system

Abstract

Provide a system. A method for receiving text data input by a user; means for analyzing the text data by natural language processing and extracting keywords and contexts; A means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge; a means for notifying a user of a list of the identified departments and personnel; The system includes a means for sharing information with the other party based on the user's intention, and for coordinating both parties' schedules to set a date and time for the meeting.

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] Within large organizations, it is often extremely difficult and time-consuming to find the right department or person to carry out an individual's idea or project. In particular, when specialized knowledge or skills are required, the process of identifying and contacting the person with that information can be complicated and cumbersome. This can delay the realization of ideas or result in missed opportunities due to not utilizing the best resources. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides a system including means for receiving text data input by a user, means for analyzing the text data using natural language processing and extracting keywords and context, means for searching a database based on the analyzed keywords and context and identifying the most appropriate department or person in charge, means for notifying the user of a list of the identified departments and people in charge, and means for sharing information with the other party based on the user's intention, adjusting both parties' schedules, and setting a date and time for a meeting.

[0006] This system allows users to quickly find the best people or departments for their ideas or projects, and to communicate and coordinate efficiently, making the most of resources within the organization.

[0007] A "user" is someone who uses the system to input information and find the most appropriate department or person in charge.

[0008] "Text data" refers to data in the form of a string of characters entered by the user, which is the subject of analysis by the system.

[0009] "Natural language processing" is a technology that enables computers to understand and process human language.

[0010] "Analysis" refers to the process of extracting keywords and context from text data and understanding its meaning.

[0011] A "keyword" is a word or phrase that has an important meaning in text data.

[0012] "Context" refers to the background and meaning of the sentence in which the keyword is used.

[0013] A "database" is a system that systematically stores and makes searchable data such as departments, job descriptions, skill sets, and staff information.

[0014] A "department" is a division within an organization that has a specific task or role.

[0015] "Person in Charge" means an individual employee with knowledge and responsibility for a particular task or skill.

[0016] "Notification" refers to the act of the system informing the user of specific information.

[0017] "Expression of intent" refers to the act of a user expressing their intentions or wishes through the system.

[0018] "Information sharing" refers to the act of conveying user requests and related information to specific personnel or departments.

[0019] "Scheduling" is the process of checking the schedules of the user and the person in charge and deciding on a meeting date and time that is convenient for both parties. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable department and person in charge. Specific program processing and its implementation will be described below.

[0042] System configuration

[0043] This system consists of the following main components:

[0044] 1. User terminal (terminal)

[0045] 2. Server

[0046] 3. Database (contained in the server)

[0047] Program processing

[0048] 1. Receiving user input

[0049] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0050] Server Validates the credentials and redirects the user to the main screen if successful.

[0051] Open the user chat window and enter what you want to do or your ideas in text format.

[0052] Terminal: Sends the entered text data to the server.

[0053] 2. Analysis using natural language processing

[0054] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[0055] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[0056] 3. Database Search

[0057] Server: Searches the database based on analyzed keywords and context to identify the most appropriate department or person.

[0058] Database: The work content, skill sets, and staff information of each department are stored, and searches are performed based on this information.

[0059] 4. Notification of Matching Results

[0060] Server Generates a list of the most suitable departments and personnel and notifies the user.

[0061] Terminal: Displays the notified information to the user.

[0062] 5. Information sharing and meeting arrangements

[0063] User If a user wishes to contact a representative, they express their desire in the chat window.

[0064] Terminal: Sends the user's intention to the server.

[0065] Server Receives user requests and shares the necessary information (such as the user's request content and contact information) with the appropriate person or department.

[0066] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0067] Server Notifies both parties of the confirmed meeting date and time and link.

[0068] Specific examples

[0069] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[0070] 1. User A's input

[0071] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[0072] The terminal sends this input to the server.

[0073] 2. Analysis and Matching

[0074] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[0075] The server searches the database to identify the digital marketing department and person B.

[0076] 3. Notice and Indication of Intention

[0077] Server: Notifies user A of the information of identified person B.

[0078] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[0079] The terminal sends this intention to the server.

[0080] 4. Information sharing and meeting arrangements

[0081] Server: Notifies user A's request to person in charge B and coordinates with them.

[0082] Server: Checks both parties' schedules and suggests suitable meeting times.

[0083] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[0084] This embodiment allows users to quickly and efficiently find the people with the expertise they need for effective collaboration.

[0085] The processing flow will be explained below.

[0086] Step 1: Receiving User Input

[0087] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0088] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[0089] Open the user chat window and enter what you want to do or your ideas in text format.

[0090] Terminal: Sends the entered text data to the server.

[0091] Step 2: Natural Language Processing Analysis

[0092] Server Analyzes text data received from users using natural language processing (NLP) technology.

[0093] Server As part of the analysis, important keywords and context are extracted from the text data.

[0094] Step 3: Database search

[0095] Server: Searches the database based on analyzed keywords and context.

[0096] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[0097] Step 4: Notification of match results

[0098] Server Generates a list of identified departments and personnel and notifies the user of that information.

[0099] Terminal: Displays the notified information to the user.

[0100] Step 5: User Indication and Processing

[0101] User If the user wishes to contact the person in charge, he / she will indicate this in the chat window.

[0102] Terminal: Sends the user's intention to the server.

[0103] Step 6: Share information and schedule a meeting

[0104] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[0105] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0106] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[0107] Through these steps, users can efficiently and quickly find the person with the expertise and schedule a meeting.

[0108] Example 1

[0109] 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."

[0110] In today's business environment, users need to be able to quickly and accurately find the right department or person to help them realize their goals or ideas. However, in many companies, department and person information is scattered, making it difficult for users to contact the person with the appropriate knowledge. The purpose of this invention is to solve this problem and make the process of achieving users' goals smoother.

[0111] 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.

[0112] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing to extract keywords and context, means for searching a data storage device based on the analyzed keywords and context to identify the most suitable organization or person in charge, means for notifying the user of a list of the identified organizations or persons in charge, and means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a meeting date and time. This enables the user to quickly and accurately find the appropriate person or department in charge and achieve effective collaboration.

[0113] "User" refers to the person who accesses the system and inputs their "ideas" and "what they want to do."

[0114] "Text data" refers to character string data that a user inputs into the system and that the server receives for processing.

[0115] "Natural language processing" refers to the technology of analyzing text data and extracting keywords, context, and important phrases.

[0116] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[0117] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[0118] "Data storage device" refers to storage that stores information such as each department's business operations, skill sets, and staff information.

[0119] "Organization" refers to a department or team responsible for a particular task or project.

[0120] "Rep" refers to an individual with expertise in a particular task or field who responds to user requests.

[0121] "List" refers to a list of organizations or personnel that the server generates as a search result and notifies the user.

[0122] "Notification" refers to the act of sending list information from the server to the user.

[0123] "Expression of intent" refers to the act of a user expressing their intention to contact or meet with a person in charge in a chat window.

[0124] "Information sharing" refers to the process of communicating user requests and contact information to relevant personnel or organizations.

[0125] "Schedule adjustment" refers to the act of the server checking the schedules of the user and the person in charge and proposing a date and time for the meeting.

[0126] "Meeting Date and Time" means the date and time set for a contact or meeting between the User and the Person in Charge.

[0127] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable organization and person in charge. Specific embodiments of the invention are described below.

[0128] This system consists of the following main components:

[0129] 1. User terminal (terminal)

[0130] 2. Server

[0131] 3. Data storage device (contained in the server)

[0132] 1. Receiving user input

[0133] The user accesses the system from their own terminal and enters their user ID and password on the login screen. The server then receives the authentication information, refers to the database, and if authentication is successful, redirects the user to the main screen.

[0134] The user opens the chat window on the main screen and enters their "ideas" or "what they want to do" in text format. For example, they might enter "I'd like to discuss a new marketing campaign." The device then sends the entered text data to the server. Specifically, it uses a POST request to transfer the text data to the API endpoint.

[0135] 2. Analysis using natural language processing

[0136] The server receives the text data sent from the device. Then, the server calls a natural language processing (NLP) engine to begin analysis. The analysis engine used here is the Google® Cloud NLP API.

[0137] As a result of the analysis, the server extracts keywords, contexts, and important phrases from the text, such as "marketing campaign," "consultation," and "digital marketing."

[0138] 3. Database Search

[0139] The server searches the data storage device based on the analyzed keywords and context. Specifically, it generates an SQL query based on the keywords and sends it to the data storage device.

[0140] This data storage device stores information on the work content, skill sets, and personnel of each organization, and the search results identify the most suitable department and personnel. For example, Person B, who belongs to the "Digital Marketing" department, is identified.

[0141] 4. Notification of Matching Results

[0142] The server generates a list of the most suitable organizations and contacts obtained as search results, converts them into a data format (such as JSON) for notifying the user, and then sends the generated contact list to the terminal. For example, the response may be JSON data including the name, job title, and contact information of contact B.

[0143] The terminal handles the received list of agents and displays it to the user. Specifically, it displays the information of agent B in the chat window and asks the user to confirm it.

[0144] 5. Information sharing and meeting arrangements

[0145] If a user wishes to contact a person in charge, they express their intention in the chat window. For example, they might type, "I would like to talk to Person B." The terminal then sends this intention to the server.

[0146] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B. Specifically, it sends a notification email to the email address of person in charge B. In addition, the server checks the schedules of the user and person in charge B and suggests the optimal date and time for the meeting. For example, it uses the "Google Calendar API" to search for free times for both parties.

[0147] The server notifies the user and person in charge B of the confirmed meeting date and time and a link. An email or chat notification containing the meeting setting link is sent. The device displays the confirmed meeting date and time and the link to the user so that the user can confirm it.

[0148] Specific examples

[0149] Let's say User A is planning a new marketing campaign and needs digital marketing expertise.

[0150] 1. User A's input

[0151] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[0152] The terminal sends this input to the server.

[0153] 2. Analysis and Matching

[0154] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[0155] The server searches the database to identify the digital marketing department and person B.

[0156] 3. Notice and Indication of Intention

[0157] Server: Notifies user A of the information of identified person B.

[0158] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[0159] The terminal sends this intention to the server.

[0160] 4. Information sharing and meeting arrangements

[0161] Server: Notifies user A's request to person in charge B and coordinates with them.

[0162] Server: Checks both parties' schedules and suggests suitable meeting times.

[0163] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[0164] This embodiment allows users to quickly and efficiently find people with the expertise they need for effective collaboration.

[0165] Prompt Sentence Examples

[0166] For example, a prompt that a user could input into a generative AI model might be something like, "I'm planning a new marketing campaign and would like contact information for someone with expertise in digital marketing."

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1:

[0169] The user accesses the system from their own terminal and enters their user ID and password on the login screen.

[0170] The terminal sends the entered authentication information to the server.

[0171] The server checks the received authentication information against its database and, if authentication is successful, redirects the user to the main screen.

[0172] Specific action: Runs an SQL query against the database to check whether the user ID and password match.

[0173] Step 2:

[0174] Users open a chat window on the main screen and enter their ideas or what they want to do in text format. For example, they might enter, "I'd like to discuss a new marketing campaign."

[0175] The terminal sends this input text data to the server.

[0176] Specific operation: Use a POST request to send text data in JSON format to an API endpoint.

[0177] Step 3:

[0178] The server receives the text data sent from the device and inputs it into a natural language processing (NLP) engine, calling the Google Cloud NLP API.

[0179] The server uses NLP analysis to extract keywords, context, and important phrases from the text data.

[0180] Specific operation: Extract keywords such as "marketing campaign," "consultation," and "digital marketing" from the response obtained by calling the API.

[0181] Step 4:

[0182] The server searches for information in the data store based on the analyzed keywords and context.

[0183] The server generates an SQL query based on the analyzed keywords and queries the data storage device, for example, to search for people in the "Digital Marketing" department.

[0184] The data storage device stores information about each organization's business, skillsets, and personnel. Search results return the most suitable departments and personnel.

[0185] What it does: Runs an SQL query to get a list of people in the "Digital Marketing" department.

[0186] Step 5:

[0187] The server generates a list of the most suitable organizations and contacts found as search results and converts them into a data format (such as JSON) for notifying the user.

[0188] The server sends the generated list of contacts to the terminal, and returns JSON data including the contact name, job title, and contact information as a response.

[0189] The terminal displays the received list of personnel to the user.

[0190] Specific actions: Display information about Representative B in the chat window and ask the user to confirm.

[0191] Step 6:

[0192] If a user wishes to contact a representative, they can express their intention in the chat window, for example by typing, "I would like to speak with Representative B."

[0193] The terminal transmits the user's intention to the server.

[0194] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B.

[0195] Specific action: Send a notification email to the email address of person in charge B.

[0196] Step 7:

[0197] The server checks the schedules of the user and person B and proposes the best time to meet. For example, it uses the Google Calendar API to search for available times for both parties.

[0198] The server notifies the user and person in charge B of the confirmed meeting date and time and link.

[0199] The device will display the confirmed meeting date and time and a link to the user.

[0200] What happens: A message appears in the chat window with a link to set up a meeting.

[0201] These steps allow users to quickly and efficiently find the people with the expertise they need and collaborate effectively.

[0202] (Application example 1)

[0203] 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."

[0204] To improve work efficiency in conventional factories, it is necessary to quickly identify the optimal work procedures and the robots responsible. However, on factory floors, determining which robots and processes are most appropriate takes time and effort, making efficient automation difficult. Furthermore, it is increasingly difficult for workers without specialized knowledge or experience to make optimal decisions. A new system is needed to solve these problems and significantly improve factory work efficiency.

[0205] 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.

[0206] In this invention, the server includes: means for receiving text data input by a user; means for analyzing the text data using natural language processing and extracting keywords and context; means for searching a database based on the analyzed keywords and context to identify the most appropriate departments and workers; means for notifying the user of a list of the identified departments and workers; means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a date and time for a work meeting; means for notifying the user and worker of the date and time of the work meeting through a process of proposing, confirming, and finalizing the date and time; and means for analyzing the user's text input and using a generative AI model to identify the most appropriate factory robots and work processes. This allows workers to automatically identify the most appropriate work procedures using the generative AI model simply by entering text, thereby enabling quick and efficient improvement of work efficiency.

[0207] "Users" refer to factory workers and managers who use the system to optimize work procedures and robots.

[0208] "Text data" refers to the written information regarding work procedures and improvements that users enter into the system.

[0209] "Natural language processing" refers to the general technology of analyzing text data to extract keywords and context.

[0210] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[0211] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[0212] A "database" refers to a collection of information that stores each department's work content, skill sets, and worker information.

[0213] A "department" refers to a department within a factory that is responsible for a specific task or operation.

[0214] "Worker" refers to an individual person or robot that is responsible for a specific task or job within a factory.

[0215] "List" refers to a list of information about identified departments and workers.

[0216] "Notification" refers to the act of conveying information about identified departments or workers to users.

[0217] "Expression of intent" refers to an action by a user that indicates to the system that they wish to take some action.

[0218] "Information sharing" refers to the act of sharing information related to a specified department or worker based on the user's expressed intention.

[0219] "Schedule adjustment" refers to the act of adjusting the schedules of the user and the worker to set the optimal meeting date and time.

[0220] "Work meeting" refers to a meeting regarding work to be performed by a user and an identified worker.

[0221] A "generative AI model" refers to a machine learning model that analyzes user text input and identifies the optimal factory robots and work processes.

[0222] The present invention is a system for improving work efficiency at a factory site, and by having the user input "what they want to do" and "points for improvement," it is possible to quickly identify the optimal work procedure and the robot in charge. Specific embodiments for realizing this system are described below.

[0223] System configuration

[0224] The system consists of the following main components:

[0225] 1. User device (smartphone, tablet, PC, etc.)

[0226] 2. Server

[0227] 3. Database (contained in the server)

[0228] Program processing

[0229] Text data entered from the user's device is first received by the server, which then analyzes the text data using natural language processing (NLP) techniques to extract keywords and context. This analysis uses NLP libraries and APIs such as "SpaCy" and "NLTK."

[0230] Based on the extracted keywords and context, the server searches a database to identify the most suitable department and robot. The database stores information about each department's work, skill sets, and robots, and searches are performed based on this information. This search is performed using a powerful search engine such as ElasticSearch (registered trademark).

[0231] The server then notifies the user of the list of identified departments and robots via a chat window or notification function on the user's device. If the user wishes to contact a person in charge or a robot, they send that request to the server.

[0232] The server receives the user's request and shares the information with the corresponding department or robot. It then checks the schedules of both parties and sets the optimal meeting date and time. The server notifies both the user and the person in charge of the confirmed meeting date and time and the link. This process is automated using the Google Calendar API and Microsoft (registered trademark) Graph API.

[0233] Specific examples

[0234] For example, User A enters "I want to improve the efficiency of assembly work" into the system from their terminal. The server receives this text data and uses NLP technology to extract "assembly work" and "efficiency improvement" as keywords.

[0235] Next, the server searches the database and identifies the "Assembly Department" as the most suitable department and "Robot B" as the most suitable robot. This result is notified to User A, who then expresses his / her desire to be contacted by Robot B.

[0236] The server receives this, informs Robot B of its role as the person in charge, checks the schedules of both parties, and sets up a meeting. The set meeting date and time and related links are notified to User A and Robot B.

[0237] Example prompt sentence:

[0238] "I want to improve the efficiency of my assembly work. Which robot is best?"

[0239] This invention allows workers to simply input text and use a generative AI model to identify optimal work procedures, enabling faster and more efficient improvements to work efficiency.

[0240] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0241] Step 1:

[0242] A user accesses the system from a terminal, enters authentication information on the login screen, and the server receives and authenticates it. The input is a username and password, and if authentication is successful, the user is redirected to the main screen. The output is the authentication success or failure status.

[0243] Step 2:

[0244] The user opens a chat window and enters "what they want to do" and "what they want to improve" in text format, and the device sends this text data to the server. The input is the specific work content and desired improvements, and the output is the transmission of text data to the server.

[0245] Step 3:

[0246] The server receives the input text data and analyzes it using NLP technology. Specifically, it segments the text data and extracts important keywords and contexts. The input is text data, and the output is a list of keywords and contexts.

[0247] Step 4:

[0248] The server searches a database based on the analyzed keywords and context. The database stores the work content, skill sets, and robot information of each department, and by comparing this, it identifies the most suitable department and robot. The input is a list of keywords and context, and the output is a list of the most suitable departments and robots.

[0249] Step 5:

[0250] The server notifies the user of the list of identified departments and robots. The information is conveyed via a chat window or notification function on the user's device. The input is a list of optimal departments and robots, and the output is a notification message to the user.

[0251] Step 6:

[0252] When a user wishes to contact a person in charge or a robot, that intention is sent to the server from the chat window. The input is the user's intention, and the output is the transmission of the intention to the server.

[0253] Step 7:

[0254] The server receives the user's intention and shares the information with the corresponding department or robot. Next, it checks the schedules of both parties and sets the optimal meeting date and time. The input is the user's intention and the schedules of both parties, and the output is the optimal meeting date and time.

[0255] Step 8:

[0256] The server notifies both the user and the person in charge of the confirmed meeting date and time. The input is the optimal meeting date and time, and the output is a notification message of the meeting date and time.

[0257] Step 9:

[0258] The user and the person in charge meet based on the confirmed meeting date and time. This allows for the provision of specific work procedures and solutions based on the user's desired improvements. The input is the meeting date and time and content, and the output is the improved work procedures and solutions.

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

[0260] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" using the system, quickly find the most suitable department and person in charge, and recognize the user's emotions to optimize the response. Specific program processing and its implementation will be described below.

[0261] System configuration

[0262] This system consists of the following main components:

[0263] 1. User terminal (terminal)

[0264] 2. Server

[0265] 3. Database (contained in the server)

[0266] 4. Emotion engine (included in the server)

[0267] Program processing

[0268] 1. Receiving user input

[0269] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0270] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[0271] Open the user chat window and enter your "ideas" or "what you want to do" in text format.

[0272] Terminal: Sends the entered text data to the server.

