system

Abstract

The system according to the embodiment aims to smoothly resolve differences that arise in discussions and meetings. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a correction unit. The reception unit accepts input of an agenda. The analysis unit analyzes the agenda accepted by the reception unit. The proposal unit organizes opinions and proposes solutions based on the agenda analyzed by the analysis unit. The correction unit modifies the discussion based on the solution proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of limited facilitator resources to smoothly resolve differences that arise during discussions and meetings, resulting in a lack of efficient means of resolution.

[0005] The system according to the embodiment aims to smoothly resolve differences that arise in discussions and meetings. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a correction unit. The reception unit accepts input of an agenda. The analysis unit analyzes the agenda accepted by the reception unit. The proposal unit organizes opinions and proposes solutions based on the agenda analyzed by the analysis unit. The correction unit corrects the discussion based on the solution proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can smoothly resolve differences that arise in discussions and meetings. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A facilitation system according to an embodiment of the present invention uses AI to smoothly resolve disagreements during discussions and meetings. This system accepts input of an agenda, and AI facilitates the discussion based on the agenda and makes suggestions for resolving disagreements between participants. This addresses the limited resources of skilled facilitators and provides cost-effective and efficient solutions. For example, participants in a discussion or meeting input the agenda. They simply enter the details and purpose of the agenda. For example, they input an agenda such as "discuss the project agenda." This information is then input into AI. The AI ​​then analyzes the input agenda and facilitates the discussion. The AI ​​makes suggestions for resolving disagreements between participants based on the agenda. For example, if participants have different opinions on a project agenda, the AI ​​organizes their opinions and proposes optimal solutions. This allows participants to efficiently advance the discussion. Furthermore, during the facilitation process, the AI ​​analyzes participants' opinions in real time and corrects the direction of the discussion as necessary. For example, if a discussion reaches a deadlock or a particular opinion is overemphasized, AI can intervene at the appropriate time and provide advice to smooth the discussion. This mechanism addresses the limited resources of skilled facilitators and provides cost-effective and efficient solutions. For example, AI can facilitate corporate meetings or project team discussions, saving time and costs while leading to better conclusions. AI can also learn from data from past discussions to improve the accuracy of its facilitation. For example, it can analyze successful and unsuccessful solutions in past discussions and apply this knowledge to the next discussion. This allows AI to continuously evolve and provide more effective facilitation. This allows the facilitation system to smoothly resolve differences in discussions and meetings and support efficient discussion progress.

[0029] A facilitation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a correction unit. The reception unit accepts input of an agenda. The agenda may include, but is not limited to, a meeting topic, an issue, or a subject of discussion. For example, the reception unit allows a user to input the agenda in text format. The reception unit can also accept the agenda using voice input. For example, the user may input the agenda through voice and convert it into text data using voice recognition technology. The analysis unit analyzes the agenda accepted by the reception unit. Examples of analysis include, but are not limited to, data analysis, text analysis, and sentiment analysis. For example, the analysis unit may analyze the content of the agenda using text analysis technology to extract important keywords and phrases. The analysis unit can also analyze emotional elements included in the agenda using sentiment analysis technology. The proposal unit organizes opinions based on the agenda analyzed by the analysis unit and proposes solutions. Examples of proposals include, but are not limited to, consensus building, compromise proposals, and facilitation techniques. The proposal unit, for example, organizes opinions among participants based on the agenda and proposes an optimal solution. The proposal unit can also refer to past discussion data and make proposals based on successful solutions and unsuccessful approaches. The correction unit corrects the discussion based on the solution proposed by the proposal unit. Corrections include, for example, changing the direction of the discussion, providing additional information, reevaluating opinions, etc., but are not limited to these examples. The correction unit, for example, analyzes the discussion in real time and corrects the direction of the discussion as necessary. The correction unit can also monitor the progress of the discussion and intervene at an appropriate time. As a result, the facilitation system according to the embodiment enables efficient discussion progress by consistently performing processes from inputting the agenda to analysis, proposals, and corrections.

[0030] The facilitation system further includes a learning unit that learns discussion data. The learning unit learns past discussion data. Examples of discussion data include, but are not limited to, meeting minutes, chat logs, and audio data. For example, the learning unit analyzes past meeting minutes using text analysis technology to extract important keywords and phrases. The learning unit can also analyze audio data using audio analysis technology to convert the content of discussions into text data. Furthermore, the learning unit can develop algorithms to improve the accuracy of facilitation based on past discussion data. For example, the learning unit can analyze successful solutions and unsuccessful approaches in past discussions and apply them to the next discussion. This enables the learning unit to continuously evolve and provide more effective facilitation. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input past discussion data into a generation AI and cause the generation AI to develop an algorithm to improve the accuracy of facilitation.

[0031] The facilitation system further includes a monitoring unit that monitors the discussion. The monitoring unit monitors the progress of the discussion. Examples of monitoring include, but are not limited to, real-time monitoring, log analysis, and sentiment analysis. For example, the monitoring unit monitors the progress of the discussion in real time and intervenes when the discussion reaches an impasse or when a particular opinion is overemphasized. The monitoring unit can also analyze the discussion log to grasp the progress of the discussion. Furthermore, the monitoring unit can use sentiment analysis technology to analyze the emotions of users participating in the discussion and identify factors that affect the progress of the discussion. This enables the monitoring unit to intervene at an appropriate time and provide advice to facilitate the progress of the discussion. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input discussion data acquired in real time into a generation AI to analyze the progress of the discussion.

[0032] The facilitation system further includes a recording unit that records the discussion. The recording unit records the results of the discussion. Recordings include, but are not limited to, text recording, audio recording, and video recording. For example, the recording unit records the content of the discussion in text format, allowing for later reference and analysis. The recording unit can also record the audio of the discussion and save it as audio data. Furthermore, the recording unit can record a video of the discussion and save it as video data. This allows the recording unit to record the results of the discussion in detail, making them useful for later reference and analysis. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the audio data of the discussion to a generation AI and have the generation AI convert the audio data into text data.

[0033] The analysis unit can make proposals for resolving disagreements between participants based on the agenda. For example, the analysis unit makes proposals for resolving differences of opinion between participants based on the agenda. Proposals include, but are not limited to, consensus building, compromise proposals, and facilitation techniques. For example, the analysis unit organizes opinions between participants based on the agenda and proposes an optimal solution. The analysis unit can also refer to past discussion data and make proposals based on successful and unsuccessful approaches. This allows the analysis unit to make proposals for resolving differences of opinion between participants, thereby improving the efficiency of discussions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can cause a generation AI to execute proposals for resolving disagreements between participants based on the agenda.

[0034] The correction unit can analyze the discussion and correct the direction of the discussion as necessary. The correction unit can, for example, analyze the discussion in real time and correct the direction of the discussion as necessary. Corrections include, but are not limited to, changing the focus of the discussion, introducing a new perspective, and managing the progress of the discussion. For example, the correction unit can provide a new perspective and restart the discussion if the discussion has reached an impasse. The correction unit can also propose corrections to balance the discussion if it has become overly one-sided. Furthermore, the correction unit can propose appropriate corrections according to the progress of the discussion while the discussion is in progress. In this way, the correction unit can correct the discussion in real time, thereby smoothing the progress of the discussion. Some or all of the above-mentioned processing in the correction unit can be performed using, for example, AI, or can be performed without AI. For example, the correction unit can input discussion data acquired in real time into the generation AI and cause the generation AI to execute suggestions to correct the direction of the discussion.

[0035] The reception unit can analyze past agenda input history and select the optimal input method. The reception unit, for example, analyzes past agenda input history and selects the optimal input method. The agenda input history includes, for example, past agendas, input dates and times, and inputters, but is not limited to these examples. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also refer to the format of agendas previously input by the user and suggest input in a similar format. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period based on the user's past input history. In this way, the reception unit can provide the user with the optimal input method by analyzing the past agenda input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past agenda input history into a generation AI and cause the generation AI to execute a suggestion for selecting the optimal input method.

