System

Abstract

A system is provided.SOLUTION: This system includes a means for acquiring past success cases and team characteristic data, a means for automatically generating a workshop idea on the basis of the acquired data, and a means for displaying the generated workshop idea.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Team building is an important activity in companies, and effective team building directly leads to improved team performance. However, planning appropriate workshops, analyzing feedback, and approaching remote teams requires a great deal of effort. It is also important to provide personalized workshops based on each member's skills and interests. To address these challenges, more efficient and effective methods are needed. [Means for solving the problem]

[0005] This system supports team building through a means for acquiring data on past success stories and team characteristics, a means for automatically generating workshop ideas based on the acquired data, and a means for displaying the generated workshop ideas. It also includes a means for generating personalized workshops based on each member's skills and interests and proposing them to each member. It also includes a means for collecting feedback after a workshop, automatically extracting important themes and trends, and displaying them as areas for improvement for the next workshop, thereby promoting continuous improvement. It automatically generates scenarios related to specific team dynamics and problems, displays them, and supports their execution, allowing the entire team to master specific problem-solving skills. It includes a means for pre-setting the timeline and steps for each workshop session and supporting their progress, enabling efficient session management. It includes a means for generating virtual team building activities based on the needs of remote teams, and displays and supports their execution, strengthening team cohesion despite physical distance. These means make it possible to comprehensively and efficiently manage and optimize corporate team building.

[0006] "Past success stories" refer to team building workshops or programs that the company has previously conducted that have produced positive results.

[0007] "Team characteristic data" refers to data such as team members' skills, abilities, interests, and performance evaluations, and is information for understanding the composition and characteristics of the team.

[0008] "Workshop ideas" refer to specific plans and proposals for activities or sessions to be held as part of team building.

[0009] A "personalized workshop" refers to a workshop that is customized based on each team member's individual skills and interests.

[0010] "Feedback" refers to the opinions and evaluations collected from participants after a workshop or program has ended, and is information used to improve the program and verify its effectiveness.

[0011] "Key themes and trends" refer to major issues, common challenges, or areas for improvement that emerge as a result of analyzing the feedback.

[0012] A "scenario" is a specific role-play setting or story created to simulate how a team should respond to a particular situation or problem.

[0013] "Timeline" refers to a schedule that shows the planned time for each step or activity in a workshop or session, and is used for time management purposes.

[0014] "Remote team" refers to a team made up of team members who work in geographically separate locations.

[0015] "Virtual team building activities" refers to team building activities conducted by remote team members in an online environment.

[0016] "System" refers to a technological arrangement that combines a set of hardware, software, and algorithms designed to implement these means and processes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

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

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

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

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0038] This invention is an AI-based system that supports corporate team building, generating workshop ideas based on past success stories and team characteristic data, and proposing personalized plans for each member. It also has the functions of automatically analyzing workshop feedback and suggesting areas for improvement, generating role-play scenarios for specific situations, managing the progress of workshops using AI, and providing virtual team building activities for remote teams.

[0039] 1. Workshop idea generation

[0040] The server accesses a database to retrieve data on past success stories and team characteristics. Based on this data, it uses an AI model to automatically generate new workshop ideas. The generated ideas are sent to the device, where the user can review and select the proposed ideas.

[0041] Examples:

[0042] The server uses an AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills." This idea is then proposed to the user via their device.

[0043] 2. Personalized Workshop Proposal

[0044] The server retrieves data about each member's skills and interests from a database and uses AI models to generate personalized workshops. The generated workshop proposals are sent to each member's device, allowing users to view the workshop content that best suits them.

[0045] Examples:

[0046] The server generates a "problem-solving workshop" based on member A's data that he is "interested in problem-solving skills," sends it to the terminal, and displays it. The user confirms the proposal.

[0047] 3. Automatic feedback analysis

[0048] After the workshop, the device collects feedback from users and sends it to the server. The server then uses an AI model to analyze the feedback data and extract important themes and trends. The results of this analysis are sent to the device as a report, allowing users to understand how to improve the next workshop.

[0049] Examples:

[0050] The device collects feedback from users that "the time to come up with ideas is short," and the server extracts this as a need for "improved time management" and notes it in the report as an issue to be addressed next time.

[0051] 4. Generating Role-Play Scenario

[0052] The server uses AI models to generate role-play scenarios based on data about team dynamics and specific problems, and the scenarios are sent to the device, where users can view and execute them to learn specific problem-solving skills.

[0053] Examples:

[0054] The server recognizes communication issues, generates role-play scenarios based on the theme of "dealing with problems during a project," and provides them to users via their terminals.

[0055] 5. Workshop Management

[0056] The server pre-determines the timeline and steps for each workshop session. The terminal supports the progress of the session according to this timeline, and displays time management and important instructions to the user, enabling efficient session progress.

[0057] Examples:

[0058] The server sets a timeline such as "Idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device notifies the user when each session ends.

[0059] 6. Creating Virtual Team Building Activities

[0060] The server uses an AI model to generate virtual team-building activities based on the needs data of the remote team, and the generated content is sent to the device and provided to the user in a format that can be executed even in a remote environment.

[0061] Examples:

[0062] The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal. The user can carry out the activity and strengthen the unity of the remote team.

[0063] As a result, the present invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling the creation of effective workshops, the analysis of feedback, and the support of remote teams.

[0064] The processing flow will be explained below.

[0065] Workshop idea generation

[0066] Step 1:

[0067] The server accesses a database to obtain past success stories and current team characteristic data.

[0068] Step 2:

[0069] The server inputs the acquired data into the AI ​​model and begins analysis.

[0070] Step 3:

[0071] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[0072] Step 4:

[0073] The server transmits the generated workshop ideas to the terminal.

[0074] Step 5:

[0075] The terminal displays the workshop ideas to the user.

[0076] Step 6:

[0077] The user selects the best idea from multiple proposed ideas.

[0078] Personalized Workshop Proposals

[0079] Step 1:

[0080] The server retrieves data about each member's skills and interests from a database.

[0081] Step 2:

[0082] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[0083] Step 3:

[0084] The server transmits the generated personalized workshop proposal to each member's terminal.

[0085] Step 4:

[0086] The terminal displays personalized workshop suggestions to each member.

[0087] Step 5:

[0088] The user checks the workshop content that best suits him or her.

[0089] Automatic analysis of feedback

[0090] Step 1:

[0091] The terminal collects feedback from users after the workshop is over.

[0092] Step 2:

[0093] The terminal transmits the collected feedback data to the server.

[0094] Step 3:

[0095] The server uses AI models to analyze the feedback data and extract important themes and trends.

[0096] Step 4:

[0097] The server generates a report based on the extraction results and sends it to the terminal.

[0098] Step 5:

[0099] The terminal displays the generated report to the user.

[0100] Step 6:

[0101] Users will know what needs to be improved for the next workshop.

[0102] Role-play scenario generation

[0103] Step 1:

[0104] The server captures data about specific team dynamics and issues.

[0105] Step 2:

[0106] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[0107] Step 3:

[0108] The server transmits the generated role-play scenario to the terminal.

[0109] Step 4:

[0110] The terminal displays the scenario and provides instructions for the user to execute.

[0111] Step 5:

[0112] Users are guided through role-play scenarios to develop problem-solving skills.

[0113] Workshop progress

[0114] Step 1:

[0115] The server pre-determines the timeline and steps for each workshop session.

[0116] Step 2:

[0117] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[0118] Step 3:

[0119] The server monitors the progress of each session and sends instructions to the terminal to proceed to the next session.

[0120] Step 4:

[0121] The user follows instructions from the terminal to proceed with the workshop.

[0122] Generate virtual team building activities

[0123] Step 1:

[0124] The server retrieves data regarding the needs of the remote team.

[0125] Step 2:

[0126] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[0127] Step 3:

[0128] The server transmits the generated activity content to the terminal.

[0129] Step 4:

[0130] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[0131] Step 5:

[0132] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[0133] Example 1

[0134] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0135] In-company team building activities require planning and running effective workshops, providing content tailored to each participant, and collecting and analyzing feedback after the activities. However, carrying out these activities efficiently is difficult and requires significant human resources and time. Furthermore, with the spread of remote work, effective team building activities must also be carried out in remote environments. However, current methods often fail to achieve their full effectiveness. The present invention aims to provide a system and method for solving these problems.

[0136] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0137] In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas using an AI model based on the acquired data, means for sending the generated workshop ideas to a terminal and displaying them, means for generating personalized workshops based on the skills and interests of each member, means for proposing personalized workshops to each member, means for collecting feedback from the terminal after the workshop, means for extracting important themes and trends using the AI ​​model based on the collected feedback, and means for displaying the extracted analysis results in a report as improvements for the next workshop. This enables an effective and efficient cycle of team building planning, execution, and feedback.

[0138] "Past success stories" are data showing successful cases obtained from workshops or projects that have been previously implemented.

[0139] "Team characteristic data" is data that includes information about team members' skills, interests, roles, performance, etc.

[0140] An "AI model" is a computational algorithm or neural network that uses artificial intelligence techniques to analyze data and generate a specific result.

[0141] "Workshop Ideas" are specific suggestions for activities or sessions designed for team building or skill development.

[0142] "Terminal" means an electronic device used by a user to input and display information, including a personal computer, smartphone, tablet, etc.

[0143] A "personalized workshop" is a workshop designed specifically to meet the skills and interests of individual members.

[0144] "Feedback" refers to data on opinions and evaluations collected from users after the workshop.

[0145] "Key themes and trends" are key issues or patterns extracted from the collected feedback data that can help improve the next workshop.

[0146] The "report" is a document displayed on the terminal that lists the analysis results and improvements for the next workshop.

[0147] A "role-play scenario" is a plot or setting for a simulated role-play based on a particular situation.

[0148] A "timeline" indicates the schedule for the start and end of each session or activity in the workshop.

[0149] "Virtual team building activities" are online team building activities that can be carried out in a remote environment.

[0150] This invention is a system for supporting corporate team building, which includes a server, terminals, and users. The system utilizes AI models to generate workshop ideas, propose personalized workshops, automatically analyze feedback, generate role-play scenarios, manage workshop progress, and generate virtual team building activities.

[0151] Data collection and workshop idea generation

[0152] The server accesses a database to retrieve past success stories and team characteristics, including each member's skills, interests, roles, and performance data. Based on the retrieved data, an AI model (e.g., GPT-4) is used to automatically generate workshop ideas. The generated ideas are sent to the device and displayed to the user.

[0153] Examples:

[0154] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[0155] Example prompt sentence:

[0156] "Generate workshop ideas aimed at improving communication skills."

[0157] Personalized Workshop Proposals

[0158] The server generates personalized workshops based on each member's skills and interests, using their skill and role data. The generated workshop proposals are sent to each member's device and notified to the user.

[0159] Examples:

[0160] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[0161] Example prompt sentence:

[0162] "Please propose a workshop for members who are interested in generating new ideas."

[0163] Feedback collection and automated analysis

[0164] After the workshop, the device collects feedback from users, including evaluations of the workshop's duration, content, and props. The collected feedback data is sent to a server and analyzed using an AI model (e.g., Transformer) to extract important themes and trends. The results of this analysis are sent to the device as a report, which users can review.

[0165] Examples:

[0166] The device asks the user how satisfied they are with the workshop time, and sends the response data to the server. The server analyzes the data and determines whether time management needs improvement, and compiles it into a report and sends it to the device.

[0167] Example prompt sentence:

[0168] "Analyze the feedback data from the workshops and extract key themes and trends."

[0169] Role-play scenario generation

[0170] The server generates role-play scenarios using AI models (e.g., T5) based on data about team dynamics and specific problems. The generated scenarios are sent to the device and made available for users to view and play.

[0171] Examples:

[0172] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[0173] Example prompt sentence:

[0174] "Generate role-play scenarios for dealing with problems during a project."

[0175] Workshop progress management

[0176] The server sets the workshop session timeline and steps in advance, and the terminal follows this timeline and supports the progress. Time management and important instructions are displayed to the user.

[0177] Examples:

[0178] The server will set up a "15-minute idea brainstorming session" or a "30-minute discussion," and the device will display a notification at the end of each session.

[0179] Example prompt sentence:

[0180] "Set the timeline for the workshop and help facilitate each session."

[0181] Generate virtual team building activities

[0182] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The generated activities are sent to the device and provided in a format that users can carry out even in a remote environment.

[0183] Examples:

[0184] The server generates an online quiz based on the characteristics of the remote team and sends it to the device. The user then checks the content and participates in the remote environment.

[0185] Example prompt sentence:

[0186] "Generate virtual team building activities for remote teams."

[0187] As described above, the system of the present invention efficiently supports team building activities through comprehensive data collection and analysis / generation using AI models, highly automating the entire process from workshop planning to execution, feedback collection, and proposals for the next activity.

[0188] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0189] Step 1:

[0190] Data collection

[0191] The server accesses the company's database to retrieve past success stories and team characteristics data, including each member's skills, interests, roles, and performance data. The input is a query from the database, and the output is the retrieved data.

[0192] Specific behavior:

[0193] The server extracts "past project success rates" and "member skill profiles" from the database.

[0194] Input: Query to the database "SELECT FROM Success_Cases"

[0195] Output: Past success story data

[0196] Step 2:

[0197] Workshop idea generation

[0198] The server automatically generates workshop ideas using an AI model (e.g., GPT-4) based on the acquired data. The acquired data is the input, and the generated workshop ideas are the output. These ideas are sent to the terminal and notified to the user.

[0199] Specific behavior:

[0200] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[0201] Input: Past success story data, team characteristics data

[0202] Output: Workshop idea "Team debate session"

[0203] Step 3:

[0204] Personalized Workshop Proposals

[0205] The server generates personalized workshops based on each member's skills and interests. The input is the member's individual skill and interest data, and the output is personalized workshop proposals. The generated proposals are sent to each member's device and notified to the user.

[0206] Specific behavior:

[0207] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[0208] Input: Individual member skills and interest data

[0209] Output: Personalized Workshop "New Idea Brainstorming Session"

[0210] Step 4:

[0211] Gathering feedback

[0212] After the workshop, the terminal collects feedback from users. The input is the feedback from users, and the output is the collected feedback data. This is then sent to the server.

[0213] Specific behavior:

[0214] The terminal asks the user about "satisfaction with the workshop time" and transmits the answer data to the server.

[0215] Input: User feedback

[0216] Output: Feedback data

[0217] Step 5:

[0218] Automatic analysis of feedback

[0219] The server analyzes the collected feedback data using an AI model (e.g., Transformer) to extract important themes and trends. The input is the feedback data, and the output is the analysis results. These analysis results are sent to the terminal in the form of a report, which the user can review.

[0220] Specific behavior:

[0221] The server extracts themes such as "time management needs improvement" from the feedback data, compiles them into a report, and sends it to the terminal.

[0222] Input: Feedback data

[0223] Output: Report of analysis results

[0224] Step 6:

[0225] Role-play scenario generation

[0226] The server generates role-play scenarios using an AI model (e.g., T5) based on data about team dynamics and specific problems. The input is the specific problem data, and the output is the generated role-play scenario. This scenario is sent to the device, where the user can view and perform it.

[0227] Specific behavior:

[0228] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[0229] Input: Specific problem data

[0230] Output: Role-play scenario

[0231] Step 7:

[0232] Workshop progress management

[0233] The server sets the timeline and steps of each workshop session in advance. The input is the workshop timeline and steps, and the output is the timeline setting data. The terminal follows this timeline and supports the progress.

[0234] Specific behavior:

[0235] The server will set up a "15-minute idea brainstorm" or "30-minute discussion," and the device will display a notification.

[0236] Input: Workshop timeline and steps

[0237] Output: Timeline setting data

[0238] Step 8:

[0239] Generate virtual team building activities

[0240] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The input is the needs data of the remote team, and the output is the generated team-building activity. The activity content is sent to the device and provided in a form that users can carry out even in a remote environment.

[0241] Specific behavior:

[0242] The server generates an "online quiz competition" based on the "remote team characteristics" and sends it to the terminal.

[0243] Input: Remote team needs data

[0244] Output: Virtual team building activity

[0245] (Application example 1)

[0246] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0247] Effective team building and training is crucial for modern companies, but it is difficult to provide personalized programs tailored to diverse needs, effectively analyze feedback, generate role-play scenarios for specific situations, and conduct training in a remote environment. In particular, employee training in a virtual environment is not effectively managed or optimized.

[0248] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0249] In this invention, the server includes means for acquiring data on past success stories and team characteristics, means for automatically generating workshop ideas based on the acquired data, means for displaying the generated workshop ideas, means for automatically generating training workshops in a virtual environment, and means for displaying the generated training workshops. This enables the provision of personalized programs that meet the diverse needs of companies, automatic analysis of feedback, generation of role-play scenarios according to situations, and effective training in remote environments.

[0250] "Past success stories" are examples of previously held workshops or trainings that have proven particularly successful.

[0251] "Team characteristic data" is data relating to a particular team that includes information such as the skills, interests, and personalities of members.

[0252] "Workshop Ideas" are specific activity proposals for team building or training designed around a specific theme or purpose.

[0253] A "personalized workshop" is a workshop that is customized based on each member's skills and interests.

[0254] "Feedback" refers to evaluations and opinions from members who participated in workshops and training, including information for improvement.

[0255] "Key themes and trends" are items or patterns extracted from the collected feedback data that deserve particular attention for improving workshops or generating new ideas.

[0256] A "role-play scenario" is an exercise or dramatization designed to recreate a specific situation for the purpose of practical skill acquisition.

[0257] A "virtual environment" refers to a virtual space or interface provided by computer technology that enables remote activities.

[0258] "Training Workshop" means an educational session designed to improve employee skills or knowledge, including work-related training.

[0259] MODE FOR CARRYING OUT THE INVENTION

[0260] This invention is an AI-based system for effective corporate team building and employee training. This system generates new workshop ideas and role-play scenarios based on past success stories and team characteristic data, and provides virtual training that can be conducted even in remote environments. Specific embodiments are described below.

[0261] 1. System Configuration

[0262] Server: A cloud server is used to install an AI model (e.g., OpenAI's GPT-4) and a database management system (e.g., MySQL).

[0263] Devices: Employee-owned smartphones, tablets, or VR headsets (e.g., Oculus, VIVE, etc.).

[0264] Software: Use feedback analysis tools (e.g., Google Cloud Natural Language) and real-time communication servers (e.g., WebSockets).

[0265] 2. Data Acquisition

[0266] The server retrieves past success stories and team characteristics data from the database, including information about employee skills and interests.

[0267] 3. Workshop idea generation

[0268] The server uses a generative AI model (e.g., GPT-4) to generate new workshop ideas based on the acquired data, and the generated ideas are sent to the terminals and made available to employees.

[0269] Example: The server uses a generative AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills."

[0270] 4. Personalized Workshop Proposals

[0271] The server acquires each member's skill and interest data and generates a personalized workshop, which is then sent to each member's device.

[0272] Example: Based on data that member A is interested in problem-solving skills, the server generates a "problem-solving workshop" and sends it to the terminal.

[0273] 5. Automatic feedback analysis

[0274] The terminals collect feedback from employees after the workshop and send it to a server, which then uses a feedback analysis tool to analyze the feedback data and extract important themes and trends.

[0275] Example: The device collects feedback from the user that "the time it takes to come up with ideas is short," and the server extracts this as a need for "improved time management."

[0276] 6. Role-play scenario generation

[0277] The server uses a generative AI model to generate role-play scenarios based on data about team dynamics and specific problems, which are then sent to the device where employees can view and act them out.

[0278] Example: The server recognizes a communication issue and generates a role-play scenario on the theme of "dealing with problems during a project."

[0279] 7. Generate virtual training workshops

[0280] The server automatically generates training workshops in a virtual environment, which can then be sent to the device and run remotely.

[0281] Example: The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal.

[0282] 8. Examples of prompts

[0283] "Generate training workshop ideas for a virtual store based on past success stories: [Case 1, Case 2, ...] and team characteristics data: [Team 1, Team 2, ...]."

[0284] This enables companies to provide personalized training programs to meet a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[0285] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0286] Step 1:

[0287] Data Acquisition:

[0288] The server retrieves data on past success stories and team characteristics from the database. In this process, it accesses the database using an SQL query and extracts the necessary data. The input is the SQL query, and the output is the data on past success stories and team characteristics.

[0289] Step 2:

[0290] Workshop idea generation:

[0291] The server generates new workshop ideas using a generative AI model (e.g., GPT-4) based on the acquired data. Here, a prompt is created and input to the AI ​​model. The input is the success case data, team characteristic data, and the created prompt, and the output is the generated workshop idea.

[0292] Step 3:

[0293] View Workshop Ideas:

[0294] The server sends the generated workshop ideas to the terminal. The terminal receives this information and displays it to the user. The input is the generated workshop ideas, and the output is the workshop ideas that the user confirms.