[0273] 2. Analysis using natural language processing

[0274] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[0275] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[0276] 3. Emotion Recognition by Emotion Engine

[0277] Server: Based on the analyzed text data, an emotion engine is used to identify the user's emotions.

[0278] Server: Generates an appropriate response to the user based on the emotion recognition results.

[0279] 4. Database Search

[0280] Server: Searches the database based on analyzed keywords and context.

[0281] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[0282] 5. Notification of Matching Results

[0283] Server Generates a list of identified departments and personnel and notifies the user of that information.

[0284] Terminal: Displays the notified information to the user.

[0285] 6. User Indications and Processing

[0286] User If a user wishes to contact a representative, they express their desire in the chat window.

[0287] Terminal: Sends the user's intention to the server.

[0288] 7. Information sharing and meeting arrangements

[0289] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[0290] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0291] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[0292] Specific examples

[0293] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[0294] 1. User A's input

[0295] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[0296] The terminal sends this input to the server.

[0297] 2. Analysis and Emotion Recognition

[0298] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[0299] Server: Using the emotion engine, identify the emotion from User A's text and determine that it is "high urgency."

[0300] 3. Database Search and Matching

[0301] The server searches the database to identify the digital marketing department and person B.

[0302] 4. Notice and Indication of Intention

[0303] Server: Notifies user A of the information of identified person B.

[0304] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[0305] The terminal sends this intention to the server.

[0306] 5. Information sharing and meeting arrangements

[0307] Server: Notifies user A's request to person in charge B and coordinates with them.

[0308] Server: Checks both parties' schedules and suggests suitable meeting times.

[0309] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[0310] This embodiment allows users to quickly and efficiently find the person with the expertise they need, receive a response that is sensitive to their emotions, and collaborate effectively.

[0311] The processing flow will be explained below.

[0312] Step 1: Receiving User Input

[0313] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0314] Server Receives the authentication information and checks it against a database to determine whether the authentication succeeds or fails.

[0315] Server If authentication is successful, redirect the user to the system's main screen.

[0316] User Open a chat window from the main screen and enter text about what you want to do or your ideas.

[0317] Terminal: Sends the entered text data to the server.

[0318] Step 2: Natural Language Processing Analysis

[0319] Server Analyzes the received text data using natural language processing (NLP) technology.

[0320] Server Extracts important keywords and context from text data.

[0321] Server: Converts the extracted keywords and context into a specific data format.

[0322] Step 3: Emotion recognition by the emotion engine

[0323] Server: Data analyzed by natural language processing is passed to the emotion engine.

[0324] The server emotion engine identifies user emotions from words and phrases in the text data.

[0325] Server: Based on the emotion recognition results, the server identifies the user's emotional state and generates an appropriate response.

[0326] Step 4: Database Search

[0327] Server Searches the database based on analyzed keywords and context.

[0328] The server database identifies the department and person having the relevant work content, skill set, and person information.

[0329] Step 5: Notification of match results

[0330] Generate a list of identified departments and personnel from the results of the server database search.

[0331] The server notifies the user of the generated list.

[0332] Device Notifications are displayed on user devices so that specific departments and personnel can view them.

[0333] Step 6: User Indication and Processing

[0334] If the user wishes to contact the identified person, he / she expresses his / her intention in the chat window.

[0335] Terminal: Sends the user's intention to the server.

[0336] Step 7: Share information and schedule a meeting

[0337] Server: Receives the user's intentions and shares the necessary information (such as the user's request, emotional state, and contact information) with the appropriate person or department.

[0338] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0339] The server may also adjust the urgency of the meeting depending on the emotional state of the user.

[0340] Server Notifies the user and contact person of the confirmed meeting date and time and link.

[0341] By following these steps, users can quickly and appropriately find the resources they need and receive emotionally sensitive support. For example, if a user types "urgent," the system uses its emotion engine to determine the level of urgency and prioritize the response.

[0342] Example 2

[0343] 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."

[0344] With conventional systems, when users communicate their "ideas" or "things they want to do," it is difficult to properly understand them and quickly find the most appropriate department or person in charge. Furthermore, because the system does not take into account the user's feelings, it is unable to respond appropriately, making efficient communication and collaboration difficult.

[0345] 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.

[0346] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing and extracting keywords and context, means for identifying the user's emotions using an emotion recognition engine and generating an appropriate response based on the results, means for searching a database based on the analyzed keywords and context and identifying the most appropriate department or person in charge, means for notifying the user of a list of the identified departments and persons in charge, and means for sharing information with the other party based on the user's intention, adjusting both parties' schedules, and setting a meeting date and time. This makes it possible to quickly and efficiently understand the user's intentions and emotions and take the most appropriate response.

[0347] A "user" is an individual or corporation that accesses the system, inputs their "ideas" or "things they want to do," and seeks the best possible response.

[0348] "Text data" refers to formatted strings of information that a user inputs into a system.

[0349] "Natural language processing" is a general term for algorithms and methods that allow computers to understand, interpret, and generate human language.

[0350] "Keywords" refer to particularly important words or phrases extracted from the text data entered by the user.

[0351] "Context" refers to information that indicates the situation and intention in which each keyword in the text data is used.

[0352] The "database" is an information repository that stores information such as each department's business operations, skill sets, and staff information.

[0353] "The most suitable department or person" refers to the department or person whose work best suits the user's input and purpose.

[0354] An "emotion recognition engine" is an algorithm or software that identifies emotions from a user's text data and provides the results.

[0355] "Response" refers to the message or action the system returns to the user based on the analysis results.

[0356] "Meeting Date and Time" means a specific date and time set for a face-to-face or online conversation between you and a representative.

[0357] This invention is a system that allows users to input their "ideas" and "things they want to do," quickly finds the most suitable department and person in charge, and recognizes the user's emotions to provide the most appropriate response.The system is composed of the following main components: a user terminal, a server, a database, and an emotion recognition engine.

[0358] Hardware and software used

[0359] User device: A PC or smartphone used by a user to access the system. The web browser used may be GOOGLE CHROME (registered trademark) or Safari.

[0360] Server: Built in Python using the Django framework. Responsible for user authentication, data processing, and NLP (Natural Language Processing).

[0361] Database: Use an SQL database (e.g., PostgreSQL) to store department, person in charge information, job details, etc.

[0362] Emotion Recognition Engine: Uses the Google Cloud Natural Language API to identify emotions from user text data.

[0363] System Embodiments and Operation

[0364] 1. Accepting user input:

[0365] Users access the system from their own devices and first enter their authentication information on the login screen. If authentication is successful, they are taken to the main screen.

[0366] After logging in, users open a chat window and enter their ideas and what they want to do.

[0367] The entered text data is sent asynchronously to the server using AJAX.

[0368] 2. Natural Language Processing Analysis:

[0369] Server The server analyzes the received text data using a Python natural language processing library (e.g., NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[0370] Server: Extracts keywords, contexts, and phrases from text using techniques such as TF-IDF and Word2Vec.

[0371] 3. Emotion recognition using emotion recognition engine:

[0372] Server: Sends the analyzed text data to the Google Cloud Natural Language API to identify sentiment.

[0373] Server: Receives emotion recognition results (e.g., "positive," "negative," or "neutral") and determines the response content and next processing steps.

[0374] 4. Database Search:

[0375] Server: Search the database (PostgreSQL) using SQLAlchemy based on the analyzed keywords and context.

[0376] The server compares the job content, skill sets, and staff information of each department in the database to identify the most suitable department and staff member.

[0377] 5. Notification of Matching Results:

[0378] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[0379] Terminal Dynamically display notification messages on the user's screen.

[0380] 6. User Indications and Processing:

[0381] User The user selects the person they wish to contact from the notification list and expresses their intention in the chat window.

[0382] The terminal transmits this intention data to the server.

[0383] 7. Information sharing and meeting scheduling:

[0384] Server Receives user intentions, notifies the appropriate person or department, and shares information.

[0385] Server: Checks both schedules and uses the Google Calendar API to suggest the best time and date for the meeting.

[0386] Server Notifies the user and contact person of the confirmed meeting date and time and sets up the meeting.

[0387] Example: Prompt sentence example

[0388] For example, suppose User A is planning a new marketing campaign and needs digital marketing expertise. User A logs in to the system and enters, "I would like to speak to someone with digital marketing expertise." This input is sent to the server, which uses natural language processing and emotion recognition to identify Person B in the digital marketing department. A meeting date and time with Person B is then proposed and finally confirmed.

[0389] In this way, users can quickly and efficiently connect with people with the expertise they need. Examples of prompts for this system include "I'd like to speak with someone in charge of market research for our new product" or "I'd like to discuss sales strategies."

[0390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0391] Step 1:

[0392] User A user accesses the system from a terminal and first enters authentication information (user name and password) on the login screen. The entered authentication information is sent from the terminal to the server.

[0393] Server The server verifies the received authentication information using the Django framework's user authentication function. If authentication is successful, it generates and returns a URL to redirect the user to the main screen. If authentication fails, it generates an error message.

[0394] If the authentication is successful, the user is redirected to the main screen; if not, an error message is displayed.

[0395] Step 2:

[0396] User The user opens a chat window on the main screen and enters their "what they want to do" or "ideas" in the text box.

[0397] The input text data is acquired from the terminal and sent asynchronously to the server using AJAX. The input here is the user's request or idea.

[0398] Server The server saves the received text data and stores it in memory in its raw form for further processing. The output is the stored text data.

[0399] Step 3:

[0400] Server The server analyzes the received text data using Python's natural language processing library (NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[0401] Input User-entered text data.

[0402] Data processing: Use NLTK to tokenize text, remove stop words, and perform stemming.

[0403] Output Keywords and contexts as analysis results after preprocessing.

[0404] Server Based on the analysis results, TF-IDF and Word2Vec techniques are introduced to extract more important phrases from the text.

[0405] Step 4:

[0406] Server: Sends the text data analyzed in the previous step to the emotion recognition engine (Google Cloud Natural Language API) and receives the results.

[0407] Input: Extracted keywords and context, further analyzed text data.

[0408] Data Calculation Analyze the sentiment of text data using an emotion recognition engine (Google Cloud Natural Language API).

[0409] Output The user's emotion recognition result (e.g., "positive", "negative", "neutral").

[0410] Server Based on the emotion recognition results, it determines the possible actions to take in order to generate the optimal response for the user.

[0411] Step 5:

[0412] Server: Using SQLAlchemy, the PostgreSQL database is searched based on the analyzed keywords and context. This is then compared with the job content, skill sets, and person information of each department in the database to identify the most suitable department and person.

[0413] Input Extracted keywords and context.

[0414] Data Calculation: Database search and matching using SQL queries.

[0415] Output: A list of the most suitable departments and personnel.

[0416] Server: Create a list of identified departments and personnel and prepare for next steps to notify users.

[0417] Step 6:

[0418] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[0419] Input a list of suitable departments and contacts.

[0420] Data processing: Generate notification messages.

[0421] Output Informational message.

[0422] Dynamically display notification messages on the user's screen. A user-friendly interface is realized using JavaScript (registered trademark).

[0423] Step 7:

[0424] User The user selects the desired contact person from the notification list and expresses their desire to be contacted in the chat window.

[0425] Input User's intention.

[0426] The device sends this data to the server using AJAX asynchronous communication.

[0427] Output User intent data.

[0428] After receiving the server user's intention, it notifies the person in charge and shares the necessary information.

[0429] Step 8:

[0430] Server The server checks the schedules of the user and the person in charge and suggests the optimal meeting date and time using the Google Calendar API.

[0431] Input: User and person in charge schedule information, user intent data.

[0432] Data calculation: Schedule matching and optimal date and time suggestions.

[0433] Output The proposed meeting time.

[0434] Server When the meeting date and time is confirmed, the information is notified to the user and the person in charge and registered in the system.

[0435] The confirmed meeting date and time will be displayed on the user's and the person in charge's device, and a reminder will be sent to the user.

[0436] (Application example 2)

[0437] 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."

[0438] In factories, employees and automated machines often have new ideas or problems, but there is a lack of a way to quickly and effectively resolve them. In particular, the process of finding the appropriate department or person in charge can be time-consuming, resulting in reduced productivity and delays in the realization of ideas. Furthermore, efficient communication becomes difficult if responses are not tailored to the emotions and situations of employees and machines. A system to overcome these challenges is needed.

[0439] 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.

[0440] In this invention, the server includes: means for receiving text data entered by a user; means for analyzing the text data using natural language processing and extracting keywords and context; emotion recognition means for identifying the user's emotion based on the analyzed text data; means for generating an appropriate response to the user based on the emotion recognition results; means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge; means for notifying the user of the list of identified departments and persons in charge; and means for sharing information with the other party based on the user's intention, coordinating schedules, and setting a meeting date and time. This enables quick and efficient resolution of issues and ideas faced by employees and automated machines, and finding the appropriate person or department. Furthermore, emotion recognition allows responses to be appropriately adjusted, improving communication efficiency.

[0441] "Means for receiving text data entered by a user" refers to the equipment or software that allows a user to enter text data into the system and receive that data.

[0442] "Means for analyzing the text data using natural language processing and extracting keywords and context" refers to equipment or software that analyzes input text data using natural language processing technology and extracts important keywords and context from it.

[0443] "Emotion recognition means" refers to algorithms or engines that analyze text data and recognize user emotions from it.

[0444] The "means for generating an appropriate response to the user" refers to equipment or software for generating an optimal response to the user based on the emotion identified by the emotion recognition means.

[0445] "Means for searching the database and identifying the most appropriate department or person" refers to algorithms or engines that search the database based on keywords or context to identify the appropriate department or person.

[0446] "Means for notifying users of a list of identified departments and personnel" refers to devices or software for notifying users of information about departments and personnel identified through a database search.

[0447] "A means of sharing information with the other party based on the user's wishes, adjusting both parties' schedules and setting a meeting date and time" refers to devices and software that share the necessary information with the person in charge based on the user's contact preferences, adjust both parties' schedules and decide the meeting date and time.

[0448] "The work content, skill information, and staff information of each department" refers to the work content of each department, related skill information, and information about the staff in each department that is stored in the database.

[0449] "Each employee and automated machine in the factory" refers to the employees working in the factory and the machines used to automate the work of the factory.

[0450] To implement this invention, a user terminal (such as a smartphone or PC), a server, and a database are required. Using this hardware and software, the following system is constructed.

[0451] Users input text data using their devices. This text data is received using a dedicated application or web interface. This application has an interface that allows users to easily input their "ideas" and "what they want to do."

[0452] The server analyzes the text data received from the user. First, it uses natural language processing (NLP) techniques to analyze the text data and extract keywords and context. For example, natural language processing libraries such as "NLTK" and "spaCy" can be used here.

[0453] Next, an emotion recognition engine identifies the user's emotions based on the analyzed text data. Emotion recognition is important for understanding the user's psychological state and the level of urgency. For example, emotion recognition engines such as "IBM Watson (registered trademark)" and "Microsoft Azure (registered trademark) Emotion Recognition API" can be used here.

[0454] Based on the results of emotion recognition, an appropriate response is generated for the user. This response generation utilizes a generative AI model (such as GPT-4 (registered trademark)). This makes it possible to communicate in appropriate words according to the user's emotions.

[0455] The server then searches a database based on the analyzed keywords and context to identify the most appropriate department and person in charge. The database contains information on each department's work, skills, and personnel. This database search function can be implemented using a search engine such as Elasticsearch.

[0456] The server notifies the user's device of the list of identified departments and contact persons. If the user receives the notification, they can enter their intention to contact the contact person again into the system. Based on this intention, the server adjusts both parties' schedules and sets the date and time of the meeting. This schedule adjustment can be done using tools such as the Google Calendar API or Microsoft Outlook Calendar API.

[0457] Specific examples

[0458] For example, consider the case where a factory employee has an idea for improving the efficiency of a new production line. The employee enters through the application, "I have an idea for improving the efficiency of the production line. I would like to speak with someone in the process control department." The system receives this input data, analyzes it using natural language processing, and extracts important keywords such as "production line," "efficiency," and "idea."

[0459] The emotion recognition engine identifies the employee as having a positive emotion and generates an appropriate response. It then searches the database to identify the process control department and the relevant person in charge. This information is sent to the employee, who then re-enters the system with a request to meet with the person in charge. The server receives this request, coordinates both parties' schedules, sets a date and time for the meeting, and notifies the employee and the person in charge.

[0460] Prompt Sentence Examples

[0461] "I have an idea for improving the efficiency of our new production line and would like to speak with someone about it. Which department would be the best fit and who should I contact?"

[0462] In this way, a system is created to quickly and efficiently solve problems and ideas from employees and automated machines.

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] The user accesses the system from a user terminal and enters authentication information on the login screen.

[0466] Input: Authentication information (username, password)

[0467] Output: Authentication result (success / failure)

[0468] How it works: The user device sends authentication information to the server, which verifies it by checking its database. If authentication is successful, the user is redirected to the main screen.

[0469] Step 2:

[0470] Users open a chat window on the main screen and enter their "ideas" and "what they want to do" in text format.

[0471] Input: Text request (e.g., "I have an idea for improving the efficiency of our production line. I'd like to speak with someone in the process control department.")

[0472] Output: Text data

[0473] Operation: The user's device sends the entered text data to the server.

[0474] Step 3:

[0475] The server analyzes the received text data using natural language processing (NLP) technology.

[0476] Input: Text data

[0477] Output: Parsed data (keywords, context)

[0478] How it works: The server uses NLP libraries such as "NLTK" and "spaCy" to parse the text data and extract keywords and context.

[0479] Step 4:

[0480] The server uses an emotion recognition engine to identify the user's emotion based on the analyzed data.

[0481] Input: Parsed data (keywords, context)

[0482] Output: Emotion recognition result (user's emotional state)

[0483] How it works: The server uses IBM Watson or Microsoft Azure Emotion Recognition API to identify emotions and retrieve the results.

[0484] Step 5:

[0485] The server generates an appropriate response to the user based on the emotion recognition results.

[0486] Input: Emotion recognition result (user's emotional state)

[0487] Output: Response message

[0488] How it works: The server uses a generative AI model (e.g., GPT-4) to generate an appropriate response message.

[0489] Step 6:

[0490] The server searches the database based on the analyzed keywords and context to identify the most appropriate department or person in charge.

[0491] Input: Parsed data (keywords, context)

[0492] Output: Best fit department and contact information

[0493] How it works: The server uses a search engine such as Elasticsearch to search the database and identify the appropriate department or person.

[0494] Step 7:

[0495] The server notifies the user of a list of identified departments and personnel.

[0496] Input: Best department and contact information

[0497] Output: Information message

[0498] Operation: The server sends the identified department and person in charge information to the user's terminal and notifies the user.

[0499] Step 8:

[0500] If the user wishes to contact the person in charge, he or she expresses this desire in the chat window.

[0501] Input: Indication of your desire to be contacted

[0502] Output: Intention data

[0503] Operation: The user device sends the intent data to the server.

[0504] Step 9:

[0505] The server receives the user's intentions and shares the necessary information with the appropriate person or department.

[0506] Input: Expression of Intention Data

[0507] Output: Information sharing content

[0508] How it works: The server notifies the person or department in charge of the user's request and contact information.

[0509] Step 10:

[0510] The server checks the schedules of the person in charge and the user and suggests the best time and date for the meeting.

[0511] Input: Both parties' schedule information

[0512] Output: Meeting time proposal

[0513] How it works: The server uses the Google Calendar API or Microsoft Outlook Calendar API to coordinate schedules and suggest suitable meeting times.

[0514] Step 11:

[0515] After the meeting date and time is confirmed, the server notifies the user and the person in charge of the information.

[0516] Input: Confirmed meeting date and time

[0517] Output: Information message

[0518] What it does: The server notifies the user and contact of the confirmed meeting date and time.

[0519] In this way, by performing specific actions at each step, users can quickly and efficiently find the appropriate department or person in charge, receive appropriate responses based on their feelings, and set up necessary contacts and meetings.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] [Second embodiment]

[0524] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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).

[0530] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0535] 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."

[0536] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable department and person in charge. Specific program processing and its implementation will be described below.

