system

Abstract

The system according to this embodiment aims to efficiently collect and analyze management information from each group company and to facilitate appropriate discussions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a discussion unit, and an suggestion unit. The collection unit collects management information of each group company. The analysis unit analyzes the management information collected by the collection unit. The provision unit displays the information analyzed by the analysis unit on a dashboard. The discussion unit conducts a discussion based on the information displayed by the provision unit. The suggestion unit summarizes the minutes of the discussion conducted by the discussion unit and provides suggestions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently collect and analyze the management information of each company in a group and conduct an appropriate discussion.

[0005] The system according to the embodiment aims to efficiently collect and analyze the management information of each company in a group and conduct an appropriate discussion.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a discussion unit, and an insight unit. The collection unit collects management information from each group company. The analysis unit analyzes the management information collected by the collection unit. The provision unit displays the information analyzed by the analysis unit on a dashboard. The discussion unit conducts a discussion based on the information displayed by the provision unit. The insight unit summarizes the minutes of the discussion conducted by the discussion unit and provides insights. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze management information from each group company and facilitate appropriate discussions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business performance and synergy exploration solution system according to the embodiment of the present invention is a system that provides the function of analyzing and visualizing the management information of each group company. This system analyzes the management information of each group company and visually displays the results on a dashboard. This allows users to easily gain insights and confirm the creation of potential synergies. The system also has a function to facilitate internal discussions. In this function, the AI ​​understands the information in a consistent context such as KPIs and business strategies, and humans conduct discussions based on the visualized management information and the information organization performed by the AI. Furthermore, the AI ​​summarizes the meeting minutes and supports the generation of insights. This makes it possible to explore various patterns and combinations between companies and evaluate how they combine to affect the overall performance of the company. For example, the system first has the AI ​​collect and analyze the management information of each group company. Next, the analysis results are visually displayed on a dashboard. Users conduct discussions based on this information, and the AI ​​summarizes the meeting minutes and provides insights. Finally, the AI ​​explores patterns and combinations between companies and evaluates their impact on the overall performance of the company. Through this mechanism, users can easily grasp management information and confirm the possibility of creating synergies. Furthermore, by having AI organize information and summarize meeting minutes, discussions proceed more efficiently, contributing to improved overall corporate performance. This solution system, which enables business performance and synergy exploration, allows users to easily grasp management information and identify potential synergies. Additionally, by having AI organize information and summarize meeting minutes, discussions proceed more efficiently, contributing to improved overall corporate performance.

[0029] The business performance and synergy exploration solution system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a discussion unit, and an insights unit. The collection unit collects management information from each group company. The collection unit can collect management information by methods such as extraction from a database or collection using an API. The analysis unit analyzes the management information collected by the collection unit. The analysis unit can analyze the management information by applying statistical analysis or machine learning algorithms, for example. The provision unit displays the information analyzed by the analysis unit on a dashboard. The provision unit can visually display the analysis results on a dashboard with functions such as graph display and real-time updates, for example. The discussion unit conducts discussions based on the information displayed by the provision unit. The discussion unit can facilitate discussions by methods such as online meetings and the creation of meeting minutes, for example. The insights unit summarizes the minutes of the discussion conducted by the discussion unit and provides insights. The insights unit can provide insights such as improvement suggestions or risk warnings, for example. As a result, the business performance and synergy exploration solution system according to this embodiment can efficiently collect, analyze, display, discuss, and provide insights from the management information of each group company.

[0030] The data collection department collects management information from each company within the group. This information can be collected through various methods, such as database extraction and API-based collection. Specifically, it covers a wide range of management information, including financial data, sales data, inventory data, and personnel data for each company. Database extraction involves efficiently obtaining necessary data using SQL queries, while API-based collection involves integrating with each company's system to obtain data in real time. This allows the data collection department to quickly collect the latest management information and integrate it into the system's overall database. Furthermore, the data collection department implements data cleansing and normalization processes to ensure data quality. For example, it removes duplicate data, fills in missing data, and standardizes data formats to enable the analysis department to perform accurate analysis. The data collection department also prioritizes data security, implementing data encryption and access control. This allows the data collection department to provide highly reliable data and improve the overall system performance.

[0031] The analysis unit analyzes the management information collected by the data collection unit. For example, the analysis unit can analyze management information by applying statistical analysis and machine learning algorithms. Specifically, based on the collected data, it performs tasks such as sales trend analysis, cost optimization, and inventory management efficiency improvements. Statistical analysis uses regression analysis and analysis of variance to clarify data correlations and causal relationships. Machine learning algorithms are used to build clustering, classification, and predictive models to forecast future business performance. For example, sales data can be used to build seasonal sales forecast models, which can help optimize inventory management. Furthermore, the analysis unit can use anomaly detection algorithms to detect abnormal data patterns and identify risks early. This allows the analysis unit to analyze management information from multiple perspectives and contribute to improving business performance. In addition, the analysis unit utilizes data visualization technology to visually display analysis results, making them intuitively understandable to users. This enables the analysis unit to analyze management information efficiently and effectively, increasing the overall value of the system.

[0032] The service provider displays the information analyzed by the analysis provider on a dashboard. The service provider can visually display analysis results on a dashboard with features such as graph display and real-time updates. Specifically, it provides a variety of visualizations, such as line graphs showing sales trends, pie charts showing cost breakdowns, and bar graphs showing inventory status. This allows users to grasp analysis results at a glance and make quick decisions. Furthermore, the service provider offers dashboard customization features, allowing users to adjust the displayed content to their needs. For example, they can filter data for specific periods or highlight specific indicators. The service provider also updates data in real time, ensuring that the latest information is always displayed. This allows users to make decisions based on the most up-to-date business information. Additionally, the service provider provides features for sharing analysis results, facilitating information sharing within teams. For example, screenshots of the dashboard can be sent via email, or links can be shared. This allows the service provider to effectively deliver analysis results and support user decision-making.