[0295] Step 4:

[0296] Personalized Workshop Suggestion:

[0297] The server acquires each member's skill and interest data and generates a personalized workshop using a generative AI model. The generated workshop is sent to each member's device and displayed. The input is the member's skill and interest data, and the output is a personalized workshop proposal.

[0298] Step 5:

[0299] Collecting feedback:

[0300] After the workshop, the terminal collects feedback from users and sends it to the server. The input is the feedback information from users, and the output is the feedback data sent to the server.

[0301] Step 6:

[0302] Feedback Analysis:

[0303] The server uses a feedback analysis tool to analyze the feedback data and extract important themes and trends. The analysis results are compiled into a report as improvements for the next workshop and sent to the terminal. The input is the feedback data, and the output is a report containing important themes and improvements.

[0304] Step 7:

[0305] Role-play scenario generation:

[0306] The server uses a generative AI model to generate role-play scenarios based on data on team dynamics and specific problems. The scenarios are sent to the device, where users can view and run them. The inputs are team dynamics data and specific problem data, and the output is the role-play scenario.

[0307] Step 8:

[0308] Generate a virtual training workshop:

[0309] The server automatically generates a training workshop in a virtual environment and sends it to the terminal. It is provided to the user in a state that can be executed in a remote environment. The input is the characteristic data of the remote team, and the output is the virtual training workshop.

[0310] These steps enable companies to deliver personalized training programs tailored to a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[0311] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0312] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans for each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. Furthermore, these functions are combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[0313] 1. Workshop idea generation

[0314] The server accesses the database to acquire data on past success stories and current team characteristics. It also uses an emotion engine to acquire user emotion data related to past success stories. Based on this data, it automatically generates new workshop ideas using an AI model and sends the ideas to the device. The device displays the workshop ideas to the user, who can then review and select from the proposed ideas.

[0315] Examples:

[0316] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[0317] 2. Personalized Workshop Proposal

[0318] The server retrieves data on each member's skills and interests from a database, and also uses an emotion engine to retrieve each member's past emotional data. Based on this data, an AI model is used to generate a personalized workshop and send it to each member's device. The device displays the proposal to each member, allowing them to check the workshop content that best suits them.

[0319] Examples:

[0320] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[0321] 3. Automatic feedback analysis

[0322] After the workshop, the device collects feedback from users and uses an emotion engine to collect emotional data during the feedback. This data is then sent to a server, which uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand how to improve their next workshop.

[0323] Examples:

[0324] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[0325] 4. Generating Role-Play Scenario

[0326] The server uses data about specific team dynamics and problems, and also uses an emotion engine to capture team emotion data. Based on this data, the AI ​​model generates role-play scenarios and sends them to the device, which displays the scenarios and provides instructions for the user to follow.

[0327] Examples:

[0328] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[0329] 5. Workshop Management

[0330] The server pre-determines the timeline and steps for each workshop session and uses an emotion engine to monitor the user's emotions in real time during the session. The device follows the pre-determined timeline and uses the emotion data to guide the session forward. Appropriate instructions are displayed to the user, ensuring efficient session progress.

[0331] Examples:

[0332] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[0333] 6. Creating Virtual Team Building Activities

[0334] The server uses an AI model to generate virtual team-building activities based on data on the needs of remote teams and emotional data acquired from an emotion engine. The generated activities are then sent to the device and provided to users so they can carry them out in a remote environment.

[0335] Examples:

[0336] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[0337] As a result, this invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling effective workshop generation, feedback analysis, and remote team support. By utilizing an emotion engine, it is possible to provide a more personalized experience based on user emotions and strengthen team cohesion.

[0338] The processing flow will be explained below.

[0339] Workshop idea generation

[0340] Step 1:

[0341] The server accesses the database to obtain historical success data and current team characteristic data.

[0342] Step 2:

[0343] The server uses an emotion engine to obtain user emotion data related to past success stories.

[0344] Step 3:

[0345] The server inputs the acquired data into the AI ​​model and begins analysis.

[0346] Step 4:

[0347] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[0348] Step 5:

[0349] The server transmits the generated workshop ideas to the terminal.

[0350] Step 6:

[0351] The terminal displays the workshop ideas to the user.

[0352] Step 7:

[0353] The user selects the best idea from the proposed ideas.

[0354] Personalized Workshop Proposals

[0355] Step 1:

[0356] The server retrieves data about each member's skills and interests from a database.

[0357] Step 2:

[0358] The server uses an emotion engine to obtain past emotion data for each member.

[0359] Step 3:

[0360] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[0361] Step 4:

[0362] The server transmits the generated personalized workshop proposal to each member's terminal.

[0363] Step 5:

[0364] The terminal displays personalized workshop suggestions to each member.

[0365] Step 6:

[0366] The user checks the workshop content that best suits him or her.

[0367] Automatic analysis of feedback

[0368] Step 1:

[0369] The terminal collects feedback from users after the workshop is over.

[0370] Step 2:

[0371] The terminal also collects the user's emotion data along with the feedback data.

[0372] Step 3:

[0373] The terminal transmits the collected data to the server.

[0374] Step 4:

[0375] The server uses AI models and sentiment engines to analyze the feedback data and extract key themes and trends.

[0376] Step 5:

[0377] The server generates a report based on the extraction results and sends it to the terminal.

[0378] Step 6:

[0379] The terminal displays the generated report to the user.

[0380] Step 7:

[0381] Users will know what needs to be improved for the next workshop.

[0382] Role-play scenario generation

[0383] Step 1:

[0384] The server captures data about specific team dynamics and issues.

[0385] Step 2:

[0386] The server uses an emotion engine to obtain emotion data within the team.

[0387] Step 3:

[0388] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[0389] Step 4:

[0390] The server transmits the generated role-play scenario to the terminal.

[0391] Step 5:

[0392] The terminal displays the scenario and provides instructions for the user to execute.

[0393] Step 6:

[0394] Users are guided through role-play scenarios to develop problem-solving skills.

[0395] Workshop progress

[0396] Step 1:

[0397] The server pre-determines the timeline and steps for each workshop session.

[0398] Step 2:

[0399] The server uses an emotion engine to monitor the user's emotions in real time during the session.

[0400] Step 3:

[0401] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[0402] Step 4:

[0403] The server determines the next action based on the emotional data during the session and sends instructions to the terminal.

[0404] Step 5:

[0405] The user follows instructions from the terminal to proceed with the workshop.

[0406] Generate virtual team building activities

[0407] Step 1:

[0408] The server retrieves data regarding the needs of the remote team.

[0409] Step 2:

[0410] The server uses an emotion engine to obtain emotion data of the remote team.

[0411] Step 3:

[0412] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[0413] Step 4:

[0414] The server transmits the generated activity content to the terminal.

[0415] Step 5:

[0416] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[0417] Step 6:

[0418] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[0419] Example 2

[0420] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0421] Current team building workshops generally have uniform content and do not fully consider the characteristics and feelings of each member, making it difficult to achieve effective team building. Furthermore, simply collecting feedback from the workshop makes it difficult to reflect specific improvements in the next workshop. Furthermore, providing appropriate virtual team building activities for remote teams is also a challenge.

[0422] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas based on the acquired data and emotion data, means for displaying the generated workshop ideas, and means for checking and selecting the proposed workshop ideas. This makes it possible to generate workshop ideas that take into account the team's characteristics and emotions, and to provide an appropriate personalized workshop for each member. In addition, feedback and emotion data can be analyzed to clearly suggest areas for improvement in the next workshop. It is also possible to provide effective virtual team building activities to remote teams.

[0423] "Past success stories" refer to successful experiences and achievements in previous workshops or projects.

[0424] "Team characteristic data" is information about team members' skills, roles, performance, interests, personalities, etc.

[0425] "Emotional data" refers to data that indicates the emotional state or emotional changes of a user or team member, and is obtained from feedback or sensor data.

[0426] "Means for automatically generating workshop ideas" refers to a method or device that uses AI models or algorithms to analyze and process acquired data and generate new workshop plans and proposals.

[0427] The "means for displaying the generated workshop idea" refers to a method or device for displaying the generated workshop idea on a user's terminal.

[0428] The "means for reviewing and selecting proposed workshop ideas" refers to a method or device that allows a user to view the generated workshop ideas and select an appropriate one from among them.

[0429] "Individual skills and interests" refers to the specific skills and interests of each team member.

[0430] A "personalized workshop" is a workshop that is individually tailored based on each member's skills, interests, and emotional data.

[0431] "Feedback" refers to opinions and impressions collected from participants after the workshop.

[0432] "Key themes and trends" are areas for improvement or noteworthy issues for the next workshop that are extracted from the collected feedback data.

[0433] An "AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions or generates results for specific tasks.

[0434] MODE FOR CARRYING OUT THE INVENTION

[0435] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans tailored to each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. In addition to these functions, it can also be combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[0436] Hardware and Software

[0437] The server mainly uses a database, a generative AI model, and an emotion engine. The database is used to store data on past success stories and team characteristics. The emotion engine is required to analyze user emotions and obtain emotional data. The generative AI model generates new workshop ideas, personalized workshops, and role-play scenarios based on the obtained data.

[0438] The terminal plays a role in displaying data sent from the server, generated workshop ideas, personalized workshops, feedback reports, etc. to the user.

[0439] Specific examples

[0440] Workshop idea generation

[0441] The server first accesses the database to retrieve data on past success stories and current team characteristics. It then uses an emotion engine to retrieve user emotion data related to past success stories. Based on this data, the server then uses an AI model to automatically generate new workshop ideas and sends them to the device. The device then displays the workshop ideas to the user, who can then review and select from the suggested ideas.

[0442] Examples:

[0443] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[0444] Personalized Workshop Proposals

[0445] The server retrieves data about each member's skills and interests from a database. It also uses an emotion engine to retrieve each member's past emotional data. Based on this data, it uses an AI model to generate personalized workshops and sends them to each member's device. The device displays the suggestions to each member, allowing them to check the workshop content that best suits them.

[0446] Examples:

[0447] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[0448] Automatic analysis of feedback

[0449] After the workshop, the device collects feedback from users. An emotion engine also collects emotional data during the feedback and sends this data to a server. The server uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand areas for improvement in the next workshop.

[0450] Examples:

[0451] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[0452] Role-play scenario generation

[0453] The server retrieves data about specific team dynamics and problems, uses an emotion engine to retrieve team emotion data, and uses this data to generate role-play scenarios using an AI model. The scenarios are then sent to the device, which displays the scenarios and provides instructions for the user to follow.

[0454] Examples:

[0455] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[0456] Workshop progress

[0457] The server pre-determines the timeline and steps for each workshop session. It uses an emotion engine to monitor the user's emotions in real time during the session. The device displays appropriate instructions on the user interface according to the pre-determined timeline, ensuring efficient session progress.

[0458] Examples:

[0459] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[0460] Generate virtual team building activities

[0461] The server uses AI models to generate virtual team-building activities based on data about the needs of remote teams and emotional data acquired through an emotion engine. The generated activities are then sent to the device and displayed to the user so that they can be carried out in a remote environment.

[0462] Examples:

[0463] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[0464] Prompt Sentence Examples

[0465] 1. "Generate workshop ideas for improving corporate project management and communication skills."

[0466] 2. "Generate a personalized workshop on problem-solving skills for Member A."

[0467] 3. "Please analyze the feedback from the workshop and identify areas for improvement for next time."

[0468] 4. "Generate role-play scenarios that address communication challenges."

[0469] 5. "Generate guidelines to help facilitate the next workshop."

[0470] 6. "Generate personalized virtual team-building activities for your remote team."

[0471] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0472] Step 1: Data Acquisition

[0473] The server accesses the database to retrieve data on past success stories and current team characteristics. Database connection information and queries are required as input. Success story data and team characteristics data are obtained as output. Specifically, the server executes SQL queries to retrieve data, structures the data (e.g., in JSON format), and saves it.

[0474] Step 2: Obtaining emotion data

[0475] The server uses the emotion engine to obtain user emotion data related to past success stories. Data about past success stories is required as input. Emotion data is obtained as output. Specifically, the server sends an API request to the emotion engine to obtain emotion data and associates it with the past success story data.

[0476] Step 3: Workshop idea generation

[0477] The server inputs the acquired success case data, team characteristic data, and emotion data into the generative AI model to generate new workshop ideas. All data obtained in the previous step is required as input. New workshop ideas are obtained as output. Specifically, the server inputs data and prompt statements into the generative AI model and runs the model to generate ideas.

[0478] Step 4: Submit and view your idea

[0479] The server sends the generated workshop ideas to the terminal, which then displays them to the user, allowing the user to review and select the proposed ideas. The generated workshop ideas are required as input, and the displayed ideas and the user's selection results are obtained as output. The specific operation is to send the generated ideas to the terminal, which then displays them on the user interface.

[0480] Step 5: Retrieving Member Data

[0481] The server accesses the database to retrieve data about each member's skills and interests. As input, it requires a query for member data. As output, it gets the data about each member's skills and interests. Specifically, it executes an SQL query to retrieve the data and organizes it into a structured data format.

[0482] Step 6: Obtaining emotion data

[0483] The server uses the emotion engine to obtain past emotion data for each member. The input requires the identification information of each member. The output is the emotion data for each member. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[0484] Step 7: Generate a personalized workshop

[0485] The server inputs the acquired member data and emotion data into the generative AI model to generate a personalized workshop. Member data and emotion data are required as input. The output is a personalized workshop proposal. Specifically, the server inputs a prompt statement into the generative AI model, runs the model, and obtains the generated result.

[0486] Step 8: Submit and view your proposal

[0487] The server sends the generated personalized workshop proposal to each member's terminal, which then displays it to the user of each member. The generated personalized workshop proposal is required as input. The displayed proposal and the user's response are obtained as output. The specific operation is to send the proposal to the terminal, which then displays it on the user interface.

[0488] Step 9: Gather feedback

[0489] The terminal collects feedback from users after the workshop ends. As input, it requires users to fill out a feedback form. As output, it obtains the collected feedback data. Specific operations include displaying the feedback form, receiving user input, and storing it in a database.

[0490] Step 10: Collect emotion data

[0491] The device uses an emotion engine to collect emotion data simultaneously with the feedback. The input requires the user's reaction and text during the feedback. The output is emotion data. Specific operations include obtaining emotion data through sensor data and text analysis, and associating it with the feedback data.

[0492] Step 11: Analyze feedback data

[0493] The server analyzes the collected feedback and emotion data using an AI model. It requires feedback and emotion data as input. It outputs analysis results that indicate key themes and trends. Specifically, it inputs data into the AI ​​model, runs the model, and analyzes the results.

[0494] Step 12: Submit and view analysis results

[0495] The server sends the analysis results to the terminal, which then displays them to the user. The analysis results are required as input. The displayed analysis results and user confirmation are obtained as output. Specifically, the analysis results are compiled into a report and sent to the terminal, which then displays them on the user interface.

[0496] Step 13: Generate role-play scenarios

[0497] The server generates role-play scenarios from data about specific team dynamics and problems. As input, it requires team dynamics data. As output, it obtains role-play scenarios. Specifically, it inputs the data into a generative AI model and runs the model to generate scenarios.

[0498] Step 14: Send and view the scenario

[0499] The server sends the generated role-play scenario to the terminal, which displays it to the user and provides instructions for execution. The generated role-play scenario is required as input. The displayed scenario and the user's reaction are obtained as output. The specific operation is to send the scenario to the terminal, which displays it on the user interface.

[0500] Step 15: Setting the Timeline and Steps

[0501] The server pre-configures the timeline and steps for each workshop session. As input, it requires the workshop schedule information. As output, it obtains the timeline and step configuration. Specific operations include retrieving the timeline and steps from a database or using pre-configured data.

[0502] Step 16: Monitoring emotional data during the session

[0503] The device uses an emotion engine to monitor the user's emotions in real time during the session. The inputs include the user's reactions during the session and sensor data. The output is real-time emotion data. Specifically, the device collects emotion data through sensor data and text analysis, and displays it on the monitoring screen.

[0504] Step 17: Display instructions

[0505] The terminal displays appropriate instructions to the user based on the configured timeline and emotional data during the session. Timeline data and emotional data are required as input. The terminal obtains the displayed instructions as output. Specific operations include displaying instructions based on the timeline on the user interface and making necessary changes according to the emotional data.

[0506] Step 18: Support the session

[0507] The terminal efficiently supports the progress of the workshop based on the timeline and emotion data. Timeline data and emotion data are required as input. The output is an efficient session progress. Specific operations include updating the interface in real time and displaying alerts about the progress of the session.

[0508] Step 19: Retrieve Remote Team Data

[0509] The server retrieves data about the needs of the remote team from the database. As input, it requires queries related to the remote team. As output, it obtains the remote team data. Specific operations include retrieving data from the database and organizing it into a structured data format.

[0510] Step 20: Capture sentiment data for your remote team

[0511] The server uses the emotion engine to obtain emotion data about the remote team. The server requires the remote team's identification information as input. The server obtains the emotion data of the remote team as output. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[0512] Step 21: Generate virtual activities

[0513] The server generates a virtual team building activity using an AI model based on the acquired remote team data and emotion data. The remote team data and emotion data are required as input. The content of the virtual team building activity is obtained as output. Specifically, the server inputs a prompt statement into the generating AI model, executes the model, and obtains the generated result.

[0514] Step 22: Submit and view your activity

[0515] The server sends the content of the generated virtual team-building activity to the terminal, and the terminal displays it to the user so that it can be executed in a remote environment. The content of the virtual team-building activity is required as input. The displayed activity content and the user's execution results are obtained as output. The specific operation is to send the activity content to the terminal, and the terminal displays it on the user interface.

[0516] (Application example 2)

[0517] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0518] Conventional team building systems typically generate and personalized workshop ideas based on past success stories and team characteristics. However, these systems lack sufficient functionality for automatically analyzing feedback, generating role-play scenarios for specific situations, and providing virtual team building activities in remote environments. As a result, users' emotions and remote team building activities are not fully considered, making it difficult to achieve effective team building.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0520] In this invention, the server includes means for acquiring data on past success stories and team characteristics, means for automatically generating workshop ideas based on the acquired data, means for providing the generated workshop ideas to a robot or a mobile information terminal, means for collecting feedback and analyzing the feedback data using an emotion recognition engine, and means for displaying improvements to the next workshop based on the analysis results. This enables more effective and personalized team building that takes into account users' emotions and activities in a remote environment.

[0521] "Past success stories" are specific examples of successful outcomes from previously implemented workshops or projects.

[0522] "Team characteristic data" is information about the skills, interests, personalities, and roles of team members, as well as the overall dynamics of the team.

[0523] A "Workshop Idea" is a proposed series of activities or exercises designed for team building or skill development purposes.

[0524] "Automatic generation" refers to the use of AI models and algorithms to mechanically generate new ideas and content based on specific input data.

[0525] "Robots or personal digital assistants" are electronic devices such as smartphones, tablets, and autonomous mobile devices.

[0526] "Feedback" refers to opinions, impressions, and evaluation information collected from users who have participated in a workshop or activity.

[0527] An "emotion recognition engine" is a technology that analyzes and recognizes a user's emotions from text, facial expressions, voice, etc.

[0528] "Analysis results" are information or insights obtained as a result of analysis performed on collected data.

[0529] "Areas for improvement for the next workshop" are specific proposals and action plans to improve the problems and issues identified based on the previous workshop.

[0530] A "personalized workshop" is a specific activity tailored to each member's individual skills and interests.

[0531] "Virtual team building activities" refer to online team building activities that can be carried out in a remote environment.

[0532] "Remote environment" refers to situations or conditions in which activities are carried out in physically separate locations.

[0533] An embodiment of the present invention relates to an AI-based system for supporting team building in a factory. This system consists of two main components: a server and a terminal.

[0534] Server configuration and functions

[0535] The server has the following features:

[0536] 1. Data acquisition function

[0537] The server has the function of retrieving past success stories and team characteristic data from a database. Past success stories include subordinates, deliverables from previous workshops and projects, and their associated emotional data.

[0538] 2. Automatic workshop idea generation function

[0539] The server uses an AI model to automatically generate new workshop ideas based on the acquired data, while also analyzing past emotional data using an emotion recognition engine to propose optimal workshop ideas.

[0540] 3. Functions provided to robots or mobile information terminals

[0541] The workshop ideas generated by the server are provided to robots or mobile information terminals in the factory and displayed to users through these devices.

[0542] 4. Feedback collection feature

[0543] After the workshop, feedback is collected from users via the terminals, including their opinions and feelings.

[0544] 5. Feedback analysis function

[0545] The collected feedback is analyzed by an emotion recognition engine, which uses technology to analyze and recognize user emotions from text, facial expressions, voice, etc.

[0546] 6. Improvements display function

[0547] Based on the analysis results, improvements for the next workshop will be displayed on the device.

[0548] Device configuration and functions

[0549] 1. Workshop idea display function

[0550] The workshop ideas provided by the server are displayed on the device screen, allowing the user to check them. The device also communicates the ideas to the user via a voice assistant or the robot's display.

