system

Abstract

The system according to this embodiment aims to provide support for facilitating the smooth progress of group work. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a monitoring unit, and a provision unit. The reception unit receives information from the user regarding the purpose, time, and participants of the group work. The generation unit generates a progress plan for the group work based on the information received by the reception unit. The monitoring unit monitors the progress based on the progress plan generated by the generation unit. The provision unit provides advice based on the progress monitored by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, sufficient support for smoothly advancing group work has not been provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide support for smoothly advancing group work.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a monitoring unit, and a provision unit. The reception unit receives information from the user regarding the purpose, time, and participants of the group work. The generation unit generates a progress plan for the group work based on the information received by the reception unit. The monitoring unit monitors the progress based on the progress plan generated by the generation unit. The provision unit provides advice based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide support for facilitating the smooth progress of group work. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The group work support system according to an embodiment of the present invention is a system that uses a generative AI to support the smooth conduct of group work. This group work support system supports the facilitator by suggesting what should be done within a given time, based on information such as the purpose, time, and participants of the group work input by the user. Specifically, first, the user inputs information such as the purpose, time, and participants of the group work. At this time, the user only needs to use a simple input form. For example, the user inputs information such as "generating new product ideas," "2 hours," and "a team of 5 people." This information is input into the generative AI. Next, the generative AI analyzes the input information and creates a progress plan for the group work. The generative AI generates an optimal progress plan based on past data and best practices. For example, a plan is generated that includes a brainstorming session for idea generation and steps for evaluating and selecting ideas. Based on the generated progress plan, specific actions for each step are presented. For example, specific actions such as "the first 30 minutes are for idea generation," "the next 30 minutes are for idea evaluation," and "the last hour is for refining the selected ideas" are presented. Furthermore, the generative AI monitors the progress in real time and provides advice as needed. For example, if the project is progressing ahead of schedule, it will advise moving on to the next step; if it is behind schedule, it will offer suggestions to accelerate progress. This ensures that group work proceeds smoothly and that objectives are achieved efficiently. In this way, the group work support system can facilitate the smooth progress of group work by generating a progress plan based on user input, monitoring progress, and providing advice.

[0029] The group work support system according to this embodiment comprises a reception unit, a generation unit, a monitoring unit, and a provision unit. The reception unit receives information from the user regarding the purpose, time, and participants of the group work. For example, the reception unit allows the user to input information such as "new product idea generation," "2 hours," and "team of 5 people" using an input form. The generation unit generates a progress plan for the group work based on the information received by the reception unit. The generation unit uses a generation AI to generate an optimal progress plan based on past data and best practices. For example, the generation unit generates a plan that includes a brainstorming session for idea generation and steps for evaluating and selecting ideas. The monitoring unit monitors the progress based on the progress plan generated by the generation unit. The monitoring unit monitors the progress in real time and provides advice as needed. For example, if the progress is ahead of schedule, the monitoring unit advises moving on to the next step, and if it is behind schedule, it makes suggestions to accelerate the progress. The provision unit provides advice based on the progress monitored by the monitoring unit. The provision unit advises moving on to the next step according to the progress and makes suggestions to accelerate the progress if it is behind schedule. As a result, the group work support system according to the embodiment can support the smooth progress of group work by generating a progress plan for group work based on user input information, monitoring the progress, and providing advice.

[0030] The generation unit can generate progress plans based on specific historical data and best practices using generational AI. For example, the generation unit can generate progress plans based on past project data and participant feedback. For example, the generation unit can generate an optimal progress plan based on data from successful group work in the past. The generation unit can also generate progress plans by referring to industry standards and best practices. For example, the generation unit can extract the optimal steps for a specific objective and incorporate them into the plan. Furthermore, the generation unit can use generational AI to develop algorithms for generating progress plans based on historical data and best practices. For example, the generation unit can analyze past success stories and propose the most effective methods of execution. In this way, the generation unit can provide optimal progress plans by generating them based on historical data and best practices.

[0031] The generation unit can use generational AI to suggest specific actions for each step. For example, the generation unit might suggest specific actions such as "the first 30 minutes are for brainstorming," "the next 30 minutes are for evaluating ideas," and "the last hour is for refining the selected ideas." The generation unit can also use generational AI to develop algorithms for suggesting specific actions for each step. For example, the generation unit can suggest task assignments and progress checkpoints. Furthermore, the generation unit can use generational AI to build a feedback loop for suggesting specific actions for each step. For example, the generation unit can evaluate the quality of the generated actions and update the actions based on the evaluation results. In this way, the generation unit can enable users to take specific actions by suggesting specific actions for each step.

[0032] The monitoring unit can monitor progress while tolerating specific time delays. For example, the monitoring unit can monitor progress in real time. For example, the monitoring unit can provide advice on proceeding to the next step based on the progress. Furthermore, the monitoring unit can also suggest ways to accelerate progress if it is behind schedule. In addition, the monitoring unit can develop algorithms for monitoring progress while tolerating specific time delays. For example, the monitoring unit can tolerate time delays ranging from a few seconds to a few minutes and monitor progress within that range. This allows the monitoring unit to monitor progress in real time and take appropriate action based on the progress.