[0033] The discussion team conducts discussions based on information displayed by the information provider team. The discussion team can facilitate discussions through methods such as online meetings and the creation of meeting minutes. Specifically, they can exchange opinions in real time while sharing the information provider team's dashboard. They can also communicate smoothly with members in remote locations using online meeting systems. Furthermore, they provide a function to automatically record the content of discussions and save it as meeting minutes. This allows for later review of the discussion content and use in future meetings. In addition, the discussion team provides tools to support the progress of discussions. For example, they can achieve efficient discussions by using features such as agenda setting, timekeeping, and voting. The discussion team also provides a function that uses AI to automatically extract the key points of the discussion and highlight important points. This allows the discussion team to promote effective discussions based on information from the information provider team and improve the quality of decision-making.

[0034] The Insights Department summarizes the minutes of the discussion conducted by the Discussion Department and provides insights. For example, the Insights Department can provide suggestions such as improvement proposals and risk warnings. Specifically, based on the discussion content, it can suggest revisions to management strategies, proposals for new business opportunities, and measures to strengthen risk management. The Insights Department uses AI to analyze the minutes and extract key points and trends. For example, it can automatically extract issues and suggestions frequently mentioned in the discussion and present concrete action plans based on them. Furthermore, the Insights Department can provide more specific and actionable insights by referencing past data and case studies from other companies. This allows the Insights Department to translate the results of the discussion into concrete actions and contribute to improved business performance. In addition, the Insights Department also provides a function to monitor the implementation status of the insights and collect feedback. This allows for evaluation of the effectiveness of the insights and implementation of improvement measures as needed. This enables the Insights Department to maximize the use of the discussion results and promote continuous improvement.

[0035] The data collection unit can collect management information from each group company. The data collection unit can collect management information by methods such as extraction from a database or collection using an API. This allows for efficient collection of management information from each group company. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform the extraction from the database.

[0036] The analysis unit can analyze collected management information and identify potential synergy creation. For example, the analysis unit can analyze management information by applying statistical analysis or machine learning algorithms. This allows for strengthening collaboration between companies by identifying potential synergy creation. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can have AI execute machine learning algorithms.

[0037] The service provider can visually display the analysis results on a dashboard. For example, the service provider can visually display the analysis results on a dashboard that has functions such as graph display and real-time updates. This allows users to easily gain insights by visually displaying the analysis results. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can have AI perform the display of the analysis results.

[0038] The discussion department can understand information within a consistent context, such as KPIs and business strategies, and facilitate discussions. The discussion department can facilitate discussions through methods such as online meetings and the creation of meeting minutes. This allows discussions to proceed efficiently by understanding information within a consistent context. Some or all of the processes described above in the discussion department may be performed using AI, or not. For example, the discussion department can have AI create meeting minutes.

[0039] The insights unit can summarize meeting minutes, explore patterns and combinations between companies, and evaluate their impact on overall company performance. The insights unit can provide insights such as improvement suggestions and risk warnings. This allows for the optimization of company strategies by exploring patterns and combinations between companies and evaluating their impact on overall company performance. Some or all of the above processing in the insights unit may be performed using AI, or not. For example, the insights unit can have AI perform the exploration of patterns and combinations between companies.

[0040] The data collection unit can analyze past data collection history and select the optimal data collection method when collecting management information from each company. For example, the data collection unit can select the most efficient data collection method from past data collection history and collect data using the same method. The data collection unit can also analyze past data collection history and prioritize the collection of information that takes a long time to collect. Furthermore, the data collection unit can optimize the frequency and timing of data collection based on past data collection history. In this way, the optimal data collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of past data collection history.

[0041] The data collection unit can filter the collected management information based on each company's current management status and industry trends. For example, the data collection unit can analyze each company's current management status and prioritize the collection of important information. The data collection unit can also filter and collect highly relevant information, taking industry trends into consideration. Furthermore, the data collection unit can combine each company's management status and industry trends to collect the most important information. This allows for the priority collection of important information by filtering based on each company's current management status and industry trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the analysis of management status and industry trends.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of each company when collecting management information. For example, the data collection unit prioritizes the collection of highly relevant information based on the geographical location of each company. The data collection unit can also prioritize the collection of information from companies that are geographically close. Furthermore, the data collection unit can collect management information by region, taking geographical location into consideration. This allows for the priority collection of highly relevant information by considering the geographical location of each company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of geographical location information.

[0043] The data collection unit can analyze each company's social media activities and collect relevant information when gathering management information. For example, the data collection unit can analyze each company's social media activities and prioritize the collection of relevant information. The data collection unit can also collect important information by considering the company's reputation on social media. Furthermore, the data collection unit can predict company trends based on social media activities and collect relevant information. This allows for the priority collection of relevant information by analyzing each company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of social media activities.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the management information during the analysis. For example, the analysis unit will perform a detailed analysis on management information of high importance. The analysis unit can also perform a simplified analysis on management information of low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the management information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the management information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of the importance of the management information.

[0045] The analysis unit can apply different analysis algorithms depending on the category of management information during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial information. It can also apply a marketing analysis algorithm to marketing information, and a human resources analysis algorithm to human resources information. By applying different analysis algorithms depending on the category of management information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI select the appropriate analysis algorithm based on the category.

[0046] The analysis unit can determine the priority of analysis based on the submission timing of management information during the analysis process. For example, the analysis unit will prioritize the analysis of the most recent management information. The analysis unit can also postpone the analysis of older information. Furthermore, the analysis unit can adjust the priority of analysis in stages based on the submission timing. This allows for the prioritization of the analysis of the most recent information by determining the priority of analysis based on the submission timing of management information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of submission timing.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the management information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the management information. This allows for the prioritization of important information by adjusting the order of analysis based on the relevance of the management information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the relevance evaluation.

