system

Abstract

We provide the system. [Solution] A means for obtaining the user's calendar information, A method for calculating the optimal meeting time based on acquired calendar information, A means of displaying the proposed meeting time, A means of accepting user approval or modification, A system that includes a means of finalizing the schedule and notifying users.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional personnel interviews, there were problems such as (1) it was difficult for the interviewee to speak their true feelings, (2) it was difficult to secure and adjust appropriate time, and (3) it was difficult to consider actions based on the interview results. As a result, there was a problem that the effect of the interview was limited and productivity was not sufficiently improved.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: A system is constructed that acquires the user's calendar information and calculates the optimal interview time based on the acquired calendar information. The system has a function to display the proposed interview time and accept the user's approval or modification. Furthermore, it includes means to confirm the final schedule and notify the user. In addition, it generates customized questions based on the user's profile and past interview data, and displays the generated questions to the user in chat format. The user's answers are encrypted and stored securely. In addition, the system analyzes the interaction during the interview in real time and generates a summary and action items. The generated summary and action items are displayed to the user and can be modified as needed. In this way, it is possible to streamline the interview process and enhance the user's sense of psychological security.

[0006] "Calendar information" refers to information that shows a user's schedule and availability, and is obtained from the schedule management system.

[0007] The "optimal meeting time" refers to a time slot that is convenient for both parties and allows for an efficient meeting.

[0008] A "question set" is a list of questions used during an interview, generated based on the user's profile and past interview data.

[0009] "Chat format" refers to a text-based dialogue method in which users answer questions using text.

[0010] "Encryption" is a technique that transforms input data using a specific algorithm in order to ensure the security of the data.

[0011] A "summary" is a concise overview of the interview content, used for later reference and action.

[0012] "Action items" refer to specific action items or next steps defined based on the results of the interview.

[0013] "Real-time analysis" is a processing method that performs analysis immediately as soon as data is entered.

[0014] "Profile" refers to detailed personal data, including the user's basic information and past work history. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This system aims to streamline user personnel interviews, ensure the interviewer's psychological comfort, and visualize interview results. The system operates through a combination of server, terminal, and user actions.

[0037] System Configuration

[0038] The main components of the system are as follows:

[0039] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[0040] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[0041] Users: These are the interviewees and interviewers who conduct interviews using the system.

[0042] Program processing

[0043] Retrieving calendar information and scheduling

[0044] The server accesses a scheduling management system (e.g., Google® Calendar, Outlook Calendar) to retrieve each user's calendar information. This calendar information includes the user's appointments and free time.

[0045] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[0046] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[0047] Question set generation

[0048] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[0049] The device displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[0050] Interview progress and data storage

[0051] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[0052] The server securely stores encrypted response data. Simultaneously, it analyzes the response data in real time, extracting important keywords and phrases.

[0053] Analysis and visualization of results

[0054] The server generates interview summaries and action items based on real-time analysis. This clarifies key points at each stage of the interview.

[0055] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[0056] Specific example

[0057] As an example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server suggests the optimal meeting time based on this information. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[0058] In this way, the entire interview process can be streamlined, reducing the burden on the user.

[0059] The following describes the processing flow.

[0060] Step 1:

[0061] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[0062] Step 2:

[0063] The server calculates a mutually convenient meeting time based on the acquired calendar information. It uses an algorithm to analyze overlapping available time slots.

[0064] Step 3:

[0065] The server sends the calculated optimal interview time to the user's device.

[0066] Step 4:

[0067] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[0068] Step 5:

[0069] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[0070] Step 6:

[0071] The device sends user approvals or modifications to the server.

[0072] Step 7:

[0073] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[0074] Step 8:

[0075] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[0076] Step 9:

[0077] The terminal displays the generated set of questions to the user in a chat format.

[0078] Step 10:

[0079] Users enter their answers to questions in a chat format.

[0080] Step 11:

[0081] The terminal encrypts the user's input and sends it to the server.

[0082] Step 12:

[0083] The server receives encrypted response data and stores it securely.

[0084] Step 13:

[0085] The server analyzes the response data in real time and extracts important keywords and phrases.

[0086] Step 14:

[0087] The server generates a summary of the interview and action items based on the extracted data.

[0088] Step 15:

[0089] The server sends the generated summary and action items to the user's device.

[0090] Step 16:

[0091] The device displays a summary and action items to the user.

[0092] Step 17:

[0093] The user reviews the displayed content and adds or corrects it as needed.

[0094] Step 18:

[0095] The terminal sends user additions and modifications to the server.

[0096] Step 19:

[0097] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[0098] (Example 1)

[0099] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] Traditional HR interview systems have faced challenges in efficiency and usability throughout the entire process, from scheduling interviews to conducting them and analyzing the results. Furthermore, they lacked the functionality to analyze interview interactions and provide meaningful feedback, making it difficult for interviewees to conduct interviews in a psychologically reassuring and relaxed environment. Additionally, the lack of sufficient visualization of interview results made it difficult to utilize the information gained from interviews across the entire organization. An effective system is needed to address these challenges.

[0101] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0102] In this invention, the server includes means for acquiring user calendar information, means for calculating the optimal interview time using an information management device that manages the acquired calendar information, means for displaying the calculated interview time on a user interface, means for accepting user approval or modification, means for finalizing the interview schedule and notifying the user, means for providing customized questions using a generation AI model based on user information and past data, means for displaying the generated questions to the user in chat format, means for receiving, encrypting, and storing the user's answers, means for analyzing the user's input data in real time, means for generating a summary and action items based on the analysis results, means for displaying the generated summary and action items to the user and receiving user modifications, and means for storing the final data. This enables increased efficiency in the entire interview process, reduced psychological burden on the user, and visualization of interview results.

[0103] "Calendar information" refers to data related to a user's schedule, such as appointments and free time.

[0104] An "information management device" refers to a system that includes hardware or software for storing, managing, and analyzing acquired calendar information.

[0105] "User interface" refers to the screens and means of operation that users use to interact with a system.

[0106] A "generative AI model" refers to an artificial intelligence model that generates question sets and analysis results based on user information and past data.

[0107] "Chat format" refers to a format in which user interaction is conducted using a text-based interface.

[0108] "Encryption" refers to a technology that transforms data using a specific algorithm to ensure data security and prevent third parties from reading it.

[0109] "Input data" refers to data such as responses and actions that users provide to the system.

[0110] A "summary" refers to a concise compilation of information obtained during an interview.

[0111] "Action items" refer to specific actions or goals that should be taken based on the results of the interview.

[0112] Modes for carrying out the invention

[0113] This system streamlines user personnel interviews, ensures the interviewer's psychological comfort, and provides visibility into interview results. The system operates through a combination of server, terminal, and user actions.

[0114] System Configuration

[0115] The main components of the system are as follows:

[0116] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[0117] Terminal: A device that accepts user input and displays the results (for example, a personal computer or smartphone).

[0118] Users: These are the interviewees and interviewers who conduct interviews using the system.

[0119] Retrieving calendar information and scheduling

[0120] The server accesses each user's schedule management system (e.g., Google Calendar, Outlook Calendar) to retrieve their calendar information. This calendar information includes the user's appointments and free time. Based on the retrieved calendar information, the server calculates a mutually convenient meeting time. The calculation uses an algorithm that analyzes overlapping available time slots. The terminal displays the calculated optimal meeting time in the user interface, allowing the user to approve or modify the proposal.

[0121] Question set generation

[0122] The server generates a customized set of questions using a generative AI model based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user. The terminal displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[0123] Interview progress and data storage

[0124] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server. The server securely stores the encrypted response data and analyzes it in real time to extract important keywords and phrases.

[0125] Analysis and visualization of results

[0126] The server generates a summary and action items for the interview based on real-time analysis. This clarifies key points at each stage of the interview. The terminal displays the generated summary and action items to the user. The user can review them and add or modify them as needed.

[0127] Specific example

[0128] For example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server uses this information to suggest the optimal meeting time. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[0129] Example of a prompt

[0130] "I'd like to schedule a meeting with my supervisor. Please link to Google Calendar and suggest available times on Monday or Wednesday afternoon of next week. Please customize the questions to include those related to my performance over the past six months."

[0131] In this way, the entire system can be made more efficient, and a form that reduces the burden on users can be implemented.

[0132] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0133] Step 1: Retrieve and save calendar information

[0134] The server establishes access to a scheduling management system (e.g., Google Calendar, Outlook Calendar) via OAuth authentication to retrieve each user's calendar information. Using the authentication credentials provided by the user, the server retrieves the calendar information via the API.

[0135] Input: User authentication information

[0136] Output: Retrieved calendar information

[0137] The server stores the retrieved calendar information in a database, organizing and saving the user's schedule and free time.

[0138] Step 2: Calculate and propose meeting time.

[0139] The server calculates the optimal meeting time between users based on stored calendar information. The calculation uses an algorithm that detects overlapping free time (e.g., binary search).

[0140] Input: Calendar information for each user

[0141] Output: Calculated optimal interview time

[0142] The terminal displays the optimal meeting time, sent from the server, in the user interface, allowing the user to approve or modify the suggestion.

[0143] Input: Suggestion for the optimal meeting time

[0144] Output: User approval or modification

[0145] Step 3: Generate the question set

[0146] The server uses user profile information and past interview data to generate a set of questions using a generative AI model. This process utilizes NLP techniques and machine learning models (e.g., GPT-3®).

[0147] Input: User profile information, past interview data

[0148] Output: Customized question set

[0149] The server saves the generated question set to a database and sends it to the terminal at the start of the interview.

[0150] Step 4: Conducting the Interview

[0151] The terminal displays the set of questions received from the server in a chat-style interface. Users can answer the questions in a relaxed, chat-style manner.

[0152] Input: Customized question set

[0153] Output: User's response

[0154] The terminal sends user input to the server in real time. Before transmission, the response data is encrypted.

[0155] Step 5: Saving and analyzing response data

[0156] The server receives encrypted response data and securely stores it in a database. It then uses a text analysis engine (e.g., spaCy) to analyze the response data in real time.

[0157] Input: Encrypted response data

[0158] Output: Analysis results, extracted important keywords and phrases

[0159] Step 6: Generating and displaying results

[0160] The server generates a summary of the meeting and action items based on the analysis results. This includes key points from the meeting and specific goals for the next meeting.

[0161] Input: Analysis results

[0162] Output: Summary and action items

[0163] The device displays the generated summary and action items to the user. The user can review the displayed content and add or modify it as needed.

[0164] Input: Summary and action items

[0165] Output: User modifications

[0166] Step 7: Save final data

[0167] The server receives the changes made by the user and saves them as the final data. This allows the necessary information to be accumulated for the next meeting.

[0168] Input: User's modifications

[0169] Output: Final data

[0170] The above is the specific processing flow of this system.

[0171] (Application Example 1)

[0172] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0173] In modern brick-and-mortar stores, smooth interviews between employees and managers are essential. However, scheduling interviews, providing high-quality feedback, and securely managing data remain challenges. Furthermore, coordination with employee shift and task management is crucial. A new system is needed to streamline these interview processes, ensure employee confidence, and provide data visibility.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0175] This invention includes a server that includes means for acquiring user time management information, means for calculating the optimal interview time based on the acquired time management information, means for displaying the proposed interview time, and an algorithm for confirming the optimal interview time. This enables more efficient interviews and data visualization. Furthermore, it is possible to provide personalized feedback to individual users using means for encrypting data with an encryption library and a generative AI model that generates customized questions. This dramatically improves the interview process between employees and managers in physical stores and enables integration with shift and task management.

[0176] "User" refers to both the person being interviewed and the person conducting the interview using the system.

[0177] "Time management information" refers to calendar information that includes the user's schedule and available time.

[0178] "Optimal meeting time" refers to the most convenient time for a meeting, calculated based on the schedules of both users.

[0179] "Suggested time" refers to the optimal meeting time calculated and presented to the user by the server.

[0180] An "encryption library" refers to a software library used for encrypting data.

[0181] "Customized questions" refer to questions that are generated based on the user's profile and past data, making them specific to each individual user.

[0182] "Chat format" refers to a form of text message exchange that provides a relaxed environment where users can respond comfortably.

[0183] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate question sets and summaries.

[0184] An "algorithm" refers to a set of computational procedures or analytical processes for solving a specific problem.

[0185] "Profile information" refers to data such as the user's personal information and past interview history.

[0186] "Shift information" refers to information about the working hours of employees at physical stores.

[0187] "Task information" refers to information about the tasks and duties that employees at physical stores are supposed to perform.

[0188] "Real-time" means that data acquisition, analysis, and display are performed instantly.

[0189] This system enables efficient interviews between employees and managers in physical stores, ensuring a sense of psychological security while also allowing for data visualization. The specific implementation of this system is described below.

[0190] System Configuration

[0191] The main components of the system are servers, terminals (such as smartphones and tablets), and users (employees and administrators).

[0192] Hardware and software to be used

[0193] Hardware: Smartphones, tablets

[0194] Software: Shift management systems (e.g., Google Calendar API, Outlook Calendar API), data analysis tools (e.g., Python, TENSORFLOW®), encryption libraries (e.g., PyCryptodome)

[0195] Specific methods for carrying out the invention

[0196] Retrieving calendar information and scheduling

[0197] The server accesses scheduling management systems such as Google Calendar and Outlook Calendar to retrieve the user's time management information. This retrieves the user's appointments and availability. Based on this information, the server uses an algorithm to calculate the optimal meeting time and suggests the most convenient time for the user. The suggested time is displayed on the terminal, and the user can review, approve, or modify it.

[0198] Generating a customized set of questions

[0199] The server uses a generative AI model to generate a customized set of questions based on the user's profile information and past conversation data. This ensures that the interview content is tailored to each individual user, promoting more effective feedback and growth. The generated questions are displayed on the device in a chat format.

[0200] Interview progress and data storage

[0201] During the interview, the user answers questions in a chat format. The device receives these answers, encrypts the response data using an encryption library, and sends it to the server. The server securely stores the received data and analyzes the response data in real time using data analysis tools.

[0202] Analysis and visualization of results

[0203] Based on the analysis results, the server generates a summary of the interview and action items. The generated content is displayed on the terminal, allowing the user to review and modify it as needed. This clarifies the key points of the interview and streamlines the entire interview process.

[0204] Specific example

[0205] As an example, consider a meeting between a store manager and an employee at a physical store. The manager and employee register their respective time management information in the system. Based on this information, the server suggests an optimal meeting time, which is displayed on the terminal. During the meeting, the terminal displays questions in a chat format, and the employee answers them. The answers are encrypted and sent to the server. The server analyzes the answers in real time and automatically generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and manager review them and make corrections as needed.

[0206] Example of a prompt

[0207] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[0208] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0209] Step 1:

[0210] The server retrieves users' time management information. Specifically, it integrates with Google Calendar and Outlook Calendar to retrieve users' appointments and free time. This involves using APIs to read each user's calendar information. The input is the user ID, and the output is time management information.

[0211] Step 2:

[0212] The server uses an algorithm to calculate the optimal interview time based on the acquired time management information. This algorithm detects overlaps in users' available time slots and identifies suitable times for interviews. The input is time management information, and the output is the optimal interview time.

[0213] Step 3:

[0214] The server sends the calculated optimal interview time to the terminal, which then displays this information to the user. The user reviews the proposed time on the terminal and approves or modifies it. The input is the optimal interview time, and the output is the time approved or modified by the user.

[0215] Step 4:

[0216] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data. This creates questions tailored to each individual user. The input is profile information and past interview data, and the output is the customized set of questions.

[0217] Step 5:

[0218] The server sends the generated set of questions to the terminal, which then displays the questions to the user in a chat format. The user enters their answers to the questions. The input is the set of questions, and the output is the user's answers.

[0219] Step 6:

[0220] The terminal receives the user's response, encrypts it, and sends it to the server. The server stores the received response data to ensure its security. The input is the user's response, and the output is the encrypted response data.

[0221] Step 7:

[0222] The server analyzes the received response data in real time and extracts important keywords and phrases. Analysis is performed using data analysis tools (e.g., Python, TensorFlow). The input is encrypted response data, and the output is the analysis results.

[0223] Step 8:

[0224] The server generates a summary of the interview and action items based on the analysis results and sends them to the terminal. The terminal displays the generated content to the user, who can review and modify it. The input is the analysis results, and the output is the summary and action items.

[0225] Example of a prompt

[0226] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[0227] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0228] This system incorporates an emotion engine to streamline user HR interviews, ensure psychological reassurance, and visualize interview results. Therefore, it can analyze the interviewee's emotional state in real time and optimize the interview process based on that data. This system operates through a combination of server, terminal, and user actions.

[0229] System Configuration

[0230] The main components of the system are as follows:

[0231] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, question generation, and operation of the sentiment engine.

[0232] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[0233] Users: These are the interviewees and interviewers who conduct interviews using the system.

[0234] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[0235] Program processing

[0236] Retrieving calendar information and scheduling

[0237] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[0238] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[0239] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[0240] Question set generation and emotion engine operation

[0241] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[0242] The device displays the generated set of questions in a chat format.

[0243] The emotion engine analyzes user responses in real time and estimates their emotional state. It extracts emotional parameters from the user's text input, facial expressions, and tone of voice.

[0244] Interview progress and data storage

[0245] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[0246] The server securely stores encrypted response data. Simultaneously, it performs real-time sentiment analysis using an emotion engine and dynamically adjusts the questions and follow-up content based on the results.

[0247] Analysis and visualization of results

[0248] The server generates interview summaries and action items based on real-time analysis. It also analyzes emotional data to generate appropriate feedback based on the emotional state.

[0249] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[0250] Users refer to the sentiment analysis results to consider their future actions and how to conduct the interview. Furthermore, data including emotional states is saved as part of the interview history, which can be used for future interviews.

[0251] Specific example

[0252] As an example, consider a meeting between a supervisor and a subordinate at a certain company. The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, an emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. As a result, the server generates a summary and action items based on the real-time analysis, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[0253] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and more effective feedback and action planning become possible.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[0257] Step 2:

[0258] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[0259] Step 3:

[0260] The server sends the calculated optimal interview time to the user's device.

[0261] Step 4:

[0262] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[0263] Step 5:

[0264] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[0265] Step 6:

[0266] The device sends user approvals or modifications to the server.

[0267] Step 7:

[0268] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[0269] Step 8:

[0270] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[0271] Step 9:

[0272] The terminal displays the generated set of questions to the user in a chat format.

[0273] Step 10:

[0274] The emotion engine analyzes the user's responses to chat-style questions, as well as their facial expressions and tone of voice, to estimate their emotional state in real time.

[0275] Step 11:

[0276] Users enter their answers to questions in a chat format.

[0277] Step 12:

[0278] The terminal encrypts the user's input content and sends it to the server.