[0033] The service provider can provide advice on how to proceed to the next step based on the progress. For example, if the progress is ahead of schedule, the service provider will advise moving on to the next step; if it is behind schedule, it will suggest ways to accelerate progress. The service provider can also develop algorithms to provide advice on how to proceed to the next step based on the progress. For example, the service provider will suggest the next task or the next meeting. Furthermore, the service provider can build a feedback loop to provide advice on how to proceed to the next step based on the progress. For example, the service provider will update the advice based on the progress evaluation. In this way, the service provider can support the smooth progress of group work by providing advice according to the progress.

[0034] The service provider can document specific delays and make suggestions to accelerate progress. For example, if progress is behind schedule, the service provider can make suggestions to accelerate progress. For instance, the service provider can suggest task reallocation or the allocation of additional resources. The service provider can also develop algorithms to make suggestions to accelerate progress when it is behind schedule. For example, the service provider can evaluate how many minutes behind schedule it is and make suggestions based on that evaluation. Furthermore, the service provider can build a feedback loop to make suggestions to accelerate progress when it is behind schedule. For example, the service provider can update its suggestions based on the progress evaluation results. In this way, the service provider can help ensure that progress stays on schedule by making suggestions to accelerate progress when it is behind schedule.

[0035] The reception desk can refer to the user's past group work history and suggest input fields. For example, the reception desk can refer to past group work history and suggest the most suitable input fields for the user. For instance, the reception desk can automatically display the purpose and time of past group work as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the purpose and participants for a specific time slot based on the user's past group work history. In this way, the reception desk can suggest the most suitable input fields for the user by referring to past history, thereby streamlining the input process.

[0036] The reception system can automatically complete estimated information when some of the entered information is missing. For example, the reception system can automatically complete an estimated list of participants based on the information entered by the user. For example, the reception system can automatically complete an estimated time based on the purpose entered by the user. The reception system can also automatically complete an estimated purpose based on the time entered by the user. Furthermore, the reception system can develop algorithms to automatically complete estimated information when some of the entered information is missing. For example, the reception system can complete the information using estimates based on past data or general estimates. In this way, the reception system can assist the user's input work by automatically completing the missing information.

[0037] The reception system can prioritize retrieving highly relevant information based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception system will prioritize suggesting participants and objectives related to that region. For example, if the user is in a specific location, the reception system will prioritize displaying the history of group work related to that location. Furthermore, if the user is on the move, the reception system can suggest the most suitable input fields based on their current location. In addition, the reception system can develop algorithms to prioritize retrieving highly relevant information based on the user's geographical location during data entry. For example, the reception system can obtain the user's geographical location using GPS data or IP addresses and provide highly relevant information based on that information. This allows the reception system to provide highly relevant information and streamline the data entry process by considering the user's geographical location.

[0038] The reception desk can analyze a user's social media activity during data entry and automatically retrieve relevant information. For example, it can analyze a user's social media posts and suggest relevant group work objectives. For example, it can refer to a user's social media friend list and suggest potential participants. It can also refer to a user's social media event information and suggest relevant times and locations. Furthermore, the reception desk can develop algorithms to analyze a user's social media activity and automatically retrieve relevant information during data entry. For example, it can analyze post content and followers and retrieve information based on the results. This allows the reception desk to automatically retrieve relevant information by analyzing a user's social media activity, thereby streamlining the data entry process.

[0039] The generation unit can generate a progress plan by referencing specific past success stories. For example, it can generate an optimal progress plan based on data from past successful group work. For instance, it can extract the optimal steps for a specific objective from past success stories and incorporate them into the plan. It can also analyze past success stories and propose the most effective methods of progress. Furthermore, the generation unit can develop algorithms for generating plans by referencing past success stories. For example, it can analyze the details and success factors of successful projects and generate a plan based on the results. In this way, the generation unit can provide an optimal progress plan by referencing past success stories.

[0040] The generation unit can optimize role assignments based on participants' skill sets when generating a progress plan. For example, it can analyze participants' skill sets and propose optimal role assignments. For instance, it can assign optimal roles based on participants' past performance data. It can also propose specific actions at each step based on participants' skill sets. Furthermore, the generation unit can develop algorithms to optimize role assignments based on participants' skill sets when generating a progress plan. For example, it can evaluate technical and soft skills and assign roles based on the results. In this way, the generation unit can achieve efficient group work by optimizing role assignments based on participants' skill sets.

[0041] The generation unit can generate a plan based on the geographical location information of the participants when generating the progress plan. For example, if the participants are in different locations, the generation unit will generate an online progress plan. For example, if the participants gather in a specific location, the generation unit will generate a progress plan that is optimal for that location. The generation unit can also suggest the optimal meeting place and time based on the geographical location information of the participants. Furthermore, the generation unit can develop algorithms for generating plans based on the geographical location information of the participants when generating the progress plan. For example, the generation unit can obtain the geographical location information of the participants using GPS data or IP addresses and generate a plan based on that information. In this way, the generation unit can provide an optimal progress plan by taking the geographical location information of the participants into consideration.

[0042] The generation unit can improve the accuracy of the plan by referencing relevant external data sources when generating the progress plan. For example, the generation unit can refer to external weather information to generate a plan optimized for outdoor activities. For example, the generation unit can refer to external traffic information to generate a plan that takes travel time into account. The generation unit can also refer to external event information to generate a plan tailored to relevant events. Furthermore, the generation unit can develop algorithms to improve the accuracy of the plan by referencing relevant external data sources when generating the progress plan. For example, the generation unit can obtain external data sources using public databases or APIs and generate a plan based on that information. In this way, the generation unit can improve the accuracy of the plan by referencing relevant external data sources.