[0537] System configuration

[0538] This system consists of the following main components:

[0539] 1. User terminal (terminal)

[0540] 2. Server

[0541] 3. Database (contained in the server)

[0542] Program processing

[0543] 1. Receiving user input

[0544] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0545] Server Validates the credentials and redirects the user to the main screen if successful.

[0546] Open the user chat window and enter what you want to do or your ideas in text format.

[0547] Terminal: Sends the entered text data to the server.

[0548] 2. Analysis using natural language processing

[0549] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[0550] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[0551] 3. Database Search

[0552] Server: Searches the database based on analyzed keywords and context to identify the most appropriate department or person.

[0553] Database: The work content, skill sets, and staff information of each department are stored, and searches are performed based on this information.

[0554] 4. Notification of Matching Results

[0555] Server Generates a list of the most suitable departments and personnel and notifies the user.

[0556] Terminal: Displays the notified information to the user.

[0557] 5. Information sharing and meeting arrangements

[0558] User If a user wishes to contact a representative, they express their desire in the chat window.

[0559] Terminal: Sends the user's intention to the server.

[0560] Server Receives user requests and shares the necessary information (such as the user's request content and contact information) with the appropriate person or department.

[0561] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0562] Server Notifies both parties of the confirmed meeting date and time and link.

[0563] Specific examples

[0564] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[0565] 1. User A's input

[0566] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[0567] The terminal sends this input to the server.

[0568] 2. Analysis and Matching

[0569] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[0570] The server searches the database to identify the digital marketing department and person B.

[0571] 3. Notice and Indication of Intention

[0572] Server: Notifies user A of the information of identified person B.

[0573] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[0574] The terminal sends this intention to the server.

[0575] 4. Information sharing and meeting arrangements

[0576] Server: Notifies user A's request to person in charge B and coordinates with them.

[0577] Server: Checks both parties' schedules and suggests suitable meeting times.

[0578] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[0579] This embodiment allows users to quickly and efficiently find the people with the expertise they need for effective collaboration.

[0580] The processing flow will be explained below.

[0581] Step 1: Receiving User Input

[0582] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0583] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[0584] Open the user chat window and enter what you want to do or your ideas in text format.

[0585] Terminal: Sends the entered text data to the server.

[0586] Step 2: Natural Language Processing Analysis

[0587] Server Analyzes text data received from users using natural language processing (NLP) technology.

[0588] Server As part of the analysis, important keywords and context are extracted from the text data.

[0589] Step 3: Database search

[0590] Server: Searches the database based on analyzed keywords and context.

[0591] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[0592] Step 4: Notification of match results

[0593] Server Generates a list of identified departments and personnel and notifies the user of that information.

[0594] Terminal: Displays the notified information to the user.

[0595] Step 5: User Indication and Processing

[0596] User If the user wishes to contact the person in charge, he / she will indicate this in the chat window.

[0597] Terminal: Sends the user's intention to the server.

[0598] Step 6: Share information and schedule a meeting

[0599] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[0600] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0601] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[0602] Through these steps, users can efficiently and quickly find the person with the expertise and schedule a meeting.

[0603] Example 1

[0604] 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."

[0605] In today's business environment, users need to be able to quickly and accurately find the right department or person to help them realize their goals or ideas. However, in many companies, department and person information is scattered, making it difficult for users to contact the person with the appropriate knowledge. The purpose of this invention is to solve this problem and make the process of achieving users' goals smoother.

[0606] 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.

[0607] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing to extract keywords and context, means for searching a data storage device based on the analyzed keywords and context to identify the most suitable organization or person in charge, means for notifying the user of a list of the identified organizations or persons in charge, and means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a meeting date and time. This enables the user to quickly and accurately find the appropriate person or department in charge and achieve effective collaboration.

[0608] "User" refers to the person who accesses the system and inputs their "ideas" and "what they want to do."

[0609] "Text data" refers to character string data that a user inputs into the system and that the server receives for processing.

[0610] "Natural language processing" refers to the technology of analyzing text data and extracting keywords, context, and important phrases.

[0611] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[0612] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[0613] "Data storage device" refers to storage that stores information such as each department's business operations, skill sets, and staff information.

[0614] "Organization" refers to a department or team responsible for a particular task or project.

[0615] "Rep" refers to an individual with expertise in a particular task or field who responds to user requests.

[0616] "List" refers to a list of organizations or personnel that the server generates as a search result and notifies the user.

[0617] "Notification" refers to the act of sending list information from the server to the user.

[0618] "Expression of intent" refers to the act of a user expressing their intention to contact or meet with a person in charge in a chat window.

[0619] "Information sharing" refers to the process of communicating user requests and contact information to relevant personnel or organizations.

[0620] "Schedule adjustment" refers to the act of the server checking the schedules of the user and the person in charge and proposing a date and time for the meeting.

[0621] "Meeting Date and Time" means the date and time set for a contact or meeting between the User and the Person in Charge.

[0622] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable organization and person in charge. Specific embodiments of the invention are described below.

[0623] This system consists of the following main components:

[0624] 1. User terminal (terminal)

[0625] 2. Server

[0626] 3. Data storage device (contained in the server)

[0627] 1. Receiving user input

[0628] The user accesses the system from their own terminal and enters their user ID and password on the login screen. The server then receives the authentication information, refers to the database, and if authentication is successful, redirects the user to the main screen.

[0629] The user opens the chat window on the main screen and enters their "ideas" or "what they want to do" in text format. For example, they might enter "I'd like to discuss a new marketing campaign." The device then sends the entered text data to the server. Specifically, it uses a POST request to transfer the text data to the API endpoint.

[0630] 2. Analysis using natural language processing

[0631] The server receives the text data sent from the device. Then, it calls the natural language processing (NLP) engine and starts analysis. The analysis engine used here uses the Google Cloud NLP API.

[0632] As a result of the analysis, the server extracts keywords, contexts, and important phrases from the text, such as "marketing campaign," "consultation," and "digital marketing."

[0633] 3. Database Search

[0634] The server searches the data storage device based on the analyzed keywords and context. Specifically, it generates an SQL query based on the keywords and sends it to the data storage device.

[0635] This data storage device stores information on the work content, skill sets, and personnel of each organization, and the search results identify the most suitable department and personnel. For example, Person B, who belongs to the "Digital Marketing" department, is identified.

[0636] 4. Notification of Matching Results

[0637] The server generates a list of the most suitable organizations and contacts obtained as search results, converts them into a data format (such as JSON) for notifying the user, and then sends the generated contact list to the terminal. For example, the response may be JSON data including the name, job title, and contact information of contact B.

[0638] The terminal handles the received list of agents and displays it to the user. Specifically, it displays the information of agent B in the chat window and asks the user to confirm it.

[0639] 5. Information sharing and meeting arrangements

[0640] If a user wishes to contact a person in charge, they express their intention in the chat window. For example, they might type, "I would like to talk to Person B." The terminal then sends this intention to the server.

[0641] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B. Specifically, it sends a notification email to the email address of person in charge B. In addition, the server checks the schedules of the user and person in charge B and suggests the optimal date and time for the meeting. For example, it uses the "Google Calendar API" to search for free times for both parties.

[0642] The server notifies the user and person in charge B of the confirmed meeting date and time and a link. An email or chat notification containing the meeting setting link is sent. The device displays the confirmed meeting date and time and the link to the user so that the user can confirm it.

[0643] Specific examples

[0644] Let's say User A is planning a new marketing campaign and needs digital marketing expertise.

[0645] 1. User A's input

[0646] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[0647] The terminal sends this input to the server.

[0648] 2. Analysis and Matching

[0649] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[0650] The server searches the database to identify the digital marketing department and person B.

[0651] 3. Notice and Indication of Intention

[0652] Server: Notifies user A of the information of identified person B.

[0653] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[0654] The terminal sends this intention to the server.

[0655] 4. Information sharing and meeting arrangements

[0656] Server: Notifies user A's request to person in charge B and coordinates with them.

[0657] Server: Checks both parties' schedules and suggests suitable meeting times.

[0658] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[0659] This embodiment allows users to quickly and efficiently find people with the expertise they need for effective collaboration.

[0660] Prompt Sentence Examples

[0661] For example, a prompt that a user could input into a generative AI model might be something like, "I'm planning a new marketing campaign and would like contact information for someone with expertise in digital marketing."

[0662] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0663] Step 1:

[0664] The user accesses the system from their own terminal and enters their user ID and password on the login screen.

[0665] The terminal sends the entered authentication information to the server.

[0666] The server checks the received authentication information against its database and, if authentication is successful, redirects the user to the main screen.

[0667] Specific action: Runs an SQL query against the database to check whether the user ID and password match.

[0668] Step 2:

[0669] Users open a chat window on the main screen and enter their ideas or what they want to do in text format. For example, they might enter, "I'd like to discuss a new marketing campaign."

[0670] The terminal sends this input text data to the server.

[0671] Specific operation: Use a POST request to send text data in JSON format to an API endpoint.

[0672] Step 3:

[0673] The server receives the text data sent from the device and inputs it into a natural language processing (NLP) engine, calling the Google Cloud NLP API.

[0674] The server uses NLP analysis to extract keywords, context, and important phrases from the text data.

[0675] Specific operation: Extract keywords such as "marketing campaign," "consultation," and "digital marketing" from the response obtained by calling the API.

[0676] Step 4:

[0677] The server searches for information in the data store based on the analyzed keywords and context.

[0678] The server generates an SQL query based on the analyzed keywords and queries the data storage device, for example, to search for people in the "Digital Marketing" department.

[0679] The data storage device stores information about each organization's business, skillsets, and personnel. Search results return the most suitable departments and personnel.

[0680] What it does: Runs an SQL query to get a list of people in the "Digital Marketing" department.

[0681] Step 5:

[0682] The server generates a list of the most suitable organizations and contacts found as search results and converts them into a data format (such as JSON) for notifying the user.

[0683] The server sends the generated list of contacts to the terminal, and returns JSON data including the contact name, job title, and contact information as a response.

[0684] The terminal displays the received list of personnel to the user.

[0685] Specific actions: Display information about Representative B in the chat window and ask the user to confirm.

[0686] Step 6:

[0687] If a user wishes to contact a representative, they can express their intention in the chat window, for example by typing, "I would like to speak with Representative B."

[0688] The terminal transmits the user's intention to the server.

[0689] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B.

[0690] Specific action: Send a notification email to the email address of person in charge B.

[0691] Step 7:

[0692] The server checks the schedules of the user and person B and proposes the best time to meet. For example, it uses the Google Calendar API to search for available times for both parties.

[0693] The server notifies the user and person in charge B of the confirmed meeting date and time and link.

[0694] The device will display the confirmed meeting date and time and a link to the user.

[0695] What happens: A message appears in the chat window with a link to set up a meeting.

[0696] These steps allow users to quickly and efficiently find the people with the expertise they need and collaborate effectively.

[0697] (Application example 1)

[0698] 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."

[0699] To improve work efficiency in conventional factories, it is necessary to quickly identify the optimal work procedures and the robots responsible. However, on factory floors, determining which robots and processes are most appropriate takes time and effort, making efficient automation difficult. Furthermore, it is increasingly difficult for workers without specialized knowledge or experience to make optimal decisions. A new system is needed to solve these problems and significantly improve factory work efficiency.

[0700] 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.

[0701] In this invention, the server includes: means for receiving text data input by a user; means for analyzing the text data using natural language processing and extracting keywords and context; means for searching a database based on the analyzed keywords and context to identify the most appropriate departments and workers; means for notifying the user of a list of the identified departments and workers; means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a date and time for a work meeting; means for notifying the user and worker of the date and time of the work meeting through a process of proposing, confirming, and finalizing the date and time; and means for analyzing the user's text input and using a generative AI model to identify the most appropriate factory robots and work processes. This allows workers to automatically identify the most appropriate work procedures using the generative AI model simply by entering text, thereby enabling quick and efficient improvement of work efficiency.

[0702] "Users" refer to factory workers and managers who use the system to optimize work procedures and robots.

[0703] "Text data" refers to the written information regarding work procedures and improvements that users enter into the system.

[0704] "Natural language processing" refers to the general technology of analyzing text data to extract keywords and context.

[0705] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[0706] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[0707] A "database" refers to a collection of information that stores each department's work content, skill sets, and worker information.

[0708] A "department" refers to a department within a factory that is responsible for a specific task or operation.

[0709] "Worker" refers to an individual person or robot that is responsible for a specific task or job within a factory.

[0710] "List" refers to a list of information about identified departments and workers.

[0711] "Notification" refers to the act of conveying information about identified departments or workers to users.

[0712] "Expression of intent" refers to an action by a user that indicates to the system that they wish to take some action.

[0713] "Information sharing" refers to the act of sharing information related to a specified department or worker based on the user's expressed intention.

[0714] "Schedule adjustment" refers to the act of adjusting the schedules of the user and the worker to set the optimal meeting date and time.

[0715] "Work meeting" refers to a meeting regarding work to be performed by a user and an identified worker.

[0716] A "generative AI model" refers to a machine learning model that analyzes user text input and identifies the optimal factory robots and work processes.

[0717] The present invention is a system for improving work efficiency at a factory site, and by having the user input "what they want to do" and "points for improvement," it is possible to quickly identify the optimal work procedure and the robot in charge. Specific embodiments for realizing this system are described below.

[0718] System configuration

[0719] The system consists of the following main components:

[0720] 1. User device (smartphone, tablet, PC, etc.)

[0721] 2. Server

[0722] 3. Database (contained in the server)

[0723] Program processing

[0724] Text data entered from the user's device is first received by the server, which then analyzes the text data using natural language processing (NLP) techniques to extract keywords and context. This analysis uses NLP libraries and APIs such as "SpaCy" and "NLTK."

[0725] Based on the extracted keywords and context, the server searches a database to identify the most suitable department and robot. The database stores information about each department's work, skill sets, and robots, and searches are performed based on this information. This search is performed using a powerful search engine such as Elasticsearch.

[0726] The server then notifies the user of the list of identified departments and robots via a chat window or notification function on the user's device. If the user wishes to contact a person in charge or a robot, they send that request to the server.

[0727] The server receives the user's request and shares the information with the appropriate department or robot. It then checks the schedules of both parties and sets the optimal meeting date and time. The server notifies both the user and the person in charge of the confirmed meeting date and time and the link. This process is automated using the Google Calendar API and Microsoft Graph API.

[0728] Specific examples

[0729] For example, User A enters "I want to improve the efficiency of assembly work" into the system from their terminal. The server receives this text data and uses NLP technology to extract "assembly work" and "efficiency improvement" as keywords.

[0730] Next, the server searches the database and identifies the "Assembly Department" as the most suitable department and "Robot B" as the most suitable robot. This result is notified to User A, who then expresses his / her desire to be contacted by Robot B.

[0731] The server receives this, informs Robot B of its role as the person in charge, checks the schedules of both parties, and sets up a meeting. The set meeting date and time and related links are notified to User A and Robot B.

[0732] Example prompt sentence:

[0733] "I want to improve the efficiency of my assembly work. Which robot is best?"

[0734] This invention allows workers to simply input text and use a generative AI model to identify optimal work procedures, enabling faster and more efficient improvements to work efficiency.

[0735] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0736] Step 1:

[0737] A user accesses the system from a terminal, enters authentication information on the login screen, and the server receives and authenticates it. The input is a username and password, and if authentication is successful, the user is redirected to the main screen. The output is the authentication success or failure status.

[0738] Step 2:

[0739] The user opens a chat window and enters "what they want to do" and "what they want to improve" in text format, and the device sends this text data to the server. The input is the specific work content and desired improvements, and the output is the transmission of text data to the server.

[0740] Step 3:

[0741] The server receives the input text data and analyzes it using NLP technology. Specifically, it segments the text data and extracts important keywords and contexts. The input is text data, and the output is a list of keywords and contexts.

[0742] Step 4:

[0743] The server searches a database based on the analyzed keywords and context. The database stores the work content, skill sets, and robot information of each department, and by comparing this, it identifies the most suitable department and robot. The input is a list of keywords and context, and the output is a list of the most suitable departments and robots.

[0744] Step 5:

[0745] The server notifies the user of the list of identified departments and robots. The information is conveyed via a chat window or notification function on the user's device. The input is a list of optimal departments and robots, and the output is a notification message to the user.

[0746] Step 6:

[0747] When a user wishes to contact a person in charge or a robot, that intention is sent to the server from the chat window. The input is the user's intention, and the output is the transmission of the intention to the server.

[0748] Step 7:

[0749] The server receives the user's intention and shares the information with the corresponding department or robot. Next, it checks the schedules of both parties and sets the optimal meeting date and time. The input is the user's intention and the schedules of both parties, and the output is the optimal meeting date and time.

[0750] Step 8:

[0751] The server notifies both the user and the person in charge of the confirmed meeting date and time. The input is the optimal meeting date and time, and the output is a notification message of the meeting date and time.

[0752] Step 9:

[0753] The user and the person in charge meet based on the confirmed meeting date and time. This allows for the provision of specific work procedures and solutions based on the user's desired improvements. The input is the meeting date and time and content, and the output is the improved work procedures and solutions.

[0754] 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.

[0755] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" using the system, quickly find the most suitable department and person in charge, and recognize the user's emotions to optimize the response. Specific program processing and its implementation will be described below.

[0756] System configuration

[0757] This system consists of the following main components:

[0758] 1. User terminal (terminal)

[0759] 2. Server

[0760] 3. Database (contained in the server)

[0761] 4. Emotion engine (included in the server)

[0762] Program processing

[0763] 1. Receiving user input

[0764] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0765] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[0766] Open the user chat window and enter your "ideas" or "what you want to do" in text format.

[0767] Terminal: Sends the entered text data to the server.

[0768] 2. Analysis using natural language processing

[0769] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[0770] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[0771] 3. Emotion Recognition by Emotion Engine

[0772] Server: Based on the analyzed text data, an emotion engine is used to identify the user's emotions.

[0773] Server: Generates an appropriate response to the user based on the emotion recognition results.

[0774] 4. Database Search

[0775] Server: Searches the database based on analyzed keywords and context.

[0776] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[0777] 5. Notification of Matching Results

[0778] Server Generates a list of identified departments and personnel and notifies the user of that information.

[0779] Terminal: Displays the notified information to the user.

[0780] 6. User Indications and Processing

[0781] User If a user wishes to contact a representative, they express their desire in the chat window.

[0782] Terminal: Sends the user's intention to the server.

[0783] 7. Information sharing and meeting arrangements

[0784] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[0785] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0786] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[0787] Specific examples

[0788] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[0789] 1. User A's input

[0790] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[0791] The terminal sends this input to the server.

[0792] 2. Analysis and Emotion Recognition

[0793] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[0794] Server: Using the emotion engine, identify the emotion from User A's text and determine that it is "high urgency."

[0795] 3. Database Search and Matching

[0796] The server searches the database to identify the digital marketing department and person B.

[0797] 4. Notice and Indication of Intention

[0798] Server: Notifies user A of the information of identified person B.

[0799] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[0800] The terminal sends this intention to the server.

[0801] 5. Information sharing and meeting arrangements

[0802] Server: Notifies user A's request to person in charge B and coordinates with them.

[0803] Server: Checks both parties' schedules and suggests suitable meeting times.

[0804] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[0805] This embodiment allows users to quickly and efficiently find the person with the expertise they need, receive a response that is sensitive to their emotions, and collaborate effectively.

[0806] The processing flow will be explained below.

[0807] Step 1: Receiving User Input

[0808] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[0809] Server Receives the authentication information and checks it against a database to determine whether the authentication succeeds or fails.

[0810] Server If authentication is successful, redirect the user to the system's main screen.

[0811] User Open a chat window from the main screen and enter text about what you want to do or your ideas.

[0812] Terminal: Sends the entered text data to the server.

[0813] Step 2: Natural Language Processing Analysis

[0814] Server Analyzes the received text data using natural language processing (NLP) technology.

[0815] Server Extracts important keywords and context from text data.