[0279] Step 13:

[0280] The server receives the encrypted response data and stores it securely.

[0281] Step 14:

[0282] The emotion engine analyzes the user's emotional state in real time and sends the result to the server.

[0283] Step 15:

[0284] Based on the analyzed emotion data and response data, the server dynamically adjusts the question content and follow-up content.

[0285] Step 16:

[0286] The server generates a summary and action items for the interview based on real-time analysis. It also analyzes the emotion data together and generates appropriate feedback based on the emotional state.

[0287] Step 17:

[0288] The server sends the generated summary and action items to the user's terminal.

[0289] Step 18:

[0290] <00009​​​​​​​​​​​​​

[0294] The terminal sends user additions and modifications to the server.

[0295] Step 21:

[0296] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[0297] (Example 2)

[0298] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0299] Traditional interview and meeting systems require manual processes such as scheduling, question generation, and sentiment analysis, which are time-consuming and labor-intensive. Furthermore, they lack mechanisms to ensure psychological comfort during interviews, making it difficult to promptly understand and respond to participants' emotional states. Simultaneously, the visualization of interview results and follow-ups is insufficient, resulting in a lack of systematically managed data that can be used for future meetings.

[0300] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0301] In this invention, the server includes means for acquiring user schedule information, means for calculating the optimal meeting time based on the acquired schedule information, means for displaying the proposed meeting time, means for accepting user approval or modification, means for confirming the final schedule and notifying the user, means for generating customized questions based on user data and past meeting data, means for displaying the generated questions to the user in a dialogue format, means for receiving, encrypting, and storing the user's answers, means for analyzing the interaction during the meeting in real time, means for performing sentiment analysis, means for generating summaries and action items, means for displaying the generated summaries and action items to the user, means for receiving user modifications and saving the final data, means for analyzing the user's emotional state in real time using a sentiment engine, means for dynamically adjusting the question content and follow-up content based on the analysis results, means for saving the final sentiment analysis results, means for utilizing the sentiment analysis results in the next meeting, means for generating appropriate feedback and action plans from the user's past data using a generative AI model, and means for displaying the generated feedback and action plans to the user. This allows users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to improve future meetings.

[0302] A "user" refers to a person who uses the system to participate in an interview or meeting.

[0303] "Schedule information" refers to data related to a user's schedule, and includes information obtained from calendar applications and other sources.

[0304] "Meeting time" refers to an appropriate date and time for users to conduct interviews or meetings.

[0305] "Schedule adjustment" refers to the process of calculating the optimal meeting time based on the user's schedule information and proposing it to the user.

[0306] "Customized questions" refer to question content individually generated based on user profile information and past interview data.

[0307] "Dialogue form" refers to the form in which the user and the system communicate through questions and answers.

[0308] "Encryption" refers to the technology that protects the user's answer data from being decoded by a third party.

[0309] "Real-time analysis" refers to the process of immediately processing and analyzing data.

[0310] "Sentiment analysis" refers to the process of estimating the emotional state from data such as the user's input, actions, voice, and expressions.

[0311] "Summary" refers to a report that concisely summarizes the content of an interview or meeting.

[0312] "Action item" refers to specific action plans or follow-up items set based on the results of an interview or meeting.

[0313] "Emotion engine" refers to software and algorithms for analyzing the user's input data and estimating the emotional state.

[0314] "Generative AI model" refers to an artificial intelligence model that uses advanced generative algorithms such as GPT-4 (registered trademark) to generate appropriate questions and feedback from data.

[0315] "Feedback" refers to the evaluation and advice provided to the user.

[0316] "Prompt text" refers to the instruction text input into the generative AI model to obtain an answer.

[0317] "Saving" refers to the process of retaining data in a certain format so that it can be reused later.

[0318] This invention is a system designed to improve the efficiency and psychological comfort of interviews and meetings. This system is implemented by combining the actions of a server, terminals, and users.

[0319] System components

[0320] Server: Responsible for key processes such as data acquisition, analysis, scheduling, question generation, and operation of the sentiment engine.

[0321] Terminal: A device (such as a PC, smartphone, or tablet) that accepts user input and displays the results.

[0322] User: The person being interviewed or interviewed using the system for interviews and meetings.

[0323] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[0324] Generative AI models: Used to generate questions and feedback.

[0325] Specific processing of the system

[0326] The server uses APIs such as Google Calendar and Outlook Calendar to retrieve user schedule information. Authentication is performed using OAuth 2.0 for API access. From the retrieved calendar information, the server uses a quicksort algorithm to analyze the overlap of users' free time and calculate the optimal meeting time.

[0327] The terminal displays the calculated optimal meeting time to the user, allowing the user to approve or modify the proposal. For example, the user's terminal displays a scheduling proposal window with approve / modify buttons. Once the schedule is confirmed, the server sends a notification to the user.

[0328] The server generates a customized set of questions based on the user's profile information and past interview data. This generation is performed using a generative AI model (e.g., the GPT-4 model). The generated questions are then displayed to the user in a conversational format using the terminal's chat UI.

[0329] The terminal conducts the interview using the user's chat input, camera images (facial expression analysis), and microphone (voice input). The emotion engine uses OpenCV, TensorFlow, etc., to analyze the user's input text, facial expressions, and voice in real time and estimate their emotional state.

[0330] The generated questions and user responses are encrypted and sent to the server. The server securely stores the data using AES-256 encryption. Meanwhile, real-time analysis results from the sentiment engine are also stored, and the questions and follow-up content are dynamically adjusted based on these results.

[0331] After the meeting concludes, the server generates a summary and action items from the analysis results. Using a generation AI model, the summary and action item list are generated by inputting user responses and sentiment data as prompts. This summary and action items are displayed on the terminal for the user to review and add or modify as needed.

[0332] The final data is saved again and used in the next meeting. By viewing sentiment analysis graphs and past interview data reports within the dashboard, users can gain the insights needed to plan their next actions.

[0333] Specific example

[0334] As an example, consider a meeting between a supervisor and a subordinate at a certain company.

[0335] The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, the emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. Based on the analysis results, the server generates a summary and action items, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[0336] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and effective feedback and action plans become possible.

[0337] Example of a prompt

[0338] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[0339] Based on the details described above, the present invention enables users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to inform future meetings.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Program processing flow

[0342] Step 1: Obtain calendar information and schedule.

[0343] Processing flow

[0344] The server uses the API key provided by the user to access calendar services (e.g., Google Calendar, Outlook Calendar) and retrieve each user's schedule information. Based on the retrieved calendar information, it calculates the optimal meeting time.

[0345] Specific actions

[0346] Input: User's API key, user's schedule information to be retrieved from the calendar service.

[0347] Data Processing: The server accesses the calendar API via OAuth 2.0 authentication to retrieve schedule information. A quicksort algorithm is used to compare each user's schedule and analyze overlapping time slots.

[0348] Output: Suggestions for optimal meeting times based on the availability of both users.

[0349] Step 2: View and approve the proposed meeting time.

[0350] Processing flow

[0351] The terminal displays the optimal meeting time sent from the server to the user. The user can approve or modify the suggested meeting time.

[0352] Specific actions

[0353] Input: Proposed meeting time

[0354] Data processing: The terminal displays a schedule proposal window, and approval / modification buttons are provided.

[0355] Output: User approval or correction response

[0356] Step 3: Confirm and notify the schedule

[0357] Processing flow

[0358] The server will finalize the schedule based on the user's approved or revised meeting times and notify the user.

[0359] Specific actions

[0360] Input: User-approved or modified meeting time

[0361] Data processing: Update confirmed meeting times in the calendar service and generate notification emails and alerts.

[0362] Output: Confirmed schedule and notifications

[0363] Step 4: Generating a customized set of questions

[0364] Processing flow

[0365] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data.

[0366] Specific actions

[0367] Input: User profile information, past interview data

[0368] Data processing: This information is input as prompts into a generative AI model (e.g., GPT-4) to generate unique questions.

[0369] Output: Individualized question list

[0370] Step 5: Displaying questions in an interactive format

[0371] Processing flow

[0372] The terminal displays the generated set of questions to the user in an interactive format. The user answers the questions.

[0373] Specific actions

[0374] Input: Customized list of questions

[0375] Data processing: Provide an interface that displays questions sequentially in the chat UI and accepts user responses.

[0376] Output: User response data

[0377] Step 6: Emotion Analysis

[0378] Processing flow

[0379] The server uses an emotion engine to analyze user responses in real time, including analyzing text input, facial expressions, and voice tone.

[0380] Specific actions

[0381] Input: User text input, camera image (facial expression), audio data

[0382] Data processing: Input data is analyzed using tools such as OpenCV and TensorFlow, and emotion parameters are extracted.

[0383] Output: Emotion analysis results

[0384] Step 7: Saving and analyzing interview results

[0385] Processing flow

[0386] The server encrypts and stores the user's responses and sentiment analysis results, and places them in a database so they can be used in future interviews.

[0387] Specific actions

[0388] Input: User response data, sentiment analysis results

[0389] Data Processing: Securely store data using AES-256 encryption. Perform necessary analysis and list areas for future improvement.

[0390] Output: Encrypted data, analysis report

[0391] Step 8: Summarize and generate action items

[0392] Processing flow

[0393] The server uses a generative AI model based on the interview results and sentiment analysis to generate summaries and action items.

[0394] Specific actions

[0395] Input: User response data, sentiment analysis results

[0396] Data processing: Input this data into a generative AI model to generate a summary and a list of action items.

[0397] Output: Summary, Action Item List

[0398] Step 9: View and correct the results

[0399] Processing flow

[0400] The terminal displays the generated summary and action items to the user, who can add or modify them as needed.

[0401] Specific actions

[0402] Input: Summary text, list of action items

[0403] Data processing: Display the data on the dashboard screen and provide an interface that allows users to edit it.

[0404] Output: User additions and modifications

[0405] Step 10: Save the final data and use it for the next meeting.

[0406] Processing flow

[0407] The server saves the final data and stores sentiment analysis data and action items in a database for use in the next interview.

[0408] Specific actions

[0409] Input: User additions and modifications, sentiment analysis results

[0410] Data processing: Integrate the final data and save it to the database so that it can be referenced during the next meeting.

[0411] Output: Saved final data, reference data for the next meeting

[0412] Examples of prompts to input into a generative AI model

[0413] Example of a prompt:

[0414] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[0415] (Application Example 2)

[0416] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0417] In conventional autonomous vehicles, it has been difficult to understand the emotional state of the passenger in real time and optimize the driving environment based on that understanding. This has led to problems such as passengers experiencing stress and difficulty in ensuring comfort and safety during driving. There is a need to provide a safer and more comfortable autonomous driving experience by appropriately monitoring the passenger's emotional state and adjusting the driving environment accordingly.

[0418] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring the user's calendar information, means for calculating the optimal meeting time based on the acquired calendar information, means for displaying the proposed meeting time, means for accepting the user's approval or modification, means for confirming the final schedule and notifying the user, means for analyzing the user's emotional state in real time using an emotion analysis module, means for automatically generating and providing feedback and advice based on the emotional state to the user, means for adjusting the driving environment and providing appropriate feedback based on the emotion analysis results, and means for safely and comfortably managing the driving situation based on the emotion analysis. This makes it possible to monitor the user's emotional state in real time and provide appropriate feedback and optimize the driving environment.

[0419] "Means for obtaining user calendar information" refers to a function that automatically retrieves user schedule information from online calendars or the organization's internal schedule management system.

[0420] The "method for calculating the optimal meeting time" is an algorithm that calculates the optimal meeting time that suits both parties, based on the acquired calendar information.

[0421] "A means of displaying suggested meeting times" refers to an interface that visually presents the user with calculated meeting time options.

[0422] "Means for accepting user approval or modification" refers to an interface that allows users to approve or modify the proposed meeting time.

[0423] "Means of finalizing the schedule and notifying the user" refers to a function that confirms the meeting time approved or modified by the user and notifies the user of that information.

[0424] The "emotion analysis module" is software that analyzes data such as the user's facial expressions and voice tone to estimate their emotional state in real time.

[0425] "Means for automatically generating feedback and advice based on emotional state" refers to a function that automatically creates and presents appropriate feedback and advice based on the results of an emotion analysis module.

[0426] "Means of adjusting the driving environment and providing appropriate feedback" refers to a function that adjusts the in-car environment (e.g., temperature, music, lighting, etc.) based on emotion analysis results to provide the user with a comfortable and safe driving experience.

[0427] "Means for safely and comfortably managing driving conditions" refers to functions that optimize the vehicle's driving state based on emotion analysis and user feedback, supporting accident prevention and comfortable operation.

[0428] This invention is a system that monitors the user's emotional state in real time within an autonomous vehicle and provides a safer and more comfortable driving experience to the user through appropriate feedback and optimization of the driving environment. The main components of this system are a server, a terminal, a user interface, and an emotion analysis module.

[0429] System Configuration

[0430] 1. Server:

[0431] Retrieving calendar information: Use a Web API to retrieve calendar information from the user's schedule management system.

[0432] Schedule Calculation: Implement an algorithm to calculate optimal meeting times and driving schedules based on acquired calendar information.

[0433] Emotion analysis module: Equipped with software to analyze the user's facial expressions and voice tone to estimate their emotional state.

[0434] Feedback generation: Based on the emotion analysis results, appropriate feedback and advice are generated.

[0435] 2. Terminal:

[0436] Information display: A device (such as an in-car display) that displays schedules and feedback obtained from a server to the user.

[0437] Dialogue Interface: Provides an interface that displays generated questions in a chat format and accepts user responses.

[0438] 3. User:

[0439] Data entry: Enter your own calendar information, profile, responses, etc. into the system.

[0440] Reviewing feedback: Review and apply any feedback or advice provided by the system.

[0441] 4. Emotion Analysis Module:

[0442] Data analysis: Real-time estimation of emotional state based on user facial expressions and tone of voice.

[0443] Data storage: Save the analysis data and use it for later feedback and analysis.

[0444] Hardware and software usage

[0445] Hardware:

[0446] In-vehicle camera: Used to capture the user's facial expressions.

[0447] Microphone: Collects and analyzes user feedback.

[0448] In-car display: Displays analysis results and feedback.

[0449] In-vehicle PC: Performs data analysis and controls the entire system.

[0450] software:

[0451] Emotion Analysis API: Used to analyze facial expressions and tone of voice to estimate emotional states.

[0452] Schedule calculation algorithm: Calculates the optimal schedule based on the user's calendar information.

[0453] Database: Stores analysis results and is used to generate feedback.

[0454] Specific example

[0455] As an example, consider a scenario where a user is riding in an autonomous vehicle. The user's facial expressions and voice data are collected in real time via the vehicle's cameras and microphones. A server's emotion analysis module analyzes this data and detects if the user is experiencing stress. Based on this, the server suggests relaxation music and displays it on the vehicle's display. It also adjusts the air conditioning temperature to provide a comfortable driving environment. The user confirms this feedback and realizes that the driving environment has become more comfortable.

[0456] Example of a prompt

[0457] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[0458] In this way, by providing real-time feedback and environmental adjustments based on emotion analysis, users can obtain a safe and comfortable driving experience.

[0459] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0460] Step 1:

[0461] The server retrieves the user's calendar information. Specifically, it accesses the user's online calendar or schedule management system via a Web API to retrieve upcoming schedule information. The input is the user ID, and the output is calendar information. This calendar information includes the time slots and content of appointments.

[0462] Step 2:

[0463] The server analyzes the acquired calendar information and executes an algorithm to calculate the optimal meeting time that suits both parties. The input is calendar information, and the output is a suggestion for the optimal meeting time. This algorithm cross-references available time slots across different time periods to select the most suitable time frame.

[0464] Step 3:

[0465] The terminal displays the calculated optimal meeting time to the user. The input is a suggested optimal meeting time, and the output is a visual notification to the user. The terminal shows the suggested time to the user through a visual interface and prompts them to approve or revise it.

[0466] Step 4:

[0467] The user approves or modifies the proposed meeting time. The input is the proposed meeting time, and the output is the approved or modified meeting time. The user interacts with the system via a terminal and provides feedback on their schedule.

[0468] Step 5:

[0469] The server confirms the final schedule from the user and notifies the user. The input is the approved or revised meeting time, and the output is a notification of the confirmed meeting schedule. The confirmed schedule is stored in the database for later access.

[0470] Step 6:

[0471] The server analyzes the user's emotional state in real time using an emotion analysis module. Inputs are the user's facial expression and voice data, and output is the result of the emotional state analysis. This data is collected via an in-vehicle camera and microphone and then processed by the emotion analysis algorithm.

[0472] Step 7:

[0473] The server automatically generates appropriate feedback and advice based on the user's emotional state and provides it to the user through the terminal. The input is the result of the emotional state analysis, and the output is feedback and advice, which includes visual notifications and audio announcements.

[0474] Step 8:

[0475] The terminal displays generated questions to the user in a chat format and receives the user's answers. The input is a customized set of questions, and the output is the user's answers. The answers are encrypted and sent to the server.

[0476] Step 9:

[0477] The server stores the user's responses and adjusts the driving environment based on the sentiment analysis results. The input consists of the user's responses and the analysis results of their emotional state, while the output is information on adjusting the driving environment. This includes adjusting the in-car air conditioning, music, and lighting.

[0478] Step 10:

[0479] The server manages driving conditions safely and comfortably based on the results of emotion analysis. Inputs are the results of the emotional state analysis and information on adjusting the driving environment, while output is an optimized driving environment. This allows the user to have a safe and comfortable driving experience.

[0480] Generative AI model prompt example:

[0481] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[0482] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0483] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0484] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0485] [Second Embodiment]

[0486] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0487] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0488] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0489] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0490] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0491] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0492] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0493] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0494] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0495] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0496] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0497] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0498] This system aims to streamline user personnel interviews, ensure the interviewer's psychological comfort, and visualize interview results. The system operates through a combination of server, terminal, and user actions.

[0499] System Configuration

[0500] The main components of the system are as follows:

[0501] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[0502] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[0503] Users: These are the interviewees and interviewers who conduct interviews using the system.

[0504] Program processing

[0505] Retrieving calendar information and scheduling

[0506] The server accesses a scheduling management system (e.g., Google Calendar, Outlook Calendar) to retrieve each user's calendar information. This calendar information includes the user's appointments and free time.

[0507] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[0508] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[0509] Question set generation

[0510] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[0511] The device displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[0512] Interview progress and data storage

[0513] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[0514] The server securely stores encrypted response data. Simultaneously, it analyzes the response data in real time, extracting important keywords and phrases.

[0515] Analysis and visualization of results

[0516] The server generates interview summaries and action items based on real-time analysis. This clarifies key points at each stage of the interview.