[0043] The monitoring unit can predict the current progress based on specific past progress data during monitoring. For example, the monitoring unit can predict the current progress based on past progress data and propose the next steps. For instance, the monitoring unit can predict delays at a specific step from past progress data and propose countermeasures. The monitoring unit can also analyze past progress data and propose the most efficient way to proceed. Furthermore, the monitoring unit can develop algorithms to predict the current progress based on past progress data during monitoring. For example, the monitoring unit can make predictions based on past project progress and participant feedback. This allows the monitoring unit to predict the current progress by referring to past progress data and take appropriate action.

[0044] The monitoring department can collect real-time feedback from participants during monitoring and reflect it in the progress. For example, the monitoring department can collect real-time feedback from participants and reflect it in the progress. For example, the monitoring department can adjust the progress plan based on participant feedback. The monitoring department can also analyze participant feedback and propose the optimal way to proceed. Furthermore, the monitoring department can develop algorithms to collect real-time feedback from participants during monitoring and reflect it in the progress. For example, the monitoring department can collect feedback using surveys or chat messages and adjust the progress based on the results. In this way, the monitoring department can collect real-time feedback from participants, reflect it in the progress, and take appropriate action.

[0045] The monitoring unit can monitor progress based on participants' geographical location information during monitoring. For example, if participants are in different locations, the monitoring unit will monitor their online progress. For example, if participants gather in a specific location, the monitoring unit will monitor progress in a way that is optimal for that location. The monitoring unit can also suggest the optimal monitoring method based on participants' geographical location information. Furthermore, the monitoring unit can develop algorithms for monitoring progress based on participants' geographical location information during monitoring. For example, the monitoring unit can obtain participants' geographical location information using GPS data or IP addresses and monitor progress based on that information. This allows the monitoring unit to perform optimal progress monitoring by taking participants' geographical location information into consideration.

[0046] The monitoring unit can improve the accuracy of monitoring by referring to relevant external data sources during monitoring. For example, the monitoring unit can refer to external weather information to provide the optimal monitoring method for outdoor activities. For example, the monitoring unit can refer to external traffic information to provide a monitoring method that takes travel time into account. The monitoring unit can also refer to external event information to provide a monitoring method tailored to relevant events. Furthermore, the monitoring unit can develop algorithms to improve the accuracy of monitoring by referring to relevant external data sources during monitoring. For example, the monitoring unit can obtain external data sources using public databases or APIs and perform monitoring based on that information. In this way, the monitoring unit can improve the accuracy of monitoring by referring to relevant external data sources.

[0047] The service provider can provide advice by referring to specific past advice history. For example, the service provider can propose the most suitable advice based on the history of advice given in the past. For example, the service provider can extract and provide the most suitable advice for a specific situation from the past advice history. The service provider can also analyze the past advice history and propose the most effective advice. Furthermore, the service provider can develop algorithms for providing advice by referring to the past advice history. For example, the service provider can analyze the details and success stories of past advice and provide advice based on the results. In this way, the service provider can provide the most suitable advice by referring to the past advice history.

[0048] The service provider can propose specific actions based on the participants' skill sets when providing advice. For example, the service provider can analyze the participants' skill sets and propose the optimal actions. For example, the service provider can propose specific actions based on the participants' past performance data. The service provider can also propose specific actions at each step based on the participants' skill sets. Furthermore, the service provider can develop algorithms for proposing specific actions based on the participants' skill sets when providing advice. For example, the service provider can evaluate technical skills and soft skills and propose actions based on the results. In this way, the service provider can achieve efficient group work by proposing specific actions based on the participants' skill sets.

[0049] The service provider can provide advice based on the geographical location of participants. For example, if participants are in different locations, the service provider will provide advice best suited to their location. For example, if participants gather in a specific location, the service provider will provide advice relevant to that location. The service provider can also suggest optimal advice based on the geographical location of participants. Furthermore, the service provider can develop algorithms to provide advice based on the geographical location of participants. For example, the service provider can obtain the geographical location of participants using GPS data or IP addresses and provide advice based on that information. This allows the service provider to provide optimal advice by taking the geographical location of participants into consideration.

[0050] The service provider can improve the accuracy of its advice by referencing relevant external data sources. For example, it can refer to external weather information to provide optimal advice for outdoor activities. For example, it can refer to external traffic information to provide advice that takes travel time into account. It can also refer to external event information to provide advice tailored to relevant events. Furthermore, the service provider can develop algorithms to improve the accuracy of its advice by referencing relevant external data sources. For example, it can obtain external data sources using public databases or APIs and provide advice based on that information. In this way, the service provider can improve the accuracy of its advice by referencing relevant external data sources.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can automatically search for successful past group work examples based on user input and provide them as reference information. For example, if a user enters "new product idea generation," the reception desk will present successful past group work examples conducted for similar purposes. The reception desk can also provide examples of optimal progress plans based on the time and number of participants entered by the user. Furthermore, the reception desk can extract and suggest best practices learned from past successes based on the information entered by the user. In this way, the reception desk can support users in conducting more effective group work by referring to past successes.