[0551] 2. Feedback collection feature

[0552] After the workshop, the device collects feedback from the users, including their opinions and feelings, and sends this information to the server.

[0553] Specific examples

[0554] For example, the server generates a "team-based production line simulation" based on success stories related to "improving production line efficiency," current team characteristic data calling for "team building to improve communication," and past workshops in which employees felt "cooperative." This idea is proposed to the user via the device. The user then runs the workshop based on the idea and provides subsequent feedback via the device.

[0555] Example prompts for generative AI models

[0556] "Based on the current team data, generate workshop ideas aimed at 'improving communication skills.' Refer to past success stories that fostered 'collaborative feelings,' and propose team-based project activities."

[0557] This invention allows for flexible and personalized workshops, enabling team building activities that effectively take into account the emotions and needs of employees.

[0558] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0559] Step 1:

[0560] The server retrieves past success stories and team characteristics data from the database. This input data includes details of past successful workshops and information on team members' skills, interests, personalities, etc. The server then analyzes this data using an AI model to prepare for the next step.

[0561] Step 2:

[0562] The server uses AI models based on the acquired data to automatically generate new workshop ideas. The AI ​​models used here include a generative AI model that combines data on past success stories with data on current team characteristics to generate optimal workshop ideas. Prompt statements are also generated, and specific workshop content is determined.

[0563] Step 3:

[0564] The server provides the generated workshop ideas to robots or mobile information terminals in the factory, which then send the content of the ideas to the terminals, which then display them to the user. The terminals then provide information to the user through a voice assistant or the robot's display.

[0565] Step 4:

[0566] The user checks the presented workshop ideas and conducts the workshop based on the contents. When the user conducts the workshop, the terminal provides necessary guidelines and instructions in real time.

[0567] Step 5:

[0568] After the workshop, the device collects feedback from users, including text, voice, and facial expression data, and sends the collected data to a server in real time.

[0569] Step 6:

[0570] The server analyzes the collected feedback data using an emotion recognition engine, which recognizes the user's emotions from text and voice data, processes the data based on various emotions, and extracts important themes and trends from the feedback.

[0571] Step 7:

[0572] Based on the analysis results, the server generates a report containing suggestions for improvement for the next workshop. This report is sent to the terminal and displayed on the terminal to the user, allowing the user to confirm specific improvements for the next workshop.

[0573] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0574] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0575] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0576] [Second embodiment]

[0577] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0578] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0579] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0580] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0581] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0582] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0583] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0584] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0585] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0586] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0587] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0588] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0589] This invention is an AI-based system that supports corporate team building, generating workshop ideas based on past success stories and team characteristic data, and proposing personalized plans for each member. It also has the functions of automatically analyzing workshop feedback and suggesting areas for improvement, generating role-play scenarios for specific situations, managing the progress of workshops using AI, and providing virtual team building activities for remote teams.

[0590] 1. Workshop idea generation

[0591] The server accesses a database to retrieve data on past success stories and team characteristics. Based on this data, it uses an AI model to automatically generate new workshop ideas. The generated ideas are sent to the device, where the user can review and select the proposed ideas.

[0592] Examples:

[0593] The server uses an AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills." This idea is then proposed to the user via their device.

[0594] 2. Personalized Workshop Proposal

[0595] The server retrieves data about each member's skills and interests from a database and uses AI models to generate personalized workshops. The generated workshop proposals are sent to each member's device, allowing users to view the workshop content that best suits them.

[0596] Examples:

[0597] The server generates a "problem-solving workshop" based on member A's data that he is "interested in problem-solving skills," sends it to the terminal, and displays it. The user confirms the proposal.

[0598] 3. Automatic feedback analysis

[0599] After the workshop, the device collects feedback from users and sends it to the server. The server then uses an AI model to analyze the feedback data and extract important themes and trends. The results of this analysis are sent to the device as a report, allowing users to understand how to improve the next workshop.

[0600] Examples:

[0601] The device collects feedback from users that "the time to come up with ideas is short," and the server extracts this as a need for "improved time management" and notes it in the report as an issue to be addressed next time.

[0602] 4. Generating Role-Play Scenario

[0603] The server uses AI models to generate role-play scenarios based on data about team dynamics and specific problems, and the scenarios are sent to the device, where users can view and execute them to learn specific problem-solving skills.

[0604] Examples:

[0605] The server recognizes communication issues, generates role-play scenarios based on the theme of "dealing with problems during a project," and provides them to users via their terminals.

[0606] 5. Workshop Management

[0607] The server pre-determines the timeline and steps for each workshop session. The terminal supports the progress of the session according to this timeline, and displays time management and important instructions to the user, enabling efficient session progress.

[0608] Examples:

[0609] The server sets a timeline such as "Idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device notifies the user when each session ends.

[0610] 6. Creating Virtual Team Building Activities

[0611] The server uses an AI model to generate virtual team-building activities based on the needs data of the remote team, and the generated content is sent to the device and provided to the user in a format that can be executed even in a remote environment.

[0612] Examples:

[0613] The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal. The user can carry out the activity and strengthen the unity of the remote team.

[0614] As a result, the present invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling the creation of effective workshops, the analysis of feedback, and the support of remote teams.

[0615] The processing flow will be explained below.

[0616] Workshop idea generation

[0617] Step 1:

[0618] The server accesses a database to obtain past success stories and current team characteristic data.

[0619] Step 2:

[0620] The server inputs the acquired data into the AI ​​model and begins analysis.

[0621] Step 3:

[0622] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[0623] Step 4:

[0624] The server transmits the generated workshop ideas to the terminal.

[0625] Step 5:

[0626] The terminal displays the workshop ideas to the user.

[0627] Step 6:

[0628] The user selects the best idea from multiple proposed ideas.

[0629] Personalized Workshop Proposals

[0630] Step 1:

[0631] The server retrieves data about each member's skills and interests from a database.

[0632] Step 2:

[0633] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[0634] Step 3:

[0635] The server transmits the generated personalized workshop proposal to each member's terminal.

[0636] Step 4:

[0637] The terminal displays personalized workshop suggestions to each member.

[0638] Step 5:

[0639] The user checks the workshop content that best suits him or her.

[0640] Automatic analysis of feedback

[0641] Step 1:

[0642] The terminal collects feedback from users after the workshop is over.

[0643] Step 2:

[0644] The terminal transmits the collected feedback data to the server.

[0645] Step 3:

[0646] The server uses AI models to analyze the feedback data and extract important themes and trends.

[0647] Step 4:

[0648] The server generates a report based on the extraction results and sends it to the terminal.

[0649] Step 5:

[0650] The terminal displays the generated report to the user.

[0651] Step 6:

[0652] Users will know what needs to be improved for the next workshop.

[0653] Role-play scenario generation

[0654] Step 1:

[0655] The server captures data about specific team dynamics and issues.

[0656] Step 2:

[0657] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[0658] Step 3:

[0659] The server transmits the generated role-play scenario to the terminal.

[0660] Step 4:

[0661] The terminal displays the scenario and provides instructions for the user to execute.

[0662] Step 5:

[0663] Users are guided through role-play scenarios to develop problem-solving skills.

[0664] Workshop progress

[0665] Step 1:

[0666] The server pre-determines the timeline and steps for each workshop session.

[0667] Step 2:

[0668] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[0669] Step 3:

[0670] The server monitors the progress of each session and sends instructions to the terminal to proceed to the next session.

[0671] Step 4:

[0672] The user follows instructions from the terminal to proceed with the workshop.

[0673] Generate virtual team building activities

[0674] Step 1:

[0675] The server retrieves data regarding the needs of the remote team.

[0676] Step 2:

[0677] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[0678] Step 3:

[0679] The server transmits the generated activity content to the terminal.

[0680] Step 4:

[0681] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[0682] Step 5:

[0683] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[0684] Example 1

[0685] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] In-company team building activities require planning and running effective workshops, providing content tailored to each participant, and collecting and analyzing feedback after the activities. However, carrying out these activities efficiently is difficult and requires significant human resources and time. Furthermore, with the spread of remote work, effective team building activities must also be carried out in remote environments. However, current methods often fail to achieve their full effectiveness. The present invention aims to provide a system and method for solving these problems.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0688] In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas using an AI model based on the acquired data, means for sending the generated workshop ideas to a terminal and displaying them, means for generating personalized workshops based on the skills and interests of each member, means for proposing personalized workshops to each member, means for collecting feedback from the terminal after the workshop, means for extracting important themes and trends using the AI ​​model based on the collected feedback, and means for displaying the extracted analysis results in a report as improvements for the next workshop. This enables an effective and efficient cycle of team building planning, execution, and feedback.

[0689] "Past success stories" are data showing successful cases obtained from workshops or projects that have been previously implemented.

[0690] "Team characteristic data" is data that includes information about team members' skills, interests, roles, performance, etc.

[0691] An "AI model" is a computational algorithm or neural network that uses artificial intelligence techniques to analyze data and generate a specific result.

[0692] "Workshop Ideas" are specific suggestions for activities or sessions designed for team building or skill development.

[0693] "Terminal" means an electronic device used by a user to input and display information, including a personal computer, smartphone, tablet, etc.

[0694] A "personalized workshop" is a workshop designed specifically to meet the skills and interests of individual members.

[0695] "Feedback" refers to data on opinions and evaluations collected from users after the workshop.

[0696] "Key themes and trends" are key issues or patterns extracted from the collected feedback data that can help improve the next workshop.

[0697] The "report" is a document displayed on the terminal that lists the analysis results and improvements for the next workshop.

[0698] A "role-play scenario" is a plot or setting for a simulated role-play based on a particular situation.

[0699] A "timeline" indicates the schedule for the start and end of each session or activity in the workshop.

[0700] "Virtual team building activities" are online team building activities that can be carried out in a remote environment.

[0701] This invention is a system for supporting corporate team building, which includes a server, terminals, and users. The system utilizes AI models to generate workshop ideas, propose personalized workshops, automatically analyze feedback, generate role-play scenarios, manage workshop progress, and generate virtual team building activities.

[0702] Data collection and workshop idea generation

[0703] The server accesses a database to retrieve past success stories and team characteristics, including each member's skills, interests, roles, and performance data. Based on the retrieved data, an AI model (e.g., GPT-4) is used to automatically generate workshop ideas. The generated ideas are sent to the device and displayed to the user.

[0704] Examples:

[0705] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[0706] Example prompt sentence:

[0707] "Generate workshop ideas aimed at improving communication skills."

[0708] Personalized Workshop Proposals

[0709] The server generates personalized workshops based on each member's skills and interests, using their skill and role data. The generated workshop proposals are sent to each member's device and notified to the user.

[0710] Examples:

[0711] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[0712] Example prompt sentence:

[0713] "Please propose a workshop for members who are interested in generating new ideas."

[0714] Feedback collection and automated analysis

[0715] After the workshop, the device collects feedback from users, including evaluations of the workshop's duration, content, and props. The collected feedback data is sent to a server and analyzed using an AI model (e.g., Transformer) to extract important themes and trends. The results of this analysis are sent to the device as a report, which users can review.

[0716] Examples:

[0717] The device asks the user how satisfied they are with the workshop time, and sends the response data to the server. The server analyzes the data and determines whether time management needs improvement, and compiles it into a report and sends it to the device.

[0718] Example prompt sentence:

[0719] "Analyze the feedback data from the workshops and extract key themes and trends."

[0720] Role-play scenario generation

[0721] The server generates role-play scenarios using AI models (e.g., T5) based on data about team dynamics and specific problems. The generated scenarios are sent to the device and made available for users to view and play.

[0722] Examples:

[0723] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[0724] Example prompt sentence:

[0725] "Generate role-play scenarios for dealing with problems during a project."

[0726] Workshop progress management

[0727] The server sets the workshop session timeline and steps in advance, and the terminal follows this timeline and supports the progress. Time management and important instructions are displayed to the user.

[0728] Examples:

[0729] The server will set up a "15-minute idea brainstorming session" or a "30-minute discussion," and the device will display a notification at the end of each session.

[0730] Example prompt sentence:

[0731] "Set the timeline for the workshop and help facilitate each session."

[0732] Generate virtual team building activities

[0733] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The generated activities are sent to the device and provided in a format that users can carry out even in a remote environment.

[0734] Examples:

[0735] The server generates an online quiz based on the characteristics of the remote team and sends it to the device. The user then checks the content and participates in the remote environment.

[0736] Example prompt sentence:

[0737] "Generate virtual team building activities for remote teams."

[0738] As described above, the system of the present invention efficiently supports team building activities through comprehensive data collection and analysis / generation using AI models, highly automating the entire process from workshop planning to execution, feedback collection, and proposals for the next activity.

[0739] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0740] Step 1:

[0741] Data collection

[0742] The server accesses the company's database to retrieve past success stories and team characteristics data, including each member's skills, interests, roles, and performance data. The input is a query from the database, and the output is the retrieved data.

[0743] Specific behavior:

[0744] The server extracts "past project success rates" and "member skill profiles" from the database.

[0745] Input: Query to the database "SELECT FROM Success_Cases"

[0746] Output: Past success story data

[0747] Step 2:

[0748] Workshop idea generation

[0749] The server automatically generates workshop ideas using an AI model (e.g., GPT-4) based on the acquired data. The acquired data is the input, and the generated workshop ideas are the output. These ideas are sent to the terminal and notified to the user.

[0750] Specific behavior:

[0751] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[0752] Input: Past success story data, team characteristics data

[0753] Output: Workshop idea "Team debate session"

[0754] Step 3:

[0755] Personalized Workshop Proposals

[0756] The server generates personalized workshops based on each member's skills and interests. The input is the member's individual skill and interest data, and the output is personalized workshop proposals. The generated proposals are sent to each member's device and notified to the user.

[0757] Specific behavior:

[0758] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[0759] Input: Individual member skills and interest data

[0760] Output: Personalized Workshop "New Idea Brainstorming Session"

[0761] Step 4:

[0762] Gathering feedback

[0763] After the workshop, the terminal collects feedback from users. The input is the feedback from users, and the output is the collected feedback data. This is then sent to the server.

[0764] Specific behavior:

[0765] The terminal asks the user about "satisfaction with the workshop time" and transmits the answer data to the server.

[0766] Input: User feedback

[0767] Output: Feedback data

[0768] Step 5:

[0769] Automatic analysis of feedback

[0770] The server analyzes the collected feedback data using an AI model (e.g., Transformer) to extract important themes and trends. The input is the feedback data, and the output is the analysis results. These analysis results are sent to the terminal in the form of a report, which the user can review.

[0771] Specific behavior:

[0772] The server extracts themes such as "time management needs improvement" from the feedback data, compiles them into a report, and sends it to the terminal.

[0773] Input: Feedback data

[0774] Output: Report of analysis results

[0775] Step 6:

[0776] Role-play scenario generation

[0777] The server generates role-play scenarios using an AI model (e.g., T5) based on data about team dynamics and specific problems. The input is the specific problem data, and the output is the generated role-play scenario. This scenario is sent to the device, where the user can view and perform it.

[0778] Specific behavior:

[0779] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[0780] Input: Specific problem data

[0781] Output: Role-play scenario

[0782] Step 7:

[0783] Workshop progress management

[0784] The server sets the timeline and steps of each workshop session in advance. The input is the workshop timeline and steps, and the output is the timeline setting data. The terminal follows this timeline and supports the progress.

[0785] Specific behavior:

[0786] The server will set up a "15-minute idea brainstorm" or "30-minute discussion," and the device will display a notification.

[0787] Input: Workshop timeline and steps

[0788] Output: Timeline setting data

[0789] Step 8:

[0790] Generate virtual team building activities

[0791] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The input is the needs data of the remote team, and the output is the generated team-building activity. The activity content is sent to the device and provided in a form that users can carry out even in a remote environment.

[0792] Specific behavior:

[0793] The server generates an "online quiz competition" based on the "remote team characteristics" and sends it to the terminal.

[0794] Input: Remote team needs data

[0795] Output: Virtual team building activity

[0796] (Application example 1)

[0797] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0798] Effective team building and training is crucial for modern companies, but it is difficult to provide personalized programs tailored to diverse needs, effectively analyze feedback, generate role-play scenarios for specific situations, and conduct training in a remote environment. In particular, employee training in a virtual environment is not effectively managed or optimized.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0800] In this invention, the server includes means for acquiring data on past success stories and team characteristics, means for automatically generating workshop ideas based on the acquired data, means for displaying the generated workshop ideas, means for automatically generating training workshops in a virtual environment, and means for displaying the generated training workshops. This enables the provision of personalized programs that meet the diverse needs of companies, automatic analysis of feedback, generation of role-play scenarios according to situations, and effective training in remote environments.

[0801] "Past success stories" are examples of previously held workshops or trainings that have proven particularly successful.

[0802] "Team characteristic data" is data relating to a particular team that includes information such as the skills, interests, and personalities of members.

[0803] "Workshop Ideas" are specific activity proposals for team building or training designed around a specific theme or purpose.

[0804] A "personalized workshop" is a workshop that is customized based on each member's skills and interests.

[0805] "Feedback" refers to evaluations and opinions from members who participated in workshops and training, including information for improvement.

[0806] "Key themes and trends" are items or patterns extracted from the collected feedback data that deserve particular attention for improving workshops or generating new ideas.

[0807] A "role-play scenario" is an exercise or dramatization designed to recreate a specific situation for the purpose of practical skill acquisition.

[0808] A "virtual environment" refers to a virtual space or interface provided by computer technology that enables remote activities.

[0809] "Training Workshop" means an educational session designed to improve employee skills or knowledge, including work-related training.

[0810] MODE FOR CARRYING OUT THE INVENTION

[0811] This invention is an AI-based system for effective corporate team building and employee training. This system generates new workshop ideas and role-play scenarios based on past success stories and team characteristic data, and provides virtual training that can be conducted even in remote environments. Specific embodiments are described below.

[0812] 1. System Configuration

[0813] Server: A cloud server is used to install an AI model (e.g., OpenAI's GPT-4) and a database management system (e.g., MySQL).

[0814] Devices: Employee-owned smartphones, tablets, or VR headsets (e.g., Oculus, VIVE, etc.).

[0815] Software: Use feedback analysis tools (e.g., Google Cloud Natural Language) and real-time communication servers (e.g., WebSockets).

[0816] 2. Data Acquisition

[0817] The server retrieves past success stories and team characteristics data from the database, including information about employee skills and interests.

[0818] 3. Workshop idea generation

[0819] The server uses a generative AI model (e.g., GPT-4) to generate new workshop ideas based on the acquired data, and the generated ideas are sent to the terminals and made available to employees.

[0820] Example: The server uses a generative AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills."

[0821] 4. Personalized Workshop Proposals

[0822] The server acquires each member's skill and interest data and generates a personalized workshop, which is then sent to each member's device.

[0823] Example: Based on data that member A is interested in problem-solving skills, the server generates a "problem-solving workshop" and sends it to the terminal.

[0824] 5. Automatic feedback analysis

[0825] The terminals collect feedback from employees after the workshop and send it to a server, which then uses a feedback analysis tool to analyze the feedback data and extract important themes and trends.

[0826] Example: The device collects feedback from the user that "the time it takes to come up with ideas is short," and the server extracts this as a need for "improved time management."

[0827] 6. Role-play scenario generation

[0828] The server uses a generative AI model to generate role-play scenarios based on data about team dynamics and specific problems, which are then sent to the device where employees can view and act them out.

[0829] Example: The server recognizes a communication issue and generates a role-play scenario on the theme of "dealing with problems during a project."

[0830] 7. Generate virtual training workshops

[0831] The server automatically generates training workshops in a virtual environment, which can then be sent to the device and run remotely.

[0832] Example: The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal.

[0833] 8. Examples of prompts

[0834] "Generate training workshop ideas for a virtual store based on past success stories: [Case 1, Case 2, ...] and team characteristics data: [Team 1, Team 2, ...]."

[0835] This enables companies to provide personalized training programs to meet a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[0836] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0837] Step 1:

[0838] Data Acquisition:

[0839] The server retrieves data on past success stories and team characteristics from the database. In this process, it accesses the database using an SQL query and extracts the necessary data. The input is the SQL query, and the output is the data on past success stories and team characteristics.

[0840] Step 2:

[0841] Workshop idea generation:

[0842] The server generates new workshop ideas using a generative AI model (e.g., GPT-4) based on the acquired data. Here, a prompt is created and input to the AI ​​model. The input is the success case data, team characteristic data, and the created prompt, and the output is the generated workshop idea.

[0843] Step 3:

[0844] View Workshop Ideas:

[0845] The server sends the generated workshop ideas to the terminal. The terminal receives this information and displays it to the user. The input is the generated workshop ideas, and the output is the workshop ideas that the user confirms.