[0816] Server: Converts the extracted keywords and context into a specific data format.

[0817] Step 3: Emotion recognition by the emotion engine

[0818] Server: Data analyzed by natural language processing is passed to the emotion engine.

[0819] The server emotion engine identifies user emotions from words and phrases in the text data.

[0820] Server: Based on the emotion recognition results, the server identifies the user's emotional state and generates an appropriate response.

[0821] Step 4: Database Search

[0822] Server Searches the database based on analyzed keywords and context.

[0823] The server database identifies the department and person having the relevant work content, skill set, and person information.

[0824] Step 5: Notification of match results

[0825] Generate a list of identified departments and personnel from the results of the server database search.

[0826] The server notifies the user of the generated list.

[0827] Device Notifications are displayed on user devices so that specific departments and personnel can view them.

[0828] Step 6: User Indication and Processing

[0829] If the user wishes to contact the identified person, he / she expresses his / her intention in the chat window.

[0830] Terminal: Sends the user's intention to the server.

[0831] Step 7: Share information and schedule a meeting

[0832] Server: Receives the user's intentions and shares the necessary information (such as the user's request, emotional state, and contact information) with the appropriate person or department.

[0833] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[0834] The server may also adjust the urgency of the meeting depending on the emotional state of the user.

[0835] Server Notifies the user and contact person of the confirmed meeting date and time and link.

[0836] By following these steps, users can quickly and appropriately find the resources they need and receive emotionally sensitive support. For example, if a user types "urgent," the system uses its emotion engine to determine the level of urgency and prioritize the response.

[0837] Example 2

[0838] 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."

[0839] With conventional systems, when users communicate their "ideas" or "things they want to do," it is difficult to properly understand them and quickly find the most appropriate department or person in charge. Furthermore, because the system does not take into account the user's feelings, it is unable to respond appropriately, making efficient communication and collaboration difficult.

[0840] 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.

[0841] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing and extracting keywords and context, means for identifying the user's emotions using an emotion recognition engine and generating an appropriate response based on the results, means for searching a database based on the analyzed keywords and context and identifying the most appropriate department or person in charge, means for notifying the user of a list of the identified departments and persons in charge, and means for sharing information with the other party based on the user's intention, adjusting both parties' schedules, and setting a meeting date and time. This makes it possible to quickly and efficiently understand the user's intentions and emotions and take the most appropriate response.

[0842] A "user" is an individual or corporation that accesses the system, inputs their "ideas" or "things they want to do," and seeks the best possible response.

[0843] "Text data" refers to formatted strings of information that a user inputs into a system.

[0844] "Natural language processing" is a general term for algorithms and methods that allow computers to understand, interpret, and generate human language.

[0845] "Keywords" refer to particularly important words or phrases extracted from the text data entered by the user.

[0846] "Context" refers to information that indicates the situation and intention in which each keyword in the text data is used.

[0847] The "database" is an information repository that stores information such as each department's business operations, skill sets, and staff information.

[0848] "The most suitable department or person" refers to the department or person whose work best suits the user's input and purpose.

[0849] An "emotion recognition engine" is an algorithm or software that identifies emotions from a user's text data and provides the results.

[0850] "Response" refers to the message or action the system returns to the user based on the analysis results.

[0851] "Meeting Date and Time" means a specific date and time set for a face-to-face or online conversation between you and a representative.

[0852] This invention is a system that allows users to input their "ideas" and "things they want to do," quickly finds the most suitable department and person in charge, and recognizes the user's emotions to provide the most appropriate response.The system is composed of the following main components: a user terminal, a server, a database, and an emotion recognition engine.

[0853] Hardware and software used

[0854] User device: A PC or smartphone used by a user to access the system. The web browser used may be Google Chrome or Safari.

[0855] Server: Built in Python using the Django framework. Responsible for user authentication, data processing, and NLP (Natural Language Processing).

[0856] Database: Use an SQL database (e.g., PostgreSQL) to store department, person in charge information, job details, etc.

[0857] Emotion Recognition Engine: Uses the Google Cloud Natural Language API to identify emotions from user text data.

[0858] System Embodiments and Operation

[0859] 1. Accepting user input:

[0860] Users access the system from their own devices and first enter their authentication information on the login screen. If authentication is successful, they are taken to the main screen.

[0861] After logging in, users open a chat window and enter their ideas and what they want to do.

[0862] The entered text data is sent asynchronously to the server using AJAX.

[0863] 2. Natural Language Processing Analysis:

[0864] Server The server analyzes the received text data using a Python natural language processing library (e.g., NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[0865] Server: Extracts keywords, contexts, and phrases from text using techniques such as TF-IDF and Word2Vec.

[0866] 3. Emotion recognition using emotion recognition engine:

[0867] Server: Sends the analyzed text data to the Google Cloud Natural Language API to identify sentiment.

[0868] Server: Receives emotion recognition results (e.g., "positive," "negative," or "neutral") and determines the response content and next processing steps.

[0869] 4. Database Search:

[0870] Server: Search the database (PostgreSQL) using SQLAlchemy based on the analyzed keywords and context.

[0871] The server compares the job content, skill sets, and staff information of each department in the database to identify the most suitable department and staff member.

[0872] 5. Notification of Matching Results:

[0873] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[0874] Terminal Dynamically display notification messages on the user's screen.

[0875] 6. User Indications and Processing:

[0876] User The user selects the person they wish to contact from the notification list and expresses their intention in the chat window.

[0877] The terminal transmits this intention data to the server.

[0878] 7. Information sharing and meeting scheduling:

[0879] Server Receives user intentions, notifies the appropriate person or department, and shares information.

[0880] Server: Checks both schedules and uses the Google Calendar API to suggest the best time and date for the meeting.

[0881] Server Notifies the user and contact person of the confirmed meeting date and time and sets up the meeting.

[0882] Example: Prompt sentence example

[0883] For example, suppose User A is planning a new marketing campaign and needs digital marketing expertise. User A logs in to the system and enters, "I would like to speak to someone with digital marketing expertise." This input is sent to the server, which uses natural language processing and emotion recognition to identify Person B in the digital marketing department. A meeting date and time with Person B is then proposed and finally confirmed.

[0884] In this way, users can quickly and efficiently connect with people with the expertise they need. Examples of prompts for this system include "I'd like to speak with someone in charge of market research for our new product" or "I'd like to discuss sales strategies."

[0885] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0886] Step 1:

[0887] User A user accesses the system from a terminal and first enters authentication information (user name and password) on the login screen. The entered authentication information is sent from the terminal to the server.

[0888] Server The server verifies the received authentication information using the Django framework's user authentication function. If authentication is successful, it generates and returns a URL to redirect the user to the main screen. If authentication fails, it generates an error message.

[0889] If the authentication is successful, the user is redirected to the main screen; if not, an error message is displayed.

[0890] Step 2:

[0891] User The user opens a chat window on the main screen and enters their "what they want to do" or "ideas" in the text box.

[0892] The input text data is acquired from the terminal and sent asynchronously to the server using AJAX. The input here is the user's request or idea.

[0893] Server The server saves the received text data and stores it in memory in its raw form for further processing. The output is the stored text data.

[0894] Step 3:

[0895] Server The server analyzes the received text data using Python's natural language processing library (NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[0896] Input User-entered text data.

[0897] Data processing: Use NLTK to tokenize text, remove stop words, and perform stemming.

[0898] Output Keywords and contexts as analysis results after preprocessing.

[0899] Server Based on the analysis results, TF-IDF and Word2Vec techniques are introduced to extract more important phrases from the text.

[0900] Step 4:

[0901] Server: Sends the text data analyzed in the previous step to the emotion recognition engine (Google Cloud Natural Language API) and receives the results.

[0902] Input: Extracted keywords and context, further analyzed text data.

[0903] Data Calculation Analyze the sentiment of text data using an emotion recognition engine (Google Cloud Natural Language API).

[0904] Output The user's emotion recognition result (e.g., "positive", "negative", "neutral").

[0905] Server Based on the emotion recognition results, it determines the possible actions to take in order to generate the optimal response for the user.

[0906] Step 5:

[0907] Server: Using SQLAlchemy, the PostgreSQL database is searched based on the analyzed keywords and context. This is then compared with the job content, skill sets, and person information of each department in the database to identify the most suitable department and person.

[0908] Input Extracted keywords and context.

[0909] Data Calculation: Database search and matching using SQL queries.

[0910] Output: A list of the most suitable departments and personnel.

[0911] Server: Create a list of identified departments and personnel and prepare for next steps to notify users.

[0912] Step 6:

[0913] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[0914] Input a list of suitable departments and contacts.

[0915] Data processing: Generate notification messages.

[0916] Output Informational message.

[0917] Dynamically display notification messages on the user's screen. A user-friendly interface is achieved using JavaScript.

[0918] Step 7:

[0919] User The user selects the desired contact person from the notification list and expresses their desire to be contacted in the chat window.

[0920] Input User's intention.

[0921] The device sends this data to the server using AJAX asynchronous communication.

[0922] Output User intent data.

[0923] After receiving the server user's intention, it notifies the person in charge and shares the necessary information.

[0924] Step 8:

[0925] Server The server checks the schedules of the user and the person in charge and suggests the optimal meeting date and time using the Google Calendar API.

[0926] Input: User and person in charge schedule information, user intent data.

[0927] Data calculation: Schedule matching and optimal date and time suggestions.

[0928] Output The proposed meeting time.

[0929] Server When the meeting date and time is confirmed, the information is notified to the user and the person in charge and registered in the system.

[0930] The confirmed meeting date and time will be displayed on the user's and the person in charge's device, and a reminder will be sent to the user.

[0931] (Application example 2)

[0932] 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."

[0933] In factories, employees and automated machines often have new ideas or problems, but there is a lack of a way to quickly and effectively resolve them. In particular, the process of finding the appropriate department or person in charge can be time-consuming, resulting in reduced productivity and delays in the realization of ideas. Furthermore, efficient communication becomes difficult if responses are not tailored to the emotions and situations of employees and machines. A system to overcome these challenges is needed.

[0934] 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.

[0935] In this invention, the server includes: means for receiving text data entered by a user; means for analyzing the text data using natural language processing and extracting keywords and context; emotion recognition means for identifying the user's emotion based on the analyzed text data; means for generating an appropriate response to the user based on the emotion recognition results; means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge; means for notifying the user of the list of identified departments and persons in charge; and means for sharing information with the other party based on the user's intention, coordinating schedules, and setting a meeting date and time. This enables quick and efficient resolution of issues and ideas faced by employees and automated machines, and finding the appropriate person or department. Furthermore, emotion recognition allows responses to be appropriately adjusted, improving communication efficiency.

[0936] "Means for receiving text data entered by a user" refers to the equipment or software that allows a user to enter text data into the system and receive that data.

[0937] "Means for analyzing the text data using natural language processing and extracting keywords and context" refers to equipment or software that analyzes input text data using natural language processing technology and extracts important keywords and context from it.

[0938] "Emotion recognition means" refers to algorithms or engines that analyze text data and recognize user emotions from it.

[0939] The "means for generating an appropriate response to the user" refers to equipment or software for generating an optimal response to the user based on the emotion identified by the emotion recognition means.

[0940] "Means for searching the database and identifying the most appropriate department or person" refers to algorithms or engines that search the database based on keywords or context to identify the appropriate department or person.

[0941] "Means for notifying users of a list of identified departments and personnel" refers to devices or software for notifying users of information about departments and personnel identified through a database search.

[0942] "A means of sharing information with the other party based on the user's wishes, adjusting both parties' schedules and setting a meeting date and time" refers to devices and software that share the necessary information with the person in charge based on the user's contact preferences, adjust both parties' schedules and decide the meeting date and time.

[0943] "The work content, skill information, and staff information of each department" refers to the work content of each department, related skill information, and information about the staff in each department that is stored in the database.

[0944] "Each employee and automated machine in the factory" refers to the employees working in the factory and the machines used to automate the work of the factory.

[0945] To implement this invention, a user terminal (such as a smartphone or PC), a server, and a database are required. Using this hardware and software, the following system is constructed.

[0946] Users input text data using their devices. This text data is received using a dedicated application or web interface. This application has an interface that allows users to easily input their "ideas" and "what they want to do."

[0947] The server analyzes the text data received from the user. First, it uses natural language processing (NLP) techniques to analyze the text data and extract keywords and context. For example, natural language processing libraries such as "NLTK" and "spaCy" can be used here.

[0948] Next, an emotion recognition engine identifies the user's emotions based on the analyzed text data. Emotion recognition is important for understanding the user's psychological state and level of urgency. For example, emotion recognition engines such as "IBM Watson" and "Microsoft Azure Emotion Recognition API" can be used here.

[0949] Based on the results of emotion recognition, an appropriate response is generated for the user. This response generation uses a generative AI model (such as GPT-4), making it possible to communicate in appropriate words according to the user's emotions.

[0950] The server then searches a database based on the analyzed keywords and context to identify the most appropriate department and person in charge. The database contains information on each department's work, skills, and personnel. This database search function can be implemented using a search engine such as Elasticsearch.

[0951] The server notifies the user's device of the list of identified departments and contact persons. If the user receives the notification, they can enter their intention to contact the contact person again into the system. Based on this intention, the server adjusts both parties' schedules and sets the date and time of the meeting. This schedule adjustment can be done using tools such as the Google Calendar API or Microsoft Outlook Calendar API.

[0952] Specific examples

[0953] For example, consider the case where a factory employee has an idea for improving the efficiency of a new production line. The employee enters through the application, "I have an idea for improving the efficiency of the production line. I would like to speak with someone in the process control department." The system receives this input data, analyzes it using natural language processing, and extracts important keywords such as "production line," "efficiency," and "idea."

[0954] The emotion recognition engine identifies the employee as having a positive emotion and generates an appropriate response. It then searches the database to identify the process control department and the relevant person in charge. This information is sent to the employee, who then re-enters the system with a request to meet with the person in charge. The server receives this request, coordinates both parties' schedules, sets a date and time for the meeting, and notifies the employee and the person in charge.

[0955] Prompt Sentence Examples

[0956] "I have an idea for improving the efficiency of our new production line and would like to speak with someone about it. Which department would be the best fit and who should I contact?"

[0957] In this way, a system is created to quickly and efficiently solve problems and ideas from employees and automated machines.

[0958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0959] Step 1:

[0960] The user accesses the system from a user terminal and enters authentication information on the login screen.

[0961] Input: Authentication information (username, password)

[0962] Output: Authentication result (success / failure)

[0963] How it works: The user device sends authentication information to the server, which verifies it by checking its database. If authentication is successful, the user is redirected to the main screen.

[0964] Step 2:

[0965] Users open a chat window on the main screen and enter their "ideas" and "what they want to do" in text format.

[0966] Input: Text request (e.g., "I have an idea for improving the efficiency of our production line. I'd like to speak with someone in the process control department.")

[0967] Output: Text data

[0968] Operation: The user's device sends the entered text data to the server.

[0969] Step 3:

[0970] The server analyzes the received text data using natural language processing (NLP) technology.

[0971] Input: Text data

[0972] Output: Parsed data (keywords, context)

[0973] How it works: The server uses NLP libraries such as "NLTK" and "spaCy" to parse the text data and extract keywords and context.

[0974] Step 4:

[0975] The server uses an emotion recognition engine to identify the user's emotion based on the analyzed data.

[0976] Input: Parsed data (keywords, context)

[0977] Output: Emotion recognition result (user's emotional state)

[0978] How it works: The server uses IBM Watson or Microsoft Azure Emotion Recognition API to identify emotions and retrieve the results.

[0979] Step 5:

[0980] The server generates an appropriate response to the user based on the emotion recognition results.

[0981] Input: Emotion recognition result (user's emotional state)

[0982] Output: Response message

[0983] How it works: The server uses a generative AI model (e.g., GPT-4) to generate an appropriate response message.

[0984] Step 6:

[0985] The server searches the database based on the analyzed keywords and context to identify the most appropriate department or person in charge.

[0986] Input: Parsed data (keywords, context)

[0987] Output: Best fit department and contact information

[0988] How it works: The server uses a search engine such as Elasticsearch to search the database and identify the appropriate department or person.

[0989] Step 7:

[0990] The server notifies the user of a list of identified departments and personnel.

[0991] Input: Best department and contact information

[0992] Output: Information message

[0993] Operation: The server sends the identified department and person in charge information to the user's terminal and notifies the user.

[0994] Step 8:

[0995] If the user wishes to contact the person in charge, he or she expresses this desire in the chat window.

[0996] Input: Indication of your desire to be contacted

[0997] Output: Intention data

[0998] Operation: The user device sends the intent data to the server.

[0999] Step 9:

[1000] The server receives the user's intentions and shares the necessary information with the appropriate person or department.

[1001] Input: Expression of Intention Data

[1002] Output: Information sharing content

[1003] How it works: The server notifies the person or department in charge of the user's request and contact information.

[1004] Step 10:

[1005] The server checks the schedules of the person in charge and the user and suggests the best time and date for the meeting.

[1006] Input: Both parties' schedule information

[1007] Output: Meeting time proposal

[1008] How it works: The server uses the Google Calendar API or Microsoft Outlook Calendar API to coordinate schedules and suggest suitable meeting times.

[1009] Step 11:

[1010] After the meeting date and time is confirmed, the server notifies the user and the person in charge of the information.

[1011] Input: Confirmed meeting date and time

[1012] Output: Information message

[1013] What it does: The server notifies the user and contact of the confirmed meeting date and time.

[1014] In this way, by performing specific actions at each step, users can quickly and efficiently find the appropriate department or person in charge, receive appropriate responses based on their feelings, and set up necessary contacts and meetings.

[1015] 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.

[1016] 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.

[1017] 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.

[1018] [Third embodiment]

[1019] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1020] 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.

[1021] 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).

[1022] 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.

[1023] 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.

[1024] 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).

[1025] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1026] 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.

[1027] 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.

[1028] 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.

[1029] 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.

[1030] 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."

[1031] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable department and person in charge. Specific program processing and its implementation will be described below.

[1032] System configuration

[1033] This system consists of the following main components:

[1034] 1. User terminal (terminal)

[1035] 2. Server

[1036] 3. Database (contained in the server)

[1037] Program processing

[1038] 1. Receiving user input

[1039] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1040] Server Validates the credentials and redirects the user to the main screen if successful.

[1041] Open the user chat window and enter what you want to do or your ideas in text format.

[1042] Terminal: Sends the entered text data to the server.

[1043] 2. Analysis using natural language processing

[1044] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[1045] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[1046] 3. Database Search

[1047] Server: Searches the database based on analyzed keywords and context to identify the most appropriate department or person.

[1048] Database: The work content, skill sets, and staff information of each department are stored, and searches are performed based on this information.

[1049] 4. Notification of Matching Results

[1050] Server Generates a list of the most suitable departments and personnel and notifies the user.

[1051] Terminal: Displays the notified information to the user.

[1052] 5. Information sharing and meeting arrangements

[1053] User If a user wishes to contact a representative, they express their desire in the chat window.

[1054] Terminal: Sends the user's intention to the server.

[1055] Server Receives user requests and shares the necessary information (such as the user's request content and contact information) with the appropriate person or department.

[1056] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1057] Server Notifies both parties of the confirmed meeting date and time and link.

[1058] Specific examples

[1059] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[1060] 1. User A's input

[1061] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[1062] The terminal sends this input to the server.

[1063] 2. Analysis and Matching

[1064] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[1065] The server searches the database to identify the digital marketing department and person B.

[1066] 3. Notice and Indication of Intention

[1067] Server: Notifies user A of the information of identified person B.

[1068] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[1069] The terminal sends this intention to the server.

[1070] 4. Information sharing and meeting arrangements

[1071] Server: Notifies user A's request to person in charge B and coordinates with them.

[1072] Server: Checks both parties' schedules and suggests suitable meeting times.

[1073] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[1074] This embodiment allows users to quickly and efficiently find the people with the expertise they need for effective collaboration.

[1075] The processing flow will be explained below.

[1076] Step 1: Receiving User Input

[1077] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1078] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[1079] Open the user chat window and enter what you want to do or your ideas in text format.