[0053] The generation unit can consider feedback from users' past group work when generating progress plans. For example, it can analyze feedback data from past group work and generate a progress plan that reflects areas for improvement. It can also extract the success factors of past group work and propose an optimal progress plan based on that. Furthermore, it can suggest specific actions at particular steps based on the user's past feedback. In this way, the generation unit can provide more effective progress plans by utilizing the user's past feedback.

[0054] The monitoring unit can acquire real-time biometric information from participants and incorporate it into the progress tracking. For example, the unit can monitor participants' heart rate and stress levels to determine if the progress is proceeding smoothly. The unit can also provide advice to adjust the progress based on the participants' biometric information. Furthermore, the unit can adjust the pace at specific steps based on the participants' biometric information. This allows the monitoring unit to perform more appropriate progress management by utilizing participants' biometric information.

[0055] The service provider can provide interactive feedback to participants based on their progress. For example, if the progress is going smoothly, they can provide positive feedback to boost participants' motivation. If the progress is behind schedule, they can also suggest specific areas for improvement. Furthermore, the service provider can build an interface to provide real-time feedback to participants based on their progress. This allows the service provider to provide appropriate feedback to participants, thereby facilitating the smooth progress of the group work.

[0056] The service provider can suggest refresh breaks to participants depending on the progress. For example, if the progress is going smoothly, the service provider can suggest a short break to participants to maintain their concentration. Conversely, if the progress is behind schedule, the service provider can suggest a refresh break to participants to help them regain their focus. Furthermore, the service provider can develop algorithms to suggest refresh breaks to participants based on the progress. This allows the service provider to maintain participants' concentration and improve the efficiency of group work.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives information from users regarding the purpose, duration, and participants of the group work. For example, users can use an input form to enter information such as "new product idea generation," "2 hours," and "team of 5 people." Step 2: The generation unit generates a progress plan for the group work based on the information received by the reception unit. The generation unit uses generation AI to generate an optimal progress plan based on past data and best practices. For example, it may generate a plan that includes a brainstorming session for idea generation and steps for evaluating and selecting ideas. Step 3: The monitoring unit monitors the progress based on the progress plan generated by the generation unit. The monitoring unit monitors the progress in real time and provides advice as needed. For example, if the progress is ahead of schedule, it advises moving on to the next step; if it is behind schedule, it makes suggestions to accelerate the progress. Step 4: The service provider provides advice based on the progress monitored by the monitoring service provider. The service provider advises on moving to the next step depending on the progress and makes suggestions to accelerate progress if it is behind schedule.

[0059] (Example of form 2) The group work support system according to an embodiment of the present invention is a system that uses a generative AI to support the smooth conduct of group work. This group work support system supports the facilitator by suggesting what should be done within a given time, based on information such as the purpose, time, and participants of the group work input by the user. Specifically, first, the user inputs information such as the purpose, time, and participants of the group work. At this time, the user only needs to use a simple input form. For example, the user inputs information such as "generating new product ideas," "2 hours," and "a team of 5 people." This information is input into the generative AI. Next, the generative AI analyzes the input information and creates a progress plan for the group work. The generative AI generates an optimal progress plan based on past data and best practices. For example, a plan is generated that includes a brainstorming session for idea generation and steps for evaluating and selecting ideas. Based on the generated progress plan, specific actions for each step are presented. For example, specific actions such as "the first 30 minutes are for idea generation," "the next 30 minutes are for idea evaluation," and "the last hour is for refining the selected ideas" are presented. Furthermore, the generative AI monitors the progress in real time and provides advice as needed. For example, if the project is progressing ahead of schedule, it will advise moving on to the next step; if it is behind schedule, it will offer suggestions to accelerate progress. This ensures that group work proceeds smoothly and that objectives are achieved efficiently. In this way, the group work support system can facilitate the smooth progress of group work by generating a progress plan based on user input, monitoring progress, and providing advice.

[0060] The group work support system according to this embodiment comprises a reception unit, a generation unit, a monitoring unit, and a provision unit. The reception unit receives information from the user regarding the purpose, time, and participants of the group work. For example, the reception unit allows the user to input information such as "new product idea generation," "2 hours," and "team of 5 people" using an input form. The generation unit generates a progress plan for the group work based on the information received by the reception unit. The generation unit uses a generation AI to generate an optimal progress plan based on past data and best practices. For example, the generation unit generates a plan that includes a brainstorming session for idea generation and steps for evaluating and selecting ideas. The monitoring unit monitors the progress based on the progress plan generated by the generation unit. The monitoring unit monitors the progress in real time and provides advice as needed. For example, if the progress is ahead of schedule, the monitoring unit advises moving on to the next step, and if it is behind schedule, it makes suggestions to accelerate the progress. The provision unit provides advice based on the progress monitored by the monitoring unit. The provision unit advises moving on to the next step according to the progress and makes suggestions to accelerate the progress if it is behind schedule. As a result, the group work support system according to the embodiment can support the smooth progress of group work by generating a progress plan for group work based on user input information, monitoring the progress, and providing advice.