[0846] Step 4:

[0847] Personalized Workshop Suggestion:

[0848] The server acquires each member's skill and interest data and generates a personalized workshop using a generative AI model. The generated workshop is sent to each member's device and displayed. The input is the member's skill and interest data, and the output is a personalized workshop proposal.

[0849] Step 5:

[0850] Collecting feedback:

[0851] After the workshop, the terminal collects feedback from users and sends it to the server. The input is the feedback information from users, and the output is the feedback data sent to the server.

[0852] Step 6:

[0853] Feedback Analysis:

[0854] The server uses a feedback analysis tool to analyze the feedback data and extract important themes and trends. The analysis results are compiled into a report as improvements for the next workshop and sent to the terminal. The input is the feedback data, and the output is a report containing important themes and improvements.

[0855] Step 7:

[0856] Role-play scenario generation:

[0857] The server uses a generative AI model to generate role-play scenarios based on data on team dynamics and specific problems. The scenarios are sent to the device, where users can view and run them. The inputs are team dynamics data and specific problem data, and the output is the role-play scenario.

[0858] Step 8:

[0859] Generate a virtual training workshop:

[0860] The server automatically generates a training workshop in a virtual environment and sends it to the terminal. It is provided to the user in a state that can be executed in a remote environment. The input is the characteristic data of the remote team, and the output is the virtual training workshop.

[0861] These steps enable companies to deliver personalized training programs tailored to a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[0862] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0863] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans for each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. Furthermore, these functions are combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[0864] 1. Workshop idea generation

[0865] The server accesses the database to acquire data on past success stories and current team characteristics. It also uses an emotion engine to acquire user emotion data related to past success stories. Based on this data, it automatically generates new workshop ideas using an AI model and sends the ideas to the device. The device displays the workshop ideas to the user, who can then review and select from the proposed ideas.

[0866] Examples:

[0867] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[0868] 2. Personalized Workshop Proposal

[0869] The server retrieves data on each member's skills and interests from a database, and also uses an emotion engine to retrieve each member's past emotional data. Based on this data, an AI model is used to generate a personalized workshop and send it to each member's device. The device displays the proposal to each member, allowing them to check the workshop content that best suits them.

[0870] Examples:

[0871] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[0872] 3. Automatic feedback analysis

[0873] After the workshop, the device collects feedback from users and uses an emotion engine to collect emotional data during the feedback. This data is then sent to a server, which uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand how to improve their next workshop.

[0874] Examples:

[0875] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[0876] 4. Generating Role-Play Scenario

[0877] The server uses data about specific team dynamics and problems, and also uses an emotion engine to capture team emotion data. Based on this data, the AI ​​model generates role-play scenarios and sends them to the device, which displays the scenarios and provides instructions for the user to follow.

[0878] Examples:

[0879] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[0880] 5. Workshop Management

[0881] The server pre-determines the timeline and steps for each workshop session and uses an emotion engine to monitor the user's emotions in real time during the session. The device follows the pre-determined timeline and uses the emotion data to guide the session forward. Appropriate instructions are displayed to the user, ensuring efficient session progress.

[0882] Examples:

[0883] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[0884] 6. Creating Virtual Team Building Activities

[0885] The server uses an AI model to generate virtual team-building activities based on data on the needs of remote teams and emotional data acquired from an emotion engine. The generated activities are then sent to the device and provided to users so they can carry them out in a remote environment.

[0886] Examples:

[0887] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[0888] As a result, this invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling effective workshop generation, feedback analysis, and remote team support. By utilizing an emotion engine, it is possible to provide a more personalized experience based on user emotions and strengthen team cohesion.

[0889] The processing flow will be explained below.

[0890] Workshop idea generation

[0891] Step 1:

[0892] The server accesses the database to obtain historical success data and current team characteristic data.

[0893] Step 2:

[0894] The server uses an emotion engine to obtain user emotion data related to past success stories.

[0895] Step 3:

[0896] The server inputs the acquired data into the AI ​​model and begins analysis.

[0897] Step 4:

[0898] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[0899] Step 5:

[0900] The server transmits the generated workshop ideas to the terminal.

[0901] Step 6:

[0902] The terminal displays the workshop ideas to the user.

[0903] Step 7:

[0904] The user selects the best idea from the proposed ideas.

[0905] Personalized Workshop Proposals

[0906] Step 1:

[0907] The server retrieves data about each member's skills and interests from a database.

[0908] Step 2:

[0909] The server uses an emotion engine to obtain past emotion data for each member.

[0910] Step 3:

[0911] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[0912] Step 4:

[0913] The server transmits the generated personalized workshop proposal to each member's terminal.

[0914] Step 5:

[0915] The terminal displays personalized workshop suggestions to each member.

[0916] Step 6:

[0917] The user checks the workshop content that best suits him or her.

[0918] Automatic analysis of feedback

[0919] Step 1:

[0920] The terminal collects feedback from users after the workshop is over.

[0921] Step 2:

[0922] The terminal also collects the user's emotion data along with the feedback data.

[0923] Step 3:

[0924] The terminal transmits the collected data to the server.

[0925] Step 4:

[0926] The server uses AI models and sentiment engines to analyze the feedback data and extract key themes and trends.

[0927] Step 5:

[0928] The server generates a report based on the extraction results and sends it to the terminal.

[0929] Step 6:

[0930] The terminal displays the generated report to the user.

[0931] Step 7:

[0932] Users will know what needs to be improved for the next workshop.

[0933] Role-play scenario generation

[0934] Step 1:

[0935] The server captures data about specific team dynamics and issues.

[0936] Step 2:

[0937] The server uses an emotion engine to obtain emotion data within the team.

[0938] Step 3:

[0939] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[0940] Step 4:

[0941] The server transmits the generated role-play scenario to the terminal.

[0942] Step 5:

[0943] The terminal displays the scenario and provides instructions for the user to execute.

[0944] Step 6:

[0945] Users are guided through role-play scenarios to develop problem-solving skills.

[0946] Workshop progress

[0947] Step 1:

[0948] The server pre-determines the timeline and steps for each workshop session.

[0949] Step 2:

[0950] The server uses an emotion engine to monitor the user's emotions in real time during the session.

[0951] Step 3:

[0952] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[0953] Step 4:

[0954] The server determines the next action based on the emotional data during the session and sends instructions to the terminal.

[0955] Step 5:

[0956] The user follows instructions from the terminal to proceed with the workshop.

[0957] Generate virtual team building activities

[0958] Step 1:

[0959] The server retrieves data regarding the needs of the remote team.

[0960] Step 2:

[0961] The server uses an emotion engine to obtain emotion data of the remote team.

[0962] Step 3:

[0963] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[0964] Step 4:

[0965] The server transmits the generated activity content to the terminal.

[0966] Step 5:

[0967] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[0968] Step 6:

[0969] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[0970] Example 2

[0971] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0972] Current team building workshops generally have uniform content and do not fully consider the characteristics and feelings of each member, making it difficult to achieve effective team building. Furthermore, simply collecting feedback from the workshop makes it difficult to reflect specific improvements in the next workshop. Furthermore, providing appropriate virtual team building activities for remote teams is also a challenge.

[0973] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas based on the acquired data and emotion data, means for displaying the generated workshop ideas, and means for checking and selecting the proposed workshop ideas. This makes it possible to generate workshop ideas that take into account the team's characteristics and emotions, and to provide an appropriate personalized workshop for each member. In addition, feedback and emotion data can be analyzed to clearly suggest areas for improvement in the next workshop. It is also possible to provide effective virtual team building activities to remote teams.

[0974] "Past success stories" refer to successful experiences and achievements in previous workshops or projects.

[0975] "Team characteristic data" is information about team members' skills, roles, performance, interests, personalities, etc.

[0976] "Emotional data" refers to data that indicates the emotional state or emotional changes of a user or team member, and is obtained from feedback or sensor data.

[0977] "Means for automatically generating workshop ideas" refers to a method or device that uses AI models or algorithms to analyze and process acquired data and generate new workshop plans and proposals.

[0978] The "means for displaying the generated workshop idea" refers to a method or device for displaying the generated workshop idea on a user's terminal.

[0979] The "means for reviewing and selecting proposed workshop ideas" refers to a method or device that allows a user to view the generated workshop ideas and select an appropriate one from among them.

[0980] "Individual skills and interests" refers to the specific skills and interests of each team member.

[0981] A "personalized workshop" is a workshop that is individually tailored based on each member's skills, interests, and emotional data.

[0982] "Feedback" refers to opinions and impressions collected from participants after the workshop.

[0983] "Key themes and trends" are areas for improvement or noteworthy issues for the next workshop that are extracted from the collected feedback data.

[0984] An "AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions or generates results for specific tasks.

[0985] MODE FOR CARRYING OUT THE INVENTION

[0986] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans tailored to each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. In addition to these functions, it can also be combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[0987] Hardware and Software

[0988] The server mainly uses a database, a generative AI model, and an emotion engine. The database is used to store data on past success stories and team characteristics. The emotion engine is required to analyze user emotions and obtain emotional data. The generative AI model generates new workshop ideas, personalized workshops, and role-play scenarios based on the obtained data.

[0989] The terminal plays a role in displaying data sent from the server, generated workshop ideas, personalized workshops, feedback reports, etc. to the user.

[0990] Specific examples

[0991] Workshop idea generation

[0992] The server first accesses the database to retrieve data on past success stories and current team characteristics. It then uses an emotion engine to retrieve user emotion data related to past success stories. Based on this data, the server then uses an AI model to automatically generate new workshop ideas and sends them to the device. The device then displays the workshop ideas to the user, who can then review and select from the suggested ideas.

[0993] Examples:

[0994] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[0995] Personalized Workshop Proposals

[0996] The server retrieves data about each member's skills and interests from a database. It also uses an emotion engine to retrieve each member's past emotional data. Based on this data, it uses an AI model to generate personalized workshops and sends them to each member's device. The device displays the suggestions to each member, allowing them to check the workshop content that best suits them.

[0997] Examples:

[0998] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[0999] Automatic analysis of feedback

[1000] After the workshop, the device collects feedback from users. An emotion engine also collects emotional data during the feedback and sends this data to a server. The server uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand areas for improvement in the next workshop.

[1001] Examples:

[1002] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[1003] Role-play scenario generation

[1004] The server retrieves data about specific team dynamics and problems, uses an emotion engine to retrieve team emotion data, and uses this data to generate role-play scenarios using an AI model. The scenarios are then sent to the device, which displays the scenarios and provides instructions for the user to follow.

[1005] Examples:

[1006] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[1007] Workshop progress

[1008] The server pre-determines the timeline and steps for each workshop session. It uses an emotion engine to monitor the user's emotions in real time during the session. The device displays appropriate instructions on the user interface according to the pre-determined timeline, ensuring efficient session progress.

[1009] Examples:

[1010] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[1011] Generate virtual team building activities

[1012] The server uses AI models to generate virtual team-building activities based on data about the needs of remote teams and emotional data acquired through an emotion engine. The generated activities are then sent to the device and displayed to the user so that they can be carried out in a remote environment.

[1013] Examples:

[1014] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[1015] Prompt Sentence Examples

[1016] 1. "Generate workshop ideas for improving corporate project management and communication skills."

[1017] 2. "Generate a personalized workshop on problem-solving skills for Member A."

[1018] 3. "Please analyze the feedback from the workshop and identify areas for improvement for next time."

[1019] 4. "Generate role-play scenarios that address communication challenges."

[1020] 5. "Generate guidelines to help facilitate the next workshop."

[1021] 6. "Generate personalized virtual team-building activities for your remote team."

[1022] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1023] Step 1: Data Acquisition

[1024] The server accesses the database to retrieve data on past success stories and current team characteristics. Database connection information and queries are required as input. Success story data and team characteristics data are obtained as output. Specifically, the server executes SQL queries to retrieve data, structures the data (e.g., in JSON format), and saves it.

[1025] Step 2: Obtaining emotion data

[1026] The server uses the emotion engine to obtain user emotion data related to past success stories. Data about past success stories is required as input. Emotion data is obtained as output. Specifically, the server sends an API request to the emotion engine to obtain emotion data and associates it with the past success story data.

[1027] Step 3: Workshop idea generation

[1028] The server inputs the acquired success case data, team characteristic data, and emotion data into the generative AI model to generate new workshop ideas. All data obtained in the previous step is required as input. New workshop ideas are obtained as output. Specifically, the server inputs data and prompt statements into the generative AI model and runs the model to generate ideas.

[1029] Step 4: Submit and view your idea

[1030] The server sends the generated workshop ideas to the terminal, which then displays them to the user, allowing the user to review and select the proposed ideas. The generated workshop ideas are required as input, and the displayed ideas and the user's selection results are obtained as output. The specific operation is to send the generated ideas to the terminal, which then displays them on the user interface.

[1031] Step 5: Retrieving Member Data

[1032] The server accesses the database to retrieve data about each member's skills and interests. As input, it requires a query for member data. As output, it gets the data about each member's skills and interests. Specifically, it executes an SQL query to retrieve the data and organizes it into a structured data format.

[1033] Step 6: Obtaining emotion data

[1034] The server uses the emotion engine to obtain past emotion data for each member. The input requires the identification information of each member. The output is the emotion data for each member. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[1035] Step 7: Generate a personalized workshop

[1036] The server inputs the acquired member data and emotion data into the generative AI model to generate a personalized workshop. Member data and emotion data are required as input. The output is a personalized workshop proposal. Specifically, the server inputs a prompt statement into the generative AI model, runs the model, and obtains the generated result.

[1037] Step 8: Submit and view your proposal

[1038] The server sends the generated personalized workshop proposal to each member's terminal, which then displays it to the user of each member. The generated personalized workshop proposal is required as input. The displayed proposal and the user's response are obtained as output. The specific operation is to send the proposal to the terminal, which then displays it on the user interface.

[1039] Step 9: Gather feedback

[1040] The terminal collects feedback from users after the workshop ends. As input, it requires users to fill out a feedback form. As output, it obtains the collected feedback data. Specific operations include displaying the feedback form, receiving user input, and storing it in a database.

[1041] Step 10: Collect emotion data

[1042] The device uses an emotion engine to collect emotion data simultaneously with the feedback. The input requires the user's reaction and text during the feedback. The output is emotion data. Specific operations include obtaining emotion data through sensor data and text analysis, and associating it with the feedback data.

[1043] Step 11: Analyze feedback data

[1044] The server analyzes the collected feedback and emotion data using an AI model. It requires feedback and emotion data as input. It outputs analysis results that indicate key themes and trends. Specifically, it inputs data into the AI ​​model, runs the model, and analyzes the results.

[1045] Step 12: Submit and view analysis results

[1046] The server sends the analysis results to the terminal, which then displays them to the user. The analysis results are required as input. The displayed analysis results and user confirmation are obtained as output. Specifically, the analysis results are compiled into a report and sent to the terminal, which then displays them on the user interface.

[1047] Step 13: Generate role-play scenarios

[1048] The server generates role-play scenarios from data about specific team dynamics and problems. As input, it requires team dynamics data. As output, it obtains role-play scenarios. Specifically, it inputs the data into a generative AI model and runs the model to generate scenarios.

[1049] Step 14: Send and view the scenario

[1050] The server sends the generated role-play scenario to the terminal, which displays it to the user and provides instructions for execution. The generated role-play scenario is required as input. The displayed scenario and the user's reaction are obtained as output. The specific operation is to send the scenario to the terminal, which displays it on the user interface.

[1051] Step 15: Setting the Timeline and Steps

[1052] The server pre-configures the timeline and steps for each workshop session. As input, it requires the workshop schedule information. As output, it obtains the timeline and step configuration. Specific operations include retrieving the timeline and steps from a database or using pre-configured data.

[1053] Step 16: Monitoring emotional data during the session

[1054] The device uses an emotion engine to monitor the user's emotions in real time during the session. The inputs include the user's reactions during the session and sensor data. The output is real-time emotion data. Specifically, the device collects emotion data through sensor data and text analysis, and displays it on the monitoring screen.

[1055] Step 17: Display instructions

[1056] The terminal displays appropriate instructions to the user based on the configured timeline and emotional data during the session. Timeline data and emotional data are required as input. The terminal obtains the displayed instructions as output. Specific operations include displaying instructions based on the timeline on the user interface and making necessary changes according to the emotional data.

[1057] Step 18: Support the session

[1058] The terminal efficiently supports the progress of the workshop based on the timeline and emotion data. Timeline data and emotion data are required as input. The output is an efficient session progress. Specific operations include updating the interface in real time and displaying alerts about the progress of the session.

[1059] Step 19: Retrieve Remote Team Data

[1060] The server retrieves data about the needs of the remote team from the database. As input, it requires queries related to the remote team. As output, it obtains the remote team data. Specific operations include retrieving data from the database and organizing it into a structured data format.

[1061] Step 20: Capture sentiment data for your remote team

[1062] The server uses the emotion engine to obtain emotion data about the remote team. The server requires the remote team's identification information as input. The server obtains the emotion data of the remote team as output. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[1063] Step 21: Generate virtual activities

[1064] The server generates a virtual team building activity using an AI model based on the acquired remote team data and emotion data. The remote team data and emotion data are required as input. The content of the virtual team building activity is obtained as output. Specifically, the server inputs a prompt statement into the generating AI model, executes the model, and obtains the generated result.

[1065] Step 22: Submit and view your activity

[1066] The server sends the content of the generated virtual team-building activity to the terminal, and the terminal displays it to the user so that it can be executed in a remote environment. The content of the virtual team-building activity is required as input. The displayed activity content and the user's execution results are obtained as output. The specific operation is to send the activity content to the terminal, and the terminal displays it on the user interface.

[1067] (Application example 2)

[1068] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1069] Conventional team building systems typically generate and personalized workshop ideas based on past success stories and team characteristics. However, these systems lack sufficient functionality for automatically analyzing feedback, generating role-play scenarios for specific situations, and providing virtual team building activities in remote environments. As a result, users' emotions and remote team building activities are not fully considered, making it difficult to achieve effective team building.

[1070] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1071] In this invention, the server includes means for acquiring data on past success stories and team characteristics, means for automatically generating workshop ideas based on the acquired data, means for providing the generated workshop ideas to a robot or a mobile information terminal, means for collecting feedback and analyzing the feedback data using an emotion recognition engine, and means for displaying improvements to the next workshop based on the analysis results. This enables more effective and personalized team building that takes into account users' emotions and activities in a remote environment.

[1072] "Past success stories" are specific examples of successful outcomes from previously implemented workshops or projects.

[1073] "Team characteristic data" is information about the skills, interests, personalities, and roles of team members, as well as the overall dynamics of the team.

[1074] A "Workshop Idea" is a proposed series of activities or exercises designed for team building or skill development purposes.

[1075] "Automatic generation" refers to the use of AI models and algorithms to mechanically generate new ideas and content based on specific input data.

[1076] "Robots or personal digital assistants" are electronic devices such as smartphones, tablets, and autonomous mobile devices.

[1077] "Feedback" refers to opinions, impressions, and evaluation information collected from users who have participated in a workshop or activity.

[1078] An "emotion recognition engine" is a technology that analyzes and recognizes a user's emotions from text, facial expressions, voice, etc.

[1079] "Analysis results" are information or insights obtained as a result of analysis performed on collected data.

[1080] "Areas for improvement for the next workshop" are specific proposals and action plans to improve the problems and issues identified based on the previous workshop.

[1081] A "personalized workshop" is a specific activity tailored to each member's individual skills and interests.

[1082] "Virtual team building activities" refer to online team building activities that can be carried out in a remote environment.

[1083] "Remote environment" refers to situations or conditions in which activities are carried out in physically separate locations.

[1084] An embodiment of the present invention relates to an AI-based system for supporting team building in a factory. This system consists of two main components: a server and a terminal.

[1085] Server configuration and functions

[1086] The server has the following features:

[1087] 1. Data acquisition function

[1088] The server has the function of retrieving past success stories and team characteristic data from a database. Past success stories include subordinates, deliverables from previous workshops and projects, and their associated emotional data.

[1089] 2. Automatic workshop idea generation function

[1090] The server uses an AI model to automatically generate new workshop ideas based on the acquired data, while also analyzing past emotional data using an emotion recognition engine to propose optimal workshop ideas.

[1091] 3. Functions provided to robots or mobile information terminals

[1092] The workshop ideas generated by the server are provided to robots or mobile information terminals in the factory and displayed to users through these devices.

[1093] 4. Feedback collection feature

[1094] After the workshop, feedback is collected from users via the terminals, including their opinions and feelings.

[1095] 5. Feedback analysis function

[1096] The collected feedback is analyzed by an emotion recognition engine, which uses technology to analyze and recognize user emotions from text, facial expressions, voice, etc.

[1097] 6. Improvements display function

[1098] Based on the analysis results, improvements for the next workshop will be displayed on the device.

[1099] Device configuration and functions

[1100] 1. Workshop idea display function

[1101] The workshop ideas provided by the server are displayed on the device screen, allowing the user to check them. The device also communicates the ideas to the user via a voice assistant or the robot's display.