[1080] Terminal: Sends the entered text data to the server.

[1081] Step 2: Natural Language Processing Analysis

[1082] Server Analyzes text data received from users using natural language processing (NLP) technology.

[1083] Server As part of the analysis, important keywords and context are extracted from the text data.

[1084] Step 3: Database search

[1085] Server: Searches the database based on analyzed keywords and context.

[1086] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[1087] Step 4: Notification of match results

[1088] Server Generates a list of identified departments and personnel and notifies the user of that information.

[1089] Terminal: Displays the notified information to the user.

[1090] Step 5: User Indication and Processing

[1091] User If the user wishes to contact the person in charge, he / she will indicate this in the chat window.

[1092] Terminal: Sends the user's intention to the server.

[1093] Step 6: Share information and schedule a meeting

[1094] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[1095] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1096] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[1097] Through these steps, users can efficiently and quickly find the person with the expertise and schedule a meeting.

[1098] Example 1

[1099] 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."

[1100] In today's business environment, users need to be able to quickly and accurately find the right department or person to help them realize their goals or ideas. However, in many companies, department and person information is scattered, making it difficult for users to contact the person with the appropriate knowledge. The purpose of this invention is to solve this problem and make the process of achieving users' goals smoother.

[1101] 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.

[1102] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing to extract keywords and context, means for searching a data storage device based on the analyzed keywords and context to identify the most suitable organization or person in charge, means for notifying the user of a list of the identified organizations or persons in charge, and means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a meeting date and time. This enables the user to quickly and accurately find the appropriate person or department in charge and achieve effective collaboration.

[1103] "User" refers to the person who accesses the system and inputs their "ideas" and "what they want to do."

[1104] "Text data" refers to character string data that a user inputs into the system and that the server receives for processing.

[1105] "Natural language processing" refers to the technology of analyzing text data and extracting keywords, context, and important phrases.

[1106] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[1107] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[1108] "Data storage device" refers to storage that stores information such as each department's business operations, skill sets, and staff information.

[1109] "Organization" refers to a department or team responsible for a particular task or project.

[1110] "Rep" refers to an individual with expertise in a particular task or field who responds to user requests.

[1111] "List" refers to a list of organizations or personnel that the server generates as a search result and notifies the user.

[1112] "Notification" refers to the act of sending list information from the server to the user.

[1113] "Expression of intent" refers to the act of a user expressing their intention to contact or meet with a person in charge in a chat window.

[1114] "Information sharing" refers to the process of communicating user requests and contact information to relevant personnel or organizations.

[1115] "Schedule adjustment" refers to the act of the server checking the schedules of the user and the person in charge and proposing a date and time for the meeting.

[1116] "Meeting Date and Time" means the date and time set for a contact or meeting between the User and the Person in Charge.

[1117] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable organization and person in charge. Specific embodiments of the invention are described below.

[1118] This system consists of the following main components:

[1119] 1. User terminal (terminal)

[1120] 2. Server

[1121] 3. Data storage device (contained in the server)

[1122] 1. Receiving user input

[1123] The user accesses the system from their own terminal and enters their user ID and password on the login screen. The server then receives the authentication information, refers to the database, and if authentication is successful, redirects the user to the main screen.

[1124] The user opens the chat window on the main screen and enters their "ideas" or "what they want to do" in text format. For example, they might enter "I'd like to discuss a new marketing campaign." The device then sends the entered text data to the server. Specifically, it uses a POST request to transfer the text data to the API endpoint.

[1125] 2. Analysis using natural language processing

[1126] The server receives the text data sent from the device. Then, it calls the natural language processing (NLP) engine and starts analysis. The analysis engine used here uses the Google Cloud NLP API.

[1127] As a result of the analysis, the server extracts keywords, contexts, and important phrases from the text, such as "marketing campaign," "consultation," and "digital marketing."

[1128] 3. Database Search

[1129] The server searches the data storage device based on the analyzed keywords and context. Specifically, it generates an SQL query based on the keywords and sends it to the data storage device.

[1130] This data storage device stores information on the work content, skill sets, and personnel of each organization, and the search results identify the most suitable department and personnel. For example, Person B, who belongs to the "Digital Marketing" department, is identified.

[1131] 4. Notification of Matching Results

[1132] The server generates a list of the most suitable organizations and contacts obtained as search results, converts them into a data format (such as JSON) for notifying the user, and then sends the generated contact list to the terminal. For example, the response may be JSON data including the name, job title, and contact information of contact B.

[1133] The terminal handles the received list of agents and displays it to the user. Specifically, it displays the information of agent B in the chat window and asks the user to confirm it.

[1134] 5. Information sharing and meeting arrangements

[1135] If a user wishes to contact a person in charge, they express their intention in the chat window. For example, they might type, "I would like to talk to Person B." The terminal then sends this intention to the server.

[1136] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B. Specifically, it sends a notification email to the email address of person in charge B. In addition, the server checks the schedules of the user and person in charge B and suggests the optimal date and time for the meeting. For example, it uses the "Google Calendar API" to search for free times for both parties.

[1137] The server notifies the user and person in charge B of the confirmed meeting date and time and a link. An email or chat notification containing the meeting setting link is sent. The device displays the confirmed meeting date and time and the link to the user so that the user can confirm it.

[1138] Specific examples

[1139] Let's say User A is planning a new marketing campaign and needs digital marketing expertise.

[1140] 1. User A's input

[1141] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[1142] The terminal sends this input to the server.

[1143] 2. Analysis and Matching

[1144] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[1145] The server searches the database to identify the digital marketing department and person B.

[1146] 3. Notice and Indication of Intention

[1147] Server: Notifies user A of the information of identified person B.

[1148] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[1149] The terminal sends this intention to the server.

[1150] 4. Information sharing and meeting arrangements

[1151] Server: Notifies user A's request to person in charge B and coordinates with them.

[1152] Server: Checks both parties' schedules and suggests suitable meeting times.

[1153] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[1154] This embodiment allows users to quickly and efficiently find people with the expertise they need for effective collaboration.

[1155] Prompt Sentence Examples

[1156] For example, a prompt that a user could input into a generative AI model might be something like, "I'm planning a new marketing campaign and would like contact information for someone with expertise in digital marketing."

[1157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1158] Step 1:

[1159] The user accesses the system from their own terminal and enters their user ID and password on the login screen.

[1160] The terminal sends the entered authentication information to the server.

[1161] The server checks the received authentication information against its database and, if authentication is successful, redirects the user to the main screen.

[1162] Specific action: Runs an SQL query against the database to check whether the user ID and password match.

[1163] Step 2:

[1164] Users open a chat window on the main screen and enter their ideas or what they want to do in text format. For example, they might enter, "I'd like to discuss a new marketing campaign."

[1165] The terminal sends this input text data to the server.

[1166] Specific operation: Use a POST request to send text data in JSON format to an API endpoint.

[1167] Step 3:

[1168] The server receives the text data sent from the device and inputs it into a natural language processing (NLP) engine, calling the Google Cloud NLP API.

[1169] The server uses NLP analysis to extract keywords, context, and important phrases from the text data.

[1170] Specific operation: Extract keywords such as "marketing campaign," "consultation," and "digital marketing" from the response obtained by calling the API.

[1171] Step 4:

[1172] The server searches for information in the data store based on the analyzed keywords and context.

[1173] The server generates an SQL query based on the analyzed keywords and queries the data storage device, for example, to search for people in the "Digital Marketing" department.

[1174] The data storage device stores information about each organization's business, skillsets, and personnel. Search results return the most suitable departments and personnel.

[1175] What it does: Runs an SQL query to get a list of people in the "Digital Marketing" department.

[1176] Step 5:

[1177] The server generates a list of the most suitable organizations and contacts found as search results and converts them into a data format (such as JSON) for notifying the user.

[1178] The server sends the generated list of contacts to the terminal, and returns JSON data including the contact name, job title, and contact information as a response.

[1179] The terminal displays the received list of personnel to the user.

[1180] Specific actions: Display information about Representative B in the chat window and ask the user to confirm.

[1181] Step 6:

[1182] If a user wishes to contact a representative, they can express their intention in the chat window, for example by typing, "I would like to speak with Representative B."

[1183] The terminal transmits the user's intention to the server.

[1184] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B.

[1185] Specific action: Send a notification email to the email address of person in charge B.

[1186] Step 7:

[1187] The server checks the schedules of the user and person B and proposes the best time to meet. For example, it uses the Google Calendar API to search for available times for both parties.

[1188] The server notifies the user and person in charge B of the confirmed meeting date and time and link.

[1189] The device will display the confirmed meeting date and time and a link to the user.

[1190] What happens: A message appears in the chat window with a link to set up a meeting.

[1191] These steps allow users to quickly and efficiently find the people with the expertise they need and collaborate effectively.

[1192] (Application example 1)

[1193] 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."

[1194] To improve work efficiency in conventional factories, it is necessary to quickly identify the optimal work procedures and the robots responsible. However, on factory floors, determining which robots and processes are most appropriate takes time and effort, making efficient automation difficult. Furthermore, it is increasingly difficult for workers without specialized knowledge or experience to make optimal decisions. A new system is needed to solve these problems and significantly improve factory work efficiency.

[1195] 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.

[1196] In this invention, the server includes: means for receiving text data input by a user; means for analyzing the text data using natural language processing and extracting keywords and context; means for searching a database based on the analyzed keywords and context to identify the most appropriate departments and workers; means for notifying the user of a list of the identified departments and workers; means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a date and time for a work meeting; means for notifying the user and worker of the date and time of the work meeting through a process of proposing, confirming, and finalizing the date and time; and means for analyzing the user's text input and using a generative AI model to identify the most appropriate factory robots and work processes. This allows workers to automatically identify the most appropriate work procedures using the generative AI model simply by entering text, thereby enabling quick and efficient improvement of work efficiency.

[1197] "Users" refer to factory workers and managers who use the system to optimize work procedures and robots.

[1198] "Text data" refers to the written information regarding work procedures and improvements that users enter into the system.

[1199] "Natural language processing" refers to the general technology of analyzing text data to extract keywords and context.

[1200] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[1201] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[1202] A "database" refers to a collection of information that stores each department's work content, skill sets, and worker information.

[1203] A "department" refers to a department within a factory that is responsible for a specific task or operation.

[1204] "Worker" refers to an individual person or robot that is responsible for a specific task or job within a factory.

[1205] "List" refers to a list of information about identified departments and workers.

[1206] "Notification" refers to the act of conveying information about identified departments or workers to users.

[1207] "Expression of intent" refers to an action by a user that indicates to the system that they wish to take some action.

[1208] "Information sharing" refers to the act of sharing information related to a specified department or worker based on the user's expressed intention.

[1209] "Schedule adjustment" refers to the act of adjusting the schedules of the user and the worker to set the optimal meeting date and time.

[1210] "Work meeting" refers to a meeting regarding work to be performed by a user and an identified worker.

[1211] A "generative AI model" refers to a machine learning model that analyzes user text input and identifies the optimal factory robots and work processes.

[1212] The present invention is a system for improving work efficiency at a factory site, and by having the user input "what they want to do" and "points for improvement," it is possible to quickly identify the optimal work procedure and the robot in charge. Specific embodiments for realizing this system are described below.

[1213] System configuration

[1214] The system consists of the following main components:

[1215] 1. User device (smartphone, tablet, PC, etc.)

[1216] 2. Server

[1217] 3. Database (contained in the server)

[1218] Program processing

[1219] Text data entered from the user's device is first received by the server, which then analyzes the text data using natural language processing (NLP) techniques to extract keywords and context. This analysis uses NLP libraries and APIs such as "SpaCy" and "NLTK."

[1220] Based on the extracted keywords and context, the server searches a database to identify the most suitable department and robot. The database stores information about each department's work, skill sets, and robots, and searches are performed based on this information. This search is performed using a powerful search engine such as Elasticsearch.

[1221] The server then notifies the user of the list of identified departments and robots via a chat window or notification function on the user's device. If the user wishes to contact a person in charge or a robot, they send that request to the server.

[1222] The server receives the user's request and shares the information with the appropriate department or robot. It then checks the schedules of both parties and sets the optimal meeting date and time. The server notifies both the user and the person in charge of the confirmed meeting date and time and the link. This process is automated using the Google Calendar API and Microsoft Graph API.

[1223] Specific examples

[1224] For example, User A enters "I want to improve the efficiency of assembly work" into the system from their terminal. The server receives this text data and uses NLP technology to extract "assembly work" and "efficiency improvement" as keywords.

[1225] Next, the server searches the database and identifies the "Assembly Department" as the most suitable department and "Robot B" as the most suitable robot. This result is notified to User A, who then expresses his / her desire to be contacted by Robot B.

[1226] The server receives this, informs Robot B of its role as the person in charge, checks the schedules of both parties, and sets up a meeting. The set meeting date and time and related links are notified to User A and Robot B.

[1227] Example prompt sentence:

[1228] "I want to improve the efficiency of my assembly work. Which robot is best?"

[1229] This invention allows workers to simply input text and use a generative AI model to identify optimal work procedures, enabling faster and more efficient improvements to work efficiency.

[1230] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1231] Step 1:

[1232] A user accesses the system from a terminal, enters authentication information on the login screen, and the server receives and authenticates it. The input is a username and password, and if authentication is successful, the user is redirected to the main screen. The output is the authentication success or failure status.

[1233] Step 2:

[1234] The user opens a chat window and enters "what they want to do" and "what they want to improve" in text format, and the device sends this text data to the server. The input is the specific work content and desired improvements, and the output is the transmission of text data to the server.

[1235] Step 3:

[1236] The server receives the input text data and analyzes it using NLP technology. Specifically, it segments the text data and extracts important keywords and contexts. The input is text data, and the output is a list of keywords and contexts.

[1237] Step 4:

[1238] The server searches a database based on the analyzed keywords and context. The database stores the work content, skill sets, and robot information of each department, and by comparing this, it identifies the most suitable department and robot. The input is a list of keywords and context, and the output is a list of the most suitable departments and robots.

[1239] Step 5:

[1240] The server notifies the user of the list of identified departments and robots. The information is conveyed via a chat window or notification function on the user's device. The input is a list of optimal departments and robots, and the output is a notification message to the user.

[1241] Step 6:

[1242] When a user wishes to contact a person in charge or a robot, that intention is sent to the server from the chat window. The input is the user's intention, and the output is the transmission of the intention to the server.

[1243] Step 7:

[1244] The server receives the user's intention and shares the information with the corresponding department or robot. Next, it checks the schedules of both parties and sets the optimal meeting date and time. The input is the user's intention and the schedules of both parties, and the output is the optimal meeting date and time.

[1245] Step 8:

[1246] The server notifies both the user and the person in charge of the confirmed meeting date and time. The input is the optimal meeting date and time, and the output is a notification message of the meeting date and time.

[1247] Step 9:

[1248] The user and the person in charge meet based on the confirmed meeting date and time. This allows for the provision of specific work procedures and solutions based on the user's desired improvements. The input is the meeting date and time and content, and the output is the improved work procedures and solutions.

[1249] 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.

[1250] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" using the system, quickly find the most suitable department and person in charge, and recognize the user's emotions to optimize the response. Specific program processing and its implementation will be described below.

[1251] System configuration

[1252] This system consists of the following main components:

[1253] 1. User terminal (terminal)

[1254] 2. Server

[1255] 3. Database (contained in the server)

[1256] 4. Emotion engine (included in the server)

[1257] Program processing

[1258] 1. Receiving user input

[1259] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1260] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[1261] Open the user chat window and enter your "ideas" or "what you want to do" in text format.

[1262] Terminal: Sends the entered text data to the server.

[1263] 2. Analysis using natural language processing

[1264] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[1265] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[1266] 3. Emotion Recognition by Emotion Engine

[1267] Server: Based on the analyzed text data, an emotion engine is used to identify the user's emotions.

[1268] Server: Generates an appropriate response to the user based on the emotion recognition results.

[1269] 4. Database Search

[1270] Server: Searches the database based on analyzed keywords and context.

[1271] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[1272] 5. Notification of Matching Results

[1273] Server Generates a list of identified departments and personnel and notifies the user of that information.

[1274] Terminal: Displays the notified information to the user.

[1275] 6. User Indications and Processing

[1276] User If a user wishes to contact a representative, they express their desire in the chat window.

[1277] Terminal: Sends the user's intention to the server.

[1278] 7. Information sharing and meeting arrangements

[1279] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[1280] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1281] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[1282] Specific examples

[1283] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[1284] 1. User A's input

[1285] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[1286] The terminal sends this input to the server.

[1287] 2. Analysis and Emotion Recognition

[1288] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[1289] Server: Using the emotion engine, identify the emotion from User A's text and determine that it is "high urgency."

[1290] 3. Database Search and Matching

[1291] The server searches the database to identify the digital marketing department and person B.

[1292] 4. Notice and Indication of Intention

[1293] Server: Notifies user A of the information of identified person B.

[1294] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[1295] The terminal sends this intention to the server.

[1296] 5. Information sharing and meeting arrangements

[1297] Server: Notifies user A's request to person in charge B and coordinates with them.

[1298] Server: Checks both parties' schedules and suggests suitable meeting times.

[1299] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[1300] This embodiment allows users to quickly and efficiently find the person with the expertise they need, receive a response that is sensitive to their emotions, and collaborate effectively.

[1301] The processing flow will be explained below.

[1302] Step 1: Receiving User Input

[1303] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1304] Server Receives the authentication information and checks it against a database to determine whether the authentication succeeds or fails.

[1305] Server If authentication is successful, redirect the user to the system's main screen.

[1306] User Open a chat window from the main screen and enter text about what you want to do or your ideas.

[1307] Terminal: Sends the entered text data to the server.

[1308] Step 2: Natural Language Processing Analysis

[1309] Server Analyzes the received text data using natural language processing (NLP) technology.

[1310] Server Extracts important keywords and context from text data.

[1311] Server: Converts the extracted keywords and context into a specific data format.

[1312] Step 3: Emotion recognition by the emotion engine

[1313] Server: Data analyzed by natural language processing is passed to the emotion engine.

[1314] The server emotion engine identifies user emotions from words and phrases in the text data.

[1315] Server: Based on the emotion recognition results, the server identifies the user's emotional state and generates an appropriate response.

[1316] Step 4: Database Search

[1317] Server Searches the database based on analyzed keywords and context.

[1318] The server database identifies the department and person having the relevant work content, skill set, and person information.

[1319] Step 5: Notification of match results

[1320] Generate a list of identified departments and personnel from the results of the server database search.

[1321] The server notifies the user of the generated list.

[1322] Device Notifications are displayed on user devices so that specific departments and personnel can view them.

[1323] Step 6: User Indication and Processing

[1324] If the user wishes to contact the identified person, he / she expresses his / her intention in the chat window.

[1325] Terminal: Sends the user's intention to the server.

[1326] Step 7: Share information and schedule a meeting

[1327] Server: Receives the user's intentions and shares the necessary information (such as the user's request, emotional state, and contact information) with the appropriate person or department.

[1328] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1329] The server may also adjust the urgency of the meeting depending on the emotional state of the user.

[1330] Server Notifies the user and contact person of the confirmed meeting date and time and link.

[1331] By following these steps, users can quickly and appropriately find the resources they need and receive emotionally sensitive support. For example, if a user types "urgent," the system uses its emotion engine to determine the level of urgency and prioritize the response.

[1332] Example 2

[1333] 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."

[1334] With conventional systems, when users communicate their "ideas" or "things they want to do," it is difficult to properly understand them and quickly find the most appropriate department or person in charge. Furthermore, because the system does not take into account the user's feelings, it is unable to respond appropriately, making efficient communication and collaboration difficult.

[1335] 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.

[1336] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing and extracting keywords and context, means for identifying the user's emotions using an emotion recognition engine and generating an appropriate response based on the results, means for searching a database based on the analyzed keywords and context and identifying the most appropriate department or person in charge, means for notifying the user of a list of the identified departments and persons in charge, and means for sharing information with the other party based on the user's intention, adjusting both parties' schedules, and setting a meeting date and time. This makes it possible to quickly and efficiently understand the user's intentions and emotions and take the most appropriate response.