[0061] The generation unit can generate progress plans based on specific historical data and best practices using generational AI. For example, the generation unit can generate progress plans based on past project data and participant feedback. For example, the generation unit can generate an optimal progress plan based on data from successful group work in the past. The generation unit can also generate progress plans by referring to industry standards and best practices. For example, the generation unit can extract the optimal steps for a specific objective and incorporate them into the plan. Furthermore, the generation unit can use generational AI to develop algorithms for generating progress plans based on historical data and best practices. For example, the generation unit can analyze past success stories and propose the most effective methods of execution. In this way, the generation unit can provide optimal progress plans by generating them based on historical data and best practices.

[0062] The generation unit can use generational AI to suggest specific actions for each step. For example, the generation unit might suggest specific actions such as "the first 30 minutes are for brainstorming," "the next 30 minutes are for evaluating ideas," and "the last hour is for refining the selected ideas." The generation unit can also use generational AI to develop algorithms for suggesting specific actions for each step. For example, the generation unit can suggest task assignments and progress checkpoints. Furthermore, the generation unit can use generational AI to build a feedback loop for suggesting specific actions for each step. For example, the generation unit can evaluate the quality of the generated actions and update the actions based on the evaluation results. In this way, the generation unit can enable users to take specific actions by suggesting specific actions for each step.

[0063] The monitoring unit can monitor progress while tolerating specific time delays. For example, the monitoring unit can monitor progress in real time. For example, the monitoring unit can provide advice on proceeding to the next step based on the progress. Furthermore, the monitoring unit can also suggest ways to accelerate progress if it is behind schedule. In addition, the monitoring unit can develop algorithms for monitoring progress while tolerating specific time delays. For example, the monitoring unit can tolerate time delays ranging from a few seconds to a few minutes and monitor progress within that range. This allows the monitoring unit to monitor progress in real time and take appropriate action based on the progress.

[0064] The service provider can provide advice on how to proceed to the next step based on the progress. For example, if the progress is ahead of schedule, the service provider will advise moving on to the next step; if it is behind schedule, it will suggest ways to accelerate progress. The service provider can also develop algorithms to provide advice on how to proceed to the next step based on the progress. For example, the service provider will suggest the next task or the next meeting. Furthermore, the service provider can build a feedback loop to provide advice on how to proceed to the next step based on the progress. For example, the service provider will update the advice based on the progress evaluation. In this way, the service provider can support the smooth progress of group work by providing advice according to the progress.

[0065] The service provider can document specific delays and make suggestions to accelerate progress. For example, if progress is behind schedule, the service provider can make suggestions to accelerate progress. For instance, the service provider can suggest task reallocation or the allocation of additional resources. The service provider can also develop algorithms to make suggestions to accelerate progress when it is behind schedule. For example, the service provider can evaluate how many minutes behind schedule it is and make suggestions based on that evaluation. Furthermore, the service provider can build a feedback loop to make suggestions to accelerate progress when it is behind schedule. For example, the service provider can update its suggestions based on the progress evaluation results. In this way, the service provider can help ensure that progress stays on schedule by making suggestions to accelerate progress when it is behind schedule.

[0066] The reception desk can estimate the user's emotions and dynamically change the design of the input form based on the estimated emotions. For example, if the user is feeling tense, the reception desk can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception desk can provide an interface with bright colors to make the input process more enjoyable. If the user is tired, the reception desk can provide a simple and highly visible interface to facilitate the input process. In this way, the reception desk can improve the user's input experience by providing an input form design that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The reception desk can refer to the user's past group work history and suggest input fields. For example, the reception desk can refer to past group work history and suggest the most suitable input fields for the user. For instance, the reception desk can automatically display the purpose and time of past group work as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the purpose and participants for a specific time slot based on the user's past group work history. In this way, the reception desk can suggest the most suitable input fields for the user by referring to past history, thereby streamlining the input process.

[0068] The reception system can automatically complete estimated information when some of the entered information is missing. For example, the reception system can automatically complete an estimated list of participants based on the information entered by the user. For example, the reception system can automatically complete an estimated time based on the purpose entered by the user. The reception system can also automatically complete an estimated purpose based on the time entered by the user. Furthermore, the reception system can develop algorithms to automatically complete estimated information when some of the entered information is missing. For example, the reception system can complete the information using estimates based on past data or general estimates. In this way, the reception system can assist the user's input work by automatically completing the missing information.

[0069] The reception desk can estimate the user's emotions and prioritize inputs based on those emotions. For example, if the user is in a hurry, the reception desk will prioritize displaying the most important input items. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is stressed, the reception desk can provide a simple interface and minimize the input steps. In this way, the reception desk can improve the user's input experience by providing input priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The reception system can prioritize retrieving highly relevant information based on the user's geographical location during data entry. For example, if the user is in a specific region, the reception system will prioritize suggesting participants and objectives related to that region. For example, if the user is in a specific location, the reception system will prioritize displaying the history of group work related to that location. Furthermore, if the user is on the move, the reception system can suggest the most suitable input fields based on their current location. In addition, the reception system can develop algorithms to prioritize retrieving highly relevant information based on the user's geographical location during data entry. For example, the reception system can obtain the user's geographical location using GPS data or IP addresses and provide highly relevant information based on that information. This allows the reception system to provide highly relevant information and streamline the data entry process by considering the user's geographical location.