[0517] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[0518] Specific example

[0519] As an example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server suggests the optimal meeting time based on this information. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[0520] In this way, the entire interview process can be streamlined, reducing the burden on the user.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[0524] Step 2:

[0525] The server calculates a mutually convenient meeting time based on the acquired calendar information. It uses an algorithm to analyze overlapping available time slots.

[0526] Step 3:

[0527] The server sends the calculated optimal interview time to the user's device.

[0528] Step 4:

[0529] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[0530] Step 5:

[0531] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[0532] Step 6:

[0533] The device sends user approvals or modifications to the server.

[0534] Step 7:

[0535] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[0536] Step 8:

[0537] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[0538] Step 9:

[0539] The terminal displays the generated set of questions to the user in a chat format.

[0540] Step 10:

[0541] Users enter their answers to questions in a chat format.

[0542] Step 11:

[0543] The terminal encrypts the user's input and sends it to the server.

[0544] Step 12:

[0545] The server receives encrypted response data and stores it securely.

[0546] Step 13:

[0547] The server analyzes the response data in real time and extracts important keywords and phrases.

[0548] Step 14:

[0549] The server generates a summary of the interview and action items based on the extracted data.

[0550] Step 15:

[0551] The server sends the generated summary and action items to the user's device.

[0552] Step 16:

[0553] The device displays a summary and action items to the user.

[0554] Step 17:

[0555] The user reviews the displayed content and adds or corrects it as needed.

[0556] Step 18:

[0557] The terminal sends user additions and modifications to the server.

[0558] Step 19:

[0559] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[0560] (Example 1)

[0561] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0562] Traditional HR interview systems have faced challenges in efficiency and usability throughout the entire process, from scheduling interviews to conducting them and analyzing the results. Furthermore, they lacked the functionality to analyze interview interactions and provide meaningful feedback, making it difficult for interviewees to conduct interviews in a psychologically reassuring and relaxed environment. Additionally, the lack of sufficient visualization of interview results made it difficult to utilize the information gained from interviews across the entire organization. An effective system is needed to address these challenges.

[0563] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0564] In this invention, the server includes means for acquiring user calendar information, means for calculating the optimal interview time using an information management device that manages the acquired calendar information, means for displaying the calculated interview time on a user interface, means for accepting user approval or modification, means for finalizing the interview schedule and notifying the user, means for providing customized questions using a generation AI model based on user information and past data, means for displaying the generated questions to the user in chat format, means for receiving, encrypting, and storing the user's answers, means for analyzing the user's input data in real time, means for generating a summary and action items based on the analysis results, means for displaying the generated summary and action items to the user and receiving user modifications, and means for storing the final data. This enables increased efficiency in the entire interview process, reduced psychological burden on the user, and visualization of interview results.

[0565] "Calendar information" refers to data related to a user's schedule, such as appointments and free time.

[0566] An "information management device" refers to a system that includes hardware or software for storing, managing, and analyzing acquired calendar information.

[0567] "User interface" refers to the screens and means of operation that users use to interact with a system.

[0568] A "generative AI model" refers to an artificial intelligence model that generates question sets and analysis results based on user information and past data.

[0569] "Chat format" refers to a format in which user interaction is conducted using a text-based interface.

[0570] "Encryption" refers to a technology that transforms data using a specific algorithm to ensure data security and prevent third parties from reading it.

[0571] "Input data" refers to data such as responses and actions that users provide to the system.

[0572] A "summary" refers to a concise compilation of information obtained during an interview.

[0573] "Action items" refer to specific actions or goals that should be taken based on the results of the interview.

[0574] Modes for carrying out the invention

[0575] This system streamlines user personnel interviews, ensures the interviewer's psychological comfort, and provides visibility into interview results. The system operates through a combination of server, terminal, and user actions.

[0576] System Configuration

[0577] The main components of the system are as follows:

[0578] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[0579] Terminal: A device that accepts user input and displays the results (for example, a personal computer or smartphone).

[0580] Users: These are the interviewees and interviewers who conduct interviews using the system.

[0581] Retrieving calendar information and scheduling

[0582] The server accesses each user's schedule management system (e.g., Google Calendar, Outlook Calendar) to retrieve their calendar information. This calendar information includes the user's appointments and free time. Based on the retrieved calendar information, the server calculates a mutually convenient meeting time. The calculation uses an algorithm that analyzes overlapping available time slots. The terminal displays the calculated optimal meeting time in the user interface, allowing the user to approve or modify the proposal.

[0583] Question set generation

[0584] The server generates a customized set of questions using a generative AI model based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user. The terminal displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[0585] Interview progress and data storage

[0586] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server. The server securely stores the encrypted response data and analyzes it in real time to extract important keywords and phrases.

[0587] Analysis and visualization of results

[0588] The server generates a summary and action items for the interview based on real-time analysis. This clarifies key points at each stage of the interview. The terminal displays the generated summary and action items to the user. The user can review them and add or modify them as needed.

[0589] Specific example

[0590] For example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server uses this information to suggest the optimal meeting time. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[0591] Example of a prompt

[0592] "I'd like to schedule a meeting with my supervisor. Please link to Google Calendar and suggest available times on Monday or Wednesday afternoon of next week. Please customize the questions to include those related to my performance over the past six months."

[0593] In this way, the entire system can be made more efficient, and a form that reduces the burden on users can be implemented.

[0594] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0595] Step 1: Retrieve and save calendar information

[0596] The server establishes access to a scheduling management system (e.g., Google Calendar, Outlook Calendar) via OAuth authentication to retrieve each user's calendar information. Using the authentication credentials provided by the user, the server retrieves the calendar information via the API.

[0597] Input: User authentication information

[0598] Output: Retrieved calendar information

[0599] The server stores the retrieved calendar information in a database, organizing and saving the user's schedule and free time.

[0600] Step 2: Calculate and propose meeting time.

[0601] The server calculates the optimal meeting time between users based on stored calendar information. The calculation uses an algorithm that detects overlapping free time (e.g., binary search).

[0602] Input: Calendar information for each user

[0603] Output: Calculated optimal interview time

[0604] The terminal displays the optimal meeting time, sent from the server, in the user interface, allowing the user to approve or modify the suggestion.

[0605] Input: Suggestion for the optimal meeting time

[0606] Output: User approval or modification

[0607] Step 3: Generate the question set

[0608] The server uses user profile information and past interview data to generate a set of questions using a generative AI model. This process utilizes NLP techniques and machine learning models (e.g., GPT-3).

[0609] Input: User profile information, past interview data

[0610] Output: Customized question set

[0611] The server saves the generated question set to a database and sends it to the terminal at the start of the interview.

[0612] Step 4: Conducting the Interview

[0613] The terminal displays the set of questions received from the server in a chat-style interface. Users can answer the questions in a relaxed, chat-style manner.

[0614] Input: Customized question set

[0615] Output: User's response

[0616] The terminal sends user input to the server in real time. Before transmission, the response data is encrypted.

[0617] Step 5: Saving and analyzing response data

[0618] The server receives encrypted response data and securely stores it in a database. It then uses a text analysis engine (e.g., spaCy) to analyze the response data in real time.

[0619] Input: Encrypted response data

[0620] Output: Analysis results, extracted important keywords and phrases

[0621] Step 6: Generating and displaying results

[0622] The server generates a summary of the meeting and action items based on the analysis results. This includes key points from the meeting and specific goals for the next meeting.

[0623] Input: Analysis results

[0624] Output: Summary and action items

[0625] The device displays the generated summary and action items to the user. The user can review the displayed content and add or modify it as needed.

[0626] Input: Summary and action items

[0627] Output: User modifications

[0628] Step 7: Save final data

[0629] The server receives the changes made by the user and saves them as the final data. This allows the necessary information to be accumulated for the next meeting.

[0630] Input: User's modifications

[0631] Output: Final data

[0632] The above is the specific processing flow of this system.

[0633] (Application Example 1)

[0634] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0635] In modern brick-and-mortar stores, smooth interviews between employees and managers are essential. However, scheduling interviews, providing high-quality feedback, and securely managing data remain challenges. Furthermore, coordination with employee shift and task management is crucial. A new system is needed to streamline these interview processes, ensure employee confidence, and provide data visibility.

[0636] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0637] This invention includes a server that includes means for acquiring user time management information, means for calculating the optimal interview time based on the acquired time management information, means for displaying the proposed interview time, and an algorithm for confirming the optimal interview time. This enables more efficient interviews and data visualization. Furthermore, it is possible to provide personalized feedback to individual users using means for encrypting data with an encryption library and a generative AI model that generates customized questions. This dramatically improves the interview process between employees and managers in physical stores and enables integration with shift and task management.

[0638] "User" refers to both the person being interviewed and the person conducting the interview using the system.

[0639] "Time management information" refers to calendar information that includes the user's schedule and available time.

[0640] "Optimal meeting time" refers to the most convenient time for a meeting, calculated based on the schedules of both users.

[0641] "Suggested time" refers to the optimal meeting time calculated and presented to the user by the server.

[0642] An "encryption library" refers to a software library used for encrypting data.

[0643] "Customized questions" refer to questions that are generated based on the user's profile and past data, making them specific to each individual user.

[0644] "Chat format" refers to a form of text message exchange that provides a relaxed environment where users can respond comfortably.

[0645] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate question sets and summaries.

[0646] An "algorithm" refers to a set of computational procedures or analytical processes for solving a specific problem.

[0647] "Profile information" refers to data such as the user's personal information and past interview history.

[0648] "Shift information" refers to information about the working hours of employees at physical stores.

[0649] "Task information" refers to information about the tasks and duties that employees at physical stores are supposed to perform.

[0650] "Real-time" means that data acquisition, analysis, and display are performed instantly.

[0651] This system enables efficient interviews between employees and managers in physical stores, ensuring a sense of psychological security while also allowing for data visualization. The specific implementation of this system is described below.

[0652] System Configuration

[0653] The main components of the system are servers, terminals (such as smartphones and tablets), and users (employees and administrators).

[0654] Hardware and software to be used

[0655] Hardware: Smartphones, tablets

[0656] Software: Shift management systems (e.g., Google Calendar API, Outlook Calendar API), data analysis tools (e.g., Python, TensorFlow), encryption libraries (e.g., PyCryptodome)

[0657] Specific methods for carrying out the invention

[0658] Retrieving calendar information and scheduling

[0659] The server accesses scheduling management systems such as Google Calendar and Outlook Calendar to retrieve the user's time management information. This retrieves the user's appointments and availability. Based on this information, the server uses an algorithm to calculate the optimal meeting time and suggests the most convenient time for the user. The suggested time is displayed on the terminal, and the user can review, approve, or modify it.

[0660] Generating a customized set of questions

[0661] The server uses a generative AI model to generate a customized set of questions based on the user's profile information and past conversation data. This ensures that the interview content is tailored to each individual user, promoting more effective feedback and growth. The generated questions are displayed on the device in a chat format.

[0662] Interview progress and data storage

[0663] During the interview, the user answers questions in a chat format. The device receives these answers, encrypts the response data using an encryption library, and sends it to the server. The server securely stores the received data and analyzes the response data in real time using data analysis tools.

[0664] Analysis and visualization of results

[0665] Based on the analysis results, the server generates a summary of the interview and action items. The generated content is displayed on the terminal, allowing the user to review and modify it as needed. This clarifies the key points of the interview and streamlines the entire interview process.

[0666] Specific example

[0667] As an example, consider a meeting between a store manager and an employee at a physical store. The manager and employee register their respective time management information in the system. Based on this information, the server suggests an optimal meeting time, which is displayed on the terminal. During the meeting, the terminal displays questions in a chat format, and the employee answers them. The answers are encrypted and sent to the server. The server analyzes the answers in real time and automatically generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and manager review them and make corrections as needed.

[0668] Example of a prompt

[0669] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[0670] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0671] Step 1:

[0672] The server retrieves users' time management information. Specifically, it integrates with Google Calendar and Outlook Calendar to retrieve users' appointments and free time. This involves using APIs to read each user's calendar information. The input is the user ID, and the output is time management information.

[0673] Step 2:

[0674] The server uses an algorithm to calculate the optimal interview time based on the acquired time management information. This algorithm detects overlaps in users' available time slots and identifies suitable times for interviews. The input is time management information, and the output is the optimal interview time.

[0675] Step 3:

[0676] The server sends the calculated optimal interview time to the terminal, which then displays this information to the user. The user reviews the proposed time on the terminal and approves or modifies it. The input is the optimal interview time, and the output is the time approved or modified by the user.

[0677] Step 4:

[0678] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data. This creates questions tailored to each individual user. The input is profile information and past interview data, and the output is the customized set of questions.

[0679] Step 5:

[0680] The server sends the generated set of questions to the terminal, which then displays the questions to the user in a chat format. The user enters their answers to the questions. The input is the set of questions, and the output is the user's answers.

[0681] Step 6:

[0682] The terminal receives the user's response, encrypts it, and sends it to the server. The server stores the received response data to ensure its security. The input is the user's response, and the output is the encrypted response data.

[0683] Step 7:

[0684] The server analyzes the received response data in real time and extracts important keywords and phrases. Analysis is performed using data analysis tools (e.g., Python, TensorFlow). The input is encrypted response data, and the output is the analysis results.

[0685] Step 8:

[0686] The server generates a summary of the interview and action items based on the analysis results and sends them to the terminal. The terminal displays the generated content to the user, who can review and modify it. The input is the analysis results, and the output is the summary and action items.

[0687] Example of a prompt

[0688] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[0689] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0690] This system incorporates an emotion engine to streamline user HR interviews, ensure psychological reassurance, and visualize interview results. Therefore, it can analyze the interviewee's emotional state in real time and optimize the interview process based on that data. This system operates through a combination of server, terminal, and user actions.

[0691] System Configuration

[0692] The main components of the system are as follows:

[0693] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, question generation, and operation of the sentiment engine.

[0694] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[0695] Users: These are the interviewees and interviewers who conduct interviews using the system.

[0696] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[0697] Program processing

[0698] Retrieving calendar information and scheduling

[0699] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[0700] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[0701] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[0702] Question set generation and emotion engine operation

[0703] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[0704] The device displays the generated set of questions in a chat format.

[0705] The emotion engine analyzes user responses in real time and estimates their emotional state. It extracts emotional parameters from the user's text input, facial expressions, and tone of voice.

[0706] Interview progress and data storage

[0707] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[0708] The server securely stores encrypted response data. Simultaneously, it performs real-time sentiment analysis using an emotion engine and dynamically adjusts the questions and follow-up content based on the results.

[0709] Analysis and visualization of results

[0710] The server generates interview summaries and action items based on real-time analysis. It also analyzes emotional data to generate appropriate feedback based on the emotional state.

[0711] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[0712] Users refer to the sentiment analysis results to consider their future actions and how to conduct the interview. Furthermore, data including emotional states is saved as part of the interview history, which can be used for future interviews.

[0713] Specific example

[0714] As an example, consider a meeting between a supervisor and a subordinate at a certain company. The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, an emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. As a result, the server generates a summary and action items based on the real-time analysis, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[0715] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and more effective feedback and action planning become possible.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[0719] Step 2:

[0720] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[0721] Step 3:

[0722] The server sends the calculated optimal interview time to the user's device.

[0723] Step 4:

[0724] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[0725] Step 5:

[0726] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[0727] Step 6:

[0728] The device sends user approvals or modifications to the server.

[0729] Step 7:

[0730] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[0731] Step 8:

[0732] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[0733] Step 9:

[0734] The terminal displays the generated set of questions to the user in a chat format.

[0735] Step 10:

[0736] The emotion engine analyzes the user's responses to chat-style questions, as well as their facial expressions and tone of voice, to estimate their emotional state in real time.

[0737] Step 11:

[0738] Users enter their answers to questions in a chat format.

[0739] Step 12:

[0740] The terminal encrypts the user's input and sends it to the server.

[0741] Step 13:

[0742] The server receives encrypted response data and stores it securely.

[0743] Step 14:

[0744] The emotion engine analyzes the user's emotional state in real time and sends the results to the server.

[0745] Step 15:

[0746] The server dynamically adjusts the questions and follow-up content based on the analyzed sentiment data and response data.

[0747] Step 16:

[0748] The server generates interview summaries and action items based on real-time analysis. It also analyzes emotional data to generate appropriate feedback based on the emotional state.

[0749] Step 17:

[0750] The server sends the generated summary and action items to the user's device.

[0751] Step 18:

[0752] The device displays a summary and action items to the user.

[0753] Step 19:

[0754] The user reviews the displayed content and adds or corrects it as needed.

[0755] Step 20:

[0756] The terminal sends user additions and modifications to the server.

[0757] Step 21:

[0758] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[0759] (Example 2)

[0760] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0761] Traditional interview and meeting systems require manual processes such as scheduling, question generation, and sentiment analysis, which are time-consuming and labor-intensive. Furthermore, they lack mechanisms to ensure psychological comfort during interviews, making it difficult to promptly understand and respond to participants' emotional states. Simultaneously, the visualization of interview results and follow-ups is insufficient, resulting in a lack of systematically managed data that can be used for future meetings.

[0762] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0763] In this invention, the server includes means for acquiring user schedule information, means for calculating the optimal meeting time based on the acquired schedule information, means for displaying the proposed meeting time, means for accepting user approval or modification, means for confirming the final schedule and notifying the user, means for generating customized questions based on user data and past meeting data, means for displaying the generated questions to the user in a dialogue format, means for receiving, encrypting, and storing the user's answers, means for analyzing the interaction during the meeting in real time, means for performing sentiment analysis, means for generating summaries and action items, means for displaying the generated summaries and action items to the user, means for receiving user modifications and saving the final data, means for analyzing the user's emotional state in real time using a sentiment engine, means for dynamically adjusting the question content and follow-up content based on the analysis results, means for saving the final sentiment analysis results, means for utilizing the sentiment analysis results in the next meeting, means for generating appropriate feedback and action plans from the user's past data using a generative AI model, and means for displaying the generated feedback and action plans to the user. This allows users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to improve future meetings.

[0764] A "user" refers to a person who uses the system to participate in an interview or meeting.

[0765] "Schedule information" refers to data related to a user's schedule, and includes information obtained from calendar applications and other sources.

[0766] "Meeting time" refers to an appropriate date and time for users to conduct interviews or meetings.

[0767] "Schedule adjustment" refers to the process of calculating the optimal meeting time based on the user's schedule information and proposing it to the user.

[0768] "Customized questions" refer to questions that are individually generated based on the user's profile information and past interview data.

[0769] "Dialogue format" refers to a form of communication between the user and the system through questions and answers.

[0770] "Encryption" refers to a technology that protects user response data from being deciphered by third parties.

[0771] "Real-time analysis" refers to the process of immediately processing and analyzing data.