[1337] A "user" is an individual or corporation that accesses the system, inputs their "ideas" or "things they want to do," and seeks the best possible response.

[1338] "Text data" refers to formatted strings of information that a user inputs into a system.

[1339] "Natural language processing" is a general term for algorithms and methods that allow computers to understand, interpret, and generate human language.

[1340] "Keywords" refer to particularly important words or phrases extracted from the text data entered by the user.

[1341] "Context" refers to information that indicates the situation and intention in which each keyword in the text data is used.

[1342] The "database" is an information repository that stores information such as each department's business operations, skill sets, and staff information.

[1343] "The most suitable department or person" refers to the department or person whose work best suits the user's input and purpose.

[1344] An "emotion recognition engine" is an algorithm or software that identifies emotions from a user's text data and provides the results.

[1345] "Response" refers to the message or action the system returns to the user based on the analysis results.

[1346] "Meeting Date and Time" means a specific date and time set for a face-to-face or online conversation between you and a representative.

[1347] This invention is a system that allows users to input their "ideas" and "things they want to do," quickly finds the most suitable department and person in charge, and recognizes the user's emotions to provide the most appropriate response.The system is composed of the following main components: a user terminal, a server, a database, and an emotion recognition engine.

[1348] Hardware and software used

[1349] User device: A PC or smartphone used by a user to access the system. The web browser used may be Google Chrome or Safari.

[1350] Server: Built in Python using the Django framework. Responsible for user authentication, data processing, and NLP (Natural Language Processing).

[1351] Database: Use an SQL database (e.g., PostgreSQL) to store department, person in charge information, job details, etc.

[1352] Emotion Recognition Engine: Uses the Google Cloud Natural Language API to identify emotions from user text data.

[1353] System Embodiments and Operation

[1354] 1. Accepting user input:

[1355] Users access the system from their own devices and first enter their authentication information on the login screen. If authentication is successful, they are taken to the main screen.

[1356] After logging in, users open a chat window and enter their ideas and what they want to do.

[1357] The entered text data is sent asynchronously to the server using AJAX.

[1358] 2. Natural Language Processing Analysis:

[1359] Server The server analyzes the received text data using a Python natural language processing library (e.g., NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[1360] Server: Extracts keywords, contexts, and phrases from text using techniques such as TF-IDF and Word2Vec.

[1361] 3. Emotion recognition using emotion recognition engine:

[1362] Server: Sends the analyzed text data to the Google Cloud Natural Language API to identify sentiment.

[1363] Server: Receives emotion recognition results (e.g., "positive," "negative," or "neutral") and determines the response content and next processing steps.

[1364] 4. Database Search:

[1365] Server: Search the database (PostgreSQL) using SQLAlchemy based on the analyzed keywords and context.

[1366] The server compares the job content, skill sets, and staff information of each department in the database to identify the most suitable department and staff member.

[1367] 5. Notification of Matching Results:

[1368] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[1369] Terminal Dynamically display notification messages on the user's screen.

[1370] 6. User Indications and Processing:

[1371] User The user selects the person they wish to contact from the notification list and expresses their intention in the chat window.

[1372] The terminal transmits this intention data to the server.

[1373] 7. Information sharing and meeting scheduling:

[1374] Server Receives user intentions, notifies the appropriate person or department, and shares information.

[1375] Server: Checks both schedules and uses the Google Calendar API to suggest the best time and date for the meeting.

[1376] Server Notifies the user and contact person of the confirmed meeting date and time and sets up the meeting.

[1377] Example: Prompt sentence example

[1378] For example, suppose User A is planning a new marketing campaign and needs digital marketing expertise. User A logs in to the system and enters, "I would like to speak to someone with digital marketing expertise." This input is sent to the server, which uses natural language processing and emotion recognition to identify Person B in the digital marketing department. A meeting date and time with Person B is then proposed and finally confirmed.

[1379] In this way, users can quickly and efficiently connect with people with the expertise they need. Examples of prompts for this system include "I'd like to speak with someone in charge of market research for our new product" or "I'd like to discuss sales strategies."

[1380] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1381] Step 1:

[1382] User A user accesses the system from a terminal and first enters authentication information (user name and password) on the login screen. The entered authentication information is sent from the terminal to the server.

[1383] Server The server verifies the received authentication information using the Django framework's user authentication function. If authentication is successful, it generates and returns a URL to redirect the user to the main screen. If authentication fails, it generates an error message.

[1384] If the authentication is successful, the user is redirected to the main screen; if not, an error message is displayed.

[1385] Step 2:

[1386] User The user opens a chat window on the main screen and enters their "what they want to do" or "ideas" in the text box.

[1387] The input text data is acquired from the terminal and sent asynchronously to the server using AJAX. The input here is the user's request or idea.

[1388] Server The server saves the received text data and stores it in memory in its raw form for further processing. The output is the stored text data.

[1389] Step 3:

[1390] Server The server analyzes the received text data using Python's natural language processing library (NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[1391] Input User-entered text data.

[1392] Data processing: Use NLTK to tokenize text, remove stop words, and perform stemming.

[1393] Output Keywords and contexts as analysis results after preprocessing.

[1394] Server Based on the analysis results, TF-IDF and Word2Vec techniques are introduced to extract more important phrases from the text.

[1395] Step 4:

[1396] Server: Sends the text data analyzed in the previous step to the emotion recognition engine (Google Cloud Natural Language API) and receives the results.

[1397] Input: Extracted keywords and context, further analyzed text data.

[1398] Data Calculation Analyze the sentiment of text data using an emotion recognition engine (Google Cloud Natural Language API).

[1399] Output The user's emotion recognition result (e.g., "positive", "negative", "neutral").

[1400] Server Based on the emotion recognition results, it determines the possible actions to take in order to generate the optimal response for the user.

[1401] Step 5:

[1402] Server: Using SQLAlchemy, the PostgreSQL database is searched based on the analyzed keywords and context. This is then compared with the job content, skill sets, and person information of each department in the database to identify the most suitable department and person.

[1403] Input Extracted keywords and context.

[1404] Data Calculation: Database search and matching using SQL queries.

[1405] Output: A list of the most suitable departments and personnel.

[1406] Server: Create a list of identified departments and personnel and prepare for next steps to notify users.

[1407] Step 6:

[1408] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[1409] Input a list of suitable departments and contacts.

[1410] Data processing: Generate notification messages.

[1411] Output Informational message.

[1412] Dynamically display notification messages on the user's screen. A user-friendly interface is achieved using JavaScript.

[1413] Step 7:

[1414] User The user selects the desired contact person from the notification list and expresses their desire to be contacted in the chat window.

[1415] Input User's intention.

[1416] The device sends this data to the server using AJAX asynchronous communication.

[1417] Output User intent data.

[1418] After receiving the server user's intention, it notifies the person in charge and shares the necessary information.

[1419] Step 8:

[1420] Server The server checks the schedules of the user and the person in charge and suggests the optimal meeting date and time using the Google Calendar API.

[1421] Input: User and person in charge schedule information, user intent data.

[1422] Data calculation: Schedule matching and optimal date and time suggestions.

[1423] Output The proposed meeting time.

[1424] Server When the meeting date and time is confirmed, the information is notified to the user and the person in charge and registered in the system.

[1425] The confirmed meeting date and time will be displayed on the user's and the person in charge's device, and a reminder will be sent to the user.

[1426] (Application example 2)

[1427] 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."

[1428] In factories, employees and automated machines often have new ideas or problems, but there is a lack of a way to quickly and effectively resolve them. In particular, the process of finding the appropriate department or person in charge can be time-consuming, resulting in reduced productivity and delays in the realization of ideas. Furthermore, efficient communication becomes difficult if responses are not tailored to the emotions and situations of employees and machines. A system to overcome these challenges is needed.

[1429] 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.

[1430] In this invention, the server includes: means for receiving text data entered by a user; means for analyzing the text data using natural language processing and extracting keywords and context; emotion recognition means for identifying the user's emotion based on the analyzed text data; means for generating an appropriate response to the user based on the emotion recognition results; means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge; means for notifying the user of the list of identified departments and persons in charge; and means for sharing information with the other party based on the user's intention, coordinating schedules, and setting a meeting date and time. This enables quick and efficient resolution of issues and ideas faced by employees and automated machines, and finding the appropriate person or department. Furthermore, emotion recognition allows responses to be appropriately adjusted, improving communication efficiency.

[1431] "Means for receiving text data entered by a user" refers to the equipment or software that allows a user to enter text data into the system and receive that data.

[1432] "Means for analyzing the text data using natural language processing and extracting keywords and context" refers to equipment or software that analyzes input text data using natural language processing technology and extracts important keywords and context from it.

[1433] "Emotion recognition means" refers to algorithms or engines that analyze text data and recognize user emotions from it.

[1434] The "means for generating an appropriate response to the user" refers to equipment or software for generating an optimal response to the user based on the emotion identified by the emotion recognition means.

[1435] "Means for searching the database and identifying the most appropriate department or person" refers to algorithms or engines that search the database based on keywords or context to identify the appropriate department or person.

[1436] "Means for notifying users of a list of identified departments and personnel" refers to devices or software for notifying users of information about departments and personnel identified through a database search.

[1437] "A means of sharing information with the other party based on the user's wishes, adjusting both parties' schedules and setting a meeting date and time" refers to devices and software that share the necessary information with the person in charge based on the user's contact preferences, adjust both parties' schedules and decide the meeting date and time.

[1438] "The work content, skill information, and staff information of each department" refers to the work content of each department, related skill information, and information about the staff in each department that is stored in the database.

[1439] "Each employee and automated machine in the factory" refers to the employees working in the factory and the machines used to automate the work of the factory.

[1440] To implement this invention, a user terminal (such as a smartphone or PC), a server, and a database are required. Using this hardware and software, the following system is constructed.

[1441] Users input text data using their devices. This text data is received using a dedicated application or web interface. This application has an interface that allows users to easily input their "ideas" and "what they want to do."

[1442] The server analyzes the text data received from the user. First, it uses natural language processing (NLP) techniques to analyze the text data and extract keywords and context. For example, natural language processing libraries such as "NLTK" and "spaCy" can be used here.

[1443] Next, an emotion recognition engine identifies the user's emotions based on the analyzed text data. Emotion recognition is important for understanding the user's psychological state and level of urgency. For example, emotion recognition engines such as "IBM Watson" and "Microsoft Azure Emotion Recognition API" can be used here.

[1444] Based on the results of emotion recognition, an appropriate response is generated for the user. This response generation uses a generative AI model (such as GPT-4), making it possible to communicate in appropriate words according to the user's emotions.

[1445] The server then searches a database based on the analyzed keywords and context to identify the most appropriate department and person in charge. The database contains information on each department's work, skills, and personnel. This database search function can be implemented using a search engine such as Elasticsearch.

[1446] The server notifies the user's device of the list of identified departments and contact persons. If the user receives the notification, they can enter their intention to contact the contact person again into the system. Based on this intention, the server adjusts both parties' schedules and sets the date and time of the meeting. This schedule adjustment can be done using tools such as the Google Calendar API or Microsoft Outlook Calendar API.

[1447] Specific examples

[1448] For example, consider the case where a factory employee has an idea for improving the efficiency of a new production line. The employee enters through the application, "I have an idea for improving the efficiency of the production line. I would like to speak with someone in the process control department." The system receives this input data, analyzes it using natural language processing, and extracts important keywords such as "production line," "efficiency," and "idea."

[1449] The emotion recognition engine identifies the employee as having a positive emotion and generates an appropriate response. It then searches the database to identify the process control department and the relevant person in charge. This information is sent to the employee, who then re-enters the system with a request to meet with the person in charge. The server receives this request, coordinates both parties' schedules, sets a date and time for the meeting, and notifies the employee and the person in charge.

[1450] Prompt Sentence Examples

[1451] "I have an idea for improving the efficiency of our new production line and would like to speak with someone about it. Which department would be the best fit and who should I contact?"

[1452] In this way, a system is created to quickly and efficiently solve problems and ideas from employees and automated machines.

[1453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1454] Step 1:

[1455] The user accesses the system from a user terminal and enters authentication information on the login screen.

[1456] Input: Authentication information (username, password)

[1457] Output: Authentication result (success / failure)

[1458] How it works: The user device sends authentication information to the server, which verifies it by checking its database. If authentication is successful, the user is redirected to the main screen.

[1459] Step 2:

[1460] Users open a chat window on the main screen and enter their "ideas" and "what they want to do" in text format.

[1461] Input: Text request (e.g., "I have an idea for improving the efficiency of our production line. I'd like to speak with someone in the process control department.")

[1462] Output: Text data

[1463] Operation: The user's device sends the entered text data to the server.

[1464] Step 3:

[1465] The server analyzes the received text data using natural language processing (NLP) technology.

[1466] Input: Text data

[1467] Output: Parsed data (keywords, context)

[1468] How it works: The server uses NLP libraries such as "NLTK" and "spaCy" to parse the text data and extract keywords and context.

[1469] Step 4:

[1470] The server uses an emotion recognition engine to identify the user's emotion based on the analyzed data.

[1471] Input: Parsed data (keywords, context)

[1472] Output: Emotion recognition result (user's emotional state)

[1473] How it works: The server uses IBM Watson or Microsoft Azure Emotion Recognition API to identify emotions and retrieve the results.

[1474] Step 5:

[1475] The server generates an appropriate response to the user based on the emotion recognition results.

[1476] Input: Emotion recognition result (user's emotional state)

[1477] Output: Response message

[1478] How it works: The server uses a generative AI model (e.g., GPT-4) to generate an appropriate response message.

[1479] Step 6:

[1480] The server searches the database based on the analyzed keywords and context to identify the most appropriate department or person in charge.

[1481] Input: Parsed data (keywords, context)

[1482] Output: Best fit department and contact information

[1483] How it works: The server uses a search engine such as Elasticsearch to search the database and identify the appropriate department or person.

[1484] Step 7:

[1485] The server notifies the user of a list of identified departments and personnel.

[1486] Input: Best department and contact information

[1487] Output: Information message

[1488] Operation: The server sends the identified department and person in charge information to the user's terminal and notifies the user.

[1489] Step 8:

[1490] If the user wishes to contact the person in charge, he or she expresses this desire in the chat window.

[1491] Input: Indication of your desire to be contacted

[1492] Output: Intention data

[1493] Operation: The user device sends the intent data to the server.

[1494] Step 9:

[1495] The server receives the user's intentions and shares the necessary information with the appropriate person or department.

[1496] Input: Expression of Intention Data

[1497] Output: Information sharing content

[1498] How it works: The server notifies the person or department in charge of the user's request and contact information.

[1499] Step 10:

[1500] The server checks the schedules of the person in charge and the user and suggests the best time and date for the meeting.

[1501] Input: Both parties' schedule information

[1502] Output: Meeting time proposal

[1503] How it works: The server uses the Google Calendar API or Microsoft Outlook Calendar API to coordinate schedules and suggest suitable meeting times.

[1504] Step 11:

[1505] After the meeting date and time is confirmed, the server notifies the user and the person in charge of the information.

[1506] Input: Confirmed meeting date and time

[1507] Output: Information message

[1508] What it does: The server notifies the user and contact of the confirmed meeting date and time.

[1509] In this way, by performing specific actions at each step, users can quickly and efficiently find the appropriate department or person in charge, receive appropriate responses based on their feelings, and set up necessary contacts and meetings.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] [Fourth embodiment]

[1514] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1515] 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.

[1516] 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).

[1517] 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.

[1518] 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.

[1519] 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).

[1520] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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.

[1526] 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."

[1527] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable department and person in charge. Specific program processing and its implementation will be described below.

[1528] System configuration

[1529] This system consists of the following main components:

[1530] 1. User terminal (terminal)

[1531] 2. Server

[1532] 3. Database (contained in the server)

[1533] Program processing

[1534] 1. Receiving user input

[1535] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1536] Server Validates the credentials and redirects the user to the main screen if successful.

[1537] Open the user chat window and enter what you want to do or your ideas in text format.

[1538] Terminal: Sends the entered text data to the server.

[1539] 2. Analysis using natural language processing

[1540] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[1541] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[1542] 3. Database Search

[1543] Server: Searches the database based on analyzed keywords and context to identify the most appropriate department or person.

[1544] Database: The work content, skill sets, and staff information of each department are stored, and searches are performed based on this information.

[1545] 4. Notification of Matching Results

[1546] Server Generates a list of the most suitable departments and personnel and notifies the user.

[1547] Terminal: Displays the notified information to the user.

[1548] 5. Information sharing and meeting arrangements

[1549] User If a user wishes to contact a representative, they express their desire in the chat window.

[1550] Terminal: Sends the user's intention to the server.

[1551] Server Receives user requests and shares the necessary information (such as the user's request content and contact information) with the appropriate person or department.

[1552] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1553] Server Notifies both parties of the confirmed meeting date and time and link.

[1554] Specific examples

[1555] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[1556] 1. User A's input

[1557] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[1558] The terminal sends this input to the server.

[1559] 2. Analysis and Matching

[1560] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[1561] The server searches the database to identify the digital marketing department and person B.

[1562] 3. Notice and Indication of Intention

[1563] Server: Notifies user A of the information of identified person B.

[1564] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[1565] The terminal sends this intention to the server.

[1566] 4. Information sharing and meeting arrangements

[1567] Server: Notifies user A's request to person in charge B and coordinates with them.

[1568] Server: Checks both parties' schedules and suggests suitable meeting times.

[1569] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[1570] This embodiment allows users to quickly and efficiently find the people with the expertise they need for effective collaboration.

[1571] The processing flow will be explained below.

[1572] Step 1: Receiving User Input

[1573] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1574] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[1575] Open the user chat window and enter what you want to do or your ideas in text format.

[1576] Terminal: Sends the entered text data to the server.

[1577] Step 2: Natural Language Processing Analysis

[1578] Server Analyzes text data received from users using natural language processing (NLP) technology.

[1579] Server As part of the analysis, important keywords and context are extracted from the text data.

[1580] Step 3: Database search

[1581] Server: Searches the database based on analyzed keywords and context.

[1582] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[1583] Step 4: Notification of match results

[1584] Server Generates a list of identified departments and personnel and notifies the user of that information.

[1585] Terminal: Displays the notified information to the user.

[1586] Step 5: User Indication and Processing

[1587] User If the user wishes to contact the person in charge, he / she will indicate this in the chat window.

[1588] Terminal: Sends the user's intention to the server.

[1589] Step 6: Share information and schedule a meeting

[1590] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[1591] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1592] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[1593] Through these steps, users can efficiently and quickly find the person with the expertise and schedule a meeting.

[1594] Example 1

[1595] 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."

[1596] In today's business environment, users need to be able to quickly and accurately find the right department or person to help them realize their goals or ideas. However, in many companies, department and person information is scattered, making it difficult for users to contact the person with the appropriate knowledge. The purpose of this invention is to solve this problem and make the process of achieving users' goals smoother.

[1597] 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.

[1598] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing to extract keywords and context, means for searching a data storage device based on the analyzed keywords and context to identify the most suitable organization or person in charge, means for notifying the user of a list of the identified organizations or persons in charge, and means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a meeting date and time. This enables the user to quickly and accurately find the appropriate person or department in charge and achieve effective collaboration.

[1599] "User" refers to the person who accesses the system and inputs their "ideas" and "what they want to do."

[1600] "Text data" refers to character string data that a user inputs into the system and that the server receives for processing.

[1601] "Natural language processing" refers to the technology of analyzing text data and extracting keywords, context, and important phrases.

[1602] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[1603] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[1604] "Data storage device" refers to storage that stores information such as each department's business operations, skill sets, and staff information.

[1605] "Organization" refers to a department or team responsible for a particular task or project.

[1606] "Rep" refers to an individual with expertise in a particular task or field who responds to user requests.

[1607] "List" refers to a list of organizations or personnel that the server generates as a search result and notifies the user.