[0048] The service provider can adjust the level of detail displayed on the dashboard based on the importance of the management information. For example, the service provider can display highly important management information in detail. It can also display less important management information in a simplified manner. Furthermore, the service provider can adjust the level of detail in stages according to the importance of the management information. This allows important information to be displayed in detail by adjusting the level of detail based on the importance of the management information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the evaluation of the importance of the management information.

[0049] The service provider can apply different display algorithms to the management information category when displaying the dashboard. For example, the service provider can apply a specific financial display algorithm to financial information. It can also apply a marketing display algorithm to marketing information, and a human resources display algorithm to human resources information. This allows for more appropriate display by applying different display algorithms to the management information category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI select the appropriate display algorithm based on the category.

[0050] The service provider can determine the display priority of management information based on the submission date when displaying the dashboard. For example, the service provider can prioritize the display of the most recent management information. It can also postpone the display of older information. Furthermore, the service provider can adjust the display priority in stages based on the submission date. This allows for the prioritization of the display of the most recent information by determining the display priority based on the submission date of management information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the evaluation of submission dates.

[0051] The service provider can adjust the display order of management information based on its relevance when displaying the dashboard. For example, the service provider can prioritize the display of highly relevant information. It can also postpone the display of less relevant information. Furthermore, the service provider can adjust the display order in stages based on the relevance of the management information. This allows important information to be displayed preferentially by adjusting the display order based on the relevance of the management information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the relevance evaluation.

[0052] The discussion unit can adjust the level of detail in its discussions based on the importance of the management information. For example, the discussion unit will conduct detailed discussions on management information of high importance. It can also conduct simplified discussions on management information of low importance. Furthermore, the discussion unit can adjust the level of detail in its discussions in stages according to the importance of the management information. This allows for detailed discussion of important information by adjusting the level of detail in the discussions based on the importance of the management information. Some or all of the above processing in the discussion unit may be performed using AI, for example, or without AI. For example, the discussion unit can have AI perform the evaluation of the importance of the management information.

[0053] The discussion unit can apply different discussion algorithms depending on the category of management information during a discussion. For example, the discussion unit can apply a specific financial discussion algorithm to financial information. It can also apply a marketing discussion algorithm to marketing information. Furthermore, it can apply a human resources discussion algorithm to human resources information. By applying different discussion algorithms depending on the category of management information, more appropriate discussions become possible. Some or all of the above processing in the discussion unit may be performed using AI, for example, or not using AI. For example, the discussion unit can have AI select a discussion algorithm according to the category.

[0054] The discussion team can prioritize discussions based on the timing of submission of management information. For example, the discussion team will prioritize the discussion of the most recent management information. The discussion team can also postpone discussions of older information. Furthermore, the discussion team can adjust the priority of discussions in stages based on the submission timing. This allows for prioritizing discussions of the most recent information by determining the priority of discussions based on the submission timing of management information. Some or all of the above processing in the discussion team may be performed using AI, for example, or not. For example, the discussion team can have AI perform the evaluation of submission timing.

[0055] The discussion unit can adjust the order of discussion based on the relevance of management information during a discussion. For example, the discussion unit can prioritize discussing highly relevant information. It can also postpone discussing less relevant information. Furthermore, the discussion unit can adjust the order of discussion in stages based on the relevance of management information. This allows important information to be discussed preferentially by adjusting the order of discussion based on the relevance of management information. Some or all of the above processing in the discussion unit may be performed using AI, for example, or not using AI. For example, the discussion unit can have AI perform the relevance evaluation.

[0056] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the meeting agenda when generating suggestions. For example, it can provide detailed suggestions for meeting agenda items of high importance. It can also provide simplified suggestions for meeting agenda items of low importance. Furthermore, the suggestion unit can adjust the level of detail of its suggestions in stages according to the importance of the meeting agenda items. This allows for the provision of detailed suggestions for important matters by adjusting the level of detail of the suggestions based on the importance of the meeting agenda items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have AI perform the evaluation of the importance of the meeting agenda items.

[0057] The suggestion unit can apply different suggestion algorithms depending on the category of the meeting agenda when generating suggestions. For example, the suggestion unit can apply a specific financial suggestion algorithm to financial agenda items. It can also apply a marketing suggestion algorithm to marketing agenda items. Furthermore, it can apply a human resources suggestion algorithm to human resources agenda items. By applying different suggestion algorithms depending on the category of the agenda items, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have AI select the appropriate suggestion algorithm based on the category.

[0058] The suggestion unit can prioritize suggestions based on the submission date of the meeting minutes when issuing suggestions. For example, the suggestion unit provides suggestions based on the most recent meeting minutes. The suggestion unit can also postpone older meeting minutes. Furthermore, the suggestion unit can adjust the priority of suggestions in stages based on the submission date. This allows for the priority provision of the most recent suggestions by prioritizing suggestions based on the submission date of the meeting minutes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can have AI perform the evaluation of submission dates.

[0059] The suggestion unit can adjust the order of suggestions based on the relevance of the meeting minutes when providing suggestions. For example, the suggestion unit provides suggestions based on highly relevant meeting minutes. The suggestion unit can also postpone less relevant meeting minutes. Furthermore, the suggestion unit can adjust the order of suggestions in stages based on the relevance of the meeting minutes. This allows important suggestions to be provided preferentially by adjusting the order of suggestions based on the relevance of the meeting minutes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can have AI perform the relevance evaluation.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze past data collection history and select the optimal data collection method when collecting management information from each company. For example, it can select the most efficient data collection method from past data collection history and collect data using the same method. It can also analyze past data collection history and prioritize the collection of information that takes a long time to collect. Furthermore, it can optimize the frequency and timing of data collection based on past data collection history. In this way, the optimal data collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the analysis of past data collection history.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the management information during the analysis. For example, it can perform a detailed analysis on management information of high importance, and a simplified analysis on management information of low importance. Furthermore, it can adjust the level of detail of the analysis in stages according to the importance of the management information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the management information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of the importance of the management information.