[0772] "Emotion analysis" refers to the process of estimating a user's emotional state from data such as user input, behavior, voice, and facial expressions.

[0773] A "summary" refers to a report that concisely summarizes the content of an interview or meeting.

[0774] "Action items" refer to specific action plans and follow-up items that are set based on the results of interviews or meetings.

[0775] An "emotion engine" refers to software and algorithms that analyze user input data and estimate their emotional state.

[0776] A "generative AI model" refers to an artificial intelligence model that uses advanced generative algorithms, such as GPT-4, to generate appropriate questions and feedback from data.

[0777] "Feedback" refers to evaluations and advice provided to users.

[0778] A "prompt" refers to a set of instructions that are input into a generative AI model to obtain a response.

[0779] "Storage" refers to the process of preserving data in a specific format so that it can be reused later.

[0780] This invention is a system designed to improve the efficiency and psychological comfort of interviews and meetings. This system is implemented by combining the actions of a server, terminals, and users.

[0781] System components

[0782] Server: Responsible for key processes such as data acquisition, analysis, scheduling, question generation, and operation of the sentiment engine.

[0783] Terminal: A device (such as a PC, smartphone, or tablet) that accepts user input and displays the results.

[0784] User: The person being interviewed or interviewed using the system for interviews and meetings.

[0785] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[0786] Generative AI models: Used to generate questions and feedback.

[0787] Specific processing of the system

[0788] The server uses APIs such as Google Calendar and Outlook Calendar to retrieve user schedule information. Authentication is performed using OAuth 2.0 for API access. From the retrieved calendar information, the server uses a quicksort algorithm to analyze the overlap of users' free time and calculate the optimal meeting time.

[0789] The terminal displays the calculated optimal meeting time to the user, allowing the user to approve or modify the proposal. For example, the user's terminal displays a scheduling proposal window with approve / modify buttons. Once the schedule is confirmed, the server sends a notification to the user.

[0790] The server generates a customized set of questions based on the user's profile information and past interview data. This generation is performed using a generative AI model (e.g., the GPT-4 model). The generated questions are then displayed to the user in a conversational format using the terminal's chat UI.

[0791] The terminal conducts the interview using the user's chat input, camera images (facial expression analysis), and microphone (voice input). The emotion engine uses OpenCV, TensorFlow, etc., to analyze the user's input text, facial expressions, and voice in real time and estimate their emotional state.

[0792] The generated questions and user responses are encrypted and sent to the server. The server securely stores the data using AES-256 encryption. Meanwhile, real-time analysis results from the sentiment engine are also stored, and the questions and follow-up content are dynamically adjusted based on these results.

[0793] After the meeting concludes, the server generates a summary and action items from the analysis results. Using a generation AI model, the summary and action item list are generated by inputting user responses and sentiment data as prompts. This summary and action items are displayed on the terminal for the user to review and add or modify as needed.

[0794] The final data is saved again and used in the next meeting. By viewing sentiment analysis graphs and past interview data reports within the dashboard, users can gain the insights needed to plan their next actions.

[0795] Specific example

[0796] As an example, consider a meeting between a supervisor and a subordinate at a certain company.

[0797] The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, the emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. Based on the analysis results, the server generates a summary and action items, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[0798] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and effective feedback and action plans become possible.

[0799] Example of a prompt

[0800] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[0801] Based on the details described above, the present invention enables users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to inform future meetings.

[0802] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0803] Program processing flow

[0804] Step 1: Obtain calendar information and schedule.

[0805] Processing flow

[0806] The server uses the API key provided by the user to access calendar services (e.g., Google Calendar, Outlook Calendar) and retrieve each user's schedule information. Based on the retrieved calendar information, it calculates the optimal meeting time.

[0807] Specific actions

[0808] Input: User's API key, user's schedule information to be retrieved from the calendar service.

[0809] Data Processing: The server accesses the calendar API via OAuth 2.0 authentication to retrieve schedule information. A quicksort algorithm is used to compare each user's schedule and analyze overlapping time slots.

[0810] Output: Suggestions for optimal meeting times based on the availability of both users.

[0811] Step 2: View and approve the proposed meeting time.

[0812] Processing flow

[0813] The terminal displays the optimal meeting time sent from the server to the user. The user can approve or modify the suggested meeting time.

[0814] Specific actions

[0815] Input: Proposed meeting time

[0816] Data processing: The terminal displays a schedule proposal window, and approval / modification buttons are provided.

[0817] Output: User approval or correction response

[0818] Step 3: Confirm and notify the schedule

[0819] Processing flow

[0820] The server will finalize the schedule based on the user's approved or revised meeting times and notify the user.

[0821] Specific actions

[0822] Input: User-approved or modified meeting time

[0823] Data processing: Update confirmed meeting times in the calendar service and generate notification emails and alerts.

[0824] Output: Confirmed schedule and notifications

[0825] Step 4: Generating a customized set of questions

[0826] Processing flow

[0827] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data.

[0828] Specific actions

[0829] Input: User profile information, past interview data

[0830] Data processing: This information is input as prompts into a generative AI model (e.g., GPT-4) to generate unique questions.

[0831] Output: Individualized question list

[0832] Step 5: Displaying questions in an interactive format

[0833] Processing flow

[0834] The terminal displays the generated set of questions to the user in an interactive format. The user answers the questions.

[0835] Specific actions

[0836] Input: Customized list of questions

[0837] Data processing: Provide an interface that displays questions sequentially in the chat UI and accepts user responses.

[0838] Output: User response data

[0839] Step 6: Emotion Analysis

[0840] Processing flow

[0841] The server uses an emotion engine to analyze user responses in real time, including analyzing text input, facial expressions, and voice tone.

[0842] Specific actions

[0843] Input: User text input, camera image (facial expression), audio data

[0844] Data processing: Input data is analyzed using tools such as OpenCV and TensorFlow, and emotion parameters are extracted.

[0845] Output: Emotion analysis results

[0846] Step 7: Saving and analyzing interview results

[0847] Processing flow

[0848] The server encrypts and stores the user's responses and sentiment analysis results, and places them in a database so they can be used in future interviews.

[0849] Specific actions

[0850] Input: User response data, sentiment analysis results

[0851] Data Processing: Securely store data using AES-256 encryption. Perform necessary analysis and list areas for future improvement.

[0852] Output: Encrypted data, analysis report

[0853] Step 8: Summarize and generate action items

[0854] Processing flow

[0855] The server uses a generative AI model based on the interview results and sentiment analysis to generate summaries and action items.

[0856] Specific actions

[0857] Input: User response data, sentiment analysis results

[0858] Data processing: Input this data into a generative AI model to generate a summary and a list of action items.

[0859] Output: Summary, Action Item List

[0860] Step 9: View and correct the results

[0861] Processing flow

[0862] The terminal displays the generated summary and action items to the user, who can add or modify them as needed.

[0863] Specific actions

[0864] Input: Summary text, list of action items

[0865] Data processing: Display the data on the dashboard screen and provide an interface that allows users to edit it.

[0866] Output: User additions and modifications

[0867] Step 10: Save the final data and use it for the next meeting.

[0868] Processing flow

[0869] The server saves the final data and stores sentiment analysis data and action items in a database for use in the next interview.

[0870] Specific actions

[0871] Input: User additions and modifications, sentiment analysis results

[0872] Data processing: Integrate the final data and save it to the database so that it can be referenced during the next meeting.

[0873] Output: Saved final data, reference data for the next meeting

[0874] Examples of prompts to input into a generative AI model

[0875] Example of a prompt:

[0876] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[0877] (Application Example 2)

[0878] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0879] In conventional autonomous vehicles, it has been difficult to understand the emotional state of the passenger in real time and optimize the driving environment based on that understanding. This has led to problems such as passengers experiencing stress and difficulty in ensuring comfort and safety during driving. There is a need to provide a safer and more comfortable autonomous driving experience by appropriately monitoring the passenger's emotional state and adjusting the driving environment accordingly.

[0880] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring the user's calendar information, means for calculating the optimal meeting time based on the acquired calendar information, means for displaying the proposed meeting time, means for accepting the user's approval or modification, means for confirming the final schedule and notifying the user, means for analyzing the user's emotional state in real time using an emotion analysis module, means for automatically generating and providing feedback and advice based on the emotional state to the user, means for adjusting the driving environment and providing appropriate feedback based on the emotion analysis results, and means for safely and comfortably managing the driving situation based on the emotion analysis. This makes it possible to monitor the user's emotional state in real time and provide appropriate feedback and optimize the driving environment.

[0881] "Means for obtaining user calendar information" refers to a function that automatically retrieves user schedule information from online calendars or the organization's internal schedule management system.

[0882] The "method for calculating the optimal meeting time" is an algorithm that calculates the optimal meeting time that suits both parties, based on the acquired calendar information.

[0883] "A means of displaying suggested meeting times" refers to an interface that visually presents the user with calculated meeting time options.

[0884] "Means for accepting user approval or modification" refers to an interface that allows users to approve or modify the proposed meeting time.

[0885] "Means of finalizing the schedule and notifying the user" refers to a function that confirms the meeting time approved or modified by the user and notifies the user of that information.

[0886] The "emotion analysis module" is software that analyzes data such as the user's facial expressions and voice tone to estimate their emotional state in real time.

[0887] "Means for automatically generating feedback and advice based on emotional state" refers to a function that automatically creates and presents appropriate feedback and advice based on the results of an emotion analysis module.

[0888] "Means of adjusting the driving environment and providing appropriate feedback" refers to a function that adjusts the in-car environment (e.g., temperature, music, lighting, etc.) based on emotion analysis results to provide the user with a comfortable and safe driving experience.

[0889] "Means for safely and comfortably managing driving conditions" refers to functions that optimize the vehicle's driving state based on emotion analysis and user feedback, supporting accident prevention and comfortable operation.

[0890] This invention is a system that monitors the user's emotional state in real time within an autonomous vehicle and provides a safer and more comfortable driving experience to the user through appropriate feedback and optimization of the driving environment. The main components of this system are a server, a terminal, a user interface, and an emotion analysis module.

[0891] System Configuration

[0892] 1. Server:

[0893] Retrieving calendar information: Use a Web API to retrieve calendar information from the user's schedule management system.

[0894] Schedule Calculation: Implement an algorithm to calculate optimal meeting times and driving schedules based on acquired calendar information.

[0895] Emotion analysis module: Equipped with software to analyze the user's facial expressions and voice tone to estimate their emotional state.

[0896] Feedback generation: Based on the emotion analysis results, appropriate feedback and advice are generated.

[0897] 2. Terminal:

[0898] Information display: A device (such as an in-car display) that displays schedules and feedback obtained from a server to the user.

[0899] Dialogue Interface: Provides an interface that displays generated questions in a chat format and accepts user responses.

[0900] 3. User:

[0901] Data entry: Enter your own calendar information, profile, responses, etc. into the system.

[0902] Reviewing feedback: Review and apply any feedback or advice provided by the system.

[0903] 4. Emotion Analysis Module:

[0904] Data analysis: Real-time estimation of emotional state based on user facial expressions and tone of voice.

[0905] Data storage: Save the analysis data and use it for later feedback and analysis.

[0906] Hardware and software usage

[0907] Hardware:

[0908] In-vehicle camera: Used to capture the user's facial expressions.

[0909] Microphone: Collects and analyzes user feedback.

[0910] In-car display: Displays analysis results and feedback.

[0911] In-vehicle PC: Performs data analysis and controls the entire system.

[0912] software:

[0913] Emotion Analysis API: Used to analyze facial expressions and tone of voice to estimate emotional states.

[0914] Schedule calculation algorithm: Calculates the optimal schedule based on the user's calendar information.

[0915] Database: Stores analysis results and is used to generate feedback.

[0916] Specific example

[0917] As an example, consider a scenario where a user is riding in an autonomous vehicle. The user's facial expressions and voice data are collected in real time via the vehicle's cameras and microphones. A server's emotion analysis module analyzes this data and detects if the user is experiencing stress. Based on this, the server suggests relaxation music and displays it on the vehicle's display. It also adjusts the air conditioning temperature to provide a comfortable driving environment. The user confirms this feedback and realizes that the driving environment has become more comfortable.

[0918] Example of a prompt

[0919] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[0920] In this way, by providing real-time feedback and environmental adjustments based on emotion analysis, users can obtain a safe and comfortable driving experience.

[0921] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0922] Step 1:

[0923] The server retrieves the user's calendar information. Specifically, it accesses the user's online calendar or schedule management system via a Web API to retrieve upcoming schedule information. The input is the user ID, and the output is calendar information. This calendar information includes the time slots and content of appointments.

[0924] Step 2:

[0925] The server analyzes the acquired calendar information and executes an algorithm to calculate the optimal meeting time that suits both parties. The input is calendar information, and the output is a suggestion for the optimal meeting time. This algorithm cross-references available time slots across different time periods to select the most suitable time frame.

[0926] Step 3:

[0927] The terminal displays the calculated optimal meeting time to the user. The input is a suggested optimal meeting time, and the output is a visual notification to the user. The terminal shows the suggested time to the user through a visual interface and prompts them to approve or revise it.

[0928] Step 4:

[0929] The user approves or modifies the proposed meeting time. The input is the proposed meeting time, and the output is the approved or modified meeting time. The user interacts with the system via a terminal and provides feedback on their schedule.

[0930] Step 5:

[0931] The server confirms the final schedule from the user and notifies the user. The input is the approved or revised meeting time, and the output is a notification of the confirmed meeting schedule. The confirmed schedule is stored in the database for later access.

[0932] Step 6:

[0933] The server analyzes the user's emotional state in real time using an emotion analysis module. Inputs are the user's facial expression and voice data, and output is the result of the emotional state analysis. This data is collected via an in-vehicle camera and microphone and then processed by the emotion analysis algorithm.

[0934] Step 7:

[0935] The server automatically generates appropriate feedback and advice based on the user's emotional state and provides it to the user through the terminal. The input is the result of the emotional state analysis, and the output is feedback and advice, which includes visual notifications and audio announcements.

[0936] Step 8:

[0937] The terminal displays generated questions to the user in a chat format and receives the user's answers. The input is a customized set of questions, and the output is the user's answers. The answers are encrypted and sent to the server.

[0938] Step 9:

[0939] The server stores the user's responses and adjusts the driving environment based on the sentiment analysis results. The input consists of the user's responses and the analysis results of their emotional state, while the output is information on adjusting the driving environment. This includes adjusting the in-car air conditioning, music, and lighting.

[0940] Step 10:

[0941] The server manages driving conditions safely and comfortably based on the results of emotion analysis. Inputs are the results of the emotional state analysis and information on adjusting the driving environment, while output is an optimized driving environment. This allows the user to have a safe and comfortable driving experience.

[0942] Generative AI model prompt example:

[0943] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[0944] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0945] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0946] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0947] [Third Embodiment]

[0948] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0949] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0950] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0951] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0952] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0953] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0954] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0955] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0956] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0957] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0958] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0959] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0960] This system aims to streamline user personnel interviews, ensure the interviewer's psychological comfort, and visualize interview results. The system operates through a combination of server, terminal, and user actions.

[0961] System Configuration

[0962] The main components of the system are as follows:

[0963] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[0964] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[0965] Users: These are the interviewees and interviewers who conduct interviews using the system.

[0966] Program processing

[0967] Retrieving calendar information and scheduling

[0968] The server accesses a scheduling management system (e.g., Google Calendar, Outlook Calendar) to retrieve each user's calendar information. This calendar information includes the user's appointments and free time.

[0969] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[0970] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[0971] Question set generation

[0972] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[0973] The device displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[0974] Interview progress and data storage

[0975] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[0976] The server securely stores encrypted response data. Simultaneously, it analyzes the response data in real time, extracting important keywords and phrases.

[0977] Analysis and visualization of results

[0978] The server generates interview summaries and action items based on real-time analysis. This clarifies key points at each stage of the interview.

[0979] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[0980] Specific example

[0981] As an example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server suggests the optimal meeting time based on this information. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[0982] In this way, the entire interview process can be streamlined, reducing the burden on the user.

[0983] The following describes the processing flow.

[0984] Step 1:

[0985] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[0986] Step 2:

[0987] The server calculates a mutually convenient meeting time based on the acquired calendar information. It uses an algorithm to analyze overlapping available time slots.

[0988] Step 3:

[0989] The server sends the calculated optimal interview time to the user's device.

[0990] Step 4:

[0991] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[0992] Step 5:

[0993] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[0994] Step 6:

[0995] The device sends user approvals or modifications to the server.

[0996] Step 7:

[0997] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[0998] Step 8:

[0999] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[1000] Step 9:

[1001] The terminal displays the generated set of questions to the user in a chat format.

[1002] Step 10:

[1003] Users enter their answers to questions in a chat format.

[1004] Step 11:

[1005] The terminal encrypts the user's input and sends it to the server.

[1006] Step 12:

[1007] The server receives encrypted response data and stores it securely.

[1008] Step 13:

[1009] The server analyzes the response data in real time and extracts important keywords and phrases.

[1010] Step 14:

[1011] The server generates a summary of the interview and action items based on the extracted data.

[1012] Step 15:

[1013] The server sends the generated summary and action items to the user's device.

[1014] Step 16:

[1015] The device displays a summary and action items to the user.

[1016] Step 17:

[1017] The user reviews the displayed content and adds or corrects it as needed.

[1018] Step 18:

[1019] The terminal sends user additions and modifications to the server.

[1020] Step 19:

[1021] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[1022] (Example 1)

[1023] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1024] Traditional HR interview systems have faced challenges in efficiency and usability throughout the entire process, from scheduling interviews to conducting them and analyzing the results. Furthermore, they lacked the functionality to analyze interview interactions and provide meaningful feedback, making it difficult for interviewees to conduct interviews in a psychologically reassuring and relaxed environment. Additionally, the lack of sufficient visualization of interview results made it difficult to utilize the information gained from interviews across the entire organization. An effective system is needed to address these challenges.

[1025] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1026] In this invention, the server includes means for acquiring user calendar information, means for calculating the optimal interview time using an information management device that manages the acquired calendar information, means for displaying the calculated interview time on a user interface, means for accepting user approval or modification, means for finalizing the interview schedule and notifying the user, means for providing customized questions using a generation AI model based on user information and past data, means for displaying the generated questions to the user in chat format, means for receiving, encrypting, and storing the user's answers, means for analyzing the user's input data in real time, means for generating a summary and action items based on the analysis results, means for displaying the generated summary and action items to the user and receiving user modifications, and means for storing the final data. This enables increased efficiency in the entire interview process, reduced psychological burden on the user, and visualization of interview results.

[1027] "Calendar information" refers to data related to a user's schedule, such as appointments and free time.

[1028] An "information management device" refers to a system that includes hardware or software for storing, managing, and analyzing acquired calendar information.

[1029] "User interface" refers to the screens and means of operation that users use to interact with a system.

[1030] A "generative AI model" refers to an artificial intelligence model that generates question sets and analysis results based on user information and past data.

[1031] "Chat format" refers to a format in which user interaction is conducted using a text-based interface.

[1032] "Encryption" refers to a technology that transforms data using a specific algorithm to ensure data security and prevent third parties from reading it.

[1033] "Input data" refers to data such as responses and actions that users provide to the system.

[1034] A "summary" refers to a concise compilation of information obtained during an interview.

[1035] "Action items" refer to specific actions or goals that should be taken based on the results of the interview.

[1036] Modes for carrying out the invention

[1037] This system streamlines user personnel interviews, ensures the interviewer's psychological comfort, and provides visibility into interview results. The system operates through a combination of server, terminal, and user actions.

[1038] System Configuration

[1039] The main components of the system are as follows:

[1040] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[1041] Terminal: A device that accepts user input and displays the results (for example, a personal computer or smartphone).

[1042] Users: These are the interviewees and interviewers who conduct interviews using the system.

[1043] Retrieving calendar information and scheduling

[1044] The server accesses each user's schedule management system (e.g., Google Calendar, Outlook Calendar) to retrieve their calendar information. This calendar information includes the user's appointments and free time. Based on the retrieved calendar information, the server calculates a mutually convenient meeting time. The calculation uses an algorithm that analyzes overlapping available time slots. The terminal displays the calculated optimal meeting time in the user interface, allowing the user to approve or modify the proposal.

[1045] Question set generation

[1046] The server generates a customized set of questions using a generative AI model based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user. The terminal displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[1047] Interview progress and data storage

[1048] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server. The server securely stores the encrypted response data and analyzes it in real time to extract important keywords and phrases.

[1049] Analysis and visualization of results

[1050] The server generates a summary and action items for the interview based on real-time analysis. This clarifies key points at each stage of the interview. The terminal displays the generated summary and action items to the user. The user can review them and add or modify them as needed.

[1051] Specific example

[1052] For example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server uses this information to suggest the optimal meeting time. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[1053] Example of a prompt

[1054] "I'd like to schedule a meeting with my supervisor. Please link to Google Calendar and suggest available times on Monday or Wednesday afternoon of next week. Please customize the questions to include those related to my performance over the past six months."

[1055] In this way, the entire system can be made more efficient, and a form that reduces the burden on users can be implemented.

[1056] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1057] Step 1: Retrieve and save calendar information

[1058] The server establishes access to a scheduling management system (e.g., Google Calendar, Outlook Calendar) via OAuth authentication to retrieve each user's calendar information. Using the authentication credentials provided by the user, the server retrieves the calendar information via the API.

[1059] Input: User authentication information

[1060] Output: Retrieved calendar information

[1061] The server stores the retrieved calendar information in a database, organizing and saving the user's schedule and free time.

[1062] Step 2: Calculate and propose meeting time.

[1063] The server calculates the optimal meeting time between users based on stored calendar information. The calculation uses an algorithm that detects overlapping free time (e.g., binary search).

[1064] Input: Calendar information for each user

[1065] Output: Calculated optimal interview time

[1066] The terminal displays the optimal meeting time, sent from the server, in the user interface, allowing the user to approve or modify the suggestion.

[1067] Input: Suggestion for the optimal meeting time

[1068] Output: User approval or modification

[1069] Step 3: Generate the question set

[1070] The server uses user profile information and past interview data to generate a set of questions using a generative AI model. This process utilizes NLP techniques and machine learning models (e.g., GPT-3).

[1071] Input: User profile information, past interview data

[1072] Output: Customized question set

[1073] The server saves the generated question set to a database and sends it to the terminal at the start of the interview.

[1074] Step 4: Conducting the Interview

[1075] The terminal displays the set of questions received from the server in a chat-style interface. Users can answer the questions in a relaxed, chat-style manner.

[1076] Input: Customized question set

[1077] Output: User's response

[1078] The terminal sends user input to the server in real time. Before transmission, the response data is encrypted.

[1079] Step 5: Saving and analyzing response data

[1080] The server receives encrypted response data and securely stores it in a database. It then uses a text analysis engine (e.g., spaCy) to analyze the response data in real time.

[1081] Input: Encrypted response data

[1082] Output: Analysis results, extracted important keywords and phrases

[1083] Step 6: Generating and displaying results

[1084] The server generates a summary of the meeting and action items based on the analysis results. This includes key points from the meeting and specific goals for the next meeting.

[1085] Input: Analysis results

[1086] Output: Summary and action items

[1087] The device displays the generated summary and action items to the user. The user can review the displayed content and add or modify it as needed.

[1088] Input: Summary and action items

[1089] Output: User modifications

[1090] Step 7: Save final data

[1091] The server receives the changes made by the user and saves them as the final data. This allows the necessary information to be accumulated for the next meeting.

[1092] Input: User's modifications

[1093] Output: Final data

[1094] The above is the specific processing flow of this system.

[1095] (Application Example 1)

[1096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1097] In modern brick-and-mortar stores, smooth interviews between employees and managers are essential. However, scheduling interviews, providing high-quality feedback, and securely managing data remain challenges. Furthermore, coordination with employee shift and task management is crucial. A new system is needed to streamline these interview processes, ensure employee confidence, and provide data visibility.

[1098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1099] This invention includes a server that includes means for acquiring user time management information, means for calculating the optimal interview time based on the acquired time management information, means for displaying the proposed interview time, and an algorithm for confirming the optimal interview time. This enables more efficient interviews and data visualization. Furthermore, it is possible to provide personalized feedback to individual users using means for encrypting data with an encryption library and a generative AI model that generates customized questions. This dramatically improves the interview process between employees and managers in physical stores and enables integration with shift and task management.

[1100] "User" refers to both the person being interviewed and the person conducting the interview using the system.

[1101] "Time management information" refers to calendar information that includes the user's schedule and available time.

[1102] "Optimal meeting time" refers to the most convenient time for a meeting, calculated based on the schedules of both users.

[1103] "Suggested time" refers to the optimal meeting time calculated and presented to the user by the server.

[1104] An "encryption library" refers to a software library used for encrypting data.

[1105] "Customized questions" refer to questions that are generated based on the user's profile and past data, making them specific to each individual user.

[1106] "Chat format" refers to a form of text message exchange that provides a relaxed environment where users can respond comfortably.

[1107] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate question sets and summaries.

[1108] An "algorithm" refers to a set of computational procedures or analytical processes for solving a specific problem.

[1109] "Profile information" refers to data such as the user's personal information and past interview history.

[1110] "Shift information" refers to information about the working hours of employees at physical stores.

[1111] "Task information" refers to information about the tasks and duties that employees at physical stores are supposed to perform.

[1112] "Real-time" means that data acquisition, analysis, and display are performed instantly.

[1113] This system enables efficient interviews between employees and managers in physical stores, ensuring a sense of psychological security while also allowing for data visualization. The specific implementation of this system is described below.

[1114] System Configuration

[1115] The main components of the system are servers, terminals (such as smartphones and tablets), and users (employees and administrators).

[1116] Hardware and software to be used

[1117] Hardware: Smartphones, tablets

[1118] Software: Shift management systems (e.g., Google Calendar API, Outlook Calendar API), data analysis tools (e.g., Python, TensorFlow), encryption libraries (e.g., PyCryptodome)

[1119] Specific methods for carrying out the invention

[1120] Retrieving calendar information and scheduling

[1121] The server accesses scheduling management systems such as Google Calendar and Outlook Calendar to retrieve the user's time management information. This retrieves the user's appointments and availability. Based on this information, the server uses an algorithm to calculate the optimal meeting time and suggests the most convenient time for the user. The suggested time is displayed on the terminal, and the user can review, approve, or modify it.

[1122] Generating a customized set of questions

[1123] The server uses a generative AI model to generate a customized set of questions based on the user's profile information and past conversation data. This ensures that the interview content is tailored to each individual user, promoting more effective feedback and growth. The generated questions are displayed on the device in a chat format.

[1124] Interview progress and data storage

[1125] During the interview, the user answers questions in a chat format. The device receives these answers, encrypts the response data using an encryption library, and sends it to the server. The server securely stores the received data and analyzes the response data in real time using data analysis tools.

[1126] Analysis and visualization of results

[1127] Based on the analysis results, the server generates a summary of the interview and action items. The generated content is displayed on the terminal, allowing the user to review and modify it as needed. This clarifies the key points of the interview and streamlines the entire interview process.

[1128] Specific example

[1129] As an example, consider a meeting between a store manager and an employee at a physical store. The manager and employee register their respective time management information in the system. Based on this information, the server suggests an optimal meeting time, which is displayed on the terminal. During the meeting, the terminal displays questions in a chat format, and the employee answers them. The answers are encrypted and sent to the server. The server analyzes the answers in real time and automatically generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and manager review them and make corrections as needed.

[1130] Example of a prompt

[1131] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[1132] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1133] Step 1:

[1134] The server retrieves users' time management information. Specifically, it integrates with Google Calendar and Outlook Calendar to retrieve users' appointments and free time. This involves using APIs to read each user's calendar information. The input is the user ID, and the output is time management information.

[1135] Step 2:

[1136] The server uses an algorithm to calculate the optimal interview time based on the acquired time management information. This algorithm detects overlaps in users' available time slots and identifies suitable times for interviews. The input is time management information, and the output is the optimal interview time.

[1137] Step 3:

[1138] The server sends the calculated optimal interview time to the terminal, which then displays this information to the user. The user reviews the proposed time on the terminal and approves or modifies it. The input is the optimal interview time, and the output is the time approved or modified by the user.

[1139] Step 4:

[1140] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data. This creates questions tailored to each individual user. The input is profile information and past interview data, and the output is the customized set of questions.

[1141] Step 5:

[1142] The server sends the generated set of questions to the terminal, which then displays the questions to the user in a chat format. The user enters their answers to the questions. The input is the set of questions, and the output is the user's answers.

[1143] Step 6:

[1144] The terminal receives the user's response, encrypts it, and sends it to the server. The server stores the received response data to ensure its security. The input is the user's response, and the output is the encrypted response data.

[1145] Step 7:

[1146] The server analyzes the received response data in real time and extracts important keywords and phrases. Analysis is performed using data analysis tools (e.g., Python, TensorFlow). The input is encrypted response data, and the output is the analysis results.

[1147] Step 8:

[1148] The server generates a summary of the interview and action items based on the analysis results and sends them to the terminal. The terminal displays the generated content to the user, who can review and modify it. The input is the analysis results, and the output is the summary and action items.

[1149] Example of a prompt

[1150] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[1151] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1152] This system incorporates an emotion engine to streamline user HR interviews, ensure psychological reassurance, and visualize interview results. Therefore, it can analyze the interviewee's emotional state in real time and optimize the interview process based on that data. This system operates through a combination of server, terminal, and user actions.

[1153] System Configuration

[1154] The main components of the system are as follows:

[1155] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, question generation, and operation of the sentiment engine.

[1156] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[1157] Users: These are the interviewees and interviewers who conduct interviews using the system.

[1158] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[1159] Program processing

[1160] Retrieving calendar information and scheduling

[1161] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[1162] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[1163] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[1164] Question set generation and emotion engine operation

[1165] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[1166] The device displays the generated set of questions in a chat format.

[1167] The emotion engine analyzes user responses in real time and estimates their emotional state. It extracts emotional parameters from the user's text input, facial expressions, and tone of voice.

[1168] Interview progress and data storage

[1169] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[1170] The server securely stores encrypted response data. Simultaneously, it performs real-time sentiment analysis using an emotion engine and dynamically adjusts the questions and follow-up content based on the results.

[1171] Analysis and visualization of results

[1172] The server generates interview summaries and action items based on real-time analysis. It also analyzes emotional data to generate appropriate feedback based on the emotional state.

[1173] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[1174] Users refer to the sentiment analysis results to consider their future actions and how to conduct the interview. Furthermore, data including emotional states is saved as part of the interview history, which can be used for future interviews.

[1175] Specific example

[1176] As an example, consider a meeting between a supervisor and a subordinate at a certain company. The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, an emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. As a result, the server generates a summary and action items based on the real-time analysis, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[1177] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and more effective feedback and action planning become possible.

[1178] The following describes the processing flow.

[1179] Step 1:

[1180] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[1181] Step 2:

[1182] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[1183] Step 3:

[1184] The server sends the calculated optimal interview time to the user's device.

[1185] Step 4:

[1186] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[1187] Step 5:

[1188] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[1189] Step 6:

[1190] The device sends user approvals or modifications to the server.

[1191] Step 7:

[1192] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[1193] Step 8:

[1194] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[1195] Step 9:

[1196] The terminal displays the generated set of questions to the user in a chat format.

[1197] Step 10:

[1198] The emotion engine analyzes the user's responses to chat-style questions, as well as their facial expressions and tone of voice, to estimate their emotional state in real time.

[1199] Step 11:

[1200] Users enter their answers to questions in a chat format.

[1201] Step 12:

[1202] The terminal encrypts the user's input and sends it to the server.

[1203] Step 13:

[1204] The server receives encrypted response data and stores it securely.

[1205] Step 14:

[1206] The emotion engine analyzes the user's emotional state in real time and sends the results to the server.

[1207] Step 15:

[1208] The server dynamically adjusts the questions and follow-up content based on the analyzed sentiment data and response data.

[1209] Step 16:

[1210] The server generates interview summaries and action items based on real-time analysis. It also analyzes emotional data to generate appropriate feedback based on the emotional state.

[1211] Step 17:

[1212] The server sends the generated summary and action items to the user's device.

[1213] Step 18:

[1214] The device displays a summary and action items to the user.

[1215] Step 19:

[1216] The user reviews the displayed content and adds or corrects it as needed.

[1217] Step 20:

[1218] The terminal sends user additions and modifications to the server.

[1219] Step 21:

[1220] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[1221] (Example 2)

[1222] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1223] Traditional interview and meeting systems require manual processes such as scheduling, question generation, and sentiment analysis, which are time-consuming and labor-intensive. Furthermore, they lack mechanisms to ensure psychological comfort during interviews, making it difficult to promptly understand and respond to participants' emotional states. Simultaneously, the visualization of interview results and follow-ups is insufficient, resulting in a lack of systematically managed data that can be used for future meetings.

[1224] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1225] In this invention, the server includes means for acquiring user schedule information, means for calculating the optimal meeting time based on the acquired schedule information, means for displaying the proposed meeting time, means for accepting user approval or modification, means for confirming the final schedule and notifying the user, means for generating customized questions based on user data and past meeting data, means for displaying the generated questions to the user in a dialogue format, means for receiving, encrypting, and storing the user's answers, means for analyzing the interaction during the meeting in real time, means for performing sentiment analysis, means for generating summaries and action items, means for displaying the generated summaries and action items to the user, means for receiving user modifications and saving the final data, means for analyzing the user's emotional state in real time using a sentiment engine, means for dynamically adjusting the question content and follow-up content based on the analysis results, means for saving the final sentiment analysis results, means for utilizing the sentiment analysis results in the next meeting, means for generating appropriate feedback and action plans from the user's past data using a generative AI model, and means for displaying the generated feedback and action plans to the user. This allows users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to improve future meetings.

[1226] A "user" refers to a person who uses the system to participate in an interview or meeting.

[1227] "Schedule information" refers to data related to a user's schedule, and includes information obtained from calendar applications and other sources.

[1228] "Meeting time" refers to an appropriate date and time for users to conduct interviews or meetings.

[1229] "Schedule adjustment" refers to the process of calculating the optimal meeting time based on the user's schedule information and proposing it to the user.

[1230] "Customized questions" refer to questions that are individually generated based on the user's profile information and past interview data.

[1231] "Dialogue format" refers to a form of communication between the user and the system through questions and answers.

[1232] "Encryption" refers to a technology that protects user response data from being deciphered by third parties.

[1233] "Real-time analysis" refers to the process of immediately processing and analyzing data.

[1234] "Emotion analysis" refers to the process of estimating a user's emotional state from data such as user input, behavior, voice, and facial expressions.

[1235] A "summary" refers to a report that concisely summarizes the content of an interview or meeting.

[1236] "Action items" refer to specific action plans and follow-up items that are set based on the results of interviews or meetings.

[1237] An "emotion engine" refers to software and algorithms that analyze user input data and estimate their emotional state.

[1238] A "generative AI model" refers to an artificial intelligence model that uses advanced generative algorithms, such as GPT-4, to generate appropriate questions and feedback from data.

[1239] "Feedback" refers to evaluations and advice provided to users.

[1240] A "prompt" refers to a set of instructions that are input into a generative AI model to obtain a response.

[1241] "Storage" refers to the process of preserving data in a specific format so that it can be reused later.

[1242] This invention is a system designed to improve the efficiency and psychological comfort of interviews and meetings. This system is implemented by combining the actions of a server, terminals, and users.

[1243] System components

[1244] Server: Responsible for key processes such as data acquisition, analysis, scheduling, question generation, and operation of the sentiment engine.

[1245] Terminal: A device (such as a PC, smartphone, or tablet) that accepts user input and displays the results.

[1246] User: The person being interviewed or interviewed using the system for interviews and meetings.

[1247] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[1248] Generative AI models: Used to generate questions and feedback.

[1249] Specific processing of the system

[1250] The server uses APIs such as Google Calendar and Outlook Calendar to retrieve user schedule information. Authentication is performed using OAuth 2.0 for API access. From the retrieved calendar information, the server uses a quicksort algorithm to analyze the overlap of users' free time and calculate the optimal meeting time.

[1251] The terminal displays the calculated optimal meeting time to the user, allowing the user to approve or modify the proposal. For example, the user's terminal displays a scheduling proposal window with approve / modify buttons. Once the schedule is confirmed, the server sends a notification to the user.

[1252] The server generates a customized set of questions based on the user's profile information and past interview data. This generation is performed using a generative AI model (e.g., the GPT-4 model). The generated questions are then displayed to the user in a conversational format using the terminal's chat UI.

[1253] The terminal conducts the interview using the user's chat input, camera images (facial expression analysis), and microphone (voice input). The emotion engine uses OpenCV, TensorFlow, etc., to analyze the user's input text, facial expressions, and voice in real time and estimate their emotional state.

[1254] The generated questions and user responses are encrypted and sent to the server. The server securely stores the data using AES-256 encryption. Meanwhile, real-time analysis results from the sentiment engine are also stored, and the questions and follow-up content are dynamically adjusted based on these results.

[1255] After the meeting concludes, the server generates a summary and action items from the analysis results. Using a generation AI model, the summary and action item list are generated by inputting user responses and sentiment data as prompts. This summary and action items are displayed on the terminal for the user to review and add or modify as needed.