[1102] 2. Feedback collection feature

[1103] After the workshop, the device collects feedback from the users, including their opinions and feelings, and sends this information to the server.

[1104] Specific examples

[1105] For example, the server generates a "team-based production line simulation" based on success stories related to "improving production line efficiency," current team characteristic data calling for "team building to improve communication," and past workshops in which employees felt "cooperative." This idea is proposed to the user via the device. The user then runs the workshop based on the idea and provides subsequent feedback via the device.

[1106] Example prompts for generative AI models

[1107] "Based on the current team data, generate workshop ideas aimed at 'improving communication skills.' Refer to past success stories that fostered 'collaborative feelings,' and propose team-based project activities."

[1108] This invention allows for flexible and personalized workshops, enabling team building activities that effectively take into account the emotions and needs of employees.

[1109] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1110] Step 1:

[1111] The server retrieves past success stories and team characteristics data from the database. This input data includes details of past successful workshops and information on team members' skills, interests, personalities, etc. The server then analyzes this data using an AI model to prepare for the next step.

[1112] Step 2:

[1113] The server uses AI models based on the acquired data to automatically generate new workshop ideas. The AI ​​models used here include a generative AI model that combines data on past success stories with data on current team characteristics to generate optimal workshop ideas. Prompt statements are also generated, and specific workshop content is determined.

[1114] Step 3:

[1115] The server provides the generated workshop ideas to robots or mobile information terminals in the factory, which then send the content of the ideas to the terminals, which then display them to the user. The terminals then provide information to the user through a voice assistant or the robot's display.

[1116] Step 4:

[1117] The user checks the presented workshop ideas and conducts the workshop based on the contents. When the user conducts the workshop, the terminal provides necessary guidelines and instructions in real time.

[1118] Step 5:

[1119] After the workshop, the device collects feedback from users, including text, voice, and facial expression data, and sends the collected data to a server in real time.

[1120] Step 6:

[1121] The server analyzes the collected feedback data using an emotion recognition engine, which recognizes the user's emotions from text and voice data, processes the data based on various emotions, and extracts important themes and trends from the feedback.

[1122] Step 7:

[1123] Based on the analysis results, the server generates a report containing suggestions for improvement for the next workshop. This report is sent to the terminal and displayed on the terminal to the user, allowing the user to confirm specific improvements for the next workshop.

[1124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1126] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1127] [Third embodiment]

[1128] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1132] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[1138] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1139] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1140] This invention is an AI-based system that supports corporate team building, generating workshop ideas based on past success stories and team characteristic data, and proposing personalized plans for each member. It also has the functions of automatically analyzing workshop feedback and suggesting areas for improvement, generating role-play scenarios for specific situations, managing the progress of workshops using AI, and providing virtual team building activities for remote teams.

[1141] 1. Workshop idea generation

[1142] The server accesses a database to retrieve data on past success stories and team characteristics. Based on this data, it uses an AI model to automatically generate new workshop ideas. The generated ideas are sent to the device, where the user can review and select the proposed ideas.

[1143] Examples:

[1144] The server uses an AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills." This idea is then proposed to the user via their device.

[1145] 2. Personalized Workshop Proposal

[1146] The server retrieves data about each member's skills and interests from a database and uses AI models to generate personalized workshops. The generated workshop proposals are sent to each member's device, allowing users to view the workshop content that best suits them.

[1147] Examples:

[1148] The server generates a "problem-solving workshop" based on member A's data that he is "interested in problem-solving skills," sends it to the terminal, and displays it. The user confirms the proposal.

[1149] 3. Automatic feedback analysis

[1150] After the workshop, the device collects feedback from users and sends it to the server. The server then uses an AI model to analyze the feedback data and extract important themes and trends. The results of this analysis are sent to the device as a report, allowing users to understand how to improve the next workshop.

[1151] Examples:

[1152] The device collects feedback from users that "the time to come up with ideas is short," and the server extracts this as a need for "improved time management" and notes it in the report as an issue to be addressed next time.

[1153] 4. Generating Role-Play Scenario

[1154] The server uses AI models to generate role-play scenarios based on data about team dynamics and specific problems, and the scenarios are sent to the device, where users can view and execute them to learn specific problem-solving skills.

[1155] Examples:

[1156] The server recognizes communication issues, generates role-play scenarios based on the theme of "dealing with problems during a project," and provides them to users via their terminals.

[1157] 5. Workshop Management

[1158] The server pre-determines the timeline and steps for each workshop session. The terminal supports the progress of the session according to this timeline, and displays time management and important instructions to the user, enabling efficient session progress.

[1159] Examples:

[1160] The server sets a timeline such as "Idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device notifies the user when each session ends.

[1161] 6. Creating Virtual Team Building Activities

[1162] The server uses an AI model to generate virtual team-building activities based on the needs data of the remote team, and the generated content is sent to the device and provided to the user in a format that can be executed even in a remote environment.

[1163] Examples:

[1164] The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal. The user can carry out the activity and strengthen the unity of the remote team.

[1165] As a result, the present invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling the creation of effective workshops, the analysis of feedback, and the support of remote teams.

[1166] The processing flow will be explained below.

[1167] Workshop idea generation

[1168] Step 1:

[1169] The server accesses a database to obtain past success stories and current team characteristic data.

[1170] Step 2:

[1171] The server inputs the acquired data into the AI ​​model and begins analysis.

[1172] Step 3:

[1173] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[1174] Step 4:

[1175] The server transmits the generated workshop ideas to the terminal.

[1176] Step 5:

[1177] The terminal displays the workshop ideas to the user.

[1178] Step 6:

[1179] The user selects the best idea from multiple proposed ideas.

[1180] Personalized Workshop Proposals

[1181] Step 1:

[1182] The server retrieves data about each member's skills and interests from a database.

[1183] Step 2:

[1184] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[1185] Step 3:

[1186] The server transmits the generated personalized workshop proposal to each member's terminal.

[1187] Step 4:

[1188] The terminal displays personalized workshop suggestions to each member.

[1189] Step 5:

[1190] The user checks the workshop content that best suits him or her.

[1191] Automatic analysis of feedback

[1192] Step 1:

[1193] The terminal collects feedback from users after the workshop is over.

[1194] Step 2:

[1195] The terminal transmits the collected feedback data to the server.

[1196] Step 3:

[1197] The server uses AI models to analyze the feedback data and extract important themes and trends.

[1198] Step 4:

[1199] The server generates a report based on the extraction results and sends it to the terminal.

[1200] Step 5:

[1201] The terminal displays the generated report to the user.

[1202] Step 6:

[1203] Users will know what needs to be improved for the next workshop.

[1204] Role-play scenario generation

[1205] Step 1:

[1206] The server captures data about specific team dynamics and issues.

[1207] Step 2:

[1208] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[1209] Step 3:

[1210] The server transmits the generated role-play scenario to the terminal.

[1211] Step 4:

[1212] The terminal displays the scenario and provides instructions for the user to execute.

[1213] Step 5:

[1214] Users are guided through role-play scenarios to develop problem-solving skills.

[1215] Workshop progress

[1216] Step 1:

[1217] The server pre-determines the timeline and steps for each workshop session.

[1218] Step 2:

[1219] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[1220] Step 3:

[1221] The server monitors the progress of each session and sends instructions to the terminal to proceed to the next session.

[1222] Step 4:

[1223] The user follows instructions from the terminal to proceed with the workshop.

[1224] Generate virtual team building activities

[1225] Step 1:

[1226] The server retrieves data regarding the needs of the remote team.

[1227] Step 2:

[1228] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[1229] Step 3:

[1230] The server transmits the generated activity content to the terminal.

[1231] Step 4:

[1232] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[1233] Step 5:

[1234] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[1235] Example 1

[1236] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1237] In-company team building activities require planning and running effective workshops, providing content tailored to each participant, and collecting and analyzing feedback after the activities. However, carrying out these activities efficiently is difficult and requires significant human resources and time. Furthermore, with the spread of remote work, effective team building activities must also be carried out in remote environments. However, current methods often fail to achieve their full effectiveness. The present invention aims to provide a system and method for solving these problems.

[1238] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1239] In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas using an AI model based on the acquired data, means for sending the generated workshop ideas to a terminal and displaying them, means for generating personalized workshops based on the skills and interests of each member, means for proposing personalized workshops to each member, means for collecting feedback from the terminal after the workshop, means for extracting important themes and trends using the AI ​​model based on the collected feedback, and means for displaying the extracted analysis results in a report as improvements for the next workshop. This enables an effective and efficient cycle of team building planning, execution, and feedback.

[1240] "Past success stories" are data showing successful cases obtained from workshops or projects that have been previously implemented.

[1241] "Team characteristic data" is data that includes information about team members' skills, interests, roles, performance, etc.

[1242] An "AI model" is a computational algorithm or neural network that uses artificial intelligence techniques to analyze data and generate a specific result.

[1243] "Workshop Ideas" are specific suggestions for activities or sessions designed for team building or skill development.

[1244] "Terminal" means an electronic device used by a user to input and display information, including a personal computer, smartphone, tablet, etc.

[1245] A "personalized workshop" is a workshop designed specifically to meet the skills and interests of individual members.

[1246] "Feedback" refers to data on opinions and evaluations collected from users after the workshop.

[1247] "Key themes and trends" are key issues or patterns extracted from the collected feedback data that can help improve the next workshop.

[1248] The "report" is a document displayed on the terminal that lists the analysis results and improvements for the next workshop.

[1249] A "role-play scenario" is a plot or setting for a simulated role-play based on a particular situation.

[1250] A "timeline" indicates the schedule for the start and end of each session or activity in the workshop.

[1251] "Virtual team building activities" are online team building activities that can be carried out in a remote environment.

[1252] This invention is a system for supporting corporate team building, which includes a server, terminals, and users. The system utilizes AI models to generate workshop ideas, propose personalized workshops, automatically analyze feedback, generate role-play scenarios, manage workshop progress, and generate virtual team building activities.

[1253] Data collection and workshop idea generation

[1254] The server accesses a database to retrieve past success stories and team characteristics, including each member's skills, interests, roles, and performance data. Based on the retrieved data, an AI model (e.g., GPT-4) is used to automatically generate workshop ideas. The generated ideas are sent to the device and displayed to the user.

[1255] Examples:

[1256] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[1257] Example prompt sentence:

[1258] "Generate workshop ideas aimed at improving communication skills."

[1259] Personalized Workshop Proposals

[1260] The server generates personalized workshops based on each member's skills and interests, using their skill and role data. The generated workshop proposals are sent to each member's device and notified to the user.

[1261] Examples:

[1262] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[1263] Example prompt sentence:

[1264] "Please propose a workshop for members who are interested in generating new ideas."

[1265] Feedback collection and automated analysis

[1266] After the workshop, the device collects feedback from users, including evaluations of the workshop's duration, content, and props. The collected feedback data is sent to a server and analyzed using an AI model (e.g., Transformer) to extract important themes and trends. The results of this analysis are sent to the device as a report, which users can review.

[1267] Examples:

[1268] The device asks the user how satisfied they are with the workshop time, and sends the response data to the server. The server analyzes the data and determines whether time management needs improvement, and compiles it into a report and sends it to the device.

[1269] Example prompt sentence:

[1270] "Analyze the feedback data from the workshops and extract key themes and trends."

[1271] Role-play scenario generation

[1272] The server generates role-play scenarios using AI models (e.g., T5) based on data about team dynamics and specific problems. The generated scenarios are sent to the device and made available for users to view and play.

[1273] Examples:

[1274] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[1275] Example prompt sentence:

[1276] "Generate role-play scenarios for dealing with problems during a project."

[1277] Workshop progress management

[1278] The server sets the workshop session timeline and steps in advance, and the terminal follows this timeline and supports the progress. Time management and important instructions are displayed to the user.

[1279] Examples:

[1280] The server will set up a "15-minute idea brainstorming session" or a "30-minute discussion," and the device will display a notification at the end of each session.

[1281] Example prompt sentence:

[1282] "Set the timeline for the workshop and help facilitate each session."

[1283] Generate virtual team building activities

[1284] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The generated activities are sent to the device and provided in a format that users can carry out even in a remote environment.

[1285] Examples:

[1286] The server generates an online quiz based on the characteristics of the remote team and sends it to the device. The user then checks the content and participates in the remote environment.

[1287] Example prompt sentence:

[1288] "Generate virtual team building activities for remote teams."

[1289] As described above, the system of the present invention efficiently supports team building activities through comprehensive data collection and analysis / generation using AI models, highly automating the entire process from workshop planning to execution, feedback collection, and proposals for the next activity.

[1290] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1291] Step 1:

[1292] Data collection

[1293] The server accesses the company's database to retrieve past success stories and team characteristics data, including each member's skills, interests, roles, and performance data. The input is a query from the database, and the output is the retrieved data.

[1294] Specific behavior:

[1295] The server extracts "past project success rates" and "member skill profiles" from the database.

[1296] Input: Query to the database "SELECT FROM Success_Cases"

[1297] Output: Past success story data

[1298] Step 2:

[1299] Workshop idea generation

[1300] The server automatically generates workshop ideas using an AI model (e.g., GPT-4) based on the acquired data. The acquired data is the input, and the generated workshop ideas are the output. These ideas are sent to the terminal and notified to the user.

[1301] Specific behavior:

[1302] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[1303] Input: Past success story data, team characteristics data

[1304] Output: Workshop idea "Team debate session"

[1305] Step 3:

[1306] Personalized Workshop Proposals

[1307] The server generates personalized workshops based on each member's skills and interests. The input is the member's individual skill and interest data, and the output is personalized workshop proposals. The generated proposals are sent to each member's device and notified to the user.

[1308] Specific behavior:

[1309] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[1310] Input: Individual member skills and interest data

[1311] Output: Personalized Workshop "New Idea Brainstorming Session"

[1312] Step 4:

[1313] Gathering feedback

[1314] After the workshop, the terminal collects feedback from users. The input is the feedback from users, and the output is the collected feedback data. This is then sent to the server.

[1315] Specific behavior:

[1316] The terminal asks the user about "satisfaction with the workshop time" and transmits the answer data to the server.

[1317] Input: User feedback

[1318] Output: Feedback data

[1319] Step 5:

[1320] Automatic analysis of feedback

[1321] The server analyzes the collected feedback data using an AI model (e.g., Transformer) to extract important themes and trends. The input is the feedback data, and the output is the analysis results. These analysis results are sent to the terminal in the form of a report, which the user can review.

[1322] Specific behavior:

[1323] The server extracts themes such as "time management needs improvement" from the feedback data, compiles them into a report, and sends it to the terminal.

[1324] Input: Feedback data

[1325] Output: Report of analysis results

[1326] Step 6:

[1327] Role-play scenario generation

[1328] The server generates role-play scenarios using an AI model (e.g., T5) based on data about team dynamics and specific problems. The input is the specific problem data, and the output is the generated role-play scenario. This scenario is sent to the device, where the user can view and perform it.

[1329] Specific behavior:

[1330] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[1331] Input: Specific problem data

[1332] Output: Role-play scenario

[1333] Step 7:

[1334] Workshop progress management

[1335] The server sets the timeline and steps of each workshop session in advance. The input is the workshop timeline and steps, and the output is the timeline setting data. The terminal follows this timeline and supports the progress.

[1336] Specific behavior:

[1337] The server will set up a "15-minute idea brainstorm" or "30-minute discussion," and the device will display a notification.

[1338] Input: Workshop timeline and steps

[1339] Output: Timeline setting data

[1340] Step 8:

[1341] Generate virtual team building activities

[1342] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The input is the needs data of the remote team, and the output is the generated team-building activity. The activity content is sent to the device and provided in a form that users can carry out even in a remote environment.

[1343] Specific behavior:

[1344] The server generates an "online quiz competition" based on the "remote team characteristics" and sends it to the terminal.

[1345] Input: Remote team needs data

[1346] Output: Virtual team building activity

[1347] (Application example 1)

[1348] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1349] Effective team building and training is crucial for modern companies, but it is difficult to provide personalized programs tailored to diverse needs, effectively analyze feedback, generate role-play scenarios for specific situations, and conduct training in a remote environment. In particular, employee training in a virtual environment is not effectively managed or optimized.

[1350] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1351] In this invention, the server includes means for acquiring data on past success stories and team characteristics, means for automatically generating workshop ideas based on the acquired data, means for displaying the generated workshop ideas, means for automatically generating training workshops in a virtual environment, and means for displaying the generated training workshops. This enables the provision of personalized programs that meet the diverse needs of companies, automatic analysis of feedback, generation of role-play scenarios according to situations, and effective training in remote environments.

[1352] "Past success stories" are examples of previously held workshops or trainings that have proven particularly successful.

[1353] "Team characteristic data" is data relating to a particular team that includes information such as the skills, interests, and personalities of members.

[1354] "Workshop Ideas" are specific activity proposals for team building or training designed around a specific theme or purpose.

[1355] A "personalized workshop" is a workshop that is customized based on each member's skills and interests.

[1356] "Feedback" refers to evaluations and opinions from members who participated in workshops and training, including information for improvement.

[1357] "Key themes and trends" are items or patterns extracted from the collected feedback data that deserve particular attention for improving workshops or generating new ideas.

[1358] A "role-play scenario" is an exercise or dramatization designed to recreate a specific situation for the purpose of practical skill acquisition.

[1359] A "virtual environment" refers to a virtual space or interface provided by computer technology that enables remote activities.

[1360] "Training Workshop" means an educational session designed to improve employee skills or knowledge, including work-related training.

[1361] MODE FOR CARRYING OUT THE INVENTION

[1362] This invention is an AI-based system for effective corporate team building and employee training. This system generates new workshop ideas and role-play scenarios based on past success stories and team characteristic data, and provides virtual training that can be conducted even in remote environments. Specific embodiments are described below.

[1363] 1. System Configuration

[1364] Server: A cloud server is used to install an AI model (e.g., OpenAI's GPT-4) and a database management system (e.g., MySQL).

[1365] Devices: Employee-owned smartphones, tablets, or VR headsets (e.g., Oculus, VIVE, etc.).

[1366] Software: Use feedback analysis tools (e.g., Google Cloud Natural Language) and real-time communication servers (e.g., WebSockets).

[1367] 2. Data Acquisition

[1368] The server retrieves past success stories and team characteristics data from the database, including information about employee skills and interests.

[1369] 3. Workshop idea generation

[1370] The server uses a generative AI model (e.g., GPT-4) to generate new workshop ideas based on the acquired data, and the generated ideas are sent to the terminals and made available to employees.

[1371] Example: The server uses a generative AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills."

[1372] 4. Personalized Workshop Proposals

[1373] The server acquires each member's skill and interest data and generates a personalized workshop, which is then sent to each member's device.

[1374] Example: Based on data that member A is interested in problem-solving skills, the server generates a "problem-solving workshop" and sends it to the terminal.

[1375] 5. Automatic feedback analysis

[1376] The terminals collect feedback from employees after the workshop and send it to a server, which then uses a feedback analysis tool to analyze the feedback data and extract important themes and trends.

[1377] Example: The device collects feedback from the user that "the time it takes to come up with ideas is short," and the server extracts this as a need for "improved time management."

[1378] 6. Role-play scenario generation

[1379] The server uses a generative AI model to generate role-play scenarios based on data about team dynamics and specific problems, which are then sent to the device where employees can view and act them out.

[1380] Example: The server recognizes a communication issue and generates a role-play scenario on the theme of "dealing with problems during a project."

[1381] 7. Generate virtual training workshops

[1382] The server automatically generates training workshops in a virtual environment, which can then be sent to the device and run remotely.

[1383] Example: The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal.

[1384] 8. Examples of prompts

[1385] "Generate training workshop ideas for a virtual store based on past success stories: [Case 1, Case 2, ...] and team characteristics data: [Team 1, Team 2, ...]."

[1386] This enables companies to provide personalized training programs to meet a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[1387] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1388] Step 1:

[1389] Data Acquisition:

[1390] The server retrieves data on past success stories and team characteristics from the database. In this process, it accesses the database using an SQL query and extracts the necessary data. The input is the SQL query, and the output is the data on past success stories and team characteristics.

[1391] Step 2:

[1392] Workshop idea generation:

[1393] The server generates new workshop ideas using a generative AI model (e.g., GPT-4) based on the acquired data. Here, a prompt is created and input to the AI ​​model. The input is the success case data, team characteristic data, and the created prompt, and the output is the generated workshop idea.

[1394] Step 3:

[1395] View Workshop Ideas:

[1396] The server sends the generated workshop ideas to the terminal. The terminal receives this information and displays it to the user. The input is the generated workshop ideas, and the output is the workshop ideas that the user confirms.

[1397] Step 4:

[1398] Personalized Workshop Suggestion:

[1399] The server acquires each member's skill and interest data and generates a personalized workshop using a generative AI model. The generated workshop is sent to each member's device and displayed. The input is the member's skill and interest data, and the output is a personalized workshop proposal.