[0036] The reception unit may filter the agenda items based on the user's projects and areas of interest when the agenda items are input. For example, the reception unit may filter the agenda items based on the user's projects and areas of interest when the agenda items are input. Examples of projects and areas of interest include, but are not limited to, ongoing projects, fields of expertise, and topics of interest. For example, the reception unit may preferentially display agenda items related to the user's ongoing projects. The reception unit may also suggest related agenda items based on the user's areas of interest. Furthermore, the reception unit may suggest highly relevant agenda items by referring to the user's past project history. This allows the reception unit to preferentially input highly relevant agenda items by filtering based on the user's current projects and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's projects and areas of interest to the generation AI and cause the generation AI to execute suggestions for filtering.

[0037] The reception unit can prioritize inputting highly relevant agenda items by taking into account the user's geographical location information when inputting agenda items. For example, the reception unit prioritizes inputting highly relevant agenda items by taking into account the user's geographical location information when inputting agenda items. Geographical location information includes, but is not limited to, GPS data, address information, location identification technology, etc. For example, if the user is in a specific area, the reception unit can prioritize inputting agenda items related to that area. Furthermore, if the user is on a business trip, the reception unit can prioritize inputting agenda items related to the business trip destination. Furthermore, if the user is at home, the reception unit can prioritize inputting agenda items related to the user's home. In this way, the reception unit can prioritize inputting highly relevant agenda items by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute a suggestion to prioritize inputting highly relevant agenda items.

[0038] The reception unit can analyze the user's social media activity when inputting an agenda and input related agenda items. For example, the reception unit can analyze the user's social media activity when inputting an agenda and input related agenda items. Social media activity includes, but is not limited to, post content, the number of likes, and the number of followers. For example, the reception unit can suggest topics that the user frequently mentions on social media as agenda items. The reception unit can also analyze the user's social media activity history and suggest related agenda items. Furthermore, the reception unit can suggest agenda items based on topics of accounts the user follows on social media. In this way, the reception unit can input related agenda items by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media activity data to a generation AI and cause the generation AI to suggest inputting related agenda items.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the agenda item during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the agenda item during analysis. The importance of an agenda item includes, but is not limited to, the scope of impact, urgency, and number of stakeholders. For example, the analysis unit performs a detailed analysis of an agenda item with high importance. The analysis unit can also perform a concise analysis of an agenda item with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the agenda item, thereby enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input agenda item importance data to the generation AI and cause the generation AI to execute a proposal for adjusting the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the topic during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the topic during analysis. Topic categories include, but are not limited to, technical issues, business strategies, and management policies. For example, the analysis unit applies a specialized analysis algorithm for technology to technical topics. The analysis unit can also apply a specialized analysis algorithm for economic topics to economic topics. Furthermore, the analysis unit can apply a specialized analysis algorithm for social science topics to social topics. This allows the analysis unit to apply different analysis algorithms depending on the category of the topic, enabling more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input topic category data into the generation AI and cause the generation AI to execute a proposal for applying a different analysis algorithm.

[0041] The analysis unit can determine the analysis priority based on the submission time of the agenda items during analysis. For example, the analysis unit determines the analysis priority based on the submission time of the agenda items during analysis. The submission time of the agenda items includes, but is not limited to, the submission date and time, the submission frequency, and the priority of the submitter. For example, the analysis unit prioritizes the analysis of recently submitted agenda items. The analysis unit can also lower the priority of older submitted agenda items. Furthermore, the analysis unit can adjust the order of analysis based on the submission time. This enables efficient analysis by the analysis unit determining the analysis priority based on the submission time of the agenda items. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the submission time of the agenda items into the generation AI and cause the generation AI to execute a proposal for determining the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the agenda items during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the agenda items during analysis. The relevance of the agenda items includes, but is not limited to, for example, similarity of topics, commonality of stakeholders, and scope of influence. For example, the analysis unit prioritizes analysis of highly relevant agenda items. The analysis unit can also lower the priority of less relevant agenda items. Furthermore, the analysis unit can adjust the order of analysis based on the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the agenda items, thereby enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input agenda relevance data into the generation AI and cause the generation AI to execute a proposal for adjusting the order of analysis.

[0043] The proposal unit may adjust the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit may adjust the level of detail of the proposal based on the importance of the solution when making the proposal. The importance of the solution may include, but is not limited to, the scope of impact, feasibility, and cost-effectiveness. For example, the proposal unit may provide a detailed proposal for a solution with high importance. The proposal unit may also provide a concise proposal for a solution with low importance. Furthermore, the proposal unit may adjust the depth of the proposal according to the importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the solution, thereby enabling a more appropriate proposal. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit may input solution importance data into the generation AI and cause the generation AI to execute a proposal to adjust the level of detail of the proposal.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the topic when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the topic when making a suggestion. Topic categories include, but are not limited to, technical issues, business strategies, and management policies. For example, the suggestion unit applies a technology-specialized suggestion algorithm to technical topics. The suggestion unit can also apply an economics-specialized suggestion algorithm to economic topics. Furthermore, the suggestion unit can apply a social science-specialized suggestion algorithm to social topics. This enables the suggestion unit to apply different suggestion algorithms depending on the category of the topic, thereby enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input topic category data into a generation AI and cause the generation AI to execute suggestions for applying different suggestion algorithms.

[0045] The suggestion unit may determine the priority of proposals based on the time of submission of the solutions at the time of proposal. For example, the suggestion unit may determine the priority of proposals based on the time of submission of the solutions at the time of proposal. The time of submission of solutions may include, but is not limited to, the submission date and time, the frequency of submission, and the priority of the submitter. For example, the suggestion unit may prioritize recently submitted solutions. The suggestion unit may also lower the priority of older submitted solutions. Furthermore, the suggestion unit may adjust the order of proposals based on the time of submission. This allows the suggestion unit to prioritize proposals based on the time of submission of the solutions, thereby enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input solution submission time data into the generation AI and cause the generation AI to execute a proposal to determine the priority of the proposals.

[0046] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the suggestion unit adjusts the order of proposals based on the relevance of the solutions when making a proposal. The relevance of the solutions includes, but is not limited to, for example, topic similarity, commonality of stakeholders, and scope of impact. For example, the suggestion unit prioritizes proposing highly relevant solutions. The suggestion unit can also lower the priority of less relevant solutions. Furthermore, the suggestion unit can adjust the order of proposals according to the relevance. This enables the suggestion unit to adjust the order of proposals based on the relevance of the solutions, thereby enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input solution relevance data into a generation AI and cause the generation AI to execute a suggestion to adjust the order of proposals.

[0047] The correction unit can analyze the progress of the discussion and select the optimal correction method during correction. For example, the correction unit analyzes the progress of the discussion and selects the optimal correction method during correction. The progress of the discussion includes, but is not limited to, the speed at which the discussion is progressing, the frequency of participants' comments, and the depth of the discussion. For example, if the discussion reaches an impasse, the correction unit can provide a new perspective to restart the discussion. If the discussion becomes overly one-sided, the correction unit can also propose a correction to balance the discussion. Furthermore, if the discussion is ongoing, the correction unit can also propose an appropriate correction according to the progress. This enables efficient correction by analyzing the progress of the discussion and selecting the optimal correction method. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input discussion progress data into the generation AI and cause the generation AI to execute a proposal to select the optimal correction method.

[0048] The correction unit can apply different correction algorithms depending on the category of the discussion during correction. For example, the correction unit applies different correction algorithms depending on the category of the discussion during correction. Discussion categories include, but are not limited to, technical issues, business strategies, and management policies. For example, the correction unit applies a technical correction algorithm to technical discussions. The correction unit can also apply an economics correction algorithm to economic discussions. Furthermore, the correction unit can apply a social science correction algorithm to social discussions. This allows the correction unit to apply different correction algorithms depending on the category of the discussion, enabling more appropriate correction. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input discussion category data to the generation AI and cause the generation AI to execute a proposal for applying a different correction algorithm.

