.

[2188] 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.

[2189] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2190] 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.

[2191] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2192] 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.

[2193] 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).

[2194] 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.

[2195] 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."

[2196] 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.

[2197] 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).

[2198] 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.

[2199] 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.

[2200] 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.

[2201] 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.

[2202] 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.

[2203] 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.

[2204] 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.

[2205] 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.

[2206] 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.

[2207] 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.

[2208] The following is further disclosed regarding the above embodiment.

[2209] (Claim 1)

[2210] A means for analyzing user comments using natural language processing and extracting related keywords;

[2211] a means for displaying the extracted related keywords on a user's screen;

[2212] a means for recording user-defined goals and key results and tracking progress;

[2213] means for generating an efficient task list based on the recorded objectives and key results;

[2214] means for displaying the task list on a user's screen;

[2215] A system including:

[2216] (Claim 2)

[2217] 2. The system according to claim 1, further comprising means for transmitting user speech content as voice data to a server in real time and converting the voice data into text at the server.

[2218] (Claim 3)

[2219] 10. The system of claim 1, further comprising means for the server to update progress against recorded goals and key results and transmit the progress data to the user's terminal.

[2220] "Example 1"

[2221] (Claim 1)

[2222] natural language processing means for analyzing user comments and extracting related keywords;

[2223] a presentation means for displaying the extracted related keywords on a user's screen;

[2224] a data storage means for recording user-defined goals and key results;

[2225] a progress visualization means for tracking progress and visualizing the data;

[2226] task generation means for generating an efficient task list based on the recorded goals and key results;

[2227] a task display means for displaying the task list on a user's screen;

[2228] A system including:

[2229] (Claim 2)

[2230] 10. The system of claim 1, further comprising speech recognition means for transmitting user utterances as voice data to a server in real time and converting the voice data into text at the server.

[2231] (Claim 3)

[2232] 10. The system of claim 1, further comprising progress update means for the server to update progress against the recorded goals and key results and transmit the progress data to the user's terminal.

[2233] "Application Example 1"

[2234] (Claim 1)

[2235] A means for analyzing user comments using natural language processing and extracting related keywords;

[2236] a means for displaying the extracted related keywords on a display device of a user;

[2237] a means of recording user-defined goals and key results and tracking progress;

[2238] means for generating an efficient task list based on the recorded objectives and key results;

[2239] means for displaying the task list on a user's display device;

[2240] A means for collecting user voice data in real time using an automated industrial device and transmitting the data to a server;

[2241] A means for converting voice data into text on the server;

[2242] means for analyzing the voice data collected by the automated industrial machine and displaying related keywords to assist brainstorming;

[2243] a means for recording data for goal setting and progress management in a database of an automated industrial machine and tracking progress;

[2244] A system including:

[2245] (Claim 2)

[2246] 2. The system according to claim 1, further comprising means for transmitting user speech content as voice data to a server in real time and converting the voice data into text at the server.

[2247] (Claim 3)

[2248] 10. The system of claim 1, further comprising means for the server to update progress against recorded goals and key results and transmit the progress data to the user's terminal.

[2249] "Example 2: Combining Emotion Engines"

[2250] (Claim 1)

[2251] A means for analyzing user comments using natural language processing and extracting related keywords;

[2252] a means for displaying the extracted related keywords on a user's screen;

[2253] a means for recording user-defined goals and key results and tracking progress;

[2254] means for generating an efficient task list based on the recorded objectives and key results;

[2255] means for displaying the task list on a user's screen;

[2256] a means for transmitting the voice data to a server in real time and converting the voice data to text at the server;

[2257] means for converting and analyzing the received voice data and recognizing the user's emotion using an emotion engine;

[2258] means for adjusting the display of related keywords based on the recognized sentiment;

[2259] means for recording the user's emotional state and displaying it together with progress data;

[2260] A system including:

[2261] (Claim 2)

[2262] 10. The system of claim 1, further comprising means for the server to update progress against recorded goals and key results and transmit the progress data to the user's terminal.

[2263] (Claim 3)

[2264] 10. The system of claim 1, wherein the emotion engine includes means for monitoring the user's emotional state in real time and adjusting the priority and content of the task list.

[2265] "Application example 2 when combining emotion engines"

[2266] (Claim 1)

[2267] A means for analyzing user comments using natural language processing and extracting related keywords;

[2268] a means for displaying the extracted related keywords on a user's screen;

[2269] a means for recording user-defined goals and key results and tracking progress;

[2270] a means for generating an efficient worklist based on the recorded objectives and key results;

[2271] means for displaying the list of tasks on a user's screen;

[2272] means for recognizing an emotional state of a user using an emotion engine and adjusting task assignments based on the emotional state;

[2273] A system including:

[2274] (Claim 2)

[2275] 2. The system according to claim 1, further comprising means for transmitting user speech content as voice data to a server in real time and converting the voice data into text at the server.

[2276] (Claim 3)

[2277] 10. The system of claim 1, further comprising means for the server to update progress against recorded goals and key results and transmit the progress data to the user's terminal. [Explanation of symbols]

[2278] 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. A means for analyzing user comments using natural language processing and extracting related keywords; means for displaying the extracted related keywords on a user's screen; a means for recording user-defined goals and key results and tracking progress; means for generating an efficient task list based on the recorded objectives and key results; means for displaying the task list on a user's screen; A system including:

2. 2. The system according to claim 1, further comprising means for transmitting user speech content as voice data to a server in real time, and converting the voice data into text at the server.

3. 2. The system of claim 1, further comprising means for the server to update progress against recorded goals and key results and transmit the progress data to the user's terminal.

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