[1400] Step 5:

[1401] Collecting feedback:

[1402] After the workshop, the terminal collects feedback from users and sends it to the server. The input is the feedback information from users, and the output is the feedback data sent to the server.

[1403] Step 6:

[1404] Feedback Analysis:

[1405] The server uses a feedback analysis tool to analyze the feedback data and extract important themes and trends. The analysis results are compiled into a report as improvements for the next workshop and sent to the terminal. The input is the feedback data, and the output is a report containing important themes and improvements.

[1406] Step 7:

[1407] Role-play scenario generation:

[1408] The server uses a generative AI model to generate role-play scenarios based on data on team dynamics and specific problems. The scenarios are sent to the device, where users can view and run them. The inputs are team dynamics data and specific problem data, and the output is the role-play scenario.

[1409] Step 8:

[1410] Generate a virtual training workshop:

[1411] The server automatically generates a training workshop in a virtual environment and sends it to the terminal. It is provided to the user in a state that can be executed in a remote environment. The input is the characteristic data of the remote team, and the output is the virtual training workshop.

[1412] These steps enable companies to deliver personalized training programs tailored to a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[1413] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1414] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans for each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. Furthermore, these functions are combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[1415] 1. Workshop idea generation

[1416] The server accesses the database to acquire data on past success stories and current team characteristics. It also uses an emotion engine to acquire user emotion data related to past success stories. Based on this data, it automatically generates new workshop ideas using an AI model and sends the ideas to the device. The device displays the workshop ideas to the user, who can then review and select from the proposed ideas.

[1417] Examples:

[1418] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[1419] 2. Personalized Workshop Proposal

[1420] The server retrieves data on each member's skills and interests from a database, and also uses an emotion engine to retrieve each member's past emotional data. Based on this data, an AI model is used to generate a personalized workshop and send it to each member's device. The device displays the proposal to each member, allowing them to check the workshop content that best suits them.

[1421] Examples:

[1422] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[1423] 3. Automatic feedback analysis

[1424] After the workshop, the device collects feedback from users and uses an emotion engine to collect emotional data during the feedback. This data is then sent to a server, which uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand how to improve their next workshop.

[1425] Examples:

[1426] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[1427] 4. Generating Role-Play Scenario

[1428] The server uses data about specific team dynamics and problems, and also uses an emotion engine to capture team emotion data. Based on this data, the AI ​​model generates role-play scenarios and sends them to the device, which displays the scenarios and provides instructions for the user to follow.

[1429] Examples:

[1430] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[1431] 5. Workshop Management

[1432] The server pre-determines the timeline and steps for each workshop session and uses an emotion engine to monitor the user's emotions in real time during the session. The device follows the pre-determined timeline and uses the emotion data to guide the session forward. Appropriate instructions are displayed to the user, ensuring efficient session progress.

[1433] Examples:

[1434] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[1435] 6. Creating Virtual Team Building Activities

[1436] The server uses an AI model to generate virtual team-building activities based on data on the needs of remote teams and emotional data acquired from an emotion engine. The generated activities are then sent to the device and provided to users so they can carry them out in a remote environment.

[1437] Examples:

[1438] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[1439] As a result, this invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling effective workshop generation, feedback analysis, and remote team support. By utilizing an emotion engine, it is possible to provide a more personalized experience based on user emotions and strengthen team cohesion.

[1440] The processing flow will be explained below.

[1441] Workshop idea generation

[1442] Step 1:

[1443] The server accesses the database to obtain historical success data and current team characteristic data.

[1444] Step 2:

[1445] The server uses an emotion engine to obtain user emotion data related to past success stories.

[1446] Step 3:

[1447] The server inputs the acquired data into the AI ​​model and begins analysis.

[1448] Step 4:

[1449] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[1450] Step 5:

[1451] The server transmits the generated workshop ideas to the terminal.

[1452] Step 6:

[1453] The terminal displays the workshop ideas to the user.

[1454] Step 7:

[1455] The user selects the best idea from the proposed ideas.

[1456] Personalized Workshop Proposals

[1457] Step 1:

[1458] The server retrieves data about each member's skills and interests from a database.

[1459] Step 2:

[1460] The server uses an emotion engine to obtain past emotion data for each member.

[1461] Step 3:

[1462] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[1463] Step 4:

[1464] The server transmits the generated personalized workshop proposal to each member's terminal.

[1465] Step 5:

[1466] The terminal displays personalized workshop suggestions to each member.

[1467] Step 6:

[1468] The user checks the workshop content that best suits him or her.

[1469] Automatic analysis of feedback

[1470] Step 1:

[1471] The terminal collects feedback from users after the workshop is over.

[1472] Step 2:

[1473] The terminal also collects the user's emotion data along with the feedback data.

[1474] Step 3:

[1475] The terminal transmits the collected data to the server.

[1476] Step 4:

[1477] The server uses AI models and sentiment engines to analyze the feedback data and extract key themes and trends.

[1478] Step 5:

[1479] The server generates a report based on the extraction results and sends it to the terminal.

[1480] Step 6:

[1481] The terminal displays the generated report to the user.

[1482] Step 7:

[1483] Users will know what needs to be improved for the next workshop.

[1484] Role-play scenario generation

[1485] Step 1:

[1486] The server captures data about specific team dynamics and issues.

[1487] Step 2:

[1488] The server uses an emotion engine to obtain emotion data within the team.

[1489] Step 3:

[1490] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[1491] Step 4:

[1492] The server transmits the generated role-play scenario to the terminal.

[1493] Step 5:

[1494] The terminal displays the scenario and provides instructions for the user to execute.

[1495] Step 6:

[1496] Users are guided through role-play scenarios to develop problem-solving skills.

[1497] Workshop progress

[1498] Step 1:

[1499] The server pre-determines the timeline and steps for each workshop session.

[1500] Step 2:

[1501] The server uses an emotion engine to monitor the user's emotions in real time during the session.

[1502] Step 3:

[1503] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[1504] Step 4:

[1505] The server determines the next action based on the emotional data during the session and sends instructions to the terminal.

[1506] Step 5:

[1507] The user follows instructions from the terminal to proceed with the workshop.

[1508] Generate virtual team building activities

[1509] Step 1:

[1510] The server retrieves data regarding the needs of the remote team.

[1511] Step 2:

[1512] The server uses an emotion engine to obtain emotion data of the remote team.

[1513] Step 3:

[1514] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[1515] Step 4:

[1516] The server transmits the generated activity content to the terminal.

[1517] Step 5:

[1518] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[1519] Step 6:

[1520] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[1521] Example 2

[1522] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1523] Current team building workshops generally have uniform content and do not fully consider the characteristics and feelings of each member, making it difficult to achieve effective team building. Furthermore, simply collecting feedback from the workshop makes it difficult to reflect specific improvements in the next workshop. Furthermore, providing appropriate virtual team building activities for remote teams is also a challenge.

[1524] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas based on the acquired data and emotion data, means for displaying the generated workshop ideas, and means for checking and selecting the proposed workshop ideas. This makes it possible to generate workshop ideas that take into account the team's characteristics and emotions, and to provide an appropriate personalized workshop for each member. In addition, feedback and emotion data can be analyzed to clearly suggest areas for improvement in the next workshop. It is also possible to provide effective virtual team building activities to remote teams.

[1525] "Past success stories" refer to successful experiences and achievements in previous workshops or projects.

[1526] "Team characteristic data" is information about team members' skills, roles, performance, interests, personalities, etc.

[1527] "Emotional data" refers to data that indicates the emotional state or emotional changes of a user or team member, and is obtained from feedback or sensor data.

[1528] "Means for automatically generating workshop ideas" refers to a method or device that uses AI models or algorithms to analyze and process acquired data and generate new workshop plans and proposals.

[1529] The "means for displaying the generated workshop idea" refers to a method or device for displaying the generated workshop idea on a user's terminal.

[1530] The "means for reviewing and selecting proposed workshop ideas" refers to a method or device that allows a user to view the generated workshop ideas and select an appropriate one from among them.

[1531] "Individual skills and interests" refers to the specific skills and interests of each team member.

[1532] A "personalized workshop" is a workshop that is individually tailored based on each member's skills, interests, and emotional data.

[1533] "Feedback" refers to opinions and impressions collected from participants after the workshop.

[1534] "Key themes and trends" are areas for improvement or noteworthy issues for the next workshop that are extracted from the collected feedback data.

[1535] An "AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions or generates results for specific tasks.

[1536] MODE FOR CARRYING OUT THE INVENTION

[1537] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans tailored to each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. In addition to these functions, it can also be combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[1538] Hardware and Software

[1539] The server mainly uses a database, a generative AI model, and an emotion engine. The database is used to store data on past success stories and team characteristics. The emotion engine is required to analyze user emotions and obtain emotional data. The generative AI model generates new workshop ideas, personalized workshops, and role-play scenarios based on the obtained data.

[1540] The terminal plays a role in displaying data sent from the server, generated workshop ideas, personalized workshops, feedback reports, etc. to the user.

[1541] Specific examples

[1542] Workshop idea generation

[1543] The server first accesses the database to retrieve data on past success stories and current team characteristics. It then uses an emotion engine to retrieve user emotion data related to past success stories. Based on this data, the server then uses an AI model to automatically generate new workshop ideas and sends them to the device. The device then displays the workshop ideas to the user, who can then review and select from the suggested ideas.

[1544] Examples:

[1545] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[1546] Personalized Workshop Proposals

[1547] The server retrieves data about each member's skills and interests from a database. It also uses an emotion engine to retrieve each member's past emotional data. Based on this data, it uses an AI model to generate personalized workshops and sends them to each member's device. The device displays the suggestions to each member, allowing them to check the workshop content that best suits them.

[1548] Examples:

[1549] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[1550] Automatic analysis of feedback

[1551] After the workshop, the device collects feedback from users. An emotion engine also collects emotional data during the feedback and sends this data to a server. The server uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand areas for improvement in the next workshop.

[1552] Examples:

[1553] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[1554] Role-play scenario generation

[1555] The server retrieves data about specific team dynamics and problems, uses an emotion engine to retrieve team emotion data, and uses this data to generate role-play scenarios using an AI model. The scenarios are then sent to the device, which displays the scenarios and provides instructions for the user to follow.

[1556] Examples:

[1557] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[1558] Workshop progress

[1559] The server pre-determines the timeline and steps for each workshop session. It uses an emotion engine to monitor the user's emotions in real time during the session. The device displays appropriate instructions on the user interface according to the pre-determined timeline, ensuring efficient session progress.

[1560] Examples:

[1561] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[1562] Generate virtual team building activities

[1563] The server uses AI models to generate virtual team-building activities based on data about the needs of remote teams and emotional data acquired through an emotion engine. The generated activities are then sent to the device and displayed to the user so that they can be carried out in a remote environment.

[1564] Examples:

[1565] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[1566] Prompt Sentence Examples

[1567] 1. "Generate workshop ideas for improving corporate project management and communication skills."

[1568] 2. "Generate a personalized workshop on problem-solving skills for Member A."

[1569] 3. "Please analyze the feedback from the workshop and identify areas for improvement for next time."

[1570] 4. "Generate role-play scenarios that address communication challenges."

[1571] 5. "Generate guidelines to help facilitate the next workshop."

[1572] 6. "Generate personalized virtual team-building activities for your remote team."

[1573] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1574] Step 1: Data Acquisition

[1575] The server accesses the database to retrieve data on past success stories and current team characteristics. Database connection information and queries are required as input. Success story data and team characteristics data are obtained as output. Specifically, the server executes SQL queries to retrieve data, structures the data (e.g., in JSON format), and saves it.

[1576] Step 2: Obtaining emotion data

[1577] The server uses the emotion engine to obtain user emotion data related to past success stories. Data about past success stories is required as input. Emotion data is obtained as output. Specifically, the server sends an API request to the emotion engine to obtain emotion data and associates it with the past success story data.

[1578] Step 3: Workshop idea generation

[1579] The server inputs the acquired success case data, team characteristic data, and emotion data into the generative AI model to generate new workshop ideas. All data obtained in the previous step is required as input. New workshop ideas are obtained as output. Specifically, the server inputs data and prompt statements into the generative AI model and runs the model to generate ideas.

[1580] Step 4: Submit and view your idea

[1581] The server sends the generated workshop ideas to the terminal, which then displays them to the user, allowing the user to review and select the proposed ideas. The generated workshop ideas are required as input, and the displayed ideas and the user's selection results are obtained as output. The specific operation is to send the generated ideas to the terminal, which then displays them on the user interface.

[1582] Step 5: Retrieving Member Data

[1583] The server accesses the database to retrieve data about each member's skills and interests. As input, it requires a query for member data. As output, it gets the data about each member's skills and interests. Specifically, it executes an SQL query to retrieve the data and organizes it into a structured data format.

[1584] Step 6: Obtaining emotion data

[1585] The server uses the emotion engine to obtain past emotion data for each member. The input requires the identification information of each member. The output is the emotion data for each member. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[1586] Step 7: Generate a personalized workshop

[1587] The server inputs the acquired member data and emotion data into the generative AI model to generate a personalized workshop. Member data and emotion data are required as input. The output is a personalized workshop proposal. Specifically, the server inputs a prompt statement into the generative AI model, runs the model, and obtains the generated result.

[1588] Step 8: Submit and view your proposal

[1589] The server sends the generated personalized workshop proposal to each member's terminal, which then displays it to the user of each member. The generated personalized workshop proposal is required as input. The displayed proposal and the user's response are obtained as output. The specific operation is to send the proposal to the terminal, which then displays it on the user interface.

[1590] Step 9: Gather feedback

[1591] The terminal collects feedback from users after the workshop ends. As input, it requires users to fill out a feedback form. As output, it obtains the collected feedback data. Specific operations include displaying the feedback form, receiving user input, and storing it in a database.

[1592] Step 10: Collect emotion data

[1593] The device uses an emotion engine to collect emotion data simultaneously with the feedback. The input requires the user's reaction and text during the feedback. The output is emotion data. Specific operations include obtaining emotion data through sensor data and text analysis, and associating it with the feedback data.

[1594] Step 11: Analyze feedback data

[1595] The server analyzes the collected feedback and emotion data using an AI model. It requires feedback and emotion data as input. It outputs analysis results that indicate key themes and trends. Specifically, it inputs data into the AI ​​model, runs the model, and analyzes the results.

[1596] Step 12: Submit and view analysis results

[1597] The server sends the analysis results to the terminal, which then displays them to the user. The analysis results are required as input. The displayed analysis results and user confirmation are obtained as output. Specifically, the analysis results are compiled into a report and sent to the terminal, which then displays them on the user interface.

[1598] Step 13: Generate role-play scenarios

[1599] The server generates role-play scenarios from data about specific team dynamics and problems. As input, it requires team dynamics data. As output, it obtains role-play scenarios. Specifically, it inputs the data into a generative AI model and runs the model to generate scenarios.

[1600] Step 14: Send and view the scenario

[1601] The server sends the generated role-play scenario to the terminal, which displays it to the user and provides instructions for execution. The generated role-play scenario is required as input. The displayed scenario and the user's reaction are obtained as output. The specific operation is to send the scenario to the terminal, which displays it on the user interface.

[1602] Step 15: Setting the Timeline and Steps

[1603] The server pre-configures the timeline and steps for each workshop session. As input, it requires the workshop schedule information. As output, it obtains the timeline and step configuration. Specific operations include retrieving the timeline and steps from a database or using pre-configured data.

[1604] Step 16: Monitoring emotional data during the session

[1605] The device uses an emotion engine to monitor the user's emotions in real time during the session. The inputs include the user's reactions during the session and sensor data. The output is real-time emotion data. Specifically, the device collects emotion data through sensor data and text analysis, and displays it on the monitoring screen.

[1606] Step 17: Display instructions

[1607] The terminal displays appropriate instructions to the user based on the configured timeline and emotional data during the session. Timeline data and emotional data are required as input. The terminal obtains the displayed instructions as output. Specific operations include displaying instructions based on the timeline on the user interface and making necessary changes according to the emotional data.

[1608] Step 18: Support the session

[1609] The terminal efficiently supports the progress of the workshop based on the timeline and emotion data. Timeline data and emotion data are required as input. The output is an efficient session progress. Specific operations include updating the interface in real time and displaying alerts about the progress of the session.

[1610] Step 19: Retrieve Remote Team Data

[1611] The server retrieves data about the needs of the remote team from the database. As input, it requires queries related to the remote team. As output, it obtains the remote team data. Specific operations include retrieving data from the database and organizing it into a structured data format.

[1612] Step 20: Capture sentiment data for your remote team

[1613] The server uses the emotion engine to obtain emotion data about the remote team. The server requires the remote team's identification information as input. The server obtains the emotion data of the remote team as output. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[1614] Step 21: Generate virtual activities

[1615] The server generates a virtual team building activity using an AI model based on the acquired remote team data and emotion data. The remote team data and emotion data are required as input. The content of the virtual team building activity is obtained as output. Specifically, the server inputs a prompt statement into the generating AI model, executes the model, and obtains the generated result.

[1616] Step 22: Submit and view your activity

[1617] The server sends the content of the generated virtual team-building activity to the terminal, and the terminal displays it to the user so that it can be executed in a remote environment. The content of the virtual team-building activity is required as input. The displayed activity content and the user's execution results are obtained as output. The specific operation is to send the activity content to the terminal, and the terminal displays it on the user interface.

[1618] (Application example 2)

[1619] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1620] Conventional team building systems typically generate and personalized workshop ideas based on past success stories and team characteristics. However, these systems lack sufficient functionality for automatically analyzing feedback, generating role-play scenarios for specific situations, and providing virtual team building activities in remote environments. As a result, users' emotions and remote team building activities are not fully considered, making it difficult to achieve effective team building.

[1621] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1622] In this invention, the server includes means for acquiring data on past success stories and team characteristics, means for automatically generating workshop ideas based on the acquired data, means for providing the generated workshop ideas to a robot or a mobile information terminal, means for collecting feedback and analyzing the feedback data using an emotion recognition engine, and means for displaying improvements to the next workshop based on the analysis results. This enables more effective and personalized team building that takes into account users' emotions and activities in a remote environment.

[1623] "Past success stories" are specific examples of successful outcomes from previously implemented workshops or projects.

[1624] "Team characteristic data" is information about the skills, interests, personalities, and roles of team members, as well as the overall dynamics of the team.

[1625] A "Workshop Idea" is a proposed series of activities or exercises designed for team building or skill development purposes.

[1626] "Automatic generation" refers to the use of AI models and algorithms to mechanically generate new ideas and content based on specific input data.

[1627] "Robots or personal digital assistants" are electronic devices such as smartphones, tablets, and autonomous mobile devices.

[1628] "Feedback" refers to opinions, impressions, and evaluation information collected from users who have participated in a workshop or activity.

[1629] An "emotion recognition engine" is a technology that analyzes and recognizes a user's emotions from text, facial expressions, voice, etc.

[1630] "Analysis results" are information or insights obtained as a result of analysis performed on collected data.

[1631] "Areas for improvement for the next workshop" are specific proposals and action plans to improve the problems and issues identified based on the previous workshop.

[1632] A "personalized workshop" is a specific activity tailored to each member's individual skills and interests.

[1633] "Virtual team building activities" refer to online team building activities that can be carried out in a remote environment.

[1634] "Remote environment" refers to situations or conditions in which activities are carried out in physically separate locations.

[1635] An embodiment of the present invention relates to an AI-based system for supporting team building in a factory. This system consists of two main components: a server and a terminal.

[1636] Server configuration and functions

[1637] The server has the following features:

[1638] 1. Data acquisition function

[1639] The server has the function of retrieving past success stories and team characteristic data from a database. Past success stories include subordinates, deliverables from previous workshops and projects, and their associated emotional data.

[1640] 2. Automatic workshop idea generation function

[1641] The server uses an AI model to automatically generate new workshop ideas based on the acquired data, while also analyzing past emotional data using an emotion recognition engine to propose optimal workshop ideas.

[1642] 3. Functions provided to robots or mobile information terminals

[1643] The workshop ideas generated by the server are provided to robots or mobile information terminals in the factory and displayed to users through these devices.

[1644] 4. Feedback collection feature

[1645] After the workshop, feedback is collected from users via the terminals, including their opinions and feelings.

[1646] 5. Feedback analysis function

[1647] The collected feedback is analyzed by an emotion recognition engine, which uses technology to analyze and recognize user emotions from text, facial expressions, voice, etc.

[1648] 6. Improvements display function

[1649] Based on the analysis results, improvements for the next workshop will be displayed on the device.

[1650] Device configuration and functions

[1651] 1. Workshop idea display function

[1652] The workshop ideas provided by the server are displayed on the device screen, allowing the user to check them. The device also communicates the ideas to the user via a voice assistant or the robot's display.