[0071] The reception desk can analyze a user's social media activity during data entry and automatically retrieve relevant information. For example, it can analyze a user's social media posts and suggest relevant group work objectives. For example, it can refer to a user's social media friend list and suggest potential participants. It can also refer to a user's social media event information and suggest relevant times and locations. Furthermore, the reception desk can develop algorithms to analyze a user's social media activity and automatically retrieve relevant information during data entry. For example, it can analyze post content and followers and retrieve information based on the results. This allows the reception desk to automatically retrieve relevant information by analyzing a user's social media activity, thereby streamlining the data entry process.

[0072] The generation unit can estimate the user's emotions and adjust the way the progress plan is presented based on those emotions. For example, if the user is relaxed, the generation unit can generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a plan that can be completed in the shortest possible time. If the user is excited, the generation unit can also generate a plan with visually stimulating effects. In this way, the generation unit can help the user understand the progress plan by providing a way of presenting it that is appropriate to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The generation unit can generate a progress plan by referencing specific past success stories. For example, it can generate an optimal progress plan based on data from past successful group work. For instance, it can extract the optimal steps for a specific objective from past success stories and incorporate them into the plan. It can also analyze past success stories and propose the most effective methods of progress. Furthermore, the generation unit can develop algorithms for generating plans by referencing past success stories. For example, it can analyze the details and success factors of successful projects and generate a plan based on the results. In this way, the generation unit can provide an optimal progress plan by referencing past success stories.

[0074] The generation unit can optimize role assignments based on participants' skill sets when generating a progress plan. For example, it can analyze participants' skill sets and propose optimal role assignments. For instance, it can assign optimal roles based on participants' past performance data. It can also propose specific actions at each step based on participants' skill sets. Furthermore, the generation unit can develop algorithms to optimize role assignments based on participants' skill sets when generating a progress plan. For example, it can evaluate technical and soft skills and assign roles based on the results. In this way, the generation unit can achieve efficient group work by optimizing role assignments based on participants' skill sets.

[0075] The generation unit can estimate the user's emotions and adjust the level of detail in the progress plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a concise plan that gets straight to the point. For example, if the user is relaxed, the generation unit will generate a plan that includes detailed explanations. Furthermore, if the user is excited, the generation unit can also generate a plan with visually stimulating effects. In this way, the generation unit can help the user understand by providing a level of detail in the progress plan that matches the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The generation unit can generate a plan based on the geographical location information of the participants when generating the progress plan. For example, if the participants are in different locations, the generation unit will generate an online progress plan. For example, if the participants gather in a specific location, the generation unit will generate a progress plan that is optimal for that location. The generation unit can also suggest the optimal meeting place and time based on the geographical location information of the participants. Furthermore, the generation unit can develop algorithms for generating plans based on the geographical location information of the participants when generating the progress plan. For example, the generation unit can obtain the geographical location information of the participants using GPS data or IP addresses and generate a plan based on that information. In this way, the generation unit can provide an optimal progress plan by taking the geographical location information of the participants into consideration.

[0077] The generation unit can improve the accuracy of the plan by referencing relevant external data sources when generating the progress plan. For example, the generation unit can refer to external weather information to generate a plan optimized for outdoor activities. For example, the generation unit can refer to external traffic information to generate a plan that takes travel time into account. The generation unit can also refer to external event information to generate a plan tailored to relevant events. Furthermore, the generation unit can develop algorithms to improve the accuracy of the plan by referencing relevant external data sources when generating the progress plan. For example, the generation unit can obtain external data sources using public databases or APIs and generate a plan based on that information. In this way, the generation unit can improve the accuracy of the plan by referencing relevant external data sources.

[0078] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit will frequently monitor the progress to provide reassurance. For example, if the user is relaxed, the monitoring unit will monitor the progress at a moderate frequency. The monitoring unit can also quickly monitor the progress and advise the user to move on to the next step if the user is in a hurry. In this way, the monitoring unit can improve the user's sense of security by providing monitoring frequency that matches the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The monitoring unit can predict the current progress based on specific past progress data during monitoring. For example, the monitoring unit can predict the current progress based on past progress data and propose the next steps. For instance, the monitoring unit can predict delays at a specific step from past progress data and propose countermeasures. The monitoring unit can also analyze past progress data and propose the most efficient way to proceed. Furthermore, the monitoring unit can develop algorithms to predict the current progress based on past progress data during monitoring. For example, the monitoring unit can make predictions based on past project progress and participant feedback. This allows the monitoring unit to predict the current progress by referring to past progress data and take appropriate action.

[0080] The monitoring department can collect real-time feedback from participants during monitoring and reflect it in the progress. For example, the monitoring department can collect real-time feedback from participants and reflect it in the progress. For example, the monitoring department can adjust the progress plan based on participant feedback. The monitoring department can also analyze participant feedback and propose the optimal way to proceed. Furthermore, the monitoring department can develop algorithms to collect real-time feedback from participants during monitoring and reflect it in the progress. For example, the monitoring department can collect feedback using surveys or chat messages and adjust the progress based on the results. In this way, the monitoring department can collect real-time feedback from participants, reflect it in the progress, and take appropriate action.