[0063] The service provider can adjust the level of detail displayed on the dashboard based on the importance of the management information. For example, highly important management information will be displayed in detail. Conversely, less important management information can be displayed in a simplified manner. Furthermore, the level of detail can be adjusted in stages according to the importance of the management information. This allows important information to be displayed in detail by adjusting the level of detail based on the importance of the management information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the evaluation of the importance of the management information.

[0064] The discussion unit can adjust the level of detail in a discussion based on the importance of the management information. For example, it can conduct a detailed discussion on management information of high importance, and a simplified discussion on management information of low importance. Furthermore, it can adjust the level of detail in a stepwise manner according to the importance of the management information. This allows for detailed discussion of important information by adjusting the level of detail in the discussion based on the importance of the management information. Some or all of the above processing in the discussion unit may be performed using AI, for example, or without AI. For example, the discussion unit can have AI perform the evaluation of the importance of the management information.

[0065] The suggestion unit can adjust the level of detail of the suggestions based on the importance of the meeting minutes when generating suggestions. For example, it can provide detailed suggestions for meeting minutes of high importance, and simplified suggestions for meeting minutes of low importance. Furthermore, it can adjust the level of detail of the suggestions in stages according to the importance of the meeting minutes. This allows for detailed provision of important suggestions by adjusting the level of detail based on the importance of the meeting minutes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have AI perform the evaluation of the importance of the meeting minutes.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects management information from each company in the group. The collection unit can collect management information by methods such as extracting from a database or collecting using an API. Step 2: The analysis unit analyzes the management information collected by the collection unit. The analysis unit can analyze the management information by applying, for example, statistical analysis or machine learning algorithms. Step 3: The service provider displays the information analyzed by the analysis unit on a dashboard. The service provider can visually display the analysis results on a dashboard that has functions such as graph display and real-time updates. Step 4: The discussion team conducts a discussion based on the information provided by the provider team. The discussion team can facilitate the discussion through methods such as online meetings or the creation of meeting minutes. Step 5: The Insights Section summarizes the minutes of the Discussion Section and provides insights. The Insights Section may provide insights such as improvement suggestions or risk warnings.

[0068] (Example of form 2) The business performance and synergy exploration solution system according to the embodiment of the present invention is a system that provides the function of analyzing and visualizing the management information of each group company. This system analyzes the management information of each group company and visually displays the results on a dashboard. This allows users to easily gain insights and confirm the creation of potential synergies. The system also has a function to facilitate internal discussions. In this function, the AI ​​understands the information in a consistent context such as KPIs and business strategies, and humans conduct discussions based on the visualized management information and the information organization performed by the AI. Furthermore, the AI ​​summarizes the meeting minutes and supports the generation of insights. This makes it possible to explore various patterns and combinations between companies and evaluate how they combine to affect the overall performance of the company. For example, the system first has the AI ​​collect and analyze the management information of each group company. Next, the analysis results are visually displayed on a dashboard. Users conduct discussions based on this information, and the AI ​​summarizes the meeting minutes and provides insights. Finally, the AI ​​explores patterns and combinations between companies and evaluates their impact on the overall performance of the company. Through this mechanism, users can easily grasp management information and confirm the possibility of creating synergies. Furthermore, by having AI organize information and summarize meeting minutes, discussions proceed more efficiently, contributing to improved overall corporate performance. This solution system, which enables business performance and synergy exploration, allows users to easily grasp management information and identify potential synergies. Additionally, by having AI organize information and summarize meeting minutes, discussions proceed more efficiently, contributing to improved overall corporate performance.

[0069] The business performance and synergy exploration solution system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a discussion unit, and an insights unit. The collection unit collects management information from each group company. The collection unit can collect management information by methods such as extraction from a database or collection using an API. The analysis unit analyzes the management information collected by the collection unit. The analysis unit can analyze the management information by applying statistical analysis or machine learning algorithms, for example. The provision unit displays the information analyzed by the analysis unit on a dashboard. The provision unit can visually display the analysis results on a dashboard with functions such as graph display and real-time updates, for example. The discussion unit conducts discussions based on the information displayed by the provision unit. The discussion unit can facilitate discussions by methods such as online meetings and the creation of meeting minutes, for example. The insights unit summarizes the minutes of the discussion conducted by the discussion unit and provides insights. The insights unit can provide insights such as improvement suggestions or risk warnings, for example. As a result, the business performance and synergy exploration solution system according to this embodiment can efficiently collect, analyze, display, discuss, and provide insights from the management information of each group company.

[0070] The data collection department collects management information from each company within the group. This information can be collected through various methods, such as database extraction and API-based collection. Specifically, it covers a wide range of management information, including financial data, sales data, inventory data, and personnel data for each company. Database extraction involves efficiently obtaining necessary data using SQL queries, while API-based collection involves integrating with each company's system to obtain data in real time. This allows the data collection department to quickly collect the latest management information and integrate it into the system's overall database. Furthermore, the data collection department implements data cleansing and normalization processes to ensure data quality. For example, it removes duplicate data, fills in missing data, and standardizes data formats to enable the analysis department to perform accurate analysis. The data collection department also prioritizes data security, implementing data encryption and access control. This allows the data collection department to provide highly reliable data and improve the overall system performance.