[1653] 2. Feedback collection feature

[1654] After the workshop, the device collects feedback from the users, including their opinions and feelings, and sends this information to the server.

[1655] Specific examples

[1656] For example, the server generates a "team-based production line simulation" based on success stories related to "improving production line efficiency," current team characteristic data calling for "team building to improve communication," and past workshops in which employees felt "cooperative." This idea is proposed to the user via the device. The user then runs the workshop based on the idea and provides subsequent feedback via the device.

[1657] Example prompts for generative AI models

[1658] "Based on the current team data, generate workshop ideas aimed at 'improving communication skills.' Refer to past success stories that fostered 'collaborative feelings,' and propose team-based project activities."

[1659] This invention allows for flexible and personalized workshops, enabling team building activities that effectively take into account the emotions and needs of employees.

[1660] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1661] Step 1:

[1662] The server retrieves past success stories and team characteristics data from the database. This input data includes details of past successful workshops and information on team members' skills, interests, personalities, etc. The server then analyzes this data using an AI model to prepare for the next step.

[1663] Step 2:

[1664] The server uses AI models based on the acquired data to automatically generate new workshop ideas. The AI ​​models used here include a generative AI model that combines data on past success stories with data on current team characteristics to generate optimal workshop ideas. Prompt statements are also generated, and specific workshop content is determined.

[1665] Step 3:

[1666] The server provides the generated workshop ideas to robots or mobile information terminals in the factory, which then send the content of the ideas to the terminals, which then display them to the user. The terminals then provide information to the user through a voice assistant or the robot's display.

[1667] Step 4:

[1668] The user checks the presented workshop ideas and conducts the workshop based on the contents. When the user conducts the workshop, the terminal provides necessary guidelines and instructions in real time.

[1669] Step 5:

[1670] After the workshop, the device collects feedback from users, including text, voice, and facial expression data, and sends the collected data to a server in real time.

[1671] Step 6:

[1672] The server analyzes the collected feedback data using an emotion recognition engine, which recognizes the user's emotions from text and voice data, processes the data based on various emotions, and extracts important themes and trends from the feedback.

[1673] Step 7:

[1674] Based on the analysis results, the server generates a report containing suggestions for improvement for the next workshop. This report is sent to the terminal and displayed on the terminal to the user, allowing the user to confirm specific improvements for the next workshop.

[1675] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1676] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1677] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1678] [Fourth embodiment]

[1679] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1680] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1681] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1682] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1683] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1684] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1685] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1686] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1687] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1688] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1689] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1690] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1691] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1692] This invention is an AI-based system that supports corporate team building, generating workshop ideas based on past success stories and team characteristic data, and proposing personalized plans for each member. It also has the functions of automatically analyzing workshop feedback and suggesting areas for improvement, generating role-play scenarios for specific situations, managing the progress of workshops using AI, and providing virtual team building activities for remote teams.

[1693] 1. Workshop idea generation

[1694] The server accesses a database to retrieve data on past success stories and team characteristics. Based on this data, it uses an AI model to automatically generate new workshop ideas. The generated ideas are sent to the device, where the user can review and select the proposed ideas.

[1695] Examples:

[1696] The server uses an AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills." This idea is then proposed to the user via their device.

[1697] 2. Personalized Workshop Proposal

[1698] The server retrieves data about each member's skills and interests from a database and uses AI models to generate personalized workshops. The generated workshop proposals are sent to each member's device, allowing users to view the workshop content that best suits them.

[1699] Examples:

[1700] The server generates a "problem-solving workshop" based on member A's data that he is "interested in problem-solving skills," sends it to the terminal, and displays it. The user confirms the proposal.

[1701] 3. Automatic feedback analysis

[1702] After the workshop, the device collects feedback from users and sends it to the server. The server then uses an AI model to analyze the feedback data and extract important themes and trends. The results of this analysis are sent to the device as a report, allowing users to understand how to improve the next workshop.

[1703] Examples:

[1704] The device collects feedback from users that "the time to come up with ideas is short," and the server extracts this as a need for "improved time management" and notes it in the report as an issue to be addressed next time.

[1705] 4. Generating Role-Play Scenario

[1706] The server uses AI models to generate role-play scenarios based on data about team dynamics and specific problems, and the scenarios are sent to the device, where users can view and execute them to learn specific problem-solving skills.

[1707] Examples:

[1708] The server recognizes communication issues, generates role-play scenarios based on the theme of "dealing with problems during a project," and provides them to users via their terminals.

[1709] 5. Workshop Management

[1710] The server pre-determines the timeline and steps for each workshop session. The terminal supports the progress of the session according to this timeline, and displays time management and important instructions to the user, enabling efficient session progress.

[1711] Examples:

[1712] The server sets a timeline such as "Idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device notifies the user when each session ends.

[1713] 6. Creating Virtual Team Building Activities

[1714] The server uses an AI model to generate virtual team-building activities based on the needs data of the remote team, and the generated content is sent to the device and provided to the user in a format that can be executed even in a remote environment.

[1715] Examples:

[1716] The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal. The user can carry out the activity and strengthen the unity of the remote team.

[1717] As a result, the present invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling the creation of effective workshops, the analysis of feedback, and the support of remote teams.

[1718] The processing flow will be explained below.

[1719] Workshop idea generation

[1720] Step 1:

[1721] The server accesses a database to obtain past success stories and current team characteristic data.

[1722] Step 2:

[1723] The server inputs the acquired data into the AI ​​model and begins analysis.

[1724] Step 3:

[1725] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[1726] Step 4:

[1727] The server transmits the generated workshop ideas to the terminal.

[1728] Step 5:

[1729] The terminal displays the workshop ideas to the user.

[1730] Step 6:

[1731] The user selects the best idea from multiple proposed ideas.

[1732] Personalized Workshop Proposals

[1733] Step 1:

[1734] The server retrieves data about each member's skills and interests from a database.

[1735] Step 2:

[1736] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[1737] Step 3:

[1738] The server transmits the generated personalized workshop proposal to each member's terminal.

[1739] Step 4:

[1740] The terminal displays personalized workshop suggestions to each member.

[1741] Step 5:

[1742] The user checks the workshop content that best suits him or her.

[1743] Automatic analysis of feedback

[1744] Step 1:

[1745] The terminal collects feedback from users after the workshop is over.

[1746] Step 2:

[1747] The terminal transmits the collected feedback data to the server.

[1748] Step 3:

[1749] The server uses AI models to analyze the feedback data and extract important themes and trends.

[1750] Step 4:

[1751] The server generates a report based on the extraction results and sends it to the terminal.

[1752] Step 5:

[1753] The terminal displays the generated report to the user.

[1754] Step 6:

[1755] Users will know what needs to be improved for the next workshop.

[1756] Role-play scenario generation

[1757] Step 1:

[1758] The server captures data about specific team dynamics and issues.

[1759] Step 2:

[1760] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[1761] Step 3:

[1762] The server transmits the generated role-play scenario to the terminal.

[1763] Step 4:

[1764] The terminal displays the scenario and provides instructions for the user to execute.

[1765] Step 5:

[1766] Users are guided through role-play scenarios to develop problem-solving skills.

[1767] Workshop progress

[1768] Step 1:

[1769] The server pre-determines the timeline and steps for each workshop session.

[1770] Step 2:

[1771] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[1772] Step 3:

[1773] The server monitors the progress of each session and sends instructions to the terminal to proceed to the next session.

[1774] Step 4:

[1775] The user follows instructions from the terminal to proceed with the workshop.

[1776] Generate virtual team building activities

[1777] Step 1:

[1778] The server retrieves data regarding the needs of the remote team.

[1779] Step 2:

[1780] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[1781] Step 3:

[1782] The server transmits the generated activity content to the terminal.

[1783] Step 4:

[1784] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[1785] Step 5:

[1786] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[1787] Example 1

[1788] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1789] In-company team building activities require planning and running effective workshops, providing content tailored to each participant, and collecting and analyzing feedback after the activities. However, carrying out these activities efficiently is difficult and requires significant human resources and time. Furthermore, with the spread of remote work, effective team building activities must also be carried out in remote environments. However, current methods often fail to achieve their full effectiveness. The present invention aims to provide a system and method for solving these problems.

[1790] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1791] In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas using an AI model based on the acquired data, means for sending the generated workshop ideas to a terminal and displaying them, means for generating personalized workshops based on the skills and interests of each member, means for proposing personalized workshops to each member, means for collecting feedback from the terminal after the workshop, means for extracting important themes and trends using the AI ​​model based on the collected feedback, and means for displaying the extracted analysis results in a report as improvements for the next workshop. This enables an effective and efficient cycle of team building planning, execution, and feedback.

[1792] "Past success stories" are data showing successful cases obtained from workshops or projects that have been previously implemented.

[1793] "Team characteristic data" is data that includes information about team members' skills, interests, roles, performance, etc.

[1794] An "AI model" is a computational algorithm or neural network that uses artificial intelligence techniques to analyze data and generate a specific result.

[1795] "Workshop Ideas" are specific suggestions for activities or sessions designed for team building or skill development.

[1796] "Terminal" means an electronic device used by a user to input and display information, including a personal computer, smartphone, tablet, etc.

[1797] A "personalized workshop" is a workshop designed specifically to meet the skills and interests of individual members.

[1798] "Feedback" refers to data on opinions and evaluations collected from users after the workshop.

[1799] "Key themes and trends" are key issues or patterns extracted from the collected feedback data that can help improve the next workshop.

[1800] The "report" is a document displayed on the terminal that lists the analysis results and improvements for the next workshop.

[1801] A "role-play scenario" is a plot or setting for a simulated role-play based on a particular situation.

[1802] A "timeline" indicates the schedule for the start and end of each session or activity in the workshop.

[1803] "Virtual team building activities" are online team building activities that can be carried out in a remote environment.

[1804] This invention is a system for supporting corporate team building, which includes a server, terminals, and users. The system utilizes AI models to generate workshop ideas, propose personalized workshops, automatically analyze feedback, generate role-play scenarios, manage workshop progress, and generate virtual team building activities.

[1805] Data collection and workshop idea generation

[1806] The server accesses a database to retrieve past success stories and team characteristics, including each member's skills, interests, roles, and performance data. Based on the retrieved data, an AI model (e.g., GPT-4) is used to automatically generate workshop ideas. The generated ideas are sent to the device and displayed to the user.

[1807] Examples:

[1808] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[1809] Example prompt sentence:

[1810] "Generate workshop ideas aimed at improving communication skills."

[1811] Personalized Workshop Proposals

[1812] The server generates personalized workshops based on each member's skills and interests, using their skill and role data. The generated workshop proposals are sent to each member's device and notified to the user.

[1813] Examples:

[1814] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[1815] Example prompt sentence:

[1816] "Please propose a workshop for members who are interested in generating new ideas."

[1817] Feedback collection and automated analysis

[1818] After the workshop, the device collects feedback from users, including evaluations of the workshop's duration, content, and props. The collected feedback data is sent to a server and analyzed using an AI model (e.g., Transformer) to extract important themes and trends. The results of this analysis are sent to the device as a report, which users can review.

[1819] Examples:

[1820] The device asks the user how satisfied they are with the workshop time, and sends the response data to the server. The server analyzes the data and determines whether time management needs improvement, and compiles it into a report and sends it to the device.

[1821] Example prompt sentence:

[1822] "Analyze the feedback data from the workshops and extract key themes and trends."

[1823] Role-play scenario generation

[1824] The server generates role-play scenarios using AI models (e.g., T5) based on data about team dynamics and specific problems. The generated scenarios are sent to the device and made available for users to view and play.

[1825] Examples:

[1826] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[1827] Example prompt sentence:

[1828] "Generate role-play scenarios for dealing with problems during a project."

[1829] Workshop progress management

[1830] The server sets the workshop session timeline and steps in advance, and the terminal follows this timeline to support the progress. Time management and important instructions are displayed to the user.

[1831] Examples:

[1832] The server will set up a "15-minute idea brainstorming session" or a "30-minute discussion," and the device will display a notification at the end of each session.

[1833] Example prompt sentence:

[1834] "Set the timeline for the workshop and help facilitate each session."

[1835] Generate virtual team building activities

[1836] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The generated activities are sent to the device and provided in a format that users can carry out even in a remote environment.

[1837] Examples:

[1838] The server generates an online quiz based on the characteristics of the remote team and sends it to the device. The user then checks the content and participates in the remote environment.

[1839] Example prompt sentence:

[1840] "Generate virtual team building activities for remote teams."

[1841] As described above, the system of the present invention efficiently supports team building activities through comprehensive data collection and analysis / generation using AI models, highly automating the entire process from workshop planning to execution, feedback collection, and proposals for the next activity.

[1842] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1843] Step 1:

[1844] Data collection

[1845] The server accesses the company's database to retrieve past success stories and team characteristics data, including each member's skills, interests, roles, and performance data. The input is a query from the database, and the output is the retrieved data.

[1846] Specific behavior:

[1847] The server extracts "past project success rates" and "member skill profiles" from the database.

[1848] Input: Query to the database "SELECT FROM Success_Cases"

[1849] Output: Past success story data

[1850] Step 2:

[1851] Workshop idea generation

[1852] The server automatically generates workshop ideas using an AI model (e.g., GPT-4) based on the acquired data. The acquired data is the input, and the generated workshop ideas are the output. These ideas are sent to the terminal and notified to the user.

[1853] Specific behavior:

[1854] The server generates a "team debate session" from data seeking "improvement of communication skills" and sends it to the device.

[1855] Input: Past success story data, team characteristics data

[1856] Output: Workshop idea "Team debate session"

[1857] Step 3:

[1858] Personalized Workshop Proposals

[1859] The server generates personalized workshops based on each member's skills and interests. The input is the member's individual skill and interest data, and the output is personalized workshop proposals. The generated proposals are sent to each member's device and notified to the user.

[1860] Specific behavior:

[1861] The server generates a "new idea brainstorming session" based on member B's data that he is "interested in creating new ideas" and sends it to the terminal.

[1862] Input: Individual member skills and interest data

[1863] Output: Personalized Workshop "New Idea Brainstorming Session"

[1864] Step 4:

[1865] Gathering feedback

[1866] After the workshop, the terminal collects feedback from users. The input is the feedback from users, and the output is the collected feedback data. This is then sent to the server.

[1867] Specific behavior:

[1868] The terminal asks the user about "satisfaction with the workshop time" and transmits the answer data to the server.

[1869] Input: User feedback

[1870] Output: Feedback data

[1871] Step 5:

[1872] Automatic analysis of feedback

[1873] The server analyzes the collected feedback data using an AI model (e.g., Transformer) to extract important themes and trends. The input is the feedback data, and the output is the analysis results. These analysis results are sent to the terminal in the form of a report, which the user can review.

[1874] Specific behavior:

[1875] The server extracts themes such as "time management needs improvement" from the feedback data, compiles them into a report, and sends it to the terminal.

[1876] Input: Feedback data

[1877] Output: Report of analysis results

[1878] Step 6:

[1879] Role-play scenario generation

[1880] The server generates role-play scenarios using an AI model (e.g., T5) based on data about team dynamics and specific problems. The input is the specific problem data, and the output is the generated role-play scenario. This scenario is sent to the device, where the user can view and perform it.

[1881] Specific behavior:

[1882] The server generates a role-play scenario with the theme of "dealing with problems during a project" and sends it to the terminal.

[1883] Input: Specific problem data

[1884] Output: Role-play scenario

[1885] Step 7:

[1886] Workshop progress management

[1887] The server sets the timeline and steps of each workshop session in advance. The input is the workshop timeline and steps, and the output is the timeline setting data. The terminal follows this timeline and supports the progress.

[1888] Specific behavior:

[1889] The server will set up a "15-minute idea brainstorm" or "30-minute discussion," and the device will display a notification.

[1890] Input: Workshop timeline and steps

[1891] Output: Timeline setting data

[1892] Step 8:

[1893] Generate virtual team building activities

[1894] The server generates virtual team-building activities using an AI model (e.g., GPT-4) based on the needs data of the remote team. The input is the needs data of the remote team, and the output is the generated team-building activity. The activity content is sent to the device and provided in a form that users can carry out even in a remote environment.

[1895] Specific behavior:

[1896] The server generates an "online quiz competition" based on the "remote team characteristics" and sends it to the terminal.

[1897] Input: Remote team needs data

[1898] Output: Virtual team building activity

[1899] (Application example 1)

[1900] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1901] Effective team building and training is crucial for modern companies, but it is difficult to provide personalized programs tailored to diverse needs, effectively analyze feedback, generate role-play scenarios for specific situations, and conduct training in a remote environment. In particular, employee training in a virtual environment is not effectively managed or optimized.

[1902] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1903] In this invention, the server includes means for acquiring data on past success stories and team characteristics, means for automatically generating workshop ideas based on the acquired data, means for displaying the generated workshop ideas, means for automatically generating training workshops in a virtual environment, and means for displaying the generated training workshops. This enables the provision of personalized programs that meet the diverse needs of companies, automatic analysis of feedback, generation of role-play scenarios according to situations, and effective training in remote environments.

[1904] "Past success stories" are examples of previously held workshops or trainings that have proven particularly successful.

[1905] "Team characteristic data" is data relating to a particular team that includes information such as the skills, interests, and personalities of members.

[1906] "Workshop Ideas" are specific activity proposals for team building or training designed around a specific theme or purpose.

[1907] A "personalized workshop" is a workshop that is customized based on each member's skills and interests.

[1908] "Feedback" refers to evaluations and opinions from members who participated in workshops and training, including information for improvement.

[1909] "Key themes and trends" are items or patterns extracted from the collected feedback data that deserve particular attention for improving workshops or generating new ideas.

[1910] A "role-play scenario" is an exercise or dramatization designed to recreate a specific situation for the purpose of practical skill acquisition.

[1911] A "virtual environment" refers to a virtual space or interface provided by computer technology that enables remote activities.

[1912] "Training Workshop" means an educational session designed to improve employee skills or knowledge, including work-related training.

[1913] MODE FOR CARRYING OUT THE INVENTION

[1914] This invention is an AI-based system for effective corporate team building and employee training. This system generates new workshop ideas and role-play scenarios based on past success stories and team characteristic data, and provides virtual training that can be conducted even in remote environments. Specific embodiments are described below.

[1915] 1. System Configuration

[1916] Server: A cloud server is used to install an AI model (e.g., OpenAI's GPT-4) and a database management system (e.g., MySQL).

[1917] Devices: Employee-owned smartphones, tablets, or VR headsets (e.g., Oculus, VIVE, etc.).

[1918] Software: Use feedback analysis tools (e.g., Google Cloud Natural Language) and real-time communication servers (e.g., WebSockets).

[1919] 2. Data Acquisition

[1920] The server retrieves past success stories and team characteristics data from the database, including information about employee skills and interests.

[1921] 3. Workshop idea generation

[1922] The server uses a generative AI model (e.g., GPT-4) to generate new workshop ideas based on the acquired data, and the generated ideas are sent to the terminals and made available to employees.

[1923] Example: The server uses a generative AI model to generate a "team project simulation" based on past examples of "project management" and current team characteristic data that calls for "improving communication skills."

[1924] 4. Personalized Workshop Proposals

[1925] The server acquires each member's skill and interest data and generates a personalized workshop, which is then sent to each member's device.

[1926] Example: Based on data that member A is interested in problem-solving skills, the server generates a "problem-solving workshop" and sends it to the terminal.

[1927] 5. Automatic feedback analysis

[1928] The terminals collect feedback from employees after the workshop and send it to a server, which then uses a feedback analysis tool to analyze the feedback data and extract important themes and trends.

[1929] Example: The device collects feedback from the user that "the time it takes to come up with ideas is short," and the server extracts this as a need for "improved time management."

[1930] 6. Generating Role-Play Scenarios

[1931] The server uses a generative AI model to generate role-play scenarios based on data about team dynamics and specific problems, which are then sent to the device where employees can view and act them out.

[1932] Example: The server recognizes a communication issue and generates a role-play scenario on the theme of "dealing with problems during a project."

[1933] 7. Generate virtual training workshops

[1934] The server automatically generates training workshops in a virtual environment, which can then be sent to the device and run remotely.

[1935] Example: The server generates an "online quiz competition" based on the characteristics of the remote team and provides it to the user through the terminal.

[1936] 8. Examples of prompts

[1937] "Generate training workshop ideas for a virtual store based on past success stories: [Case 1, Case 2, ...] and team characteristics data: [Team 1, Team 2, ...]."

[1938] This enables companies to provide personalized training programs to meet a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[1939] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1940] Step 1:

[1941] Data Acquisition:

[1942] The server retrieves data on past success stories and team characteristics from the database. In this process, it accesses the database using an SQL query and extracts the necessary data. The input is the SQL query, and the output is the data on past success stories and team characteristics.

[1943] Step 2:

[1944] Workshop idea generation:

[1945] The server generates new workshop ideas using a generative AI model (e.g., GPT-4) based on the acquired data. Here, a prompt is created and input to the AI ​​model. The input is the success case data, team characteristic data, and the created prompt, and the output is the generated workshop idea.