[1256] The final data is saved again and used in the next meeting. By viewing sentiment analysis graphs and past interview data reports within the dashboard, users can gain the insights needed to plan their next actions.

[1257] Specific example

[1258] As an example, consider a meeting between a supervisor and a subordinate at a certain company.

[1259] The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, the emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. Based on the analysis results, the server generates a summary and action items, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[1260] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and effective feedback and action plans become possible.

[1261] Example of a prompt

[1262] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[1263] Based on the details described above, the present invention enables users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to inform future meetings.

[1264] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1265] Program processing flow

[1266] Step 1: Obtain calendar information and schedule.

[1267] Processing flow

[1268] The server uses the API key provided by the user to access calendar services (e.g., Google Calendar, Outlook Calendar) and retrieve each user's schedule information. Based on the retrieved calendar information, it calculates the optimal meeting time.

[1269] Specific actions

[1270] Input: User's API key, user's schedule information to be retrieved from the calendar service.

[1271] Data Processing: The server accesses the calendar API via OAuth 2.0 authentication to retrieve schedule information. A quicksort algorithm is used to compare each user's schedule and analyze overlapping time slots.

[1272] Output: Suggestions for optimal meeting times based on the availability of both users.

[1273] Step 2: View and approve the proposed meeting time.

[1274] Processing flow

[1275] The terminal displays the optimal meeting time sent from the server to the user. The user can approve or modify the suggested meeting time.

[1276] Specific actions

[1277] Input: Proposed meeting time

[1278] Data processing: The terminal displays a schedule proposal window, and approval / modification buttons are provided.

[1279] Output: User approval or correction response

[1280] Step 3: Confirm and notify the schedule

[1281] Processing flow

[1282] The server will finalize the schedule based on the user's approved or revised meeting times and notify the user.

[1283] Specific actions

[1284] Input: User-approved or modified meeting time

[1285] Data processing: Update confirmed meeting times in the calendar service and generate notification emails and alerts.

[1286] Output: Confirmed schedule and notifications

[1287] Step 4: Generating a customized set of questions

[1288] Processing flow

[1289] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data.

[1290] Specific actions

[1291] Input: User profile information, past interview data

[1292] Data processing: This information is input as prompts into a generative AI model (e.g., GPT-4) to generate unique questions.

[1293] Output: Individualized question list

[1294] Step 5: Displaying questions in an interactive format

[1295] Processing flow

[1296] The terminal displays the generated set of questions to the user in an interactive format. The user answers the questions.

[1297] Specific actions

[1298] Input: Customized list of questions

[1299] Data processing: Provide an interface that displays questions sequentially in the chat UI and accepts user responses.

[1300] Output: User response data

[1301] Step 6: Emotion Analysis

[1302] Processing flow

[1303] The server uses an emotion engine to analyze user responses in real time, including analyzing text input, facial expressions, and voice tone.

[1304] Specific actions

[1305] Input: User text input, camera image (facial expression), audio data

[1306] Data processing: Input data is analyzed using tools such as OpenCV and TensorFlow, and emotion parameters are extracted.

[1307] Output: Emotion analysis results

[1308] Step 7: Saving and analyzing interview results

[1309] Processing flow

[1310] The server encrypts and stores the user's responses and sentiment analysis results, and places them in a database so they can be used in future interviews.

[1311] Specific actions

[1312] Input: User response data, sentiment analysis results

[1313] Data Processing: Securely store data using AES-256 encryption. Perform necessary analysis and list areas for future improvement.

[1314] Output: Encrypted data, analysis report

[1315] Step 8: Summarize and generate action items

[1316] Processing flow

[1317] The server uses a generative AI model based on the interview results and sentiment analysis to generate summaries and action items.

[1318] Specific actions

[1319] Input: User response data, sentiment analysis results

[1320] Data processing: Input this data into a generative AI model to generate a summary and a list of action items.

[1321] Output: Summary, Action Item List

[1322] Step 9: View and correct the results

[1323] Processing flow

[1324] The terminal displays the generated summary and action items to the user, who can add or modify them as needed.

[1325] Specific actions

[1326] Input: Summary text, list of action items

[1327] Data processing: Display the data on the dashboard screen and provide an interface that allows users to edit it.

[1328] Output: User additions and modifications

[1329] Step 10: Save the final data and use it for the next meeting.

[1330] Processing flow

[1331] The server saves the final data and stores sentiment analysis data and action items in a database for use in the next interview.

[1332] Specific actions

[1333] Input: User additions and modifications, sentiment analysis results

[1334] Data processing: Integrate the final data and save it to the database so that it can be referenced during the next meeting.

[1335] Output: Saved final data, reference data for the next meeting

[1336] Examples of prompts to input into a generative AI model

[1337] Example of a prompt:

[1338] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[1339] (Application Example 2)

[1340] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1341] In conventional autonomous vehicles, it has been difficult to understand the emotional state of the passenger in real time and optimize the driving environment based on that understanding. This has led to problems such as passengers experiencing stress and difficulty in ensuring comfort and safety during driving. There is a need to provide a safer and more comfortable autonomous driving experience by appropriately monitoring the passenger's emotional state and adjusting the driving environment accordingly.

[1342] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring the user's calendar information, means for calculating the optimal meeting time based on the acquired calendar information, means for displaying the proposed meeting time, means for accepting the user's approval or modification, means for confirming the final schedule and notifying the user, means for analyzing the user's emotional state in real time using an emotion analysis module, means for automatically generating and providing feedback and advice based on the emotional state to the user, means for adjusting the driving environment and providing appropriate feedback based on the emotion analysis results, and means for safely and comfortably managing the driving situation based on the emotion analysis. This makes it possible to monitor the user's emotional state in real time and provide appropriate feedback and optimize the driving environment.

[1343] "Means for obtaining user calendar information" refers to a function that automatically retrieves user schedule information from online calendars or the organization's internal schedule management system.

[1344] The "method for calculating the optimal meeting time" is an algorithm that calculates the optimal meeting time that suits both parties, based on the acquired calendar information.

[1345] "A means of displaying suggested meeting times" refers to an interface that visually presents the user with calculated meeting time options.

[1346] "Means for accepting user approval or modification" refers to an interface that allows users to approve or modify the proposed meeting time.

[1347] "Means of finalizing the schedule and notifying the user" refers to a function that confirms the meeting time approved or modified by the user and notifies the user of that information.

[1348] The "emotion analysis module" is software that analyzes data such as the user's facial expressions and voice tone to estimate their emotional state in real time.

[1349] "Means for automatically generating feedback and advice based on emotional state" refers to a function that automatically creates and presents appropriate feedback and advice based on the results of an emotion analysis module.

[1350] "Means of adjusting the driving environment and providing appropriate feedback" refers to a function that adjusts the in-car environment (e.g., temperature, music, lighting, etc.) based on emotion analysis results to provide the user with a comfortable and safe driving experience.

[1351] "Means for safely and comfortably managing driving conditions" refers to functions that optimize the vehicle's driving state based on emotion analysis and user feedback, supporting accident prevention and comfortable operation.

[1352] This invention is a system that monitors the user's emotional state in real time within an autonomous vehicle and provides a safer and more comfortable driving experience to the user through appropriate feedback and optimization of the driving environment. The main components of this system are a server, a terminal, a user interface, and an emotion analysis module.

[1353] System Configuration

[1354] 1. Server:

[1355] Retrieving calendar information: Use a Web API to retrieve calendar information from the user's schedule management system.

[1356] Schedule Calculation: Implement an algorithm to calculate optimal meeting times and driving schedules based on acquired calendar information.

[1357] Emotion analysis module: Equipped with software to analyze the user's facial expressions and voice tone to estimate their emotional state.

[1358] Feedback generation: Based on the emotion analysis results, appropriate feedback and advice are generated.

[1359] 2. Terminal:

[1360] Information display: A device (such as an in-car display) that displays schedules and feedback obtained from a server to the user.

[1361] Dialogue Interface: Provides an interface that displays generated questions in a chat format and accepts user responses.

[1362] 3. User:

[1363] Data entry: Enter your own calendar information, profile, responses, etc. into the system.

[1364] Reviewing feedback: Review and apply any feedback or advice provided by the system.

[1365] 4. Emotion Analysis Module:

[1366] Data analysis: Real-time estimation of emotional state based on user facial expressions and tone of voice.

[1367] Data storage: Save the analysis data and use it for later feedback and analysis.

[1368] Hardware and software usage

[1369] Hardware:

[1370] In-vehicle camera: Used to capture the user's facial expressions.

[1371] Microphone: Collects and analyzes user feedback.

[1372] In-car display: Displays analysis results and feedback.

[1373] In-vehicle PC: Performs data analysis and controls the entire system.

[1374] software:

[1375] Emotion Analysis API: Used to analyze facial expressions and tone of voice to estimate emotional states.

[1376] Schedule calculation algorithm: Calculates the optimal schedule based on the user's calendar information.

[1377] Database: Stores analysis results and is used to generate feedback.

[1378] Specific example

[1379] As an example, consider a scenario where a user is riding in an autonomous vehicle. The user's facial expressions and voice data are collected in real time via the vehicle's cameras and microphones. A server's emotion analysis module analyzes this data and detects if the user is experiencing stress. Based on this, the server suggests relaxation music and displays it on the vehicle's display. It also adjusts the air conditioning temperature to provide a comfortable driving environment. The user confirms this feedback and realizes that the driving environment has become more comfortable.

[1380] Example of a prompt

[1381] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[1382] In this way, by providing real-time feedback and environmental adjustments based on emotion analysis, users can obtain a safe and comfortable driving experience.

[1383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1384] Step 1:

[1385] The server retrieves the user's calendar information. Specifically, it accesses the user's online calendar or schedule management system via a Web API to retrieve upcoming schedule information. The input is the user ID, and the output is calendar information. This calendar information includes the time slots and content of appointments.

[1386] Step 2:

[1387] The server analyzes the acquired calendar information and executes an algorithm to calculate the optimal meeting time that suits both parties. The input is calendar information, and the output is a suggestion for the optimal meeting time. This algorithm cross-references available time slots across different time periods to select the most suitable time frame.

[1388] Step 3:

[1389] The terminal displays the calculated optimal meeting time to the user. The input is a suggested optimal meeting time, and the output is a visual notification to the user. The terminal shows the suggested time to the user through a visual interface and prompts them to approve or revise it.

[1390] Step 4:

[1391] The user approves or modifies the proposed meeting time. The input is the proposed meeting time, and the output is the approved or modified meeting time. The user interacts with the system via a terminal and provides feedback on their schedule.

[1392] Step 5:

[1393] The server confirms the final schedule from the user and notifies the user. The input is the approved or revised meeting time, and the output is a notification of the confirmed meeting schedule. The confirmed schedule is stored in the database for later access.

[1394] Step 6:

[1395] The server analyzes the user's emotional state in real time using an emotion analysis module. Inputs are the user's facial expression and voice data, and output is the result of the emotional state analysis. This data is collected via an in-vehicle camera and microphone and then processed by the emotion analysis algorithm.

[1396] Step 7:

[1397] The server automatically generates appropriate feedback and advice based on the user's emotional state and provides it to the user through the terminal. The input is the result of the emotional state analysis, and the output is feedback and advice, which includes visual notifications and audio announcements.

[1398] Step 8:

[1399] The terminal displays generated questions to the user in a chat format and receives the user's answers. The input is a customized set of questions, and the output is the user's answers. The answers are encrypted and sent to the server.

[1400] Step 9:

[1401] The server stores the user's responses and adjusts the driving environment based on the sentiment analysis results. The input consists of the user's responses and the analysis results of their emotional state, while the output is information on adjusting the driving environment. This includes adjusting the in-car air conditioning, music, and lighting.

[1402] Step 10:

[1403] The server manages driving conditions safely and comfortably based on the results of emotion analysis. Inputs are the results of the emotional state analysis and information on adjusting the driving environment, while output is an optimized driving environment. This allows the user to have a safe and comfortable driving experience.

[1404] Generative AI model prompt example:

[1405] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[1406] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1409] [Fourth Embodiment]

[1410] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1411] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1413] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1416] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1417] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1418] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1419] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1420] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1421] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1422] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1423] This system aims to streamline user personnel interviews, ensure the interviewer's psychological comfort, and visualize interview results. The system operates through a combination of server, terminal, and user actions.

[1424] System Configuration

[1425] The main components of the system are as follows:

[1426] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[1427] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[1428] Users: These are the interviewees and interviewers who conduct interviews using the system.

[1429] Program processing

[1430] Retrieving calendar information and scheduling

[1431] The server accesses a scheduling management system (e.g., Google Calendar, Outlook Calendar) to retrieve each user's calendar information. This calendar information includes the user's appointments and free time.

[1432] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[1433] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[1434] Question set generation

[1435] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[1436] The device displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[1437] Interview progress and data storage

[1438] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[1439] The server securely stores encrypted response data. Simultaneously, it analyzes the response data in real time, extracting important keywords and phrases.

[1440] Analysis and visualization of results

[1441] The server generates interview summaries and action items based on real-time analysis. This clarifies key points at each stage of the interview.

[1442] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[1443] Specific example

[1444] As an example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server suggests the optimal meeting time based on this information. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[1445] In this way, the entire interview process can be streamlined, reducing the burden on the user.

[1446] The following describes the processing flow.

[1447] Step 1:

[1448] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[1449] Step 2:

[1450] The server calculates a mutually convenient meeting time based on the acquired calendar information. It uses an algorithm to analyze overlapping available time slots.

[1451] Step 3:

[1452] The server sends the calculated optimal interview time to the user's device.

[1453] Step 4:

[1454] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[1455] Step 5:

[1456] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[1457] Step 6:

[1458] The device sends user approvals or modifications to the server.

[1459] Step 7:

[1460] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[1461] Step 8:

[1462] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[1463] Step 9:

[1464] The terminal displays the generated set of questions to the user in a chat format.

[1465] Step 10:

[1466] Users enter their answers to questions in a chat format.

[1467] Step 11:

[1468] The terminal encrypts the user's input and sends it to the server.

[1469] Step 12:

[1470] The server receives encrypted response data and stores it securely.

[1471] Step 13:

[1472] The server analyzes the response data in real time and extracts important keywords and phrases.

[1473] Step 14:

[1474] The server generates a summary of the interview and action items based on the extracted data.

[1475] Step 15:

[1476] The server sends the generated summary and action items to the user's device.

[1477] Step 16:

[1478] The device displays a summary and action items to the user.

[1479] Step 17:

[1480] The user reviews the displayed content and adds or corrects it as needed.

[1481] Step 18:

[1482] The terminal sends user additions and modifications to the server.

[1483] Step 19:

[1484] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[1485] (Example 1)

[1486] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1487] Traditional HR interview systems have faced challenges in efficiency and usability throughout the entire process, from scheduling interviews to conducting them and analyzing the results. Furthermore, they lacked the functionality to analyze interview interactions and provide meaningful feedback, making it difficult for interviewees to conduct interviews in a psychologically reassuring and relaxed environment. Additionally, the lack of sufficient visualization of interview results made it difficult to utilize the information gained from interviews across the entire organization. An effective system is needed to address these challenges.

[1488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1489] In this invention, the server includes means for acquiring user calendar information, means for calculating the optimal interview time using an information management device that manages the acquired calendar information, means for displaying the calculated interview time on a user interface, means for accepting user approval or modification, means for finalizing the interview schedule and notifying the user, means for providing customized questions using a generation AI model based on user information and past data, means for displaying the generated questions to the user in chat format, means for receiving, encrypting, and storing the user's answers, means for analyzing the user's input data in real time, means for generating a summary and action items based on the analysis results, means for displaying the generated summary and action items to the user and receiving user modifications, and means for storing the final data. This enables increased efficiency in the entire interview process, reduced psychological burden on the user, and visualization of interview results.

[1490] "Calendar information" refers to data related to a user's schedule, such as appointments and free time.

[1491] An "information management device" refers to a system that includes hardware or software for storing, managing, and analyzing acquired calendar information.

[1492] "User interface" refers to the screens and means of operation that users use to interact with a system.

[1493] A "generative AI model" refers to an artificial intelligence model that generates question sets and analysis results based on user information and past data.

[1494] "Chat format" refers to a format in which user interaction is conducted using a text-based interface.

[1495] "Encryption" refers to a technology that transforms data using a specific algorithm to ensure data security and prevent third parties from reading it.

[1496] "Input data" refers to data such as responses and actions that users provide to the system.

[1497] A "summary" refers to a concise compilation of information obtained during an interview.

[1498] "Action items" refer to specific actions or goals that should be taken based on the results of the interview.

[1499] Modes for carrying out the invention

[1500] This system streamlines user personnel interviews, ensures the interviewer's psychological comfort, and provides visibility into interview results. The system operates through a combination of server, terminal, and user actions.

[1501] System Configuration

[1502] The main components of the system are as follows:

[1503] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, and query generation.

[1504] Terminal: A device that accepts user input and displays the results (for example, a personal computer or smartphone).

[1505] Users: These are the interviewees and interviewers who conduct interviews using the system.

[1506] Retrieving calendar information and scheduling

[1507] The server accesses each user's schedule management system (e.g., Google Calendar, Outlook Calendar) to retrieve their calendar information. This calendar information includes the user's appointments and free time. Based on the retrieved calendar information, the server calculates a mutually convenient meeting time. The calculation uses an algorithm that analyzes overlapping available time slots. The terminal displays the calculated optimal meeting time in the user interface, allowing the user to approve or modify the proposal.

[1508] Question set generation

[1509] The server generates a customized set of questions using a generative AI model based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user. The terminal displays the generated question set in a chat format, providing a relaxed environment for the user to answer.

[1510] Interview progress and data storage

[1511] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server. The server securely stores the encrypted response data and analyzes it in real time to extract important keywords and phrases.

[1512] Analysis and visualization of results

[1513] The server generates a summary and action items for the interview based on real-time analysis. This clarifies key points at each stage of the interview. The terminal displays the generated summary and action items to the user. The user can review them and add or modify them as needed.

[1514] Specific example

[1515] For example, consider a meeting between a supervisor and an employee at a certain company. The supervisor and employee register their respective calendar information in the system. The server uses this information to suggest the optimal meeting time. The suggested time is displayed on the terminal, and the supervisor and employee approve it. During the meeting, the terminal displays questions in a chat format, and the employee enters their answers. These answers are encrypted and sent to the server in real time. The server analyzes the answer data and generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and supervisor review them and make corrections as needed.

[1516] Example of a prompt

[1517] "I'd like to schedule a meeting with my supervisor. Please link to Google Calendar and suggest available times on Monday or Wednesday afternoon of next week. Please customize the questions to include those related to my performance over the past six months."