[0071] The analysis unit analyzes the management information collected by the data collection unit. For example, the analysis unit can analyze management information by applying statistical analysis and machine learning algorithms. Specifically, based on the collected data, it performs tasks such as sales trend analysis, cost optimization, and inventory management efficiency improvements. Statistical analysis uses regression analysis and analysis of variance to clarify data correlations and causal relationships. Machine learning algorithms are used to build clustering, classification, and predictive models to forecast future business performance. For example, sales data can be used to build seasonal sales forecast models, which can help optimize inventory management. Furthermore, the analysis unit can use anomaly detection algorithms to detect abnormal data patterns and identify risks early. This allows the analysis unit to analyze management information from multiple perspectives and contribute to improving business performance. In addition, the analysis unit utilizes data visualization technology to visually display analysis results, making them intuitively understandable to users. This enables the analysis unit to analyze management information efficiently and effectively, increasing the overall value of the system.

[0072] The service provider displays the information analyzed by the analysis provider on a dashboard. The service provider can visually display analysis results on a dashboard with features such as graph display and real-time updates. Specifically, it provides a variety of visualizations, such as line graphs showing sales trends, pie charts showing cost breakdowns, and bar graphs showing inventory status. This allows users to grasp analysis results at a glance and make quick decisions. Furthermore, the service provider offers dashboard customization features, allowing users to adjust the displayed content to their needs. For example, they can filter data for specific periods or highlight specific indicators. The service provider also updates data in real time, ensuring that the latest information is always displayed. This allows users to make decisions based on the most up-to-date business information. Additionally, the service provider provides features for sharing analysis results, facilitating information sharing within teams. For example, screenshots of the dashboard can be sent via email, or links can be shared. This allows the service provider to effectively deliver analysis results and support user decision-making.

[0073] The discussion team conducts discussions based on information displayed by the information provider team. The discussion team can facilitate discussions through methods such as online meetings and the creation of meeting minutes. Specifically, they can exchange opinions in real time while sharing the information provider team's dashboard. They can also communicate smoothly with members in remote locations using online meeting systems. Furthermore, they provide a function to automatically record the content of discussions and save it as meeting minutes. This allows for later review of the discussion content and use in future meetings. In addition, the discussion team provides tools to support the progress of discussions. For example, they can achieve efficient discussions by using features such as agenda setting, timekeeping, and voting. The discussion team also provides a function that uses AI to automatically extract the key points of the discussion and highlight important points. This allows the discussion team to promote effective discussions based on information from the information provider team and improve the quality of decision-making.

[0074] The Insights Department summarizes the minutes of the discussion conducted by the Discussion Department and provides insights. For example, the Insights Department can provide suggestions such as improvement proposals and risk warnings. Specifically, based on the discussion content, it can suggest revisions to management strategies, proposals for new business opportunities, and measures to strengthen risk management. The Insights Department uses AI to analyze the minutes and extract key points and trends. For example, it can automatically extract issues and suggestions frequently mentioned in the discussion and present concrete action plans based on them. Furthermore, the Insights Department can provide more specific and actionable insights by referencing past data and case studies from other companies. This allows the Insights Department to translate the results of the discussion into concrete actions and contribute to improved business performance. In addition, the Insights Department also provides a function to monitor the implementation status of the insights and collect feedback. This allows for evaluation of the effectiveness of the insights and implementation of improvement measures as needed. This enables the Insights Department to maximize the use of the discussion results and promote continuous improvement.

[0075] The data collection unit can collect management information from each group company. The data collection unit can collect management information by methods such as extraction from a database or collection using an API. This allows for efficient collection of management information from each group company. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform the extraction from the database.

[0076] The analysis unit can analyze collected management information and identify potential synergy creation. For example, the analysis unit can analyze management information by applying statistical analysis or machine learning algorithms. This allows for strengthening collaboration between companies by identifying potential synergy creation. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can have AI execute machine learning algorithms.

[0077] The service provider can visually display the analysis results on a dashboard. For example, the service provider can visually display the analysis results on a dashboard that has functions such as graph display and real-time updates. This allows users to easily gain insights by visually displaying the analysis results. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can have AI perform the display of the analysis results.

[0078] The discussion department can understand information within a consistent context, such as KPIs and business strategies, and facilitate discussions. The discussion department can facilitate discussions through methods such as online meetings and the creation of meeting minutes. This allows discussions to proceed efficiently by understanding information within a consistent context. Some or all of the processes described above in the discussion department may be performed using AI, or not. For example, the discussion department can have AI create meeting minutes.

[0079] The insights unit can summarize meeting minutes, explore patterns and combinations between companies, and evaluate their impact on overall company performance. The insights unit can provide insights such as improvement suggestions and risk warnings. This allows for the optimization of company strategies by exploring patterns and combinations between companies and evaluating their impact on overall company performance. Some or all of the above processing in the insights unit may be performed using AI, or not. For example, the insights unit can have AI perform the exploration of patterns and combinations between companies.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect data when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can immediately collect data and begin analysis quickly. If the user is in a hurry, the data collection unit can accelerate collection to quickly collect data. By adjusting the collection timing according to the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform the estimation of the user's emotions.

[0081] The data collection unit can analyze past data collection history and select the optimal data collection method when collecting management information from each company. For example, the data collection unit can select the most efficient data collection method from past data collection history and collect data using the same method. The data collection unit can also analyze past data collection history and prioritize the collection of information that takes a long time to collect. Furthermore, the data collection unit can optimize the frequency and timing of data collection based on past data collection history. In this way, the optimal data collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of past data collection history.

[0082] The data collection unit can filter the collected management information based on each company's current management status and industry trends. For example, the data collection unit can analyze each company's current management status and prioritize the collection of important information. The data collection unit can also filter and collect highly relevant information, taking industry trends into consideration. Furthermore, the data collection unit can combine each company's management status and industry trends to collect the most important information. This allows for the priority collection of important information by filtering based on each company's current management status and industry trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the analysis of management status and industry trends.