[0049] The correction unit can select the optimal correction method by taking into account the geographic distribution of the discussion during correction. For example, the correction unit selects the optimal correction method by taking into account the geographic distribution of the discussion during correction. Geographic distribution includes, but is not limited to, the locations of participants, the region covered by the discussion, and the geographical scope of influence. For example, if the discussion is concentrated in a specific region, the correction unit prioritizes corrections related to that region. Furthermore, if the discussion is distributed across multiple regions, the correction unit can also make corrections by taking into account the characteristics of each region. Furthermore, if the discussion is international, the correction unit can make corrections by taking into account the culture and regulations of each country. This enables efficient correction by selecting the optimal correction method by taking into account the geographic distribution of the discussion. Some or all of the above-described processing by the correction unit may be performed using, for example, AI, or without AI. For example, the correction unit can input geographic distribution data of the discussion into the generation AI and have the generation AI execute a proposal to select the optimal correction method.

[0050] The correction unit can improve the accuracy of the correction by referring to literature related to the discussion during the correction. For example, the correction unit can improve the accuracy of the correction by referring to literature related to the discussion during the correction. Examples of related literature include, but are not limited to, academic papers, technical reports, and industry white papers. For example, the correction unit can make the correction by referring to the latest research papers related to the discussion. The correction unit can also make the correction by referring to past cases related to the discussion. Furthermore, the correction unit can make the correction by referring to specialized books related to the discussion. Thus, the correction unit can improve the accuracy of the correction by referring to literature related to the discussion. Some or all of the above-described processing in the correction unit can be performed using, for example, AI, or without AI. For example, the correction unit can input literature data related to the discussion into the generation AI and cause the generation AI to make suggestions for improving the accuracy of the correction.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Examples of learning data include, but are not limited to, past discussion data, successful solutions, and unsuccessful approaches. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also analyze past learning data and adjust parameters of the learning algorithm. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. Thus, the learning unit improves the accuracy of the learning algorithm by referring to the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to execute suggestions for optimizing the learning algorithm.

[0052] The learning unit can weight the learning data based on the submission time of the discussion during learning. For example, the learning unit weights the learning data based on the submission time of the discussion during learning. The submission time of the discussion includes, but is not limited to, for example, the submission date and time, the submission frequency, and the priority of the submitter. For example, the learning unit weights recent discussion data more highly during learning. The learning unit can also weight older discussion data less highly during learning. Furthermore, the learning unit can adjust the weight of the learning data depending on the submission time. This enables efficient learning by weighting the learning data based on the submission time of the discussion. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input discussion submission time data into the generation AI and cause the generation AI to execute a proposal for weighting the learning data.

[0053] The monitoring unit can analyze the progress of the discussion during monitoring and select the optimal monitoring method. For example, the monitoring unit can analyze the progress of the discussion during monitoring and select the optimal monitoring method. The progress of the discussion includes, but is not limited to, the speed at which the discussion is progressing, the frequency of participants' comments, and the depth of the discussion. For example, if the discussion reaches an impasse, the monitoring unit can provide a new perspective to restart the discussion. If the discussion becomes overly one-sided, the monitoring unit can also perform monitoring to balance the discussion. Furthermore, if the discussion is ongoing, the monitoring unit can perform appropriate monitoring according to the progress of the discussion. This enables efficient monitoring by analyzing the progress of the discussion and selecting the optimal monitoring method. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input discussion progress data into the generation AI and cause the generation AI to execute a proposal for selecting the optimal monitoring method.

[0054] The monitoring unit can select the optimal monitoring method by taking into account the geographic distribution of the discussion during monitoring. For example, the monitoring unit selects the optimal monitoring method by taking into account the geographic distribution of the discussion during monitoring. Geographic distribution includes, but is not limited to, the locations of participants, the region the discussion is focused on, and the geographical scope of influence. For example, if the discussion is concentrated in a specific region, the monitoring unit prioritizes monitoring related to that region. Furthermore, if the discussion is distributed across multiple regions, the monitoring unit can also perform monitoring by taking into account the characteristics of each region. Furthermore, if the discussion is international, the monitoring unit can also perform monitoring by taking into account the cultures and regulations of each country. This enables efficient monitoring by the monitoring unit selecting the optimal monitoring method by taking into account the geographic distribution of the discussion. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input geographic distribution data of the discussion into a generation AI and have the generation AI execute a proposal to select the optimal monitoring method.

[0055] The recording unit can analyze the progress of the discussion and select the optimal recording method when recording. For example, the recording unit can analyze the progress of the discussion and select the optimal recording method when recording. The progress of the discussion can include, but is not limited to, the speed at which the discussion is progressing, the frequency of participants' comments, and the depth of the discussion. For example, if the discussion reaches an impasse, the recording unit can provide a new perspective and record. If the discussion becomes overly one-sided, the recording unit can also record to balance the discussion. Furthermore, if the discussion is ongoing, the recording unit can make appropriate recordings according to the progress of the discussion. This allows the recording unit to analyze the progress of the discussion and select the optimal recording method, enabling efficient recording. Some or all of the above-described processing in the recording unit can be performed using, or without, AI. For example, the recording unit can input discussion progress data into the generation AI and cause the generation AI to execute a suggestion for selecting the optimal recording method.

[0056] The recording unit can select the optimal recording method by taking into account the geographic distribution of the discussion when recording. For example, the recording unit selects the optimal recording method by taking into account the geographic distribution of the discussion when recording. Geographic distribution includes, but is not limited to, the locations of participants, the region of the discussion, and the geographical scope of influence. For example, if the discussion is concentrated in a specific region, the recording unit prioritizes recording related to that region. Furthermore, if the discussion is distributed across multiple regions, the recording unit can also record while taking into account the characteristics of each region. Furthermore, if the discussion is international, the recording unit can record while taking into account the cultures and regulations of each country. This allows the recording unit to select the optimal recording method by taking into account the geographic distribution of the discussion, enabling efficient recording. Some or all of the above-described processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input geographic distribution data of the discussion into the generation AI and have the generation AI execute a proposal to select the optimal recording method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The facilitation system may further include a statement history analysis unit that analyzes a user's past statement history. The statement history analysis unit analyzes past statement data to extract the user's statement tendencies and patterns. For example, it can analyze what topics the user has actively spoken about in the past and what opinions they have held. This allows the statement history analysis unit to make suggestions to support the progress of the discussion based on the user's statement tendencies. For example, if a user has had strong opinions about a particular topic in the past, the system can actively incorporate the user's opinions when a discussion related to that topic takes place. Furthermore, if a user has made statements in the past that disrupt the progress of the discussion, the system can provide advice to avoid such statements. Furthermore, the statement history analysis unit can make suggestions to smooth the progress of the discussion based on the user's statement patterns. This allows the facilitation system to utilize the user's past statement history to support the progress of the discussion more effectively.

[0059] The facilitation system can further include a schedule acquisition unit that acquires the user's schedule information. The schedule acquisition unit acquires information from the user's calendar or schedule management app and optimizes the discussion schedule. For example, the schedule acquisition unit can schedule discussions to avoid busy times for the user. The schedule acquisition unit can also determine the priority of discussions based on the user's schedule. For example, if the user has an important meeting or event coming up, the schedule acquisition unit can prioritize important discussions before that. The schedule acquisition unit can also adjust the pace of the discussion based on the user's schedule. This enables the facilitation system to utilize the user's schedule information to support efficient discussion progress.

[0060] The facilitation system may further include a learning style analysis unit that analyzes the user's learning style. The learning style analysis unit analyzes the user's past learning data and performance data to identify the user's learning style and preferences. For example, if the user prefers visual information, it may make suggestions for visually expressing the content of the discussion. Alternatively, if the user prefers auditory information, it may prioritize audio explanations. Furthermore, the learning style analysis unit may adjust the way the discussion proceeds based on the user's learning style. For example, if the user prefers a practical approach, it may suggest discussions using actual cases or simulations. This enables the facilitation system to support more effective discussion proceedings by taking the user's learning style into consideration.

[0061] The facilitation system can further include a cultural background consideration unit that takes into account the user's cultural background. The cultural background consideration unit adjusts the way the discussion proceeds based on the user's place of origin and cultural background. For example, if users have different cultural backgrounds, it can suggest a way to proceed with the discussion that takes that culture into consideration. The cultural background consideration unit can also select an appropriate communication style based on the user's cultural background. For example, if a user prefers direct communication, it can suggest a clear and frank discussion. Furthermore, the cultural background consideration unit can select the content and topic of the discussion based on the user's cultural background. This enables the facilitation system to support more appropriate discussion proceedings by taking into account the user's cultural background.

[0062] The facilitation system may further include a discussion participation history analysis unit that analyzes a user's past discussion participation history. The discussion participation history analysis unit analyzes data on discussions in which the user has participated in the past to identify the user's discussion participation tendencies and patterns. For example, it can analyze what topics the user has actively participated in in the past and what opinions they have held. As a result, the discussion participation history analysis unit can make suggestions to support the progress of the discussion based on the user's discussion participation tendencies. For example, if a user has had strong opinions on a particular topic in the past, the user's opinions can be actively incorporated when a discussion related to that topic takes place. Furthermore, if a user has behaved in a way that hinders the progress of the discussion in the past, advice can be provided to avoid such behavior. Furthermore, the discussion participation history analysis unit can make suggestions to smooth the progress of the discussion based on the user's discussion participation patterns. As a result, the facilitation system can utilize the user's past discussion participation history to support the progress of the discussion more effectively.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit accepts input of the agenda. The agenda includes the meeting topic, issues, and subjects of discussion. The reception unit allows users to input the agenda in text format. It can also accept the agenda via voice input, which is converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the agenda received by the reception unit. The analysis includes data analysis, text analysis, and sentiment analysis. The analysis unit analyzes the content of the agenda using text analysis technology and extracts important keywords and phrases. It can also use sentiment analysis technology to analyze the emotional elements contained in the agenda. Step 3: The proposal section organizes opinions based on the agenda analyzed by the analysis section and proposes solutions. Proposals include consensus building, presenting compromises, and facilitation techniques. The proposal section organizes opinions among participants based on the agenda and proposes optimal solutions. It can also refer to past discussion data and make proposals based on successful solutions and unsuccessful approaches. Step 4: The Revision Department revises the discussion based on the solutions proposed by the Proposal Department. Revisions can include changing the direction of the discussion, providing additional information, or reevaluating opinions. The Revision Department analyzes the discussion in real time and revises the direction of the discussion as necessary. It can also monitor the progress of the discussion and intervene at the appropriate time.

[0065] (Example 2) A facilitation system according to an embodiment of the present invention uses AI to smoothly resolve disagreements during discussions and meetings. This system accepts input of an agenda, and AI facilitates the discussion based on the agenda and makes suggestions for resolving disagreements between participants. This addresses the limited resources of skilled facilitators and provides cost-effective and efficient solutions. For example, participants in a discussion or meeting input the agenda. They simply enter the details and purpose of the agenda. For example, they input an agenda such as "discuss the project agenda." This information is then input into AI. The AI ​​then analyzes the input agenda and facilitates the discussion. The AI ​​makes suggestions for resolving disagreements between participants based on the agenda. For example, if participants have different opinions on a project agenda, the AI ​​organizes their opinions and proposes optimal solutions. This allows participants to efficiently advance the discussion. Furthermore, during the facilitation process, the AI ​​analyzes participants' opinions in real time and corrects the direction of the discussion as necessary. For example, if a discussion reaches a deadlock or a particular opinion is overemphasized, AI can intervene at the appropriate time and provide advice to smooth the discussion. This mechanism addresses the limited resources of skilled facilitators and provides cost-effective and efficient solutions. For example, AI can facilitate corporate meetings or project team discussions, saving time and costs while leading to better conclusions. AI can also learn from data from past discussions to improve the accuracy of its facilitation. For example, it can analyze successful and unsuccessful solutions in past discussions and apply this knowledge to the next discussion. This allows AI to continuously evolve and provide more effective facilitation. This allows the facilitation system to smoothly resolve differences in discussions and meetings and support efficient discussion progress.

[0066] A facilitation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a correction unit. The reception unit accepts input of an agenda. The agenda may include, but is not limited to, a meeting topic, an issue, or a subject of discussion. For example, the reception unit allows a user to input the agenda in text format. The reception unit can also accept the agenda using voice input. For example, the user may input the agenda through voice and convert it into text data using voice recognition technology. The analysis unit analyzes the agenda accepted by the reception unit. Examples of analysis include, but are not limited to, data analysis, text analysis, and sentiment analysis. For example, the analysis unit may analyze the content of the agenda using text analysis technology to extract important keywords and phrases. The analysis unit can also analyze emotional elements included in the agenda using sentiment analysis technology. The proposal unit organizes opinions based on the agenda analyzed by the analysis unit and proposes solutions. Examples of proposals include, but are not limited to, consensus building, compromise proposals, and facilitation techniques. The proposal unit, for example, organizes opinions among participants based on the agenda and proposes an optimal solution. The proposal unit can also refer to past discussion data and make proposals based on successful solutions and unsuccessful approaches. The correction unit corrects the discussion based on the solution proposed by the proposal unit. Corrections include, for example, changing the direction of the discussion, providing additional information, reevaluating opinions, etc., but are not limited to these examples. The correction unit, for example, analyzes the discussion in real time and corrects the direction of the discussion as necessary. The correction unit can also monitor the progress of the discussion and intervene at an appropriate time. As a result, the facilitation system according to the embodiment enables efficient discussion progress by consistently performing processes from inputting the agenda to analysis, proposals, and corrections.

[0067] The facilitation system further includes a learning unit that learns discussion data. The learning unit learns past discussion data. Examples of discussion data include, but are not limited to, meeting minutes, chat logs, and audio data. For example, the learning unit analyzes past meeting minutes using text analysis technology to extract important keywords and phrases. The learning unit can also analyze audio data using audio analysis technology to convert the content of discussions into text data. Furthermore, the learning unit can develop algorithms to improve the accuracy of facilitation based on past discussion data. For example, the learning unit can analyze successful solutions and unsuccessful approaches in past discussions and apply them to the next discussion. This enables the learning unit to continuously evolve and provide more effective facilitation. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input past discussion data into a generation AI and cause the generation AI to develop an algorithm to improve the accuracy of facilitation.

[0068] The facilitation system further includes a monitoring unit that monitors the discussion. The monitoring unit monitors the progress of the discussion. Examples of monitoring include, but are not limited to, real-time monitoring, log analysis, and sentiment analysis. For example, the monitoring unit monitors the progress of the discussion in real time and intervenes when the discussion reaches an impasse or when a particular opinion is overemphasized. The monitoring unit can also analyze the discussion log to grasp the progress of the discussion. Furthermore, the monitoring unit can use sentiment analysis technology to analyze the emotions of users participating in the discussion and identify factors that affect the progress of the discussion. This enables the monitoring unit to intervene at an appropriate time and provide advice to facilitate the progress of the discussion. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input discussion data acquired in real time into a generation AI to analyze the progress of the discussion.

[0069] The facilitation system further includes a recording unit that records the discussion. The recording unit records the results of the discussion. Recordings include, but are not limited to, text recording, audio recording, and video recording. For example, the recording unit records the content of the discussion in text format, allowing for later reference and analysis. The recording unit can also record the audio of the discussion and save it as audio data. Furthermore, the recording unit can record a video of the discussion and save it as video data. This allows the recording unit to record the results of the discussion in detail, making them useful for later reference and analysis. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the audio data of the discussion to a generation AI and have the generation AI convert the audio data into text data.

[0070] The analysis unit can make proposals for resolving disagreements between participants based on the agenda. For example, the analysis unit makes proposals for resolving differences of opinion between participants based on the agenda. Proposals include, but are not limited to, consensus building, compromise proposals, and facilitation techniques. For example, the analysis unit organizes opinions between participants based on the agenda and proposes an optimal solution. The analysis unit can also refer to past discussion data and make proposals based on successful and unsuccessful approaches. This allows the analysis unit to make proposals for resolving differences of opinion between participants, thereby improving the efficiency of discussions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can cause a generation AI to execute proposals for resolving disagreements between participants based on the agenda.

[0071] The correction unit can analyze the discussion and correct the direction of the discussion as necessary. The correction unit can, for example, analyze the discussion in real time and correct the direction of the discussion as necessary. Corrections include, but are not limited to, changing the focus of the discussion, introducing a new perspective, and managing the progress of the discussion. For example, the correction unit can provide a new perspective and restart the discussion if the discussion has reached an impasse. The correction unit can also propose corrections to balance the discussion if it has become overly one-sided. Furthermore, the correction unit can propose appropriate corrections according to the progress of the discussion while the discussion is in progress. In this way, the correction unit can correct the discussion in real time, thereby smoothing the progress of the discussion. Some or all of the above-mentioned processing in the correction unit can be performed using, for example, AI, or can be performed without AI. For example, the correction unit can input discussion data acquired in real time into the generation AI and cause the generation AI to execute suggestions to correct the direction of the discussion.

[0072] The reception unit can estimate the user's emotions and adjust the timing of inputting the agenda. The reception unit, for example, estimates the user's emotions and adjusts the timing of inputting the agenda. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit delays inputting the agenda to create a relaxing environment. Furthermore, if the user is relaxed, the reception unit can prompt the user to input the agenda immediately to start a smooth discussion. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input the agenda quickly to start an efficient discussion. In this way, the reception unit can adjust the timing of inputting the agenda according to the user's emotions, thereby allowing the agenda to be input at a more appropriate time. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data into a generation AI and cause the generation AI to execute a suggestion for adjusting the timing of inputting the agenda.

[0073] The reception unit can analyze past agenda input history and select the optimal input method. The reception unit, for example, analyzes past agenda input history and selects the optimal input method. The agenda input history includes, for example, past agendas, input dates and times, and inputters, but is not limited to these examples. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also refer to the format of agendas previously input by the user and suggest input in a similar format. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period based on the user's past input history. In this way, the reception unit can provide the user with the optimal input method by analyzing the past agenda input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past agenda input history into a generation AI and cause the generation AI to execute a suggestion for selecting the optimal input method.

[0074] The reception unit may filter the agenda items based on the user's projects and areas of interest when the agenda items are input. For example, the reception unit may filter the agenda items based on the user's projects and areas of interest when the agenda items are input. Examples of projects and areas of interest include, but are not limited to, ongoing projects, fields of expertise, and topics of interest. For example, the reception unit may preferentially display agenda items related to the user's ongoing projects. The reception unit may also suggest related agenda items based on the user's areas of interest. Furthermore, the reception unit may suggest highly relevant agenda items by referring to the user's past project history. This allows the reception unit to preferentially input highly relevant agenda items by filtering based on the user's current projects and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input data on the user's projects and areas of interest to the generation AI and cause the generation AI to execute suggestions for filtering.

[0075] The reception unit can estimate the user's emotions and determine the priority of the agenda items. The reception unit, for example, estimates the user's emotions and determines the priority of the agenda items. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is feeling stressed, the reception unit postpones less important agenda items. Furthermore, when the user is relaxed, the reception unit can prioritize input of more important agenda items. Furthermore, when the user is in a hurry, the reception unit can prioritize input of more urgent agenda items. In this way, the reception unit can prioritize the agenda items according to the user's emotions, thereby enabling more appropriate agenda items to be input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data into a generation AI and cause the generation AI to execute a proposal for determining the priority of the agenda items.

[0076] The reception unit can prioritize inputting highly relevant agenda items by taking into account the user's geographical location information when inputting agenda items. For example, the reception unit prioritizes inputting highly relevant agenda items by taking into account the user's geographical location information when inputting agenda items. Geographical location information includes, but is not limited to, GPS data, address information, location identification technology, etc. For example, if the user is in a specific area, the reception unit can prioritize inputting agenda items related to that area. Furthermore, if the user is on a business trip, the reception unit can prioritize inputting agenda items related to the business trip destination. Furthermore, if the user is at home, the reception unit can prioritize inputting agenda items related to the user's home. In this way, the reception unit can prioritize inputting highly relevant agenda items by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute a suggestion to prioritize inputting highly relevant agenda items.

[0077] The reception unit can analyze the user's social media activity when inputting an agenda and input related agenda items. For example, the reception unit can analyze the user's social media activity when inputting an agenda and input related agenda items. Social media activity includes, but is not limited to, post content, the number of likes, and the number of followers. For example, the reception unit can suggest topics that the user frequently mentions on social media as agenda items. The reception unit can also analyze the user's social media activity history and suggest related agenda items. Furthermore, the reception unit can suggest agenda items based on topics of accounts the user follows on social media. In this way, the reception unit can input related agenda items by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media activity data to a generation AI and cause the generation AI to suggest inputting related agenda items.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user's emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions, thereby providing a more appropriate analysis result. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's emotion data into a generation AI and cause the generation AI to execute a suggestion for adjusting the presentation method of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the agenda item during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the agenda item during analysis. The importance of an agenda item includes, but is not limited to, the scope of impact, urgency, and number of stakeholders. For example, the analysis unit performs a detailed analysis of an agenda item with high importance. The analysis unit can also perform a concise analysis of an agenda item with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the agenda item, thereby enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input agenda item importance data to the generation AI and cause the generation AI to execute a proposal for adjusting the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the topic during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the topic during analysis. Topic categories include, but are not limited to, technical issues, business strategies, and management policies. For example, the analysis unit applies a specialized analysis algorithm for technology to technical topics. The analysis unit can also apply a specialized analysis algorithm for economic topics to economic topics. Furthermore, the analysis unit can apply a specialized analysis algorithm for social science topics to social topics. This allows the analysis unit to apply different analysis algorithms depending on the category of the topic, enabling more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input topic category data into the generation AI and cause the generation AI to execute a proposal for applying a different analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. This allows the analysis unit to adjust the length of the analysis according to the user's emotions, thereby providing a more appropriate analysis result. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to make a suggestion for adjusting the length of the analysis.

[0082] The analysis unit can determine the analysis priority based on the submission time of the agenda items during analysis. For example, the analysis unit determines the analysis priority based on the submission time of the agenda items during analysis. The submission time of the agenda items includes, but is not limited to, the submission date and time, the submission frequency, and the priority of the submitter. For example, the analysis unit prioritizes the analysis of recently submitted agenda items. The analysis unit can also lower the priority of older submitted agenda items. Furthermore, the analysis unit can adjust the order of analysis based on the submission time. This enables efficient analysis by the analysis unit determining the analysis priority based on the submission time of the agenda items. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the submission time of the agenda items into the generation AI and cause the generation AI to execute a proposal for determining the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the agenda items during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the agenda items during analysis. The relevance of the agenda items includes, but is not limited to, for example, similarity of topics, commonality of stakeholders, and scope of influence. For example, the analysis unit prioritizes analysis of highly relevant agenda items. The analysis unit can also lower the priority of less relevant agenda items. Furthermore, the analysis unit can adjust the order of analysis based on the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the agenda items, thereby enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input agenda relevance data into the generation AI and cause the generation AI to execute a proposal for adjusting the order of analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the way the suggestions are expressed based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise suggestion that focuses on the main points. This allows the suggestion unit to adjust the way the suggestions are expressed based on the user's emotions, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's emotion data into a generation AI and cause the generation AI to execute a suggestion to adjust the way the suggestions are expressed.

[0085] The proposal unit may adjust the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit may adjust the level of detail of the proposal based on the importance of the solution when making the proposal. The importance of the solution may include, but is not limited to, the scope of impact, feasibility, and cost-effectiveness. For example, the proposal unit may provide a detailed proposal for a solution with high importance. The proposal unit may also provide a concise proposal for a solution with low importance. Furthermore, the proposal unit may adjust the depth of the proposal according to the importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the solution, thereby enabling a more appropriate proposal. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit may input solution importance data into the generation AI and cause the generation AI to execute a proposal to adjust the level of detail of the proposal.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of the topic when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the topic when making a suggestion. Topic categories include, but are not limited to, technical issues, business strategies, and management policies. For example, the suggestion unit applies a technology-specialized suggestion algorithm to technical topics. The suggestion unit can also apply an economics-specialized suggestion algorithm to economic topics. Furthermore, the suggestion unit can apply a social science-specialized suggestion algorithm to social topics. This enables the suggestion unit to apply different suggestion algorithms depending on the category of the topic, thereby enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input topic category data into a generation AI and cause the generation AI to execute suggestions for applying different suggestion algorithms.

[0087] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the suggestion unit can provide a short and concise suggestion when the user is in a hurry. The suggestion unit can also provide a detailed suggestion when the user is relaxed. Furthermore, the suggestion unit can provide a visually stimulating suggestion when the user is excited. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's emotion data into a generation AI and cause the generation AI to execute a suggestion to adjust the length of the suggestion.

[0088] The suggestion unit may determine the priority of proposals based on the time of submission of the solutions at the time of proposal. For example, the suggestion unit may determine the priority of proposals based on the time of submission of the solutions at the time of proposal. The time of submission of solutions may include, but is not limited to, the submission date and time, the frequency of submission, and the priority of the submitter. For example, the suggestion unit may prioritize recently submitted solutions. The suggestion unit may also lower the priority of older submitted solutions. Furthermore, the suggestion unit may adjust the order of proposals based on the time of submission. This allows the suggestion unit to prioritize proposals based on the time of submission of the solutions, thereby enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input solution submission time data into the generation AI and cause the generation AI to execute a proposal to determine the priority of the proposals.

[0089] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the suggestion unit adjusts the order of proposals based on the relevance of the solutions when making a proposal. The relevance of the solutions includes, but is not limited to, for example, topic similarity, commonality of stakeholders, and scope of impact. For example, the suggestion unit prioritizes proposing highly relevant solutions. The suggestion unit can also lower the priority of less relevant solutions. Furthermore, the suggestion unit can adjust the order of proposals according to the relevance. This enables the suggestion unit to adjust the order of proposals based on the relevance of the solutions, thereby enabling efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input solution relevance data into a generation AI and cause the generation AI to execute a suggestion to adjust the order of proposals.

[0090] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated user emotions. The correction unit, for example, estimates the user's emotions and adjusts the correction method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the correction unit can provide a simple, highly visible correction method. Furthermore, if the user is relaxed, the correction unit can provide a detailed correction method. Furthermore, if the user is in a hurry, the correction unit can provide a concise correction method that focuses on the main points. This allows the correction unit to adjust the correction method according to the user's emotions, enabling more appropriate correction. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the user's emotion data into the generation AI and cause the generation AI to execute a suggestion for adjusting the correction method.

[0091] The correction unit can analyze the progress of the discussion and select the optimal correction method during correction. For example, the correction unit analyzes the progress of the discussion and selects the optimal correction method during correction. The progress of the discussion includes, but is not limited to, the speed at which the discussion is progressing, the frequency of participants' comments, and the depth of the discussion. For example, if the discussion reaches an impasse, the correction unit can provide a new perspective to restart the discussion. If the discussion becomes overly one-sided, the correction unit can also propose a correction to balance the discussion. Furthermore, if the discussion is ongoing, the correction unit can also propose an appropriate correction according to the progress. This enables efficient correction by analyzing the progress of the discussion and selecting the optimal correction method. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input discussion progress data into the generation AI and cause the generation AI to execute a proposal to select the optimal correction method.

[0092] The correction unit can apply different correction algorithms depending on the category of the discussion during correction. For example, the correction unit applies different correction algorithms depending on the category of the discussion during correction. Discussion categories include, but are not limited to, technical issues, business strategies, and management policies. For example, the correction unit applies a technical correction algorithm to technical discussions. The correction unit can also apply an economics correction algorithm to economic discussions. Furthermore, the correction unit can apply a social science correction algorithm to social discussions. This allows the correction unit to apply different correction algorithms depending on the category of the discussion, enabling more appropriate correction. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input discussion category data to the generation AI and cause the generation AI to execute a proposal for applying a different correction algorithm.

[0093] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. The correction unit, for example, estimates the user's emotions and determines the priority of corrections based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the correction unit postpones corrections of lower importance. Furthermore, when the user is relaxed, the correction unit can prioritize corrections of higher importance. Furthermore, when the user is in a hurry, the correction unit can prioritize corrections of higher urgency. In this way, the correction unit can determine the priority of corrections according to the user's emotions, thereby enabling more appropriate corrections. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the user's emotion data into the generation AI and cause the generation AI to execute suggestions for determining the priority of corrections.

[0094] The correction unit can select the optimal correction method by taking into account the geographic distribution of the discussion during correction. For example, the correction unit selects the optimal correction method by taking into account the geographic distribution of the discussion during correction. Geographic distribution includes, but is not limited to, the locations of participants, the region covered by the discussion, and the geographical scope of influence. For example, if the discussion is concentrated in a specific region, the correction unit prioritizes corrections related to that region. Furthermore, if the discussion is distributed across multiple regions, the correction unit can also make corrections by taking into account the characteristics of each region. Furthermore, if the discussion is international, the correction unit can make corrections by taking into account the culture and regulations of each country. This enables efficient correction by selecting the optimal correction method by taking into account the geographic distribution of the discussion. Some or all of the above-described processing by the correction unit may be performed using, for example, AI, or without AI. For example, the correction unit can input geographic distribution data of the discussion into the generation AI and have the generation AI execute a proposal to select the optimal correction method.

[0095] The correction unit can improve the accuracy of the correction by referring to literature related to the discussion during the correction. For example, the correction unit can improve the accuracy of the correction by referring to literature related to the discussion during the correction. Examples of related literature include, but are not limited to, academic papers, technical reports, and industry white papers. For example, the correction unit can make the correction by referring to the latest research papers related to the discussion. The correction unit can also make the correction by referring to past cases related to the discussion. Furthermore, the correction unit can make the correction by referring to specialized books related to the discussion. Thus, the correction unit can improve the accuracy of the correction by referring to literature related to the discussion. Some or all of the above-described processing in the correction unit can be performed using, for example, AI, or without AI. For example, the correction unit can input literature data related to the discussion into the generation AI and cause the generation AI to make suggestions for improving the accuracy of the correction.

[0096] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the learning unit selects detailed training data when the user is relaxed. Furthermore, the learning unit can select training data that focuses on the key points when the user is in a hurry. Furthermore, the learning unit can select visually stimulating training data when the user is excited. This allows the learning unit to select training data according to the user's emotions, enabling more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the user's emotional data into a generation AI and cause the generation AI to make suggestions for selecting training data.

[0097] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Examples of learning data include, but are not limited to, past discussion data, successful solutions, and unsuccessful approaches. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also analyze past learning data and adjust parameters of the learning algorithm. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. Thus, the learning unit improves the accuracy of the learning algorithm by referring to the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to execute suggestions for optimizing the learning algorithm.

[0098] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. This allows the learning unit to adjust the frequency of learning according to the user's emotions, enabling more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to execute suggestions for adjusting the frequency of learning.

[0099] The learning unit can weight the learning data based on the submission time of the discussion during learning. For example, the learning unit weights the learning data based on the submission time of the discussion during learning. The submission time of the discussion includes, but is not limited to, for example, the submission date and time, the submission frequency, and the priority of the submitter. For example, the learning unit weights recent discussion data more highly during learning. The learning unit can also weight older discussion data less highly during learning. Furthermore, the learning unit can adjust the weight of the learning data depending on the submission time. This enables efficient learning by weighting the learning data based on the submission time of the discussion. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input discussion submission time data into the generation AI and cause the generation AI to execute a proposal for weighting the learning data.

[0100] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the monitoring method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible monitoring method. Furthermore, if the user is relaxed, the monitoring unit can provide a detailed monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise monitoring method that focuses on the main points. This allows the monitoring unit to adjust the monitoring method according to the user's emotions, enabling more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input the user's emotion data into a generation AI and cause the generation AI to execute a suggestion for adjusting the monitoring method.

[0101] The monitoring unit can analyze the progress of the discussion during monitoring and select the optimal monitoring method. For example, the monitoring unit can analyze the progress of the discussion during monitoring and select the optimal monitoring method. The progress of the discussion includes, but is not limited to, the speed at which the discussion is progressing, the frequency of participants' comments, and the depth of the discussion. For example, if the discussion reaches an impasse, the monitoring unit can provide a new perspective to restart the discussion. If the discussion becomes overly one-sided, the monitoring unit can also perform monitoring to balance the discussion. Furthermore, if the discussion is ongoing, the monitoring unit can perform appropriate monitoring according to the progress of the discussion. This enables efficient monitoring by analyzing the progress of the discussion and selecting the optimal monitoring method. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input discussion progress data into the generation AI and cause the generation AI to execute a proposal for selecting the optimal monitoring method.

[0102] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and determines monitoring priorities based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the monitoring unit postpones monitoring of less important items. Furthermore, when the user is relaxed, the monitoring unit can prioritize monitoring of more important items. Furthermore, when the user is in a hurry, the monitoring unit can prioritize monitoring of more urgent items. This allows the monitoring unit to determine monitoring priorities based on the user's emotions, enabling more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit may input the user's emotion data into a generation AI and cause the generation AI to execute suggestions for determining monitoring priorities.

[0103] The monitoring unit can select the optimal monitoring method by taking into account the geographic distribution of the discussion during monitoring. For example, the monitoring unit selects the optimal monitoring method by taking into account the geographic distribution of the discussion during monitoring. Geographic distribution includes, but is not limited to, the locations of participants, the region the discussion is focused on, and the geographical scope of influence. For example, if the discussion is concentrated in a specific region, the monitoring unit prioritizes monitoring related to that region. Furthermore, if the discussion is distributed across multiple regions, the monitoring unit can also perform monitoring by taking into account the characteristics of each region. Furthermore, if the discussion is international, the monitoring unit can also perform monitoring by taking into account the cultures and regulations of each country. This enables efficient monitoring by the monitoring unit selecting the optimal monitoring method by taking into account the geographic distribution of the discussion. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input geographic distribution data of the discussion into a generation AI and have the generation AI execute a proposal to select the optimal monitoring method.

[0104] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated user emotions. The recording unit, for example, estimates the user's emotions and adjusts the recording method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is nervous, the recording unit provides a simple, highly visible recording method. Furthermore, when the user is relaxed, the recording unit can provide a detailed recording method. Furthermore, when the user is in a hurry, the recording unit can provide a concise recording method that focuses on the main points. This allows the recording unit to adjust the recording method according to the user's emotions, enabling more appropriate recording. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's emotion data into a generation AI and cause the generation AI to execute a suggestion for adjusting the recording method.

[0105] The recording unit can analyze the progress of the discussion and select the optimal recording method when recording. For example, the recording unit can analyze the progress of the discussion and select the optimal recording method when recording. The progress of the discussion can include, but is not limited to, the speed at which the discussion is progressing, the frequency of participants' comments, and the depth of the discussion. For example, if the discussion reaches an impasse, the recording unit can provide a new perspective and record. If the discussion becomes overly one-sided, the recording unit can also record to balance the discussion. Furthermore, if the discussion is ongoing, the recording unit can make appropriate recordings according to the progress of the discussion. This allows the recording unit to analyze the progress of the discussion and select the optimal recording method, enabling efficient recording. Some or all of the above-described processing in the recording unit can be performed using, or without, AI. For example, the recording unit can input discussion progress data into the generation AI and cause the generation AI to execute a suggestion for selecting the optimal recording method.

[0106] The recording unit can estimate the user's emotions and determine the recording priority based on the estimated user emotions. The recording unit, for example, estimates the user's emotions and determines the recording priority based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the recording unit postpones recording of less important items. Furthermore, when the user is relaxed, the recording unit can prioritize recording of more important items. Furthermore, when the user is in a hurry, the recording unit can prioritize recording of more urgent items. This allows the recording unit to determine the recording priority based on the user's emotions, enabling more appropriate recording. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's emotion data into a generation AI and cause the generation AI to execute suggestions for determining the recording priority.

[0107] The recording unit can select the optimal recording method by taking into account the geographic distribution of the discussion when recording. For example, the recording unit selects the optimal recording method by taking into account the geographic distribution of the discussion when recording. Geographic distribution includes, but is not limited to, the locations of participants, the region of the discussion, and the geographical scope of influence. For example, if the discussion is concentrated in a specific region, the recording unit prioritizes recording related to that region. Furthermore, if the discussion is distributed across multiple regions, the recording unit can also record while taking into account the characteristics of each region. Furthermore, if the discussion is international, the recording unit can record while taking into account the cultures and regulations of each country. This allows the recording unit to select the optimal recording method by taking into account the geographic distribution of the discussion, enabling efficient recording. Some or all of the above-described processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input geographic distribution data of the discussion into the generation AI and have the generation AI execute a proposal to select the optimal recording method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, correction unit, learning unit, monitoring unit, and recording unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows a user to input an agenda. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the content of the agenda. The proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a solution. The correction unit is realized by the control unit 46A of the smart device 14, and corrects the direction of the discussion. The learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns past discussion data. The monitoring unit is realized by the control unit 46A of the smart device 14, and monitors the progress of the discussion. The recording unit is realized by the specific processing unit 290 of the data processing device 12, and records the results of the discussion. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, correction unit, learning unit, monitoring unit, and recording unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to input an agenda. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the content of the agenda. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a solution. The correction unit is realized, for example, by the control unit 46A of the smart glasses 214, and corrects the direction of the discussion. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns past discussion data. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the progress of the discussion. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the results of the discussion. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, correction unit, learning unit, monitoring unit, and recording unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows a user to input an agenda. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the content of the agenda. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a solution. The correction unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and corrects the direction of the discussion. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns past discussion data. The monitoring unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and monitors the progress of the discussion. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the results of the discussion. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, correction unit, learning unit, monitoring unit, and recording unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to input an agenda. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the content of the agenda. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a solution. The correction unit is realized, for example, by the control unit 46A of the robot 414, and corrects the direction of the discussion. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns past discussion data. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and monitors the progress of the discussion. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the results of the discussion.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The facilitation system may further include a statement history analysis unit that analyzes a user's past statement history. The statement history analysis unit analyzes past statement data to extract the user's statement tendencies and patterns. For example, it can analyze what topics the user has actively spoken about in the past and what opinions they have held. This allows the statement history analysis unit to make suggestions to support the progress of the discussion based on the user's statement tendencies. For example, if a user has had strong opinions about a particular topic in the past, the system can actively incorporate the user's opinions when a discussion related to that topic takes place. Furthermore, if a user has made statements in the past that disrupt the progress of the discussion, the system can provide advice to avoid such statements. Furthermore, the statement history analysis unit can make suggestions to smooth the progress of the discussion based on the user's statement patterns. This allows the facilitation system to utilize the user's past statement history to support the progress of the discussion more effectively.

[0110] The facilitation system can further include a schedule acquisition unit that acquires the user's schedule information. The schedule acquisition unit acquires information from the user's calendar or schedule management app and optimizes the discussion schedule. For example, the schedule acquisition unit can schedule discussions to avoid busy times for the user. The schedule acquisition unit can also determine the priority of discussions based on the user's schedule. For example, if the user has an important meeting or event coming up, the schedule acquisition unit can prioritize important discussions before that. The schedule acquisition unit can also adjust the pace of the discussion based on the user's schedule. This enables the facilitation system to utilize the user's schedule information to support efficient discussion progress.

[0111] The facilitation system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires health data, such as the user's heart rate, blood pressure, and stress level, in real time and identifies factors that affect the progress of the discussion. For example, if the user shows a high stress level, the health monitoring unit may temporarily halt the discussion and provide time for the user to relax. The health monitoring unit may also adjust the speed and content of the discussion based on the user's health condition. For example, if the user is tired, the health monitoring unit may slow down the discussion and make the content concise. Furthermore, the health monitoring unit may analyze the user's health data and provide advice for long-term health management. This enables the facilitation system to support more appropriate discussion progress by taking the user's health condition into consideration.

[0112] The facilitation system may further include a learning style analysis unit that analyzes the user's learning style. The learning style analysis unit analyzes the user's past learning data and performance data to identify the user's learning style and preferences. For example, if the user prefers visual information, it may make suggestions for visually expressing the content of the discussion. Alternatively, if the user prefers auditory information, it may prioritize audio explanations. Furthermore, the learning style analysis unit may adjust the way the discussion proceeds based on the user's learning style. For example, if the user prefers a practical approach, it may suggest discussions using actual cases or simulations. This enables the facilitation system to support more effective discussion proceedings by taking the user's learning style into consideration.

[0113] The facilitation system can further include a cultural background consideration unit that takes into account the user's cultural background. The cultural background consideration unit adjusts the way the discussion proceeds based on the user's place of origin and cultural background. For example, if users have different cultural backgrounds, it can suggest a way to proceed with the discussion that takes that culture into consideration. The cultural background consideration unit can also select an appropriate communication style based on the user's cultural background. For example, if a user prefers direct communication, it can suggest a clear and frank discussion. Furthermore, the cultural background consideration unit can select the content and topic of the discussion based on the user's cultural background. This enables the facilitation system to support more appropriate discussion proceedings by taking into account the user's cultural background.

[0114] The facilitation system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the progress of the discussion based on the estimated emotions. The emotion adjustment unit analyzes the user's facial expressions, voice, and text data to estimate emotions in real time. For example, if the user is feeling anxious, the emotion adjustment unit may temporarily halt the discussion and provide time for the user to relax. Furthermore, if the user is excited, the emotion adjustment unit may make suggestions to accelerate the discussion and utilize energy. Furthermore, the emotion adjustment unit may adjust the content and topic of the discussion based on the user's emotions. This enables the facilitation system to take the user's emotions into consideration and support more appropriate discussion progress.

[0115] The facilitation system may further include a discussion participation history analysis unit that analyzes a user's past discussion participation history. The discussion participation history analysis unit analyzes data on discussions in which the user has participated in the past to identify the user's discussion participation tendencies and patterns. For example, it can analyze what topics the user has actively participated in in the past and what opinions they have held. As a result, the discussion participation history analysis unit can make suggestions to support the progress of the discussion based on the user's discussion participation tendencies. For example, if a user has had strong opinions on a particular topic in the past, the user's opinions can be actively incorporated when a discussion related to that topic takes place. Furthermore, if a user has behaved in a way that hinders the progress of the discussion in the past, advice can be provided to avoid such behavior. Furthermore, the discussion participation history analysis unit can make suggestions to smooth the progress of the discussion based on the user's discussion participation patterns. As a result, the facilitation system can utilize the user's past discussion participation history to support the progress of the discussion more effectively.

[0116] The facilitation system may further include an emotional content adjustment unit that estimates the user's emotions and adjusts the content of the discussion based on the estimated emotions. The emotional content adjustment unit analyzes the user's facial expressions, voice, and text data to estimate emotions in real time. For example, if the user is feeling stressed, the content of the discussion can be lightened and changed to a relaxing topic. The emotional content adjustment unit can also suggest content for deeper discussion if the user is relaxed. Furthermore, if the user is excited, the emotional content adjustment unit can suggest a stimulating topic to utilize the user's energy. This enables the facilitation system to provide more appropriate discussion content by taking the user's emotions into consideration.

[0117] The facilitation system may further include an emotion progression speed adjustment unit that estimates the user's emotions and adjusts the speed of the discussion based on the estimated emotions. The emotion progression speed adjustment unit analyzes the user's facial expressions, voice, and text data to estimate emotions in real time. For example, if the user is nervous, the emotion progression speed adjustment unit can slow down the speed of the discussion to allow the user to relax. Furthermore, if the user is relaxed, the emotion progression speed adjustment unit can also speed up the speed of the discussion to enable an efficient discussion. Furthermore, if the user is excited, the emotion progression speed adjustment unit can adjust the speed of the discussion and make suggestions to utilize energy. This enables the facilitation system to provide a more appropriate speed of the discussion by taking the user's emotions into consideration.

[0118] The facilitation system may further include an emotion format adjustment unit that estimates the user's emotion and adjusts the discussion format based on the estimated emotion. The emotion format adjustment unit analyzes the user's facial expressions, voice, and text data to estimate the emotion in real time. For example, if the user is nervous, it may provide a simple, highly visible format. If the user is relaxed, the emotion format adjustment unit may also provide a detailed format. If the user is excited, the emotion format adjustment unit may also provide a visually stimulating format. This enables the facilitation system to provide a more appropriate discussion format by taking the user's emotion into consideration.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit accepts input of the agenda. The agenda includes the meeting topic, issues, and subjects of discussion. The reception unit allows users to input the agenda in text format. It can also accept the agenda via voice input, which is converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the agenda received by the reception unit. The analysis includes data analysis, text analysis, and sentiment analysis. The analysis unit analyzes the content of the agenda using text analysis technology and extracts important keywords and phrases. It can also use sentiment analysis technology to analyze the emotional elements contained in the agenda. Step 3: The proposal section organizes opinions based on the agenda analyzed by the analysis section and proposes solutions. Proposals include consensus building, presenting compromises, and facilitation techniques. The proposal section organizes opinions among participants based on the agenda and proposes optimal solutions. It can also refer to past discussion data and make proposals based on successful solutions and unsuccessful approaches. Step 4: The Revision Department revises the discussion based on the solutions proposed by the Proposal Department. Revisions can include changing the direction of the discussion, providing additional information, or reevaluating opinions. The Revision Department analyzes the discussion in real time and revises the direction of the discussion as necessary. It can also monitor the progress of the discussion and intervene at the appropriate time.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] 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.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving input of an agenda; an analysis unit that analyzes the agenda accepted by the acceptance unit; a proposal unit that organizes opinions and proposes solutions based on the agenda analyzed by the analysis unit; a correction unit that corrects the argument based on the solution proposed by the proposal unit; Equipped with system.

2. Further provided is a learning unit that learns the discussion data. The system of claim 1 .

3. Further establishing a monitoring department to monitor discussions The system of claim 1 .

4. Further provided is a recording unit for recording the discussion. The system of claim 1 .

5. The analysis unit Make proposals to resolve disagreements among participants based on the agenda The system of claim 1 .

6. The correction unit Analyze the discussion and steer it in the right direction if necessary The system of claim 1 .

7. The reception unit Estimate user emotions and adjust the timing of agenda entry The system of claim 1 .

8. The reception unit Analyze agenda entry history and select the optimal entry method The system of claim 1 .

9. The reception unit Filtering based on user projects and interests as you enter your agenda The system of claim 1 .

Images