[1518] In this way, the entire system can be made more efficient, and a form that reduces the burden on users can be implemented.

[1519] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1520] Step 1: Retrieve and save calendar information

[1521] The server establishes access to a scheduling management system (e.g., Google Calendar, Outlook Calendar) via OAuth authentication to retrieve each user's calendar information. Using the authentication credentials provided by the user, the server retrieves the calendar information via the API.

[1522] Input: User authentication information

[1523] Output: Retrieved calendar information

[1524] The server stores the retrieved calendar information in a database, organizing and saving the user's schedule and free time.

[1525] Step 2: Calculate and propose meeting time.

[1526] The server calculates the optimal meeting time between users based on stored calendar information. The calculation uses an algorithm that detects overlapping free time (e.g., binary search).

[1527] Input: Calendar information for each user

[1528] Output: Calculated optimal interview time

[1529] The terminal displays the optimal meeting time, sent from the server, in the user interface, allowing the user to approve or modify the suggestion.

[1530] Input: Suggestion for the optimal meeting time

[1531] Output: User approval or modification

[1532] Step 3: Generate the question set

[1533] The server uses user profile information and past interview data to generate a set of questions using a generative AI model. This process utilizes NLP techniques and machine learning models (e.g., GPT-3).

[1534] Input: User profile information, past interview data

[1535] Output: Customized question set

[1536] The server saves the generated question set to a database and sends it to the terminal at the start of the interview.

[1537] Step 4: Conducting the Interview

[1538] The terminal displays the set of questions received from the server in a chat-style interface. Users can answer the questions in a relaxed, chat-style manner.

[1539] Input: Customized question set

[1540] Output: User's response

[1541] The terminal sends user input to the server in real time. Before transmission, the response data is encrypted.

[1542] Step 5: Saving and analyzing response data

[1543] The server receives encrypted response data and securely stores it in a database. It then uses a text analysis engine (e.g., spaCy) to analyze the response data in real time.

[1544] Input: Encrypted response data

[1545] Output: Analysis results, extracted important keywords and phrases

[1546] Step 6: Generating and displaying results

[1547] The server generates a summary of the meeting and action items based on the analysis results. This includes key points from the meeting and specific goals for the next meeting.

[1548] Input: Analysis results

[1549] Output: Summary and action items

[1550] The device displays the generated summary and action items to the user. The user can review the displayed content and add or modify it as needed.

[1551] Input: Summary and action items

[1552] Output: User modifications

[1553] Step 7: Save final data

[1554] The server receives the changes made by the user and saves them as the final data. This allows the necessary information to be accumulated for the next meeting.

[1555] Input: User's modifications

[1556] Output: Final data

[1557] The above is the specific processing flow of this system.

[1558] (Application Example 1)

[1559] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1560] In modern brick-and-mortar stores, smooth interviews between employees and managers are essential. However, scheduling interviews, providing high-quality feedback, and securely managing data remain challenges. Furthermore, coordination with employee shift and task management is crucial. A new system is needed to streamline these interview processes, ensure employee confidence, and provide data visibility.

[1561] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1562] This invention includes a server that includes means for acquiring user time management information, means for calculating the optimal interview time based on the acquired time management information, means for displaying the proposed interview time, and an algorithm for confirming the optimal interview time. This enables more efficient interviews and data visualization. Furthermore, it is possible to provide personalized feedback to individual users using means for encrypting data with an encryption library and a generative AI model that generates customized questions. This dramatically improves the interview process between employees and managers in physical stores and enables integration with shift and task management.

[1563] "User" refers to both the person being interviewed and the person conducting the interview using the system.

[1564] "Time management information" refers to calendar information that includes the user's schedule and available time.

[1565] "Optimal meeting time" refers to the most convenient time for a meeting, calculated based on the schedules of both users.

[1566] "Suggested time" refers to the optimal meeting time calculated and presented to the user by the server.

[1567] An "encryption library" refers to a software library used for encrypting data.

[1568] "Customized questions" refer to questions that are generated based on the user's profile and past data, making them specific to each individual user.

[1569] "Chat format" refers to a form of text message exchange that provides a relaxed environment where users can respond comfortably.

[1570] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate question sets and summaries.

[1571] An "algorithm" refers to a set of computational procedures or analytical processes for solving a specific problem.

[1572] "Profile information" refers to data such as the user's personal information and past interview history.

[1573] "Shift information" refers to information about the working hours of employees at physical stores.

[1574] "Task information" refers to information about the tasks and duties that employees at physical stores are supposed to perform.

[1575] "Real-time" means that data acquisition, analysis, and display are performed instantly.

[1576] This system enables efficient interviews between employees and managers in physical stores, ensuring a sense of psychological security while also allowing for data visualization. The specific implementation of this system is described below.

[1577] System Configuration

[1578] The main components of the system are servers, terminals (such as smartphones and tablets), and users (employees and administrators).

[1579] Hardware and software to be used

[1580] Hardware: Smartphones, tablets

[1581] Software: Shift management systems (e.g., Google Calendar API, Outlook Calendar API), data analysis tools (e.g., Python, TensorFlow), encryption libraries (e.g., PyCryptodome)

[1582] Specific methods for carrying out the invention

[1583] Retrieving calendar information and scheduling

[1584] The server accesses scheduling management systems such as Google Calendar and Outlook Calendar to retrieve the user's time management information. This retrieves the user's appointments and availability. Based on this information, the server uses an algorithm to calculate the optimal meeting time and suggests the most convenient time for the user. The suggested time is displayed on the terminal, and the user can review, approve, or modify it.

[1585] Generating a customized set of questions

[1586] The server uses a generative AI model to generate a customized set of questions based on the user's profile information and past conversation data. This ensures that the interview content is tailored to each individual user, promoting more effective feedback and growth. The generated questions are displayed on the device in a chat format.

[1587] Interview progress and data storage

[1588] During the interview, the user answers questions in a chat format. The device receives these answers, encrypts the response data using an encryption library, and sends it to the server. The server securely stores the received data and analyzes the response data in real time using data analysis tools.

[1589] Analysis and visualization of results

[1590] Based on the analysis results, the server generates a summary of the interview and action items. The generated content is displayed on the terminal, allowing the user to review and modify it as needed. This clarifies the key points of the interview and streamlines the entire interview process.

[1591] Specific example

[1592] As an example, consider a meeting between a store manager and an employee at a physical store. The manager and employee register their respective time management information in the system. Based on this information, the server suggests an optimal meeting time, which is displayed on the terminal. During the meeting, the terminal displays questions in a chat format, and the employee answers them. The answers are encrypted and sent to the server. The server analyzes the answers in real time and automatically generates a meeting summary and action items. The final results are displayed again on the terminal, and the employee and manager review them and make corrections as needed.

[1593] Example of a prompt

[1594] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[1595] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1596] Step 1:

[1597] The server retrieves users' time management information. Specifically, it integrates with Google Calendar and Outlook Calendar to retrieve users' appointments and free time. This involves using APIs to read each user's calendar information. The input is the user ID, and the output is time management information.

[1598] Step 2:

[1599] The server uses an algorithm to calculate the optimal interview time based on the acquired time management information. This algorithm detects overlaps in users' available time slots and identifies suitable times for interviews. The input is time management information, and the output is the optimal interview time.

[1600] Step 3:

[1601] The server sends the calculated optimal interview time to the terminal, which then displays this information to the user. The user reviews the proposed time on the terminal and approves or modifies it. The input is the optimal interview time, and the output is the time approved or modified by the user.

[1602] Step 4:

[1603] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data. This creates questions tailored to each individual user. The input is profile information and past interview data, and the output is the customized set of questions.

[1604] Step 5:

[1605] The server sends the generated set of questions to the terminal, which then displays the questions to the user in a chat format. The user enters their answers to the questions. The input is the set of questions, and the output is the user's answers.

[1606] Step 6:

[1607] The terminal receives the user's response, encrypts it, and sends it to the server. The server stores the received response data to ensure its security. The input is the user's response, and the output is the encrypted response data.

[1608] Step 7:

[1609] The server analyzes the received response data in real time and extracts important keywords and phrases. Analysis is performed using data analysis tools (e.g., Python, TensorFlow). The input is encrypted response data, and the output is the analysis results.

[1610] Step 8:

[1611] The server generates a summary of the interview and action items based on the analysis results and sends them to the terminal. The terminal displays the generated content to the user, who can review and modify it. The input is the analysis results, and the output is the summary and action items.

[1612] Example of a prompt

[1613] "Design a meeting system to streamline employee shift and task management, as well as regular feedback. Features should include schedule synchronization using the Google Calendar API, generation of customized question sets, and real-time analysis and encrypted storage of response data."

[1614] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1615] This system incorporates an emotion engine to streamline user HR interviews, ensure psychological reassurance, and visualize interview results. Therefore, it can analyze the interviewee's emotional state in real time and optimize the interview process based on that data. This system operates through a combination of server, terminal, and user actions.

[1616] System Configuration

[1617] The main components of the system are as follows:

[1618] Server: Responsible for key processes such as data acquisition, analysis, schedule calculation, question generation, and operation of the sentiment engine.

[1619] Terminal: A device that accepts user input and displays the results (for example, a PC or smartphone).

[1620] Users: These are the interviewees and interviewers who conduct interviews using the system.

[1621] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[1622] Program processing

[1623] Retrieving calendar information and scheduling

[1624] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[1625] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[1626] The device displays the calculated optimal meeting time to the user and allows the user to approve or modify the suggestion.

[1627] Question set generation and emotion engine operation

[1628] The server generates a customized set of questions based on the user's profile information and past interview data. This ensures that the interview content is tailored to each individual user.

[1629] The device displays the generated set of questions in a chat format.

[1630] The emotion engine analyzes user responses in real time and estimates their emotional state. It extracts emotional parameters from the user's text input, facial expressions, and tone of voice.

[1631] Interview progress and data storage

[1632] The device receives responses entered by the user in a chat format. The received responses are encrypted and sent to the server.

[1633] The server securely stores encrypted response data. Simultaneously, it performs real-time sentiment analysis using an emotion engine and dynamically adjusts the questions and follow-up content based on the results.

[1634] Analysis and visualization of results

[1635] The server generates interview summaries and action items based on real-time analysis. It also analyzes emotional data to generate appropriate feedback based on the emotional state.

[1636] The terminal displays the generated summary and action items to the user. The user can review these and add or modify them as needed.

[1637] Users refer to the sentiment analysis results to consider their future actions and how to conduct the interview. Furthermore, data including emotional states is saved as part of the interview history, which can be used for future interviews.

[1638] Specific example

[1639] As an example, consider a meeting between a supervisor and a subordinate at a certain company. The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, an emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. As a result, the server generates a summary and action items based on the real-time analysis, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[1640] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and more effective feedback and action planning become possible.

[1641] The following describes the processing flow.

[1642] Step 1:

[1643] The server uses an API to access each user's calendar (e.g., Google Calendar, Outlook Calendar) and retrieve their appointments and free time.

[1644] Step 2:

[1645] The server calculates a mutually convenient meeting time based on the acquired calendar information. The calculation uses an algorithm that analyzes overlapping available time slots.

[1646] Step 3:

[1647] The server sends the calculated optimal interview time to the user's device.

[1648] Step 4:

[1649] The device displays the optimal meeting time suggested to the user. The user can then approve or modify the suggestion.

[1650] Step 5:

[1651] The user reviews the proposed schedule and enters any approvals or modifications into the terminal.

[1652] Step 6:

[1653] The device sends user approvals or modifications to the server.

[1654] Step 7:

[1655] The server will finalize the schedule based on user feedback and reflect it in both parties' calendars.

[1656] Step 8:

[1657] The server generates a customized set of questions based on the user's profile information and past interview data. Appropriate questions are selected from the template management system.

[1658] Step 9:

[1659] The terminal displays the generated set of questions to the user in a chat format.

[1660] Step 10:

[1661] The emotion engine analyzes the user's responses to chat-style questions, as well as their facial expressions and tone of voice, to estimate their emotional state in real time.

[1662] Step 11:

[1663] Users enter their answers to questions in a chat format.

[1664] Step 12:

[1665] The terminal encrypts the user's input and sends it to the server.

[1666] Step 13:

[1667] The server receives encrypted response data and stores it securely.

[1668] Step 14:

[1669] The emotion engine analyzes the user's emotional state in real time and sends the results to the server.

[1670] Step 15:

[1671] The server dynamically adjusts the questions and follow-up content based on the analyzed sentiment data and response data.

[1672] Step 16:

[1673] The server generates interview summaries and action items based on real-time analysis. It also analyzes emotional data to generate appropriate feedback based on the emotional state.

[1674] Step 17:

[1675] The server sends the generated summary and action items to the user's device.

[1676] Step 18:

[1677] The device displays a summary and action items to the user.

[1678] Step 19:

[1679] The user reviews the displayed content and adds or corrects it as needed.

[1680] Step 20:

[1681] The terminal sends user additions and modifications to the server.

[1682] Step 21:

[1683] The server stores the final data and sends notifications to relevant administrators and personnel as needed.

[1684] (Example 2)

[1685] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1686] Traditional interview and meeting systems require manual processes such as scheduling, question generation, and sentiment analysis, which are time-consuming and labor-intensive. Furthermore, they lack mechanisms to ensure psychological comfort during interviews, making it difficult to promptly understand and respond to participants' emotional states. Simultaneously, the visualization of interview results and follow-ups is insufficient, resulting in a lack of systematically managed data that can be used for future meetings.

[1687] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1688] In this invention, the server includes means for acquiring user schedule information, means for calculating the optimal meeting time based on the acquired schedule information, means for displaying the proposed meeting time, means for accepting user approval or modification, means for confirming the final schedule and notifying the user, means for generating customized questions based on user data and past meeting data, means for displaying the generated questions to the user in a dialogue format, means for receiving, encrypting, and storing the user's answers, means for analyzing the interaction during the meeting in real time, means for performing sentiment analysis, means for generating summaries and action items, means for displaying the generated summaries and action items to the user, means for receiving user modifications and saving the final data, means for analyzing the user's emotional state in real time using a sentiment engine, means for dynamically adjusting the question content and follow-up content based on the analysis results, means for saving the final sentiment analysis results, means for utilizing the sentiment analysis results in the next meeting, means for generating appropriate feedback and action plans from the user's past data using a generative AI model, and means for displaying the generated feedback and action plans to the user. This allows users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to improve future meetings.

[1689] A "user" refers to a person who uses the system to participate in an interview or meeting.

[1690] "Schedule information" refers to data related to a user's schedule, and includes information obtained from calendar applications and other sources.

[1691] "Meeting time" refers to an appropriate date and time for users to conduct interviews or meetings.

[1692] "Schedule adjustment" refers to the process of calculating the optimal meeting time based on the user's schedule information and proposing it to the user.

[1693] "Customized questions" refer to questions that are individually generated based on the user's profile information and past interview data.

[1694] "Dialogue format" refers to a form of communication between the user and the system through questions and answers.

[1695] "Encryption" refers to a technology that protects user response data from being deciphered by third parties.

[1696] "Real-time analysis" refers to the process of immediately processing and analyzing data.

[1697] "Emotion analysis" refers to the process of estimating a user's emotional state from data such as user input, behavior, voice, and facial expressions.

[1698] A "summary" refers to a report that concisely summarizes the content of an interview or meeting.

[1699] "Action items" refer to specific action plans and follow-up items that are set based on the results of interviews or meetings.

[1700] An "emotion engine" refers to software and algorithms that analyze user input data and estimate their emotional state.

[1701] A "generative AI model" refers to an artificial intelligence model that uses advanced generative algorithms, such as GPT-4, to generate appropriate questions and feedback from data.

[1702] "Feedback" refers to evaluations and advice provided to users.

[1703] A "prompt" refers to a set of instructions that are input into a generative AI model to obtain a response.

[1704] "Storage" refers to the process of preserving data in a specific format so that it can be reused later.

[1705] This invention is a system designed to improve the efficiency and psychological comfort of interviews and meetings. This system is implemented by combining the actions of a server, terminals, and users.

[1706] System components

[1707] Server: Responsible for key processes such as data acquisition, analysis, scheduling, question generation, and operation of the sentiment engine.

[1708] Terminal: A device (such as a PC, smartphone, or tablet) that accepts user input and displays the results.

[1709] User: The person being interviewed or interviewed using the system for interviews and meetings.

[1710] Emotion Engine: This function analyzes user input, facial expressions, voice tone, etc., to estimate the user's emotional state.

[1711] Generative AI models: Used to generate questions and feedback.

[1712] Specific processing of the system

[1713] The server uses APIs such as Google Calendar and Outlook Calendar to retrieve user schedule information. Authentication is performed using OAuth 2.0 for API access. From the retrieved calendar information, the server uses a quicksort algorithm to analyze the overlap of users' free time and calculate the optimal meeting time.

[1714] The terminal displays the calculated optimal meeting time to the user, allowing the user to approve or modify the proposal. For example, the user's terminal displays a scheduling proposal window with approve / modify buttons. Once the schedule is confirmed, the server sends a notification to the user.

[1715] The server generates a customized set of questions based on the user's profile information and past interview data. This generation is performed using a generative AI model (e.g., the GPT-4 model). The generated questions are then displayed to the user in a conversational format using the terminal's chat UI.

[1716] The terminal conducts the interview using the user's chat input, camera images (facial expression analysis), and microphone (voice input). The emotion engine uses OpenCV, TensorFlow, etc., to analyze the user's input text, facial expressions, and voice in real time and estimate their emotional state.

[1717] The generated questions and user responses are encrypted and sent to the server. The server securely stores the data using AES-256 encryption. Meanwhile, real-time analysis results from the sentiment engine are also stored, and the questions and follow-up content are dynamically adjusted based on these results.

[1718] After the meeting concludes, the server generates a summary and action items from the analysis results. Using a generation AI model, the summary and action item list are generated by inputting user responses and sentiment data as prompts. This summary and action items are displayed on the terminal for the user to review and add or modify as needed.

[1719] The final data is saved again and used in the next meeting. By viewing sentiment analysis graphs and past interview data reports within the dashboard, users can gain the insights needed to plan their next actions.

[1720] Specific example

[1721] As an example, consider a meeting between a supervisor and a subordinate at a certain company.

[1722] The supervisor and subordinate register their respective calendar information in the system. Based on this information, the server suggests the optimal meeting time, which is displayed on the terminal. Once the suggested time is approved, the meeting begins. The terminal displays questions in a chat format, and the subordinate enters their answers. Simultaneously, the emotion engine analyzes the subordinate's answers, facial expressions, and tone of voice to estimate their emotional state. Based on the analysis results, the server generates a summary and action items, which can be viewed on the terminal. The subordinate and supervisor review the displayed results and add or modify them as needed.