[0083] The data collection unit can estimate the user's emotions and determine the priority of the business information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information and prioritize collecting more important information. If the user is relaxed, the data collection unit can collect all information equally. If the user is in a hurry, the data collection unit can prioritize collecting the most important information. In this way, by determining the priority of the business information to collect according to the user's emotions, important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI perform the estimation of the user's emotions.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of each company when collecting management information. For example, the data collection unit prioritizes the collection of highly relevant information based on the geographical location of each company. The data collection unit can also prioritize the collection of information from companies that are geographically close. Furthermore, the data collection unit can collect management information by region, taking geographical location into consideration. This allows for the priority collection of highly relevant information by considering the geographical location of each company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of geographical location information.

[0085] The data collection unit can analyze each company's social media activities and collect relevant information when gathering management information. For example, the data collection unit can analyze each company's social media activities and prioritize the collection of relevant information. The data collection unit can also collect important information by considering the company's reputation on social media. Furthermore, the data collection unit can predict company trends based on social media activities and collect relevant information. This allows for the priority collection of relevant information by analyzing each company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI perform the analysis of social media activities.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the estimation of the user's emotions.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the management information during the analysis. For example, the analysis unit will perform a detailed analysis on management information of high importance. The analysis unit can also perform a simplified analysis on management information of low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the management information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the management information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of the importance of the management information.

[0088] The analysis unit can apply different analysis algorithms depending on the category of management information during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial information. It can also apply a marketing analysis algorithm to marketing information, and a human resources analysis algorithm to human resources information. By applying different analysis algorithms depending on the category of management information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI select the appropriate analysis algorithm based on the category.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI perform the estimation of the user's emotions.

[0090] The analysis unit can determine the priority of analysis based on the submission timing of management information during the analysis process. For example, the analysis unit will prioritize the analysis of the most recent management information. The analysis unit can also postpone the analysis of older information. Furthermore, the analysis unit can adjust the priority of analysis in stages based on the submission timing. This allows for the prioritization of the analysis of the most recent information by determining the priority of analysis based on the submission timing of management information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of submission timing.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the management information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the management information. This allows for the prioritization of important information by adjusting the order of analysis based on the relevance of the management information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the relevance evaluation.

[0092] The service provider can estimate the user's emotions and adjust the dashboard display based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can also provide a display that includes detailed information. If the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting the dashboard display according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the estimation of the user's emotions.

[0093] The service provider can adjust the level of detail displayed on the dashboard based on the importance of the management information. For example, the service provider can display highly important management information in detail. It can also display less important management information in a simplified manner. Furthermore, the service provider can adjust the level of detail in stages according to the importance of the management information. This allows important information to be displayed in detail by adjusting the level of detail based on the importance of the management information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the evaluation of the importance of the management information.

[0094] The service provider can apply different display algorithms to the management information category when displaying the dashboard. For example, the service provider can apply a specific financial display algorithm to financial information. It can also apply a marketing display algorithm to marketing information, and a human resources display algorithm to human resources information. This allows for more appropriate display by applying different display algorithms to the management information category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI select the appropriate display algorithm based on the category.

[0095] The service provider can estimate the user's emotions and adjust the display order of the dashboard based on the estimated emotions. For example, if the user is stressed, the service provider can display important information first. If the user is relaxed, the service provider can also display all information evenly. If the user is in a hurry, the service provider can also display the most important information first. This allows for a more appropriate display by adjusting the dashboard display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the estimation of the user's emotions.

[0096] The service provider can determine the display priority of management information based on the submission date when displaying the dashboard. For example, the service provider can prioritize the display of the most recent management information. It can also postpone the display of older information. Furthermore, the service provider can adjust the display priority in stages based on the submission date. This allows for the prioritization of the display of the most recent information by determining the display priority based on the submission date of management information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the evaluation of submission dates.

[0097] The service provider can adjust the display order of management information based on its relevance when displaying the dashboard. For example, the service provider can prioritize the display of highly relevant information. It can also postpone the display of less relevant information. Furthermore, the service provider can adjust the display order in stages based on the relevance of the management information. This allows important information to be displayed preferentially by adjusting the display order based on the relevance of the management information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the relevance evaluation.

[0098] The discussion unit can estimate the user's emotions and adjust the discussion process based on those emotions. For example, if the user is nervous, the discussion unit may adopt a relaxing approach. If the user is relaxed, the discussion unit may adopt a method that encourages free discussion. If the user is in a hurry, the discussion unit may adopt a method that moves the discussion forward quickly. By adjusting the discussion process according to the user's emotions, a more appropriate discussion becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the discussion unit may be performed using AI, or not using AI. For example, the discussion unit can have AI perform the estimation of the user's emotions.

[0099] The discussion unit can adjust the level of detail in its discussions based on the importance of the management information. For example, the discussion unit will conduct detailed discussions on management information of high importance. It can also conduct simplified discussions on management information of low importance. Furthermore, the discussion unit can adjust the level of detail in its discussions in stages according to the importance of the management information. This allows for detailed discussion of important information by adjusting the level of detail in the discussions based on the importance of the management information. Some or all of the above processing in the discussion unit may be performed using AI, for example, or without AI. For example, the discussion unit can have AI perform the evaluation of the importance of the management information.

[0100] The discussion unit can apply different discussion algorithms depending on the category of management information during a discussion. For example, the discussion unit can apply a specific financial discussion algorithm to financial information. It can also apply a marketing discussion algorithm to marketing information. Furthermore, it can apply a human resources discussion algorithm to human resources information. By applying different discussion algorithms depending on the category of management information, more appropriate discussions become possible. Some or all of the above processing in the discussion unit may be performed using AI, for example, or not using AI. For example, the discussion unit can have AI select a discussion algorithm according to the category.

[0101] The discussion unit can estimate the user's emotions and determine the priority of the discussion based on the estimated emotions. For example, if the user is nervous, the discussion unit will discuss important topics first. If the user is relaxed, the discussion unit can also discuss all topics equally. If the user is in a hurry, the discussion unit can also discuss the most important topics first. In this way, by determining the priority of the discussion according to the user's emotions, important topics can be discussed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discussion unit may be performed using AI, for example, or not using AI. For example, the discussion unit can have AI perform the estimation of the user's emotions.

[0102] The discussion team can prioritize discussions based on the timing of submission of management information. For example, the discussion team will prioritize the discussion of the most recent management information. The discussion team can also postpone discussions of older information. Furthermore, the discussion team can adjust the priority of discussions in stages based on the submission timing. This allows for prioritizing discussions of the most recent information by determining the priority of discussions based on the submission timing of management information. Some or all of the above processing in the discussion team may be performed using AI, for example, or not. For example, the discussion team can have AI perform the evaluation of submission timing.

[0103] The discussion unit can adjust the order of discussion based on the relevance of management information during a discussion. For example, the discussion unit can prioritize discussing highly relevant information. It can also postpone discussing less relevant information. Furthermore, the discussion unit can adjust the order of discussion in stages based on the relevance of management information. This allows important information to be discussed preferentially by adjusting the order of discussion based on the relevance of management information. Some or all of the above processing in the discussion unit may be performed using AI, for example, or not using AI. For example, the discussion unit can have AI perform the relevance evaluation.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is tense, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can have AI perform the estimation of the user's emotions.

[0105] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the meeting agenda when generating suggestions. For example, it can provide detailed suggestions for meeting agenda items of high importance. It can also provide simplified suggestions for meeting agenda items of low importance. Furthermore, the suggestion unit can adjust the level of detail of its suggestions in stages according to the importance of the meeting agenda items. This allows for the provision of detailed suggestions for important matters by adjusting the level of detail of the suggestions based on the importance of the meeting agenda items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have AI perform the evaluation of the importance of the meeting agenda items.

[0106] The suggestion unit can apply different suggestion algorithms depending on the category of the meeting agenda when generating suggestions. For example, the suggestion unit can apply a specific financial suggestion algorithm to financial agenda items. It can also apply a marketing suggestion algorithm to marketing agenda items. Furthermore, it can apply a human resources suggestion algorithm to human resources agenda items. By applying different suggestion algorithms depending on the category of the agenda items, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have AI select the appropriate suggestion algorithm based on the category.

[0107] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is tense, the suggestion unit can provide important suggestions first. If the user is relaxed, the suggestion unit can provide all suggestions equally. If the user is in a hurry, the suggestion unit can provide the most important suggestions first. This allows for the priority of important suggestions to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can have AI perform the estimation of the user's emotions.

[0108] The suggestion unit can prioritize suggestions based on the submission date of the meeting minutes when issuing suggestions. For example, the suggestion unit provides suggestions based on the most recent meeting minutes. The suggestion unit can also postpone older meeting minutes. Furthermore, the suggestion unit can adjust the priority of suggestions in stages based on the submission date. This allows for the priority provision of the most recent suggestions by prioritizing suggestions based on the submission date of the meeting minutes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can have AI perform the evaluation of submission dates.

[0109] The suggestion unit can adjust the order of suggestions based on the relevance of the meeting minutes when providing suggestions. For example, the suggestion unit provides suggestions based on highly relevant meeting minutes. The suggestion unit can also postpone less relevant meeting minutes. Furthermore, the suggestion unit can adjust the order of suggestions in stages based on the relevance of the meeting minutes. This allows important suggestions to be provided preferentially by adjusting the order of suggestions based on the relevance of the meeting minutes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can have AI perform the relevance evaluation.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection timing can be delayed until the user is relaxed. Alternatively, if the user is relaxed, data can be collected immediately and analysis can begin quickly. Furthermore, if the user is in a hurry, the data collection timing can be advanced to quickly collect data. By adjusting the data collection timing according to the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform the estimation of the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI perform the estimation of the user's emotions.

[0113] The service provider can estimate the user's emotions and adjust the dashboard display based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display can be provided. If the user is relaxed, a display with detailed information can be provided. Furthermore, if the user is in a hurry, a display that gets straight to the point can be provided. By adjusting the dashboard display according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the estimation of the user's emotions.

[0114] The discussion unit can estimate the user's emotions and adjust the discussion process based on those emotions. For example, if the user is nervous, it can adopt a relaxing approach. If the user is relaxed, it can adopt a process that encourages free discussion. Furthermore, if the user is in a hurry, it can adopt a process that moves the discussion forward quickly. By adjusting the discussion process according to the user's emotions, a more appropriate discussion becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the discussion unit may be performed using AI, or not. For example, the discussion unit can have AI perform the estimation of the user's emotions.

[0115] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is tense, it can provide simple and easily visible suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can have AI perform the estimation of the user's emotions.

[0116] The data collection unit can analyze past data collection history and select the optimal data collection method when collecting management information from each company. For example, it can select the most efficient data collection method from past data collection history and collect data using the same method. It can also analyze past data collection history and prioritize the collection of information that takes a long time to collect. Furthermore, it can optimize the frequency and timing of data collection based on past data collection history. In this way, the optimal data collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the analysis of past data collection history.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the management information during the analysis. For example, it can perform a detailed analysis on management information of high importance, and a simplified analysis on management information of low importance. Furthermore, it can adjust the level of detail of the analysis in stages according to the importance of the management information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the management information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of the importance of the management information.