[1608] "Notification" refers to the act of sending list information from the server to the user.

[1609] "Expression of intent" refers to the act of a user expressing their intention to contact or meet with a person in charge in a chat window.

[1610] "Information sharing" refers to the process of communicating user requests and contact information to relevant personnel or organizations.

[1611] "Schedule adjustment" refers to the act of the server checking the schedules of the user and the person in charge and proposing a date and time for the meeting.

[1612] "Meeting Date and Time" means the date and time set for a contact or meeting between the User and the Person in Charge.

[1613] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" and quickly find the most suitable organization and person in charge. Specific embodiments of the invention are described below.

[1614] This system consists of the following main components:

[1615] 1. User terminal (terminal)

[1616] 2. Server

[1617] 3. Data storage device (contained in the server)

[1618] 1. Receiving user input

[1619] The user accesses the system from their own terminal and enters their user ID and password on the login screen. The server then receives the authentication information, refers to the database, and if authentication is successful, redirects the user to the main screen.

[1620] The user opens the chat window on the main screen and enters their "ideas" or "what they want to do" in text format. For example, they might enter "I'd like to discuss a new marketing campaign." The device then sends the entered text data to the server. Specifically, it uses a POST request to transfer the text data to the API endpoint.

[1621] 2. Analysis using natural language processing

[1622] The server receives the text data sent from the device. Then, it calls the natural language processing (NLP) engine and starts analysis. The analysis engine used here uses the Google Cloud NLP API.

[1623] As a result of the analysis, the server extracts keywords, contexts, and important phrases from the text, such as "marketing campaign," "consultation," and "digital marketing."

[1624] 3. Database Search

[1625] The server searches the data storage device based on the analyzed keywords and context. Specifically, it generates an SQL query based on the keywords and sends it to the data storage device.

[1626] This data storage device stores information on the work content, skill sets, and personnel of each organization, and the search results identify the most suitable department and personnel. For example, Person B, who belongs to the "Digital Marketing" department, is identified.

[1627] 4. Notification of Matching Results

[1628] The server generates a list of the most suitable organizations and contacts obtained as search results, converts them into a data format (such as JSON) for notifying the user, and then sends the generated contact list to the terminal. For example, the response may be JSON data including the name, job title, and contact information of contact B.

[1629] The terminal handles the received list of agents and displays it to the user. Specifically, it displays the information of agent B in the chat window and asks the user to confirm it.

[1630] 5. Information sharing and meeting arrangements

[1631] If a user wishes to contact a person in charge, they express their intention in the chat window. For example, they might type, "I would like to talk to Person B." The terminal then sends this intention to the server.

[1632] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B. Specifically, it sends a notification email to the email address of person in charge B. In addition, the server checks the schedules of the user and person in charge B and suggests the optimal date and time for the meeting. For example, it uses the "Google Calendar API" to search for free times for both parties.

[1633] The server notifies the user and person in charge B of the confirmed meeting date and time and a link. An email or chat notification containing the meeting setting link is sent. The device displays the confirmed meeting date and time and the link to the user so that the user can confirm it.

[1634] Specific examples

[1635] Let's say User A is planning a new marketing campaign and needs digital marketing expertise.

[1636] 1. User A's input

[1637] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[1638] The terminal sends this input to the server.

[1639] 2. Analysis and Matching

[1640] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[1641] The server searches the database to identify the digital marketing department and person B.

[1642] 3. Notice and Indication of Intention

[1643] Server: Notifies user A of the information of identified person B.

[1644] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[1645] The terminal sends this intention to the server.

[1646] 4. Information sharing and meeting arrangements

[1647] Server: Notifies user A's request to person in charge B and coordinates with them.

[1648] Server: Checks both parties' schedules and suggests suitable meeting times.

[1649] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[1650] This embodiment allows users to quickly and efficiently find people with the expertise they need for effective collaboration.

[1651] Prompt Sentence Examples

[1652] For example, a prompt that a user could input into a generative AI model might be something like, "I'm planning a new marketing campaign and would like contact information for someone with expertise in digital marketing."

[1653] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1654] Step 1:

[1655] The user accesses the system from their own terminal and enters their user ID and password on the login screen.

[1656] The terminal sends the entered authentication information to the server.

[1657] The server checks the received authentication information against its database and, if authentication is successful, redirects the user to the main screen.

[1658] Specific action: Runs an SQL query against the database to check whether the user ID and password match.

[1659] Step 2:

[1660] Users open a chat window on the main screen and enter their ideas or what they want to do in text format. For example, they might enter, "I'd like to discuss a new marketing campaign."

[1661] The terminal sends this input text data to the server.

[1662] Specific operation: Use a POST request to send text data in JSON format to an API endpoint.

[1663] Step 3:

[1664] The server receives the text data sent from the device and inputs it into a natural language processing (NLP) engine, calling the Google Cloud NLP API.

[1665] The server uses NLP analysis to extract keywords, context, and important phrases from the text data.

[1666] Specific operation: Extract keywords such as "marketing campaign," "consultation," and "digital marketing" from the response obtained by calling the API.

[1667] Step 4:

[1668] The server searches for information in the data store based on the analyzed keywords and context.

[1669] The server generates an SQL query based on the analyzed keywords and queries the data storage device, for example, to search for people in the "Digital Marketing" department.

[1670] The data storage device stores information about each organization's business, skillsets, and personnel. Search results return the most suitable departments and personnel.

[1671] What it does: Runs an SQL query to get a list of people in the "Digital Marketing" department.

[1672] Step 5:

[1673] The server generates a list of the most suitable organizations and contacts found as search results and converts them into a data format (such as JSON) for notifying the user.

[1674] The server sends the generated list of contacts to the terminal, and returns JSON data including the contact name, job title, and contact information as a response.

[1675] The terminal displays the received list of personnel to the user.

[1676] Specific actions: Display information about Representative B in the chat window and ask the user to confirm.

[1677] Step 6:

[1678] If a user wishes to contact a representative, they can express their intention in the chat window, for example by typing, "I would like to speak with Representative B."

[1679] The terminal transmits the user's intention to the server.

[1680] The server receives the user's request and shares the necessary information (request content and contact information) with the relevant person in charge B.

[1681] Specific action: Send a notification email to the email address of person in charge B.

[1682] Step 7:

[1683] The server checks the schedules of the user and person B and proposes the best time to meet. For example, it uses the Google Calendar API to search for available times for both parties.

[1684] The server notifies the user and person in charge B of the confirmed meeting date and time and link.

[1685] The device will display the confirmed meeting date and time and a link to the user.

[1686] What happens: A message appears in the chat window with a link to set up a meeting.

[1687] These steps allow users to quickly and efficiently find the people with the expertise they need and collaborate effectively.

[1688] (Application example 1)

[1689] 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."

[1690] To improve work efficiency in conventional factories, it is necessary to quickly identify the optimal work procedures and the robots responsible. However, on factory floors, determining which robots and processes are most appropriate takes time and effort, making efficient automation difficult. Furthermore, it is increasingly difficult for workers without specialized knowledge or experience to make optimal decisions. A new system is needed to solve these problems and significantly improve factory work efficiency.

[1691] 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.

[1692] In this invention, the server includes: means for receiving text data input by a user; means for analyzing the text data using natural language processing and extracting keywords and context; means for searching a database based on the analyzed keywords and context to identify the most appropriate departments and workers; means for notifying the user of a list of the identified departments and workers; means for sharing information with the other party based on the user's intention and adjusting both parties' schedules to set a date and time for a work meeting; means for notifying the user and worker of the date and time of the work meeting through a process of proposing, confirming, and finalizing the date and time; and means for analyzing the user's text input and using a generative AI model to identify the most appropriate factory robots and work processes. This allows workers to automatically identify the most appropriate work procedures using the generative AI model simply by entering text, thereby enabling quick and efficient improvement of work efficiency.

[1693] "Users" refer to factory workers and managers who use the system to optimize work procedures and robots.

[1694] "Text data" refers to the written information regarding work procedures and improvements that users enter into the system.

[1695] "Natural language processing" refers to the general technology of analyzing text data to extract keywords and context.

[1696] "Keywords" refer to important words and phrases extracted from text data using natural language processing.

[1697] "Context" refers to information that indicates the meaning and relationships of keywords within text data.

[1698] A "database" refers to a collection of information that stores each department's work content, skill sets, and worker information.

[1699] A "department" refers to a department within a factory that is responsible for a specific task or operation.

[1700] "Worker" refers to an individual person or robot that is responsible for a specific task or job within a factory.

[1701] "List" refers to a list of information about identified departments and workers.

[1702] "Notification" refers to the act of conveying information about identified departments or workers to users.

[1703] "Expression of intent" refers to an action by a user that indicates to the system that they wish to take some action.

[1704] "Information sharing" refers to the act of sharing information related to a specified department or worker based on the user's expressed intention.

[1705] "Schedule adjustment" refers to the act of adjusting the schedules of the user and the worker to set the optimal meeting date and time.

[1706] "Work meeting" refers to a meeting regarding work to be performed by a user and an identified worker.

[1707] A "generative AI model" refers to a machine learning model that analyzes user text input and identifies the optimal factory robots and work processes.

[1708] The present invention is a system for improving work efficiency at a factory site, and by having the user input "what they want to do" and "points for improvement," it is possible to quickly identify the optimal work procedure and the robot in charge. Specific embodiments for realizing this system are described below.

[1709] System configuration

[1710] The system consists of the following main components:

[1711] 1. User device (smartphone, tablet, PC, etc.)

[1712] 2. Server

[1713] 3. Database (contained in the server)

[1714] Program processing

[1715] Text data entered from the user's device is first received by the server, which then analyzes the text data using natural language processing (NLP) techniques to extract keywords and context. This analysis uses NLP libraries and APIs such as "SpaCy" and "NLTK."

[1716] Based on the extracted keywords and context, the server searches a database to identify the most suitable department and robot. The database stores information about each department's work, skill sets, and robots, and searches are performed based on this information. This search is performed using a powerful search engine such as Elasticsearch.

[1717] The server then notifies the user of the list of identified departments and robots via a chat window or notification function on the user's device. If the user wishes to contact a person in charge or a robot, they send that request to the server.

[1718] The server receives the user's request and shares the information with the appropriate department or robot. It then checks the schedules of both parties and sets the optimal meeting date and time. The server notifies both the user and the person in charge of the confirmed meeting date and time and the link. This process is automated using the Google Calendar API and Microsoft Graph API.

[1719] Specific examples

[1720] For example, User A enters "I want to improve the efficiency of assembly work" into the system from their terminal. The server receives this text data and uses NLP technology to extract "assembly work" and "efficiency improvement" as keywords.

[1721] Next, the server searches the database and identifies the "Assembly Department" as the most suitable department and "Robot B" as the most suitable robot. This result is notified to User A, who then expresses his / her desire to be contacted by Robot B.

[1722] The server receives this, informs Robot B of its role as the person in charge, checks the schedules of both parties, and sets up a meeting. The set meeting date and time and related links are notified to User A and Robot B.

[1723] Example prompt sentence:

[1724] "I want to improve the efficiency of my assembly work. Which robot is best?"

[1725] This invention allows workers to simply input text and use a generative AI model to identify optimal work procedures, enabling faster and more efficient improvements to work efficiency.

[1726] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1727] Step 1:

[1728] A user accesses the system from a terminal, enters authentication information on the login screen, and the server receives and authenticates it. The input is a username and password, and if authentication is successful, the user is redirected to the main screen. The output is the authentication success or failure status.

[1729] Step 2:

[1730] The user opens a chat window and enters "what they want to do" and "what they want to improve" in text format, and the device sends this text data to the server. The input is the specific work content and desired improvements, and the output is the transmission of text data to the server.

[1731] Step 3:

[1732] The server receives the input text data and analyzes it using NLP technology. Specifically, it segments the text data and extracts important keywords and contexts. The input is text data, and the output is a list of keywords and contexts.

[1733] Step 4:

[1734] The server searches a database based on the analyzed keywords and context. The database stores the work content, skill sets, and robot information of each department, and by comparing this, it identifies the most suitable department and robot. The input is a list of keywords and context, and the output is a list of the most suitable departments and robots.

[1735] Step 5:

[1736] The server notifies the user of the list of identified departments and robots. The information is conveyed via a chat window or notification function on the user's device. The input is a list of optimal departments and robots, and the output is a notification message to the user.

[1737] Step 6:

[1738] When a user wishes to contact a person in charge or a robot, that intention is sent to the server from the chat window. The input is the user's intention, and the output is the transmission of the intention to the server.

[1739] Step 7:

[1740] The server receives the user's intention and shares the information with the corresponding department or robot. Next, it checks the schedules of both parties and sets the optimal meeting date and time. The input is the user's intention and the schedules of both parties, and the output is the optimal meeting date and time.

[1741] Step 8:

[1742] The server notifies both the user and the person in charge of the confirmed meeting date and time. The input is the optimal meeting date and time, and the output is a notification message of the meeting date and time.

[1743] Step 9:

[1744] The user and the person in charge meet based on the confirmed meeting date and time. This allows for the provision of specific work procedures and solutions based on the user's desired improvements. The input is the meeting date and time and content, and the output is the improved work procedures and solutions.

[1745] 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.

[1746] The present invention relates to a system that allows users to input their "ideas" and "things they want to do" using the system, quickly find the most suitable department and person in charge, and recognize the user's emotions to optimize the response. Specific program processing and its implementation will be described below.

[1747] System configuration

[1748] This system consists of the following main components:

[1749] 1. User terminal (terminal)

[1750] 2. Server

[1751] 3. Database (contained in the server)

[1752] 4. Emotion engine (included in the server)

[1753] Program processing

[1754] 1. Receiving user input

[1755] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1756] Server Validates the authentication information and redirects the user to the main screen if authentication is successful.

[1757] Open the user chat window and enter your "ideas" or "what you want to do" in text format.

[1758] Terminal: Sends the entered text data to the server.

[1759] 2. Analysis using natural language processing

[1760] Server: Receives the input text data and analyzes it using natural language processing (NLP) technology.

[1761] Server: As a result of the analysis, keywords, context, and important phrases are extracted.

[1762] 3. Emotion Recognition by Emotion Engine

[1763] Server: Based on the analyzed text data, an emotion engine is used to identify the user's emotions.

[1764] Server: Generates an appropriate response to the user based on the emotion recognition results.

[1765] 4. Database Search

[1766] Server: Searches the database based on analyzed keywords and context.

[1767] The server compares the job content, skill set, and staff information of each department in the database to identify the most suitable department and staff member.

[1768] 5. Notification of Matching Results

[1769] Server Generates a list of identified departments and personnel and notifies the user of that information.

[1770] Terminal: Displays the notified information to the user.

[1771] 6. User Indications and Processing

[1772] User If a user wishes to contact a representative, they express their desire in the chat window.

[1773] Terminal: Sends the user's intention to the server.

[1774] 7. Information sharing and meeting arrangements

[1775] Server: Receives the user's intentions and shares the necessary information (such as the user's request and contact information) with the appropriate person or department.

[1776] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1777] Server After the meeting date and time is confirmed, the information is notified to the user and the person in charge.

[1778] Specific examples

[1779] For example, say User A is planning a new marketing campaign and needs digital marketing expertise.

[1780] 1. User A's input

[1781] User User A accesses the system from a terminal and types in the chat window, "I would like to speak to someone with expertise in digital marketing."

[1782] The terminal sends this input to the server.

[1783] 2. Analysis and Emotion Recognition

[1784] Server: Analyzes the received text data using natural language processing and extracts the key keywords "digital marketing" and "expertise."

[1785] Server: Using the emotion engine, identify the emotion from User A's text and determine that it is "high urgency."

[1786] 3. Database Search and Matching

[1787] The server searches the database to identify the digital marketing department and person B.

[1788] 4. Notice and Indication of Intention

[1789] Server: Notifies user A of the information of identified person B.

[1790] Terminal User A receives the notification and indicates his / her intention to speak with Person B.

[1791] The terminal sends this intention to the server.

[1792] 5. Information sharing and meeting arrangements

[1793] Server: Notifies user A's request to person in charge B and coordinates with them.

[1794] Server: Checks both parties' schedules and suggests suitable meeting times.

[1795] Server Notifies user A and person in charge B of the confirmed meeting date and time and sets up the meeting.

[1796] This embodiment allows users to quickly and efficiently find the person with the expertise they need, receive a response that is sensitive to their emotions, and collaborate effectively.

[1797] The processing flow will be explained below.

[1798] Step 1: Receiving User Input

[1799] User The user accesses the system from their own terminal and enters their authentication information on the login screen.

[1800] Server Receives the authentication information and checks it against a database to determine whether the authentication succeeds or fails.

[1801] Server If authentication is successful, redirect the user to the system's main screen.

[1802] User Open a chat window from the main screen and enter text about what you want to do or your ideas.

[1803] Terminal: Sends the entered text data to the server.

[1804] Step 2: Natural Language Processing Analysis

[1805] Server Analyzes the received text data using natural language processing (NLP) technology.

[1806] Server Extracts important keywords and context from text data.

[1807] Server: Converts the extracted keywords and context into a specific data format.

[1808] Step 3: Emotion recognition by the emotion engine

[1809] Server: Data analyzed by natural language processing is passed to the emotion engine.

[1810] The server emotion engine identifies user emotions from words and phrases in the text data.

[1811] Server: Based on the emotion recognition results, the server identifies the user's emotional state and generates an appropriate response.

[1812] Step 4: Database Search

[1813] Server Searches the database based on analyzed keywords and context.

[1814] The server database identifies the department and person having the relevant work content, skill set, and person information.

[1815] Step 5: Notification of match results

[1816] Generate a list of identified departments and personnel from the results of the server database search.

[1817] The server notifies the user of the generated list.

[1818] Device Notifications are displayed on user devices so that specific departments and personnel can view them.

[1819] Step 6: User Indication and Processing

[1820] If the user wishes to contact the identified person, he / she expresses his / her intention in the chat window.

[1821] Terminal: Sends the user's intention to the server.

[1822] Step 7: Share information and schedule a meeting

[1823] Server: Receives the user's intentions and shares the necessary information (such as the user's request, emotional state, and contact information) with the appropriate person or department.

[1824] Check the schedules of the server staff and users and suggest the best time and date for the meeting.

[1825] The server may also adjust the urgency of the meeting depending on the emotional state of the user.

[1826] Server Notifies the user and contact person of the confirmed meeting date and time and link.

[1827] By following these steps, users can quickly and appropriately find the resources they need and receive emotionally sensitive support. For example, if a user types "urgent," the system uses its emotion engine to determine the level of urgency and prioritize the response.

[1828] Example 2

[1829] 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."

[1830] With conventional systems, when users communicate their "ideas" or "things they want to do," it is difficult to properly understand them and quickly find the most appropriate department or person in charge. Furthermore, because the system does not take into account the user's feelings, it is unable to respond appropriately, making efficient communication and collaboration difficult.

[1831] 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.

[1832] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the text data using natural language processing and extracting keywords and context, means for identifying the user's emotions using an emotion recognition engine and generating an appropriate response based on the results, means for searching a database based on the analyzed keywords and context and identifying the most appropriate department or person in charge, means for notifying the user of a list of the identified departments and persons in charge, and means for sharing information with the other party based on the user's intention, adjusting both parties' schedules, and setting a meeting date and time. This makes it possible to quickly and efficiently understand the user's intentions and emotions and take the most appropriate response.

[1833] A "user" is an individual or corporation that accesses the system, inputs their "ideas" or "things they want to do," and seeks the best possible response.

[1834] "Text data" refers to formatted strings of information that a user inputs into a system.

[1835] "Natural language processing" is a general term for algorithms and methods that allow computers to understand, interpret, and generate human language.

[1836] "Keywords" refer to particularly important words or phrases extracted from the text data entered by the user.

[1837] "Context" refers to information that indicates the situation and intention in which each keyword in the text data is used.

[1838] The "database" is an information repository that stores information such as each department's business operations, skill sets, and staff information.

[1839] "The most suitable department or person" refers to the department or person whose work best suits the user's input and purpose.

[1840] An "emotion recognition engine" is an algorithm or software that identifies emotions from a user's text data and provides the results.

[1841] "Response" refers to the message or action the system returns to the user based on the analysis results.

[1842] "Meeting Date and Time" means a specific date and time set for a face-to-face or online conversation between you and a representative.

[1843] This invention is a system that allows users to input their "ideas" and "things they want to do," quickly finds the most suitable department and person in charge, and recognizes the user's emotions to provide the most appropriate response.The system is composed of the following main components: a user terminal, a server, a database, and an emotion recognition engine.

[1844] Hardware and software used

[1845] User device: A PC or smartphone used by a user to access the system. The web browser used may be Google Chrome or Safari.

[1846] Server: Built in Python using the Django framework. Responsible for user authentication, data processing, and NLP (Natural Language Processing).

[1847] Database: Use an SQL database (e.g., PostgreSQL) to store department, person in charge information, job details, etc.

[1848] Emotion Recognition Engine: Uses the Google Cloud Natural Language API to identify emotions from user text data.

[1849] System Embodiments and Operation

[1850] 1. Accepting user input:

[1851] Users access the system from their own devices and first enter their authentication information on the login screen. If authentication is successful, they are taken to the main screen.

[1852] After logging in, users open a chat window and enter their ideas and what they want to do.

[1853] The entered text data is sent asynchronously to the server using AJAX.

[1854] 2. Natural Language Processing Analysis:

[1855] Server The server analyzes the received text data using a Python natural language processing library (e.g., NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[1856] Server: Extracts keywords, contexts, and phrases from text using techniques such as TF-IDF and Word2Vec.

[1857] 3. Emotion recognition using emotion recognition engine:

[1858] Server: Sends the analyzed text data to the Google Cloud Natural Language API to identify sentiment.

[1859] Server: Receives emotion recognition results (e.g., "positive," "negative," or "neutral") and determines the response content and next processing steps.

[1860] 4. Database Search:

[1861] Server: Search the database (PostgreSQL) using SQLAlchemy based on the analyzed keywords and context.

[1862] The server compares the job content, skill sets, and staff information of each department in the database to identify the most suitable department and staff member.

[1863] 5. Notification of Matching Results:

[1864] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[1865] Terminal Dynamically display notification messages on the user's screen.

[1866] 6. User Indications and Processing:

[1867] User The user selects the person they wish to contact from the notification list and expresses their intention in the chat window.

[1868] The terminal transmits this intention data to the server.

[1869] 7. Information sharing and meeting scheduling:

[1870] Server Receives user intentions, notifies the appropriate person or department, and shares information.

[1871] Server: Checks both schedules and uses the Google Calendar API to suggest the best time and date for the meeting.

[1872] Server Notifies the user and contact person of the confirmed meeting date and time and sets up the meeting.

[1873] Example: Prompt sentence example

[1874] For example, suppose User A is planning a new marketing campaign and needs digital marketing expertise. User A logs in to the system and enters, "I would like to speak to someone with digital marketing expertise." This input is sent to the server, which uses natural language processing and emotion recognition to identify Person B in the digital marketing department. A meeting date and time with Person B is then proposed and finally confirmed.

[1875] In this way, users can quickly and efficiently connect with people with the expertise they need. Examples of prompts for this system include "I'd like to speak with someone in charge of market research for our new product" or "I'd like to discuss sales strategies."

[1876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1877] Step 1:

[1878] User A user accesses the system from a terminal and first enters authentication information (user name and password) on the login screen. The entered authentication information is sent from the terminal to the server.

[1879] Server The server verifies the received authentication information using the Django framework's user authentication function. If authentication is successful, it generates and returns a URL to redirect the user to the main screen. If authentication fails, it generates an error message.

[1880] If the authentication is successful, the user is redirected to the main screen; if not, an error message is displayed.

[1881] Step 2:

[1882] User The user opens a chat window on the main screen and enters their "what they want to do" or "ideas" in the text box.

[1883] The input text data is acquired from the terminal and sent asynchronously to the server using AJAX. The input here is the user's request or idea.

[1884] Server The server saves the received text data and stores it in memory in its raw form for further processing. The output is the stored text data.

[1885] Step 3:

[1886] Server The server analyzes the received text data using Python's natural language processing library (NLTK), performing preprocessing such as tokenization, stop word removal, and stemming.

[1887] Input User-entered text data.

[1888] Data processing: Use NLTK to tokenize text, remove stop words, and perform stemming.

[1889] Output Keywords and contexts as analysis results after preprocessing.

[1890] Server Based on the analysis results, TF-IDF and Word2Vec techniques are introduced to extract more important phrases from the text.

[1891] Step 4:

[1892] Server: Sends the text data analyzed in the previous step to the emotion recognition engine (Google Cloud Natural Language API) and receives the results.

[1893] Input: Extracted keywords and context, further analyzed text data.

[1894] Data Calculation Analyze the sentiment of text data using an emotion recognition engine (Google Cloud Natural Language API).

[1895] Output The user's emotion recognition result (e.g., "positive", "negative", "neutral").

[1896] Server Based on the emotion recognition results, it determines the possible actions to take in order to generate the optimal response for the user.

[1897] Step 5:

[1898] Server: Using SQLAlchemy, the PostgreSQL database is searched based on the analyzed keywords and context. This is then compared with the job content, skill sets, and person information of each department in the database to identify the most suitable department and person.

[1899] Input Extracted keywords and context.

[1900] Data Calculation: Database search and matching using SQL queries.

[1901] Output: A list of the most suitable departments and personnel.

[1902] Server: Create a list of identified departments and personnel and prepare for next steps to notify users.

[1903] Step 6:

[1904] Server Organizes the information of the identified departments and personnel and generates a message to notify the user.

[1905] Input a list of suitable departments and contacts.

[1906] Data processing: Generate notification messages.

[1907] Output Informational message.

[1908] Dynamically display notification messages on the user's screen. A user-friendly interface is achieved using JavaScript.

[1909] Step 7:

[1910] User The user selects the desired contact person from the notification list and expresses their desire to be contacted in the chat window.

[1911] Input User's intention.

[1912] The device sends this data to the server using AJAX asynchronous communication.

[1913] Output User intent data.

[1914] After receiving the server user's intention, it notifies the person in charge and shares the necessary information.

[1915] Step 8:

[1916] Server The server checks the schedules of the user and the person in charge and suggests the optimal meeting date and time using the Google Calendar API.

[1917] Input: User and person in charge schedule information, user intent data.

[1918] Data calculation: Schedule matching and optimal date and time suggestions.

[1919] Output The proposed meeting time.

[1920] Server When the meeting date and time is confirmed, the information is notified to the user and the person in charge and registered in the system.

[1921] The confirmed meeting date and time will be displayed on the user's and the person in charge's device, and a reminder will be sent to the user.

[1922] (Application example 2)

[1923] 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."

[1924] In factories, employees and automated machines often have new ideas or problems, but there is a lack of a way to quickly and effectively resolve them. In particular, the process of finding the appropriate department or person in charge can be time-consuming, resulting in reduced productivity and delays in the realization of ideas. Furthermore, efficient communication becomes difficult if responses are not tailored to the emotions and situations of employees and machines. A system to overcome these challenges is needed.

[1925] 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.

[1926] In this invention, the server includes: means for receiving text data entered by a user; means for analyzing the text data using natural language processing and extracting keywords and context; emotion recognition means for identifying the user's emotion based on the analyzed text data; means for generating an appropriate response to the user based on the emotion recognition results; means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge; means for notifying the user of the list of identified departments and persons in charge; and means for sharing information with the other party based on the user's intention, coordinating schedules, and setting a meeting date and time. This enables quick and efficient resolution of issues and ideas faced by employees and automated machines, and finding the appropriate person or department. Furthermore, emotion recognition allows responses to be appropriately adjusted, improving communication efficiency.

[1927] "Means for receiving text data entered by a user" refers to the equipment or software that allows a user to enter text data into the system and receive that data.

[1928] "Means for analyzing the text data using natural language processing and extracting keywords and context" refers to equipment or software that analyzes input text data using natural language processing technology and extracts important keywords and context from it.

[1929] "Emotion recognition means" refers to algorithms or engines that analyze text data and recognize user emotions from it.

[1930] The "means for generating an appropriate response to the user" refers to equipment or software for generating an optimal response to the user based on the emotion identified by the emotion recognition means.

[1931] "Means for searching the database and identifying the most appropriate department or person" refers to algorithms or engines that search the database based on keywords or context to identify the appropriate department or person.

[1932] "Means for notifying users of a list of identified departments and personnel" refers to devices or software for notifying users of information about departments and personnel identified through a database search.

[1933] "A means of sharing information with the other party based on the user's wishes, adjusting both parties' schedules and setting a meeting date and time" refers to devices and software that share the necessary information with the person in charge based on the user's contact preferences, adjust both parties' schedules and decide the meeting date and time.

[1934] "The work content, skill information, and staff information of each department" refers to the work content of each department, related skill information, and information about the staff in each department that is stored in the database.

[1935] "Each employee and automated machine in the factory" refers to the employees working in the factory and the machines used to automate the work of the factory.

[1936] To implement this invention, a user terminal (such as a smartphone or PC), a server, and a database are required. Using this hardware and software, the following system is constructed.

[1937] Users input text data using their devices. This text data is received using a dedicated application or web interface. This application has an interface that allows users to easily input their "ideas" and "what they want to do."

[1938] The server analyzes the text data received from the user. First, it uses natural language processing (NLP) techniques to analyze the text data and extract keywords and context. For example, natural language processing libraries such as "NLTK" and "spaCy" can be used here.

[1939] Next, an emotion recognition engine identifies the user's emotions based on the analyzed text data. Emotion recognition is important for understanding the user's psychological state and level of urgency. For example, emotion recognition engines such as "IBM Watson" and "Microsoft Azure Emotion Recognition API" can be used here.

[1940] Based on the results of emotion recognition, an appropriate response is generated for the user. This response generation uses a generative AI model (such as GPT-4), making it possible to communicate in appropriate words according to the user's emotions.

[1941] The server then searches a database based on the analyzed keywords and context to identify the most appropriate department and person in charge. The database contains information on each department's work, skills, and personnel. This database search function can be implemented using a search engine such as Elasticsearch.

[1942] The server notifies the user's device of the list of identified departments and contact persons. If the user receives the notification, they can enter their intention to contact the contact person again into the system. Based on this intention, the server adjusts both parties' schedules and sets the date and time of the meeting. This schedule adjustment can be done using tools such as the Google Calendar API or Microsoft Outlook Calendar API.

[1943] Specific examples

[1944] For example, consider the case where a factory employee has an idea for improving the efficiency of a new production line. The employee enters through the application, "I have an idea for improving the efficiency of the production line. I would like to speak with someone in the process control department." The system receives this input data, analyzes it using natural language processing, and extracts important keywords such as "production line," "efficiency," and "idea."

[1945] The emotion recognition engine identifies the employee as having a positive emotion and generates an appropriate response. It then searches the database to identify the process control department and the relevant person in charge. This information is sent to the employee, who then re-enters the system with a request to meet with the person in charge. The server receives this request, coordinates both parties' schedules, sets a date and time for the meeting, and notifies the employee and the person in charge.

[1946] Prompt Sentence Examples

[1947] "I have an idea for improving the efficiency of our new production line and would like to speak with someone about it. Which department would be the best fit and who should I contact?"

[1948] In this way, a system is created to quickly and efficiently solve problems and ideas from employees and automated machines.

[1949] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1950] Step 1:

[1951] The user accesses the system from a user terminal and enters authentication information on the login screen.

[1952] Input: Authentication information (username, password)

[1953] Output: Authentication result (success / failure)

[1954] How it works: The user device sends authentication information to the server, which verifies it by checking its database. If authentication is successful, the user is redirected to the main screen.

[1955] Step 2:

[1956] Users open a chat window on the main screen and enter their "ideas" and "what they want to do" in text format.

[1957] Input: Text request (e.g., "I have an idea for improving the efficiency of our production line. I'd like to speak with someone in the process control department.")

[1958] Output: Text data

[1959] Operation: The user's device sends the entered text data to the server.

[1960] Step 3:

[1961] The server analyzes the received text data using natural language processing (NLP) technology.

[1962] Input: Text data

[1963] Output: Parsed data (keywords, context)

[1964] How it works: The server uses NLP libraries such as "NLTK" and "spaCy" to parse the text data and extract keywords and context.

[1965] Step 4:

[1966] The server uses an emotion recognition engine to identify the user's emotion based on the analyzed data.

[1967] Input: Parsed data (keywords, context)

[1968] Output: Emotion recognition result (user's emotional state)

[1969] How it works: The server uses IBM Watson or Microsoft Azure Emotion Recognition API to identify emotions and retrieve the results.

[1970] Step 5:

[1971] The server generates an appropriate response to the user based on the emotion recognition results.

[1972] Input: Emotion recognition result (user's emotional state)

[1973] Output: Response message

[1974] How it works: The server uses a generative AI model (e.g., GPT-4) to generate an appropriate response message.

[1975] Step 6:

[1976] The server searches the database based on the analyzed keywords and context to identify the most appropriate department or person in charge.

[1977] Input: Parsed data (keywords, context)

[1978] Output: Best fit department and contact information

[1979] How it works: The server uses a search engine such as Elasticsearch to search the database and identify the appropriate department or person.

[1980] Step 7:

[1981] The server notifies the user of a list of identified departments and personnel.

[1982] Input: Best department and contact information

[1983] Output: Information message

[1984] Operation: The server sends the identified department and person in charge information to the user's terminal and notifies the user.

[1985] Step 8:

[1986] If the user wishes to contact the person in charge, he or she expresses this desire in the chat window.

[1987] Input: Indication of your desire to be contacted

[1988] Output: Intention data

[1989] Operation: The user device sends the intent data to the server.

[1990] Step 9:

[1991] The server receives the user's intentions and shares the necessary information with the appropriate person or department.

[1992] Input: Expression of Intention Data

[1993] Output: Information sharing content

[1994] How it works: The server notifies the person or department in charge of the user's request and contact information.

[1995] Step 10:

[1996] The server checks the schedules of the person in charge and the user and suggests the best time and date for the meeting.

[1997] Input: Both parties' schedule information

[1998] Output: Meeting time proposal

[1999] How it works: The server uses the Google Calendar API or Microsoft Outlook Calendar API to coordinate schedules and suggest suitable meeting times.

[2000] Step 11:

[2001] After the meeting date and time is confirmed, the server notifies the user and the person in charge of the information.

[2002] Input: Confirmed meeting date and time

[2003] Output: Information message

[2004] What it does: The server notifies the user and contact of the confirmed meeting date and time.

[2005] In this way, by performing specific actions at each step, users can quickly and efficiently find the appropriate department or person in charge, receive appropriate responses based on their feelings, and set up necessary contacts and meetings.

[2006] 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.

[2007] 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.

[2008] 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.

[2009] 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.

[2010] 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.

[2011] 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.

[2012] 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).

[2013] 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.

[2014] 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."

[2015] 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.

[2016] 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).

[2017] 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.

[2018] 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.

[2019] 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.

[2020] 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.

[2021] 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.

[2022] 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.

[2023] 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.

[2024] 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.

[2025] 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.

[2026] 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.

[2027] The following is further disclosed regarding the above embodiment.

[2028] (Claim 1)

[2029] means for receiving text data input from a user;

[2030] means for analyzing the text data by natural language processing and extracting keywords and contexts;

[2031] A means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge;

[2032] a means for notifying a user of a list of the identified departments and personnel;

[2033] The system includes a means for sharing information with the other party based on the user's intention, and for coordinating both parties' schedules to set a date and time for the meeting.

[2034] (Claim 2)

[2035] 2. The system according to claim 1, wherein the database includes information on the work content, skill sets, and personnel of each department.

[2036] (Claim 3)

[2037] 2. The system according to claim 1, wherein the date and time of the meeting is notified to the user and the person in charge after going through a process of proposal, confirmation, and confirmation.

[2038] "Example 1"

[2039] (Claim 1)

[2040] means for receiving text data input from a user;

[2041] means for analyzing the text data by natural language processing and extracting keywords and contexts;

[2042] A means for searching a data storage device based on the analyzed keywords and context to identify the most suitable organization or person;

[2043] means for notifying a user of the list of identified organizations or personnel;

[2044] The system includes a means for sharing information with the other party based on the user's intention, and adjusting both parties' schedules to set a date and time for the meeting.

[2045] (Claim 2)

[2046] 2. The system according to claim 1, wherein the data storage device includes information on the business operations, skill sets, and personnel of each organization.

[2047] (Claim 3)

[2048] 2. The system according to claim 1, wherein the date and time of the meeting is notified to the user and the person in charge after going through a process of proposal, confirmation, and confirmation.

[2049] "Application Example 1"

[2050] (Claim 1)

[2051] means for receiving text data input from a user;

[2052] means for analyzing the text data by natural language processing and extracting keywords and contexts;

[2053] A means for searching a database based on the analyzed keywords and context to identify the most suitable department and worker;

[2054] a means for notifying a user of a list of the identified departments and workers;

[2055] a means for sharing information with the other party based on the user's intention and adjusting the schedules of both parties to set a date and time for a work meeting;

[2056] The system includes a means for notifying users and workers of the date and time of the work meeting through a process of proposing, confirming, and confirming the date and time.

[2057] (Claim 2)

[2058] 2. The system according to claim 1, wherein the database includes information on the work content, skill sets, and workers of each department.

[2059] (Claim 3)

[2060] The system of claim 1, further comprising a generative AI model that analyzes user text input and identifies optimal factory robots and work processes.

[2061] "Example 2: Combining Emotion Engines"

[2062] (Claim 1)

[2063] means for receiving text data input from a user;

[2064] means for analyzing the text data by natural language processing and extracting keywords and contexts;

[2065] A means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge;

[2066] a means for notifying a user of a list of the identified departments and personnel;

[2067] a means for sharing information with the other party based on the user's intention and adjusting the schedules of both parties to set a date and time for a meeting;

[2068] means for identifying a user's emotion using an emotion recognition engine and generating an appropriate response based on the identified emotion;

[2069] The system further includes a means for dynamically changing response content based on the emotion recognition result.

[2070] (Claim 2)

[2071] 2. The system according to claim 1, wherein the database includes information on the work content, skill sets, and personnel of each department.

[2072] (Claim 3)

[2073] 2. The system according to claim 1, wherein the date and time of the meeting is notified to the user and the person in charge after going through a process of proposal, confirmation, and confirmation.

[2074] "Application example 2 when combining emotion engines"

[2075] (Claim 1)

[2076] means for receiving text data input from a user;

[2077] means for analyzing the text data by natural language processing and extracting keywords and contexts;

[2078] an emotion recognition means for identifying the emotion of the user based on the analyzed text data;

[2079] means for generating an appropriate response to a user based on the emotion recognition result;

[2080] A means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge;

[2081] a means for notifying a user of a list of the identified departments and personnel;

[2082] The system includes a means for sharing information with the other party based on the user's intention, and for coordinating both parties' schedules to set a date and time for the meeting.

[2083] (Claim 2)

[2084] 2. The system according to claim 1, wherein the database includes information on the work content, skills, and personnel of each department.

[2085] (Claim 3)

[2086] 2. The system according to claim 1, further comprising: each employee and automated machine in a factory corresponding to said user and said person in charge. [Explanation of symbols]

[2087] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving text data input from a user; means for analyzing the text data by natural language processing and extracting keywords and contexts; A means for searching a database based on the analyzed keywords and context to identify the most appropriate department or person in charge; a means for notifying a user of a list of the identified departments and personnel; The system includes a means for sharing information with the other party based on the user's intention, and for coordinating both parties' schedules to set a date and time for the meeting.

2. 2. The system according to claim 1, wherein the database includes information on the business operations, skill sets, and personnel of each department.

3. 2. The system according to claim 1, wherein the date and time of the meeting is notified to the user and the person in charge after going through a process of proposal, confirmation, and confirmation.

Images