[1723] In this way, by combining the emotion engine, the interview process becomes more personalized, users feel more psychologically secure, and effective feedback and action plans become possible.

[1724] Example of a prompt

[1725] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[1726] Based on the details described above, the present invention enables users to efficiently conduct meetings through an automated system, ensure a sense of psychological security, and utilize emotional data to inform future meetings.

[1727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1728] Program processing flow

[1729] Step 1: Obtain calendar information and schedule.

[1730] Processing flow

[1731] The server uses the API key provided by the user to access calendar services (e.g., Google Calendar, Outlook Calendar) and retrieve each user's schedule information. Based on the retrieved calendar information, it calculates the optimal meeting time.

[1732] Specific actions

[1733] Input: User's API key, user's schedule information to be retrieved from the calendar service.

[1734] Data Processing: The server accesses the calendar API via OAuth 2.0 authentication to retrieve schedule information. A quicksort algorithm is used to compare each user's schedule and analyze overlapping time slots.

[1735] Output: Suggestions for optimal meeting times based on the availability of both users.

[1736] Step 2: View and approve the proposed meeting time.

[1737] Processing flow

[1738] The terminal displays the optimal meeting time sent from the server to the user. The user can approve or modify the suggested meeting time.

[1739] Specific actions

[1740] Input: Proposed meeting time

[1741] Data processing: The terminal displays a schedule proposal window, and approval / modification buttons are provided.

[1742] Output: User approval or correction response

[1743] Step 3: Confirm and notify the schedule

[1744] Processing flow

[1745] The server will finalize the schedule based on the user's approved or revised meeting times and notify the user.

[1746] Specific actions

[1747] Input: User-approved or modified meeting time

[1748] Data processing: Update confirmed meeting times in the calendar service and generate notification emails and alerts.

[1749] Output: Confirmed schedule and notifications

[1750] Step 4: Generating a customized set of questions

[1751] Processing flow

[1752] The server generates a customized set of questions using an AI model based on the user's profile information and past interview data.

[1753] Specific actions

[1754] Input: User profile information, past interview data

[1755] Data processing: This information is input as prompts into a generative AI model (e.g., GPT-4) to generate unique questions.

[1756] Output: Individualized question list

[1757] Step 5: Displaying questions in an interactive format

[1758] Processing flow

[1759] The terminal displays the generated set of questions to the user in an interactive format. The user answers the questions.

[1760] Specific actions

[1761] Input: Customized list of questions

[1762] Data processing: Provide an interface that displays questions sequentially in the chat UI and accepts user responses.

[1763] Output: User response data

[1764] Step 6: Emotion Analysis

[1765] Processing flow

[1766] The server uses an emotion engine to analyze user responses in real time, including analyzing text input, facial expressions, and voice tone.

[1767] Specific actions

[1768] Input: User text input, camera image (facial expression), audio data

[1769] Data processing: Input data is analyzed using tools such as OpenCV and TensorFlow, and emotion parameters are extracted.

[1770] Output: Emotion analysis results

[1771] Step 7: Saving and analyzing interview results

[1772] Processing flow

[1773] The server encrypts and stores the user's responses and sentiment analysis results, and places them in a database so they can be used in future interviews.

[1774] Specific actions

[1775] Input: User response data, sentiment analysis results

[1776] Data Processing: Securely store data using AES-256 encryption. Perform necessary analysis and list areas for future improvement.

[1777] Output: Encrypted data, analysis report

[1778] Step 8: Summarize and generate action items

[1779] Processing flow

[1780] The server uses a generative AI model based on the interview results and sentiment analysis to generate summaries and action items.

[1781] Specific actions

[1782] Input: User response data, sentiment analysis results

[1783] Data processing: Input this data into a generative AI model to generate a summary and a list of action items.

[1784] Output: Summary, Action Item List

[1785] Step 9: View and correct the results

[1786] Processing flow

[1787] The terminal displays the generated summary and action items to the user, who can add or modify them as needed.

[1788] Specific actions

[1789] Input: Summary text, list of action items

[1790] Data processing: Display the data on the dashboard screen and provide an interface that allows users to edit it.

[1791] Output: User additions and modifications

[1792] Step 10: Save the final data and use it for the next meeting.

[1793] Processing flow

[1794] The server saves the final data and stores sentiment analysis data and action items in a database for use in the next interview.

[1795] Specific actions

[1796] Input: User additions and modifications, sentiment analysis results

[1797] Data processing: Integrate the final data and save it to the database so that it can be referenced during the next meeting.

[1798] Output: Saved final data, reference data for the next meeting

[1799] Examples of prompts to input into a generative AI model

[1800] Example of a prompt:

[1801] "When giving feedback to a subordinate during a meeting, what points should a supervisor keep in mind to provide optimal feedback while considering the subordinate's emotional state?"

[1802] (Application Example 2)

[1803] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1804] In conventional autonomous vehicles, it has been difficult to understand the emotional state of the passenger in real time and optimize the driving environment based on that understanding. This has led to problems such as passengers experiencing stress and difficulty in ensuring comfort and safety during driving. There is a need to provide a safer and more comfortable autonomous driving experience by appropriately monitoring the passenger's emotional state and adjusting the driving environment accordingly.

[1805] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring the user's calendar information, means for calculating the optimal meeting time based on the acquired calendar information, means for displaying the proposed meeting time, means for accepting the user's approval or modification, means for confirming the final schedule and notifying the user, means for analyzing the user's emotional state in real time using an emotion analysis module, means for automatically generating and providing feedback and advice based on the emotional state to the user, means for adjusting the driving environment and providing appropriate feedback based on the emotion analysis results, and means for safely and comfortably managing the driving situation based on the emotion analysis. This makes it possible to monitor the user's emotional state in real time and provide appropriate feedback and optimize the driving environment.

[1806] "Means for obtaining user calendar information" refers to a function that automatically retrieves user schedule information from online calendars or the organization's internal schedule management system.

[1807] The "method for calculating the optimal meeting time" is an algorithm that calculates the optimal meeting time that suits both parties, based on the acquired calendar information.

[1808] "A means of displaying suggested meeting times" refers to an interface that visually presents the user with calculated meeting time options.

[1809] "Means for accepting user approval or modification" refers to an interface that allows users to approve or modify the proposed meeting time.

[1810] "Means of finalizing the schedule and notifying the user" refers to a function that confirms the meeting time approved or modified by the user and notifies the user of that information.

[1811] The "emotion analysis module" is software that analyzes data such as the user's facial expressions and voice tone to estimate their emotional state in real time.

[1812] "Means for automatically generating feedback and advice based on emotional state" refers to a function that automatically creates and presents appropriate feedback and advice based on the results of an emotion analysis module.

[1813] "Means of adjusting the driving environment and providing appropriate feedback" refers to a function that adjusts the in-car environment (e.g., temperature, music, lighting, etc.) based on emotion analysis results to provide the user with a comfortable and safe driving experience.

[1814] "Means for safely and comfortably managing driving conditions" refers to functions that optimize the vehicle's driving state based on emotion analysis and user feedback, supporting accident prevention and comfortable operation.

[1815] This invention is a system that monitors the user's emotional state in real time within an autonomous vehicle and provides a safer and more comfortable driving experience to the user through appropriate feedback and optimization of the driving environment. The main components of this system are a server, a terminal, a user interface, and an emotion analysis module.

[1816] System Configuration

[1817] 1. Server:

[1818] Retrieving calendar information: Use a Web API to retrieve calendar information from the user's schedule management system.

[1819] Schedule Calculation: Implement an algorithm to calculate optimal meeting times and driving schedules based on acquired calendar information.

[1820] Emotion analysis module: Equipped with software to analyze the user's facial expressions and voice tone to estimate their emotional state.

[1821] Feedback generation: Based on the emotion analysis results, appropriate feedback and advice are generated.

[1822] 2. Terminal:

[1823] Information display: A device (such as an in-car display) that displays schedules and feedback obtained from a server to the user.

[1824] Dialogue Interface: Provides an interface that displays generated questions in a chat format and accepts user responses.

[1825] 3. User:

[1826] Data entry: Enter your own calendar information, profile, responses, etc. into the system.

[1827] Reviewing feedback: Review and apply any feedback or advice provided by the system.

[1828] 4. Emotion Analysis Module:

[1829] Data analysis: Real-time estimation of emotional state based on user facial expressions and tone of voice.

[1830] Data storage: Save the analysis data and use it for later feedback and analysis.

[1831] Hardware and software usage

[1832] Hardware:

[1833] In-vehicle camera: Used to capture the user's facial expressions.

[1834] Microphone: Collects and analyzes user feedback.

[1835] In-car display: Displays analysis results and feedback.

[1836] In-vehicle PC: Performs data analysis and controls the entire system.

[1837] software:

[1838] Emotion Analysis API: Used to analyze facial expressions and tone of voice to estimate emotional states.

[1839] Schedule calculation algorithm: Calculates the optimal schedule based on the user's calendar information.

[1840] Database: Stores analysis results and is used to generate feedback.

[1841] Specific example

[1842] As an example, consider a scenario where a user is riding in an autonomous vehicle. The user's facial expressions and voice data are collected in real time via the vehicle's cameras and microphones. A server's emotion analysis module analyzes this data and detects if the user is experiencing stress. Based on this, the server suggests relaxation music and displays it on the vehicle's display. It also adjusts the air conditioning temperature to provide a comfortable driving environment. The user confirms this feedback and realizes that the driving environment has become more comfortable.

[1843] Example of a prompt

[1844] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[1845] In this way, by providing real-time feedback and environmental adjustments based on emotion analysis, users can obtain a safe and comfortable driving experience.

[1846] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1847] Step 1:

[1848] The server retrieves the user's calendar information. Specifically, it accesses the user's online calendar or schedule management system via a Web API to retrieve upcoming schedule information. The input is the user ID, and the output is calendar information. This calendar information includes the time slots and content of appointments.

[1849] Step 2:

[1850] The server analyzes the acquired calendar information and executes an algorithm to calculate the optimal meeting time that suits both parties. The input is calendar information, and the output is a suggestion for the optimal meeting time. This algorithm cross-references available time slots across different time periods to select the most suitable time frame.

[1851] Step 3:

[1852] The terminal displays the calculated optimal meeting time to the user. The input is a suggested optimal meeting time, and the output is a visual notification to the user. The terminal shows the suggested time to the user through a visual interface and prompts them to approve or revise it.

[1853] Step 4:

[1854] The user approves or modifies the proposed meeting time. The input is the proposed meeting time, and the output is the approved or modified meeting time. The user interacts with the system via a terminal and provides feedback on their schedule.

[1855] Step 5:

[1856] The server confirms the final schedule from the user and notifies the user. The input is the approved or revised meeting time, and the output is a notification of the confirmed meeting schedule. The confirmed schedule is stored in the database for later access.

[1857] Step 6:

[1858] The server analyzes the user's emotional state in real time using an emotion analysis module. Inputs are the user's facial expression and voice data, and output is the result of the emotional state analysis. This data is collected via an in-vehicle camera and microphone and then processed by the emotion analysis algorithm.

[1859] Step 7:

[1860] The server automatically generates appropriate feedback and advice based on the user's emotional state and provides it to the user through the terminal. The input is the result of the emotional state analysis, and the output is feedback and advice, which includes visual notifications and audio announcements.

[1861] Step 8:

[1862] The terminal displays generated questions to the user in a chat format and receives the user's answers. The input is a customized set of questions, and the output is the user's answers. The answers are encrypted and sent to the server.

[1863] Step 9:

[1864] The server stores the user's responses and adjusts the driving environment based on the sentiment analysis results. The input consists of the user's responses and the analysis results of their emotional state, while the output is information on adjusting the driving environment. This includes adjusting the in-car air conditioning, music, and lighting.

[1865] Step 10:

[1866] The server manages driving conditions safely and comfortably based on the results of emotion analysis. Inputs are the results of the emotional state analysis and information on adjusting the driving environment, while output is an optimized driving environment. This allows the user to have a safe and comfortable driving experience.

[1867] Generative AI model prompt example:

[1868] "Please describe how to design and implement a system that monitors the user's relaxation while driving and provides appropriate feedback as needed. Explain how to analyze the user's facial expressions and voice in real time and optimize the driving environment based on their emotional state. Also, please describe the hardware and software used for this purpose."

[1869] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1870] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1871] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1872] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1873] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1874] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1875] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1876] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1877] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1878] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1879] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1880] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1881] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1882] 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.

[1883] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1884] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1885] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1886] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1887] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1888] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1889] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1890] The following is further disclosed regarding the embodiments described above.

[1891] (Claim 1)

[1892] A means of obtaining the user's calendar information,

[1893] A method for calculating the optimal meeting time based on acquired calendar information,

[1894] A means of displaying the proposed meeting time,

[1895] A means of accepting user approval or modification,

[1896] A system that includes a means of finalizing the schedule and notifying users.

[1897] (Claim 2)

[1898] A means of generating customized questions based on the user's profile and past interview data,

[1899] A means of displaying the generated questions to the user in a chat format,

[1900] The system according to claim 1, comprising means for receiving, encrypting, and storing a user's response.

[1901] (Claim 3)

[1902] A means of analyzing the interaction during the interview in real time,

[1903] Means for generating summaries and action items,

[1904] A means of displaying the generated summary and action items to the user,

[1905] The system according to claim 1, comprising means for receiving user modifications and storing the final data.

[1906] "Example 1"

[1907] (Claim 1)

[1908] A means of obtaining the user's calendar information,

[1909] An information management device that manages acquired calendar information provides a means for calculating the optimal meeting time,

[1910] A means of displaying the calculated interview time on the user interface,

[1911] A means of accepting user approval or modification,

[1912] A system that includes a means of finalizing the interview schedule and notifying the user.

[1913] (Claim 2)

[1914] A means of providing customized questions using a generative AI model based on user information and past data,

[1915] A means of displaying the generated questions to the user in a chat format,

[1916] The system according to claim 1, comprising means for receiving, encrypting, and storing a user's response.

[1917] (Claim 3)

[1918] A means of analyzing user input data in real time,

[1919] A means for generating summaries and action items based on analysis results,

[1920] A means for displaying the generated summary and action items to the user and receiving the user's modifications,

[1921] The system according to claim 1, comprising means for storing the final data.

[1922] "Application Example 1"

[1923] (Claim 1)

[1924] A means of obtaining user time management information,

[1925] A means of calculating the optimal interview time based on acquired time management information,

[1926] A means of displaying the proposed meeting time,

[1927] A means of accepting user approval or modification,

[1928] A means to finalize the schedule and notify users,

[1929] A means for detecting overlaps in free time in calendar information,

[1930] A means of displaying the generated optimal time on the terminal,

[1931] A system that includes a means of encrypting data using an encryption library.

[1932] (Claim 2)

[1933] A means of generating customized questions based on the user's profile information and past conversation data,

[1934] A means of displaying the generated questions to the user in a chat format,

[1935] A means of receiving, encrypting, and storing user responses,

[1936] The system according to claim 1, comprising means for updating profile information based on the user's shift and task information.

[1937] (Claim 3)

[1938] A means of analyzing the interaction during a conversation in real time,

[1939] Means for generating summaries and action items,

[1940] A means of displaying the generated summary and action items to the user,

[1941] A means of receiving user modifications and saving the final data,

[1942] An algorithm to determine the optimal interview time,

[1943] The system according to claim 1, comprising a generative AI model for automatically generating text for interview questions.

[1944] "Example 2 of combining an emotion engine"

[1945] (Claim 1)

[1946] A means of obtaining the user's schedule information,

[1947] A means of calculating the optimal meeting time based on the acquired schedule information,

[1948] A means of displaying the proposed meeting time,

[1949] A means of accepting user approval or modification,

[1950] A system that includes a means of finalizing the schedule and notifying users.

[1951] (Claim 2)

[1952] A means of generating customized questions based on user data and past meeting data,

[1953] A means of displaying the generated questions to the user in an interactive format,

[1954] The system according to claim 1, comprising means for receiving, encrypting, and storing a user's response.

[1955] (Claim 3)

[1956] A means of analyzing the interactions during a meeting in real time,

[1957] Means for performing emotion analysis,

[1958] Means for generating summaries and action items,

[1959] A means of displaying the generated summary and action items to the user,

[1960] The system according to claim 1, comprising means for receiving user modifications and storing the final data.

[1961] (Claim 4)

[1962] A method for analyzing a user's emotional state in real time using an emotion engine,

[1963] A means to dynamically adjust the content of questions and follow-up based on the analysis results,

[1964] A means of saving the final emotion analysis results,

[1965] The system according to claim 1, comprising means for utilizing the emotion analysis results in the next meeting.

[1966] (Claim 5)

[1967] A means of generating appropriate feedback and action plans from a user's past data using a generative AI model,

[1968] The system according to claim 1, comprising means for displaying the generated feedback and action plan to the user.

[1969] "Application example 2 when combining with an emotional engine"

[1970] (Claim 1)

[1971] A means of obtaining the user's calendar information,

[1972] A method for calculating the optimal meeting time based on acquired calendar information,

[1973] A means of displaying the proposed meeting time,

[1974] A means of accepting user approval or modification,

[1975] A means to finalize the schedule and notify users,

[1976] A means of analyzing a user's emotional state in real time using an emotion analysis module,

[1977] A system that includes means for automatically generating and providing feedback and advice to users based on their emotional state.

[1978] (Claim 2)

[1979] A means of generating customized questions based on the user's profile and past interview data,

[1980] A means of displaying the generated questions to the user in a chat format,

[1981] A means of receiving, encrypting, and storing user responses,

[1982] The system according to claim 1, comprising means for adjusting the driving environment and providing appropriate feedback based on the results of emotion analysis.

[1983] (Claim 3)

[1984] A means of analyzing the interaction during the interview in real time,

[1985] Means for generating summaries and action items,

[1986] A means of displaying the generated summary and action items to the user,

[1987] A means of receiving user modifications and saving the final data,

[1988] The system according to claim 1, comprising means for safely and comfortably managing driving conditions based on emotion analysis. [Explanation of Symbols]

[1989] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining the user's calendar information, A method for calculating the optimal meeting time based on acquired calendar information, A means of displaying the proposed meeting time, A means of accepting user approval or modification, A system that includes a means of finalizing the schedule and notifying users.

2. A means of generating customized questions based on the user's profile and past interview data, A means of displaying the generated questions to the user in a chat format, The system according to claim 1, comprising means for receiving, encrypting, and storing a user's response.

3. A means of analyzing the interaction during the interview in real time, Means for generating summaries and action items, A means of displaying the generated summary and action items to the user, The system according to claim 1, comprising means for receiving user modifications and saving the final data.

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