[0081] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit provides a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit provides a display method that includes detailed information. The monitoring unit can also provide a concise display method if the user is in a hurry. In this way, the monitoring unit can help the user understand the situation by providing a display method of monitoring results that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The monitoring unit can monitor progress based on participants' geographical location information during monitoring. For example, if participants are in different locations, the monitoring unit will monitor their online progress. For example, if participants gather in a specific location, the monitoring unit will monitor progress in a way that is optimal for that location. The monitoring unit can also suggest the optimal monitoring method based on participants' geographical location information. Furthermore, the monitoring unit can develop algorithms for monitoring progress based on participants' geographical location information during monitoring. For example, the monitoring unit can obtain participants' geographical location information using GPS data or IP addresses and monitor progress based on that information. This allows the monitoring unit to perform optimal progress monitoring by taking participants' geographical location information into consideration.

[0083] The monitoring unit can improve the accuracy of monitoring by referring to relevant external data sources during monitoring. For example, the monitoring unit can refer to external weather information to provide the optimal monitoring method for outdoor activities. For example, the monitoring unit can refer to external traffic information to provide a monitoring method that takes travel time into account. The monitoring unit can also refer to external event information to provide a monitoring method tailored to relevant events. Furthermore, the monitoring unit can develop algorithms to improve the accuracy of monitoring by referring to relevant external data sources during monitoring. For example, the monitoring unit can obtain external data sources using public databases or APIs and perform monitoring based on that information. In this way, the monitoring unit can improve the accuracy of monitoring by referring to relevant external data sources.

[0084] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is nervous, the service provider will provide advice in a calm voice. For example, if the user is relaxed, the service provider will provide advice in a cheerful voice. The service provider can also provide quick and concise advice if the user is in a hurry. In this way, the service provider can help the user understand the advice by providing an expression of advice that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The service provider can provide advice by referring to specific past advice history. For example, the service provider can propose the most suitable advice based on the history of advice given in the past. For example, the service provider can extract and provide the most suitable advice for a specific situation from the past advice history. The service provider can also analyze the past advice history and propose the most effective advice. Furthermore, the service provider can develop algorithms for providing advice by referring to the past advice history. For example, the service provider can analyze the details and success stories of past advice and provide advice based on the results. In this way, the service provider can provide the most suitable advice by referring to the past advice history.

[0086] The service provider can propose specific actions based on the participants' skill sets when providing advice. For example, the service provider can analyze the participants' skill sets and propose the optimal actions. For example, the service provider can propose specific actions based on the participants' past performance data. The service provider can also propose specific actions at each step based on the participants' skill sets. Furthermore, the service provider can develop algorithms for proposing specific actions based on the participants' skill sets when providing advice. For example, the service provider can evaluate technical skills and soft skills and propose actions based on the results. In this way, the service provider can achieve efficient group work by proposing specific actions based on the participants' skill sets.

[0087] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is in a hurry, the service provider will prioritize providing the most important advice. For example, if the user is relaxed, the service provider will provide detailed advice. Furthermore, if the user is stressed, the service provider can provide simple and effective advice. This allows the service provider to help the user understand themselves by prioritizing advice according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The service provider can provide advice based on the geographical location of participants. For example, if participants are in different locations, the service provider will provide advice best suited to their location. For example, if participants gather in a specific location, the service provider will provide advice relevant to that location. The service provider can also suggest optimal advice based on the geographical location of participants. Furthermore, the service provider can develop algorithms to provide advice based on the geographical location of participants. For example, the service provider can obtain the geographical location of participants using GPS data or IP addresses and provide advice based on that information. This allows the service provider to provide optimal advice by taking the geographical location of participants into consideration.

[0089] The service provider can improve the accuracy of its advice by referencing relevant external data sources. For example, it can refer to external weather information to provide optimal advice for outdoor activities. For example, it can refer to external traffic information to provide advice that takes travel time into account. It can also refer to external event information to provide advice tailored to relevant events. Furthermore, the service provider can develop algorithms to improve the accuracy of its advice by referencing relevant external data sources. For example, it can obtain external data sources using public databases or APIs and provide advice based on that information. In this way, the service provider can improve the accuracy of its advice by referencing relevant external data sources. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, enabling the user to input information using an input form. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, generating an optimal progress plan using generation AI. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14, monitoring the progress in real time. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing advice according to the progress. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, enabling the user to input information using an input form. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, generating an optimal progress plan using generation AI. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214, monitoring the progress in real time. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing advice according to the progress. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, enabling the user to input information using an input form. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, generating an optimal progress plan using a generation AI. The monitoring unit is implemented, for example, by the control unit 46A of the headset terminal 314, monitoring the progress in real time. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing advice according to the progress. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, enabling the user to input information using an input form. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, generating an optimal progress plan using a generation AI. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414, monitoring the progress in real time. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing advice according to the progress.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception desk can automatically search for successful past group work examples based on user input and provide them as reference information. For example, if a user enters "new product idea generation," the reception desk will present successful past group work examples conducted for similar purposes. The reception desk can also provide examples of optimal progress plans based on the time and number of participants entered by the user. Furthermore, the reception desk can extract and suggest best practices learned from past successes based on the information entered by the user. In this way, the reception desk can support users in conducting more effective group work by referring to past successes.