[0118] The service provider can adjust the level of detail displayed on the dashboard based on the importance of the management information. For example, highly important management information will be displayed in detail. Conversely, less important management information can be displayed in a simplified manner. Furthermore, the level of detail can be adjusted in stages according to the importance of the management information. This allows important information to be displayed in detail by adjusting the level of detail based on the importance of the management information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI perform the evaluation of the importance of the management information.

[0119] The discussion unit can adjust the level of detail in a discussion based on the importance of the management information. For example, it can conduct a detailed discussion on management information of high importance, and a simplified discussion on management information of low importance. Furthermore, it can adjust the level of detail in a stepwise manner according to the importance of the management information. This allows for detailed discussion of important information by adjusting the level of detail in the discussion based on the importance of the management information. Some or all of the above processing in the discussion unit may be performed using AI, for example, or without AI. For example, the discussion unit can have AI perform the evaluation of the importance of the management information.

[0120] The suggestion unit can adjust the level of detail of the suggestions based on the importance of the meeting minutes when generating suggestions. For example, it can provide detailed suggestions for meeting minutes of high importance, and simplified suggestions for meeting minutes of low importance. Furthermore, it can adjust the level of detail of the suggestions in stages according to the importance of the meeting minutes. This allows for detailed provision of important suggestions by adjusting the level of detail based on the importance of the meeting minutes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have AI perform the evaluation of the importance of the meeting minutes.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects management information from each company in the group. The collection unit can collect management information by methods such as extracting from a database or collecting using an API. Step 2: The analysis unit analyzes the management information collected by the collection unit. The analysis unit can analyze the management information by applying, for example, statistical analysis or machine learning algorithms. Step 3: The service provider displays the information analyzed by the analysis unit on a dashboard. The service provider can visually display the analysis results on a dashboard that has functions such as graph display and real-time updates. Step 4: The discussion team conducts a discussion based on the information provided by the provider team. The discussion team can facilitate the discussion through methods such as online meetings or the creation of meeting minutes. Step 5: The Insights Section summarizes the minutes of the Discussion Section and provides insights. The Insights Section may provide insights such as improvement suggestions or risk warnings.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, discussion unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The discussion unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, discussion unit, and suggestion unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The discussion unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, discussion unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The discussion unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, discussion unit, and suggestion unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The discussion unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The collection department collects management information from each company in the group, An analysis unit analyzes the management information collected by the aforementioned collection unit, A providing unit that displays the information analyzed by the aforementioned analysis unit on a dashboard, A discussion unit conducts a discussion based on the information displayed by the aforementioned provision unit, The system comprises a discussion section that summarizes the proceedings and provides suggestions. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect management information on each company within the group. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected management information to identify potential synergies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Visually display the analysis results on the dashboard. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned discussion section is, Understanding information within a consistent context, such as KPIs and business strategies, facilitates discussion. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned suggestion unit is, We summarize the meeting minutes, explore patterns and combinations between companies, and evaluate their impact on overall company performance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of collecting business information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting management information from each company, we analyze past collection history and select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting management information, filtering is performed based on each company's current management situation and industry trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of business information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting management information, we prioritize collecting highly relevant information, taking into account the geographical location of each company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting management information, we analyze each company's social media activities and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the management information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of management information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the timing of the submission of management information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of management information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the dashboard is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When displaying the dashboard, adjust the level of detail based on the importance of the business information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When displaying the dashboard, different display algorithms are applied depending on the category of business information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display order of the dashboard based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When displaying the dashboard, the display priority is determined based on the timing of the submission of management information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When displaying the dashboard, the display order is adjusted based on the relevance of the business information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned discussion section is, It estimates the user's emotions and adjusts the discussion's progression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned discussion section is, During discussions, adjust the level of detail based on the importance of the management information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned discussion section is, During discussions, different discussion algorithms are applied depending on the category of management information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned discussion section is, It estimates user sentiment and determines discussion priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned discussion section is, During discussions, prioritizing discussions will be based on the timing of the submission of management information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned discussion section is, During discussions, adjust the order of discussion based on the relevance of management information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned suggestion unit is, It estimates the user's emotions and adjusts how suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned suggestion unit is, When providing insights, adjust the level of detail based on the importance of the meeting agenda. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned suggestion unit is, When generating insights, different insight algorithms are applied depending on the category of the meeting content. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned suggestion unit is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned suggestion unit is, When issuing suggestions, prioritize them based on when the meeting minutes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned suggestion unit is, When offering suggestions, adjust the order of suggestions based on their relevance to the meeting agenda. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects management information from each company in the group, An analysis unit analyzes the management information collected by the aforementioned collection unit, A providing unit that displays the information analyzed by the aforementioned analysis unit on a dashboard, A discussion unit conducts a discussion based on the information displayed by the aforementioned provision unit, The system comprises a discussion section that summarizes the proceedings and provides suggestions. A system characterized by the following features.

2. The aforementioned collection unit is Collect management information on each company within the group. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected management information to identify potential synergies. The system according to feature 1.

4. The aforementioned supply unit is, Visually display the analysis results on the dashboard. The system according to feature 1.

5. The aforementioned discussion section is, Understanding information within a consistent context, such as KPIs and business strategies, facilitates discussion. The system according to feature 1.

6. The aforementioned suggestion unit is, We summarize the meeting minutes, explore patterns and combinations between companies, and evaluate their impact on overall company performance. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of collecting business information based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is When collecting management information from each company, we analyze past collection history and select the most suitable collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting management information, filtering is performed based on each company's current management situation and industry trends. The system according to feature 1.

10. The aforementioned collection unit is It estimates user sentiment and determines the priority of business information to collect based on the estimated user sentiment. The system according to feature 1.

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