[1946] Step 3:

[1947] View Workshop Ideas:

[1948] The server sends the generated workshop ideas to the terminal. The terminal receives this information and displays it to the user. The input is the generated workshop ideas, and the output is the workshop ideas that the user confirms.

[1949] Step 4:

[1950] Personalized Workshop Suggestion:

[1951] The server acquires each member's skill and interest data and generates a personalized workshop using a generative AI model. The generated workshop is sent to each member's device and displayed. The input is the member's skill and interest data, and the output is a personalized workshop proposal.

[1952] Step 5:

[1953] Collecting feedback:

[1954] After the workshop, the terminal collects feedback from users and sends it to the server. The input is the feedback information from users, and the output is the feedback data sent to the server.

[1955] Step 6:

[1956] Feedback Analysis:

[1957] The server uses a feedback analysis tool to analyze the feedback data and extract important themes and trends. The analysis results are compiled into a report as improvements for the next workshop and sent to the terminal. The input is the feedback data, and the output is a report containing important themes and improvements.

[1958] Step 7:

[1959] Role-play scenario generation:

[1960] The server uses a generative AI model to generate role-play scenarios based on data on team dynamics and specific problems. The scenarios are sent to the device, where users can view and run them. The inputs are team dynamics data and specific problem data, and the output is the role-play scenario.

[1961] Step 8:

[1962] Generate a virtual training workshop:

[1963] The server automatically generates a training workshop in a virtual environment and sends it to the terminal. It is provided to the user in a state that can be executed in a remote environment. The input is the characteristic data of the remote team, and the output is the virtual training workshop.

[1964] These steps enable companies to deliver personalized training programs tailored to a wide range of needs, automatically analyze feedback, generate role-play scenarios for specific situations, and effectively train in remote environments.

[1965] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1966] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans for each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. Furthermore, these functions are combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[1967] 1. Workshop idea generation

[1968] The server accesses the database to acquire data on past success stories and current team characteristics. It also uses an emotion engine to acquire user emotion data related to past success stories. Based on this data, it automatically generates new workshop ideas using an AI model and sends the ideas to the device. The device displays the workshop ideas to the user, who can then review and select from the proposed ideas.

[1969] Examples:

[1970] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[1971] 2. Personalized Workshop Proposal

[1972] The server retrieves data on each member's skills and interests from a database, and also uses an emotion engine to retrieve each member's past emotional data. Based on this data, an AI model is used to generate a personalized workshop and send it to each member's device. The device displays the proposal to each member, allowing them to check the workshop content that best suits them.

[1973] Examples:

[1974] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[1975] 3. Automatic feedback analysis

[1976] After the workshop, the device collects feedback from users and uses an emotion engine to collect emotional data during the feedback. This data is then sent to a server, which uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand how to improve their next workshop.

[1977] Examples:

[1978] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[1979] 4. Generating Role-Play Scenario

[1980] The server uses data about specific team dynamics and problems, and also uses an emotion engine to capture team emotion data. Based on this data, the AI ​​model generates role-play scenarios and sends them to the device, which displays the scenarios and provides instructions for the user to follow.

[1981] Examples:

[1982] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[1983] 5. Workshop Management

[1984] The server pre-determines the timeline and steps for each workshop session and uses an emotion engine to monitor the user's emotions in real time during the session. The device follows the pre-determined timeline and uses the emotion data to guide the session forward. Appropriate instructions are displayed to the user, ensuring efficient session progress.

[1985] Examples:

[1986] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[1987] 6. Creating Virtual Team Building Activities

[1988] The server uses an AI model to generate virtual team-building activities based on data on the needs of remote teams and emotional data acquired from an emotion engine. The generated activities are then sent to the device and provided to users so they can carry them out in a remote environment.

[1989] Examples:

[1990] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[1991] As a result, this invention provides a system for comprehensively and efficiently managing and optimizing corporate team building, enabling effective workshop generation, feedback analysis, and remote team support. By utilizing an emotion engine, it is possible to provide a more personalized experience based on user emotions and strengthen team cohesion.

[1992] The processing flow will be explained below.

[1993] Workshop idea generation

[1994] Step 1:

[1995] The server accesses the database to obtain historical success data and current team characteristic data.

[1996] Step 2:

[1997] The server uses an emotion engine to obtain user emotion data related to past success stories.

[1998] Step 3:

[1999] The server inputs the acquired data into the AI ​​model and begins analysis.

[2000] Step 4:

[2001] Based on the analysis results, the server uses an AI model to generate new workshop ideas.

[2002] Step 5:

[2003] The server transmits the generated workshop ideas to the terminal.

[2004] Step 6:

[2005] The terminal displays the workshop ideas to the user.

[2006] Step 7:

[2007] The user selects the best idea from the proposed ideas.

[2008] Personalized Workshop Proposals

[2009] Step 1:

[2010] The server retrieves data about each member's skills and interests from a database.

[2011] Step 2:

[2012] The server uses an emotion engine to obtain past emotion data for each member.

[2013] Step 3:

[2014] The server inputs the acquired data into an AI model and generates workshop content that is optimal for each member.

[2015] Step 4:

[2016] The server transmits the generated personalized workshop proposal to each member's terminal.

[2017] Step 5:

[2018] The terminal displays personalized workshop suggestions to each member.

[2019] Step 6:

[2020] The user checks the workshop content that best suits him or her.

[2021] Automatic analysis of feedback

[2022] Step 1:

[2023] The terminal collects feedback from users after the workshop is over.

[2024] Step 2:

[2025] The terminal also collects the user's emotion data along with the feedback data.

[2026] Step 3:

[2027] The terminal transmits the collected data to the server.

[2028] Step 4:

[2029] The server uses AI models and sentiment engines to analyze the feedback data and extract key themes and trends.

[2030] Step 5:

[2031] The server generates a report based on the extraction results and sends it to the terminal.

[2032] Step 6:

[2033] The terminal displays the generated report to the user.

[2034] Step 7:

[2035] Users will know what needs to be improved for the next workshop.

[2036] Role-play scenario generation

[2037] Step 1:

[2038] The server captures data about specific team dynamics and issues.

[2039] Step 2:

[2040] The server uses an emotion engine to obtain emotion data within the team.

[2041] Step 3:

[2042] The server inputs the acquired data into an AI model, analyzes it, and generates specific scenarios.

[2043] Step 4:

[2044] The server transmits the generated role-play scenario to the terminal.

[2045] Step 5:

[2046] The terminal displays the scenario and provides instructions for the user to execute.

[2047] Step 6:

[2048] Users are guided through role-play scenarios to develop problem-solving skills.

[2049] Workshop progress

[2050] Step 1:

[2051] The server pre-determines the timeline and steps for each workshop session.

[2052] Step 2:

[2053] The server uses an emotion engine to monitor the user's emotions in real time during the session.

[2054] Step 3:

[2055] The terminal supports the progress of the session according to the set timeline and displays appropriate instructions to the user.

[2056] Step 4:

[2057] The server determines the next action based on the emotional data during the session and sends instructions to the terminal.

[2058] Step 5:

[2059] The user follows instructions from the terminal to proceed with the workshop.

[2060] Generate virtual team building activities

[2061] Step 1:

[2062] The server retrieves data regarding the needs of the remote team.

[2063] Step 2:

[2064] The server uses an emotion engine to obtain emotion data of the remote team.

[2065] Step 3:

[2066] The server inputs the acquired data into an AI model for analysis and generates virtual team-building activities.

[2067] Step 4:

[2068] The server transmits the generated activity content to the terminal.

[2069] Step 5:

[2070] The terminal displays the contents of the virtual team building activity to the user and provides instructions on how to carry it out.

[2071] Step 6:

[2072] The user follows instructions on the terminal to carry out virtual team building activities and strengthen the cohesion of the remote team.

[2073] Example 2

[2074] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2075] Current team building workshops generally have uniform content and do not fully consider the characteristics and feelings of each member, making it difficult to achieve effective team building. Furthermore, simply collecting feedback from the workshop makes it difficult to reflect specific improvements in the next workshop. Furthermore, providing appropriate virtual team building activities for remote teams is also a challenge.

[2076] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring past success stories and team characteristic data, means for automatically generating workshop ideas based on the acquired data and emotion data, means for displaying the generated workshop ideas, and means for checking and selecting the proposed workshop ideas. This makes it possible to generate workshop ideas that take into account the team's characteristics and emotions, and to provide an appropriate personalized workshop for each member. In addition, feedback and emotion data can be analyzed to clearly suggest areas for improvement in the next workshop. It is also possible to provide effective virtual team building activities to remote teams.

[2077] "Past success stories" refer to successful experiences and achievements in previous workshops or projects.

[2078] "Team characteristic data" is information about team members' skills, roles, performance, interests, personalities, etc.

[2079] "Emotional data" refers to data that indicates the emotional state or emotional changes of a user or team member, and is obtained from feedback or sensor data.

[2080] "Means for automatically generating workshop ideas" refers to a method or device that uses AI models or algorithms to analyze and process acquired data and generate new workshop plans and proposals.

[2081] The "means for displaying the generated workshop idea" refers to a method or device for displaying the generated workshop idea on a user's terminal.

[2082] The "means for reviewing and selecting proposed workshop ideas" refers to a method or device that allows a user to view the generated workshop ideas and select an appropriate one from among them.

[2083] "Individual skills and interests" refers to the specific skills and interests of each team member.

[2084] A "personalized workshop" is a workshop that is individually tailored based on each member's skills, interests, and emotional data.

[2085] "Feedback" refers to opinions and impressions collected from participants after the workshop.

[2086] "Key themes and trends" are areas for improvement or noteworthy issues for the next workshop that are extracted from the collected feedback data.

[2087] An "AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions or generates results for specific tasks.

[2088] MODE FOR CARRYING OUT THE INVENTION

[2089] This invention is an AI-based system that supports corporate team building. It generates workshop ideas based on past success stories and team characteristic data, and proposes personalized plans tailored to each member. It also has the ability to automatically analyze workshop feedback and suggest areas for improvement, generate role-play scenarios for specific situations, manage workshop progress using AI, and provide virtual team building activities for remote teams. In addition to these functions, it can also be combined with an emotion engine that recognizes user emotions to provide a more effective and personalized team building experience.

[2090] Hardware and Software

[2091] The server mainly uses a database, a generative AI model, and an emotion engine. The database is used to store data on past success stories and team characteristics. The emotion engine is required to analyze user emotions and obtain emotional data. The generative AI model generates new workshop ideas, personalized workshops, and role-play scenarios based on the obtained data.

[2092] The terminal plays a role in displaying data sent from the server, generated workshop ideas, personalized workshops, feedback reports, etc. to the user.

[2093] Specific examples

[2094] Workshop idea generation

[2095] The server first accesses the database to retrieve data on past success stories and current team characteristics. It then uses an emotion engine to retrieve user emotion data related to past success stories. Based on this data, the server then uses an AI model to automatically generate new workshop ideas and sends them to the device. The device then displays the workshop ideas to the user, who can then review and select from the suggested ideas.

[2096] Examples:

[2097] The server uses an AI model to generate a "team project simulation" based on past examples of "project management," data on the characteristics of current teams seeking "improvement in communication skills," and workshops in which the user previously felt "cooperative." This idea is then proposed to the user via their device.

[2098] Personalized Workshop Proposals

[2099] The server retrieves data about each member's skills and interests from a database. It also uses an emotion engine to retrieve each member's past emotional data. Based on this data, it uses an AI model to generate personalized workshops and sends them to each member's device. The device displays the suggestions to each member, allowing them to check the workshop content that best suits them.

[2100] Examples:

[2101] The server generates a "problem-solving workshop" based on data that member A is "interested in problem-solving skills" and past workshop data that showed "high satisfaction," and sends it to the terminal for display. The user confirms the proposal.

[2102] Automatic analysis of feedback

[2103] After the workshop, the device collects feedback from users. An emotion engine also collects emotional data during the feedback and sends this data to a server. The server uses an AI model to analyze the feedback data and extract important themes and trends. The analysis results are sent to the device as a report, allowing users to understand areas for improvement in the next workshop.

[2104] Examples:

[2105] The device collects feedback from users, such as "the time to come up with ideas is short," as well as emotional data on "dissatisfaction." The server then analyzes this information to identify the need for "improved time management," which is then recorded in a report as an issue to be addressed next time.

[2106] Role-play scenario generation

[2107] The server retrieves data about specific team dynamics and problems, uses an emotion engine to retrieve team emotion data, and uses this data to generate role-play scenarios using an AI model. The scenarios are then sent to the device, which displays the scenarios and provides instructions for the user to follow.

[2108] Examples:

[2109] The server recognizes communication issues and emotional data indicating rising tension within the team, generates a role-play scenario on the theme of "dealing with problems during a project," and provides it to the user via the device.

[2110] Workshop progress

[2111] The server pre-determines the timeline and steps for each workshop session. It uses an emotion engine to monitor the user's emotions in real time during the session. The device displays appropriate instructions on the user interface according to the pre-determined timeline, ensuring efficient session progress.

[2112] Examples:

[2113] The server sets a timeline of "idea brainstorming - 15 minutes, discussion - 30 minutes, presentation - 15 minutes," and the device detects emotional data indicating "declining concentration" during the session and suggests a refreshing break to the user.

[2114] Generate virtual team building activities

[2115] The server uses AI models to generate virtual team-building activities based on data about the needs of remote teams and emotional data acquired through an emotion engine. The generated activities are then sent to the device and displayed to the user so that they can be carried out in a remote environment.

[2116] Examples:

[2117] The server generates an "online quiz competition" based on the characteristics of the remote team, selects "team members who are likely to have a relaxed mood" based on past emotional data, and provides it to the user via their device. The user then carries out the activity, enhancing the cohesion of the remote team.

[2118] Prompt Sentence Examples

[2119] 1. "Generate workshop ideas for improving corporate project management and communication skills."

[2120] 2. "Generate a personalized workshop on problem-solving skills for Member A."

[2121] 3. "Please analyze the feedback from the workshop and identify areas for improvement for next time."

[2122] 4. "Generate role-play scenarios that address communication challenges."

[2123] 5. "Generate guidelines to help facilitate the next workshop."

[2124] 6. "Generate personalized virtual team-building activities for your remote team."

[2125] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2126] Step 1: Data Acquisition

[2127] The server accesses the database to retrieve data on past success stories and current team characteristics. Database connection information and queries are required as input. Success story data and team characteristics data are obtained as output. Specifically, the server executes SQL queries to retrieve data, structures the data (e.g., in JSON format), and saves it.

[2128] Step 2: Obtaining emotion data

[2129] The server uses the emotion engine to obtain user emotion data related to past success stories. Data about past success stories is required as input. Emotion data is obtained as output. Specifically, the server sends an API request to the emotion engine to obtain emotion data and associates it with the past success story data.

[2130] Step 3: Workshop idea generation

[2131] The server inputs the acquired success case data, team characteristic data, and emotion data into the generative AI model to generate new workshop ideas. All data obtained in the previous step is required as input. New workshop ideas are obtained as output. Specifically, the server inputs data and prompt statements into the generative AI model and runs the model to generate ideas.

[2132] Step 4: Submit and view your idea

[2133] The server sends the generated workshop ideas to the terminal, which then displays them to the user, allowing the user to review and select the proposed ideas. The generated workshop ideas are required as input, and the displayed ideas and the user's selection results are obtained as output. The specific operation is to send the generated ideas to the terminal, which then displays them on the user interface.

[2134] Step 5: Retrieving Member Data

[2135] The server accesses the database to retrieve data about each member's skills and interests. As input, it requires a query for member data. As output, it gets the data about each member's skills and interests. Specifically, it executes an SQL query to retrieve the data and organizes it into a structured data format.

[2136] Step 6: Obtaining emotion data

[2137] The server uses the emotion engine to obtain past emotion data for each member. The input requires the identification information of each member. The output is the emotion data for each member. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[2138] Step 7: Generate a personalized workshop

[2139] The server inputs the acquired member data and emotion data into the generative AI model to generate a personalized workshop. Member data and emotion data are required as input. The output is a personalized workshop proposal. Specifically, the server inputs a prompt statement into the generative AI model, runs the model, and obtains the generated result.

[2140] Step 8: Submit and view your proposal

[2141] The server sends the generated personalized workshop proposal to each member's terminal, which then displays it to the user of each member. The generated personalized workshop proposal is required as input. The displayed proposal and the user's response are obtained as output. The specific operation is to send the proposal to the terminal, which then displays it on the user interface.

[2142] Step 9: Gather feedback

[2143] The terminal collects feedback from users after the workshop ends. As input, it requires users to fill out a feedback form. As output, it obtains the collected feedback data. Specific operations include displaying the feedback form, receiving user input, and storing it in a database.

[2144] Step 10: Collect emotion data

[2145] The device uses an emotion engine to collect emotion data simultaneously with the feedback. The input requires the user's reaction and text during the feedback. The output is emotion data. Specific operations include obtaining emotion data through sensor data and text analysis, and associating it with the feedback data.

[2146] Step 11: Analyze feedback data

[2147] The server analyzes the collected feedback and emotion data using an AI model. It requires feedback and emotion data as input. It outputs analysis results that indicate key themes and trends. Specifically, it inputs data into the AI ​​model, runs the model, and analyzes the results.

[2148] Step 12: Submit and view analysis results

[2149] The server sends the analysis results to the terminal, which then displays them to the user. The analysis results are required as input. The displayed analysis results and user confirmation are obtained as output. Specifically, the analysis results are compiled into a report and sent to the terminal, which then displays them on the user interface.

[2150] Step 13: Generate role-play scenarios

[2151] The server generates role-play scenarios from data about specific team dynamics and problems. As input, it requires team dynamics data. As output, it obtains role-play scenarios. Specifically, it inputs the data into a generative AI model and runs the model to generate scenarios.

[2152] Step 14: Send and view the scenario

[2153] The server sends the generated role-play scenario to the terminal, which displays it to the user and provides instructions for execution. The generated role-play scenario is required as input. The displayed scenario and the user's reaction are obtained as output. The specific operation is to send the scenario to the terminal, which displays it on the user interface.

[2154] Step 15: Setting the Timeline and Steps

[2155] The server pre-configures the timeline and steps for each workshop session. As input, it requires the workshop schedule information. As output, it obtains the timeline and step configuration. Specific operations include retrieving the timeline and steps from a database or using pre-configured data.

[2156] Step 16: Monitoring emotional data during the session

[2157] The device uses an emotion engine to monitor the user's emotions in real time during the session. The inputs include the user's reactions during the session and sensor data. The output is real-time emotion data. Specifically, the device collects emotion data through sensor data and text analysis, and displays it on the monitoring screen.

[2158] Step 17: Display instructions

[2159] The terminal displays appropriate instructions to the user based on the configured timeline and emotional data during the session. Timeline data and emotional data are required as input. The terminal obtains the displayed instructions as output. Specific operations include displaying instructions based on the timeline on the user interface and making necessary changes according to the emotional data.

[2160] Step 18: Support the session

[2161] The terminal efficiently supports the progress of the workshop based on the timeline and emotion data. Timeline data and emotion data are required as input. The output is an efficient session progress. Specific operations include updating the interface in real time and displaying alerts about the progress of the session.

[2162] Step 19: Retrieve Remote Team Data

[2163] The server retrieves data about the needs of the remote team from the database. As input, it requires queries related to the remote team. As output, it obtains the remote team data. Specific operations include retrieving data from the database and organizing it into a structured data format.

[2164] Step 20: Capture sentiment data for your remote team

[2165] The server uses the emotion engine to obtain emotion data about the remote team. The server requires the remote team's identification information as input. The server obtains the emotion data of the remote team as output. Specifically, the server sends an API request to the emotion engine to obtain the emotion data.

[2166] Step 21: Generate virtual activities

[2167] The server generates a virtual team building activity using an AI model based on the acquired remote team data and emo...

Claims

1. A means of obtaining data on past success stories and team characteristics; A means to automatically generate workshop ideas based on the acquired data, and a means for displaying the generated workshop ideas; A system including:

2. A way to generate personalized workshops based on each member's skills and interests; A way to propose personalized workshops to each member, The system of claim 1 , comprising:

3. a means of gathering feedback after the workshop; A means to extract important themes and trends from the collected feedback; A means to display the extracted analysis results as points to improve for the next workshop, and The system of claim 1 , comprising:

4. A means to automatically generate scenarios for specific team dynamics and problems; a means for displaying the generated scenario and supporting its execution; The system of claim 1 , comprising:

5. A means to pre-determine the timeline and steps for each workshop session; A means to support the progression of the session according to a set timeline; means for displaying instructions for proceeding to the next session; The system of claim 1 , comprising:

6. A means of generating virtual team building activities based on the needs of remote teams; A means for displaying the generated activities and supporting their execution; The system of claim 1 , comprising:

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