[0092] The generation unit can consider feedback from users' past group work when generating progress plans. For example, it can analyze feedback data from past group work and generate a progress plan that reflects areas for improvement. It can also extract the success factors of past group work and propose an optimal progress plan based on that. Furthermore, it can suggest specific actions at particular steps based on the user's past feedback. In this way, the generation unit can provide more effective progress plans by utilizing the user's past feedback.

[0093] The monitoring unit can acquire real-time biometric information from participants and incorporate it into the progress tracking. For example, the unit can monitor participants' heart rate and stress levels to determine if the progress is proceeding smoothly. The unit can also provide advice to adjust the progress based on the participants' biometric information. Furthermore, the unit can adjust the pace at specific steps based on the participants' biometric information. This allows the monitoring unit to perform more appropriate progress management by utilizing participants' biometric information.

[0094] The service provider can provide interactive feedback to participants based on their progress. For example, if the progress is going smoothly, they can provide positive feedback to boost participants' motivation. If the progress is behind schedule, they can also suggest specific areas for improvement. Furthermore, the service provider can build an interface to provide real-time feedback to participants based on their progress. This allows the service provider to provide appropriate feedback to participants, thereby facilitating the smooth progress of the group work.

[0095] The service provider can suggest refresh breaks to participants depending on the progress. For example, if the progress is going smoothly, the service provider can suggest a short break to participants to maintain their concentration. Conversely, if the progress is behind schedule, the service provider can suggest a refresh break to participants to help them regain their focus. Furthermore, the service provider can develop algorithms to suggest refresh breaks to participants based on the progress. This allows the service provider to maintain participants' concentration and improve the efficiency of group work.

[0096] The reception desk can estimate the user's emotions and dynamically change the guidance messages on the input form based on those emotions. For example, if the reception desk is tense, it can display a gentle message to help the user relax. If the user is enjoying themselves, it can display an encouraging message to make the input process more enjoyable. Furthermore, if the user is tired, it can display a concise and easy-to-understand message to facilitate the input process. In this way, the reception desk can improve the user's input experience by providing guidance messages that are tailored to the user's emotions.

[0097] The generation unit can estimate the user's emotions and adjust the flexibility of the progress plan based on those emotions. For example, if the user is stressed, the generation unit can generate a flexible progress plan to help the user relax. If the user is relaxed, the generation unit can also generate a detailed progress plan to help them stay on track. Furthermore, if the user is in a hurry, the generation unit can generate a plan that allows for quick progress. In this way, the generation unit can provide support tailored to the user's needs by offering flexibility in the progress plan that responds to the user's emotions.

[0098] The monitoring unit can estimate the user's emotions and adjust the timing of monitoring based on those emotions. For example, if the user is feeling stressed, the monitoring unit can frequently monitor the progress to provide reassurance. If the user is relaxed, the monitoring unit can monitor the progress at appropriate times. Furthermore, if the user is in a hurry, the monitoring unit can quickly monitor the progress and advise them to move on to the next step. In this way, the monitoring unit can enhance the user's sense of security by providing monitoring timing that is tailored to the user's emotions.

[0099] The service provider can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is feeling tense, the service provider can offer advice to help them relax. If the user is enjoying themselves, the service provider can also offer advice to boost their motivation. Furthermore, if the user is feeling tired, the service provider can offer concise and effective advice. In this way, the service provider can help understand the user by providing advice tailored to their emotions.

[0100] The service provider can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is feeling anxious, the service provider can provide advice at the appropriate time to reassure them. It can also provide advice at a suitable time if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can provide advice quickly. In this way, the service provider can help users understand the situation by providing advice at the right time, tailored to their emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives information from users regarding the purpose, duration, and participants of the group work. For example, users can use an input form to enter information such as "new product idea generation," "2 hours," and "team of 5 people." Step 2: The generation unit generates a progress plan for the group work based on the information received by the reception unit. The generation unit uses generation AI to generate an optimal progress plan based on past data and best practices. For example, it may generate a plan that includes a brainstorming session for idea generation and steps for evaluating and selecting ideas. Step 3: The monitoring unit monitors the progress based on the progress plan generated by the generation unit. The monitoring unit monitors the progress in real time and provides advice as needed. For example, if the progress is ahead of schedule, it advises moving on to the next step; if it is behind schedule, it makes suggestions to accelerate the progress. Step 4: The service provider provides advice based on the progress monitored by the monitoring service provider. The service provider advises on moving to the next step depending on the progress and makes suggestions to accelerate progress if it is behind schedule.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users regarding the purpose, time, and participants of group work, Based on the information received by the reception unit, a generation unit generates a group work progress plan, A monitoring unit monitors the progress based on the progress plan generated by the generation unit, A providing unit that provides advice based on the progress monitored by the aforementioned monitoring unit, Equipped with A system characterized by the following features.

2. The generating unit is The AI ​​generates a progress plan based on specific historical data and best practices. The system according to feature 1.

3. The generating unit is The generated AI will present specific actions for each step. The system according to feature 1.

4. The aforementioned monitoring unit, Allow for specific time delays and monitor progress. The system according to feature 1.

5. The aforementioned supply unit is, Provide advice on how to proceed to the next step depending on your progress. The system according to feature 1.

6. The aforementioned supply unit is, We will describe the specific delays and propose ways to accelerate progress. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input form based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Refer to specific past group work history and suggest input fields to the user. The system according to feature 1.

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