system

Abstract

The system according to the embodiment aims to effectively provide the knowledge of the CEO and founder to employees and improve the quality of work. [Solution] A system according to an embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit collects data on past statements or actions of the CEO or founder. The analysis unit analyzes the data collected by the data collection unit and reproduces specific insights. The provision unit provides employees with the insights reproduced by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to effectively convey the knowledge of CEOs and founders to employees, which limited the improvement in work quality.

[0005] The system according to the embodiment aims to effectively provide the knowledge of the CEO and founder to employees and improve the quality of work. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit collects data on past statements or actions of the CEO or founder. The analysis unit analyzes the data collected by the data collection unit and reproduces specific findings. The provision unit provides employees with the findings reproduced by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively provide the knowledge of the CEO or founder to employees and improve the quality of work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A system according to an embodiment of the present invention dramatically improves the quality of employees' work by creating a personality model of a CEO, founder, or director (hereinafter "MASA") in a generative AI and utilizing the model as an advisor in daily operations and business processes. This system includes a data collection module for creating the MASA's personality, which collects data on the CEO's and founder's past statements and actions. Next, an analysis module analyzes the collected data, allowing the generative AI to recreate MASA's knowledge. Finally, a provision module provides the recreated knowledge to employees, enabling rapid decision-making and problem-solving. The system also includes an expansion function for services and commercialization, such as for business (external sales) and for the public (global audience). For example, consulting services utilizing MASA's knowledge could be provided to businesses, and educational content and advice based on MASA's ideas could be provided to the general public. For example, a data collection module for creating the MASA's personality could collect data on the CEO's and founder's past statements and actions. Next, an analysis module analyzes the collected data, allowing the generative AI to recreate MASA's knowledge. Furthermore, a provision department will be established to provide employees with the reproduced knowledge. This will enable quick decision-making and problem-solving. Furthermore, as an extension function, services and commercialization will be implemented for biz (external sales) and for the public (for people all over the world). For example, consulting services utilizing MASA's knowledge can be provided to companies, and educational content and advice based on MASA's ideas can be provided to the general public. This will enable the system to dramatically improve the quality of employees' work.

[0029] The system according to the embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit collects data on past statements and behavior of CEOs and founders. For example, the data collection unit can collect statements made in meetings, email content, and social media posts. For example, the data collection unit can analyze recordings of meetings and save the statements as text data. The data collection unit can also automatically analyze email content and extract important information. The data collection unit can also collect social media posts and analyze the posts. For example, the data collection unit collects social media posts in real time and analyzes the posts. The analysis unit analyzes the collected data and reproduces MASA's knowledge. For example, the analysis unit uses natural language processing technology to analyze the statements and extract important knowledge. The analysis unit uses text mining technology to extract important keywords from the statements. The analysis unit can also classify the statements and reproduce the knowledge using a machine learning algorithm. For example, the analysis unit learns from past statement data and reproduces knowledge based on new statement data. The providing unit provides the reproduced knowledge to the employee. For example, the providing unit can provide the knowledge in text format. For example, the providing unit can send the knowledge to the employee by email. The providing unit can also provide the knowledge through a web application. For example, the providing unit can make the knowledge viewable on the web application. In this way, the system according to the embodiment can dramatically improve the work quality of employees.

[0030] The service provider includes a consulting department that provides consulting services utilizing specific expertise. The consulting department provides consulting services that leverage MASA's expertise. For example, the consulting department can provide advice on a company's management strategy. For example, the consulting department can analyze a company's management strategy and propose the optimal strategy. The consulting department can also provide advice on marketing methods. For example, the consulting department can analyze a company's marketing strategy and propose effective marketing methods. Furthermore, the consulting department can also provide advice on technical know-how. For example, the consulting department can provide advice on a company's technology development. In this way, by providing consulting services that leverage MASA's expertise, the quality of business operations can be improved.

[0031] The provision department includes an education department that provides educational content based on specific ideas. The education department provides educational content based on MASA's ideas. For example, the education department can provide educational content on new product concepts. For example, the education department can create educational content explaining new product concepts and provide it to employees. The education department can also provide educational content on business improvement suggestions. For example, the education department can create educational content explaining business improvement suggestions and provide it to employees. Furthermore, the education department can also provide educational content on technical know-how. For example, the education department can create educational content explaining technical know-how and provide it to employees. In this way, by providing educational content based on MASA's ideas, it is possible to improve the skills of employees.

[0032] The data collection unit filters data based on specific events or situations. For example, the data collection unit prioritizes collecting comments and actions at the start and end of an important project. For example, the data collection unit checks the project schedule and collects comments and actions at the start and end. The data collection unit can also collect comments and actions at the time of a company's earnings announcement. For example, the data collection unit checks the schedule of the earnings announcement and collects comments and actions at the time of the announcement. Furthermore, the data collection unit can filter and collect comments and actions at company events or anniversaries. For example, the data collection unit checks the schedule of company events and collects comments and actions at the time of the event. In this way, by filtering data based on specific events or situations, important data can be collected preferentially.

[0033] The data collection department determines the priority of data to be collected based on past projects or performance. For example, the data collection department prioritizes the collection of comments and actions related to successful projects. For example, the data collection department checks the records of successful projects and collects related comments and actions. The data collection department can also collect comments about points to reflect on and areas for improvement related to failed projects. For example, the data collection department checks the records of failed projects and collects comments about points to reflect on and areas for improvement. Furthermore, the data collection department can prioritize the collection of comments and actions during periods when performance fluctuated significantly. For example, the data collection department checks periods when performance fluctuated and collects comments and actions during those periods. In this way, important data can be collected efficiently by determining the priority of data based on past projects and performance.

[0034] The data collection department prioritizes collecting highly relevant data based on geographical location information. For example, if the CEO is on a business trip, the data collection department will collect statements and actions taken at that location. For instance, the data collection department will check the geographical location of the business trip destination and collect statements and actions taken at that location. The data collection department can also collect statements and actions taken by founders if they are attending an event in a specific region. For example, the data collection department will check the geographical location of the event and collect statements and actions taken at that location. Furthermore, if the CEO is at headquarters, the data collection department can prioritize collecting statements and actions taken at headquarters. For example, the data collection department will check the geographical location of headquarters and collect statements and actions taken at that location. This allows for the efficient collection of highly relevant data by considering geographical location information.

[0035] The data collection department analyzes social media activity and collects relevant data. For example, it collects content posted by the CEO on social media. For instance, it monitors the CEO's social media accounts and collects their posts. The data collection department can also analyze the founder's social media reactions and collect relevant statements and actions. For example, it analyzes social media comments and retweets and collects relevant statements and actions. Furthermore, the data collection department can collect the CEO's statements about social media trends. For example, it monitors social media trends and collects relevant statements. This allows for the efficient collection of relevant data by analyzing social media activity.

[0036] The analysis unit adjusts the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data related to important projects in detail. For example, the analysis unit evaluates the importance of the project and analyzes the related data in detail. The analysis unit can also simplify and analyze data related to daily operations. For example, the analysis unit simplifies and analyzes data of daily operations. Furthermore, the analysis unit can also analyze in detail data that has a major impact on the company's performance. For example, the analysis unit analyzes in detail data that has a major impact on performance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, efficient analysis is possible.

[0037] The analysis unit applies different analysis algorithms depending on the category of data. For example, the analysis unit applies a financial analysis algorithm to financial data. For example, the analysis unit applies a dedicated algorithm for analyzing financial data. The analysis unit can also apply a personnel analysis algorithm to personnel data. For example, the analysis unit applies a dedicated algorithm for analyzing personnel data. The analysis unit can also apply a marketing analysis algorithm to marketing data. For example, the analysis unit applies a dedicated algorithm for analyzing marketing data. In this way, applying an analysis algorithm depending on the category of data enables more appropriate analysis.

[0038] The analysis unit determines the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. For example, the analysis unit checks the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also prioritize analyzing data of important events or projects. For example, the analysis unit checks the schedule of the event or project and prioritizes analyzing the related data. Furthermore, the analysis unit can also analyze data including past data to grasp overall trends. For example, the analysis unit collects past data and analyzes it to grasp overall trends. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the data was collected.

[0039] The analysis unit adjusts the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of directly related data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of directly related data. The analysis unit can also analyze indirectly related data. For example, the analysis unit collects and analyzes indirectly related data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data in real time and dynamically adjusts the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0040] The provision department adjusts the level of detail of the knowledge provided based on the importance of the knowledge. For example, the provision department provides detailed knowledge related to important projects. For example, the provision department evaluates the importance of a project and provides detailed related knowledge. The provision department can also provide simplified knowledge related to daily operations. For example, the provision department provides simplified knowledge of daily operations. Furthermore, the provision department can also provide detailed knowledge that will have a major impact on the company's performance. For example, the provision department provides detailed knowledge that will have a major impact on performance. In this way, efficient knowledge provision is possible by adjusting the level of detail of the knowledge provided based on the importance of the knowledge.

[0041] The provision unit applies different provision methods depending on the category of knowledge. For example, the provision unit provides financial knowledge in the form of a financial report. For example, the provision unit analyzes financial data and provides the knowledge in the form of a financial report. The provision unit can also provide human resources knowledge in the form of a human resources report. For example, the provision unit analyzes human resources data and provides the knowledge in the form of a human resources report. Furthermore, the provision unit can also provide marketing knowledge in the form of a marketing report. For example, the provision unit analyzes marketing data and provides the knowledge in the form of a marketing report. This makes it possible to provide more appropriate knowledge by applying a provision method depending on the category of knowledge.

[0042] The provision unit determines the priority of provision based on the time when the knowledge was collected. The provision unit, for example, prioritizes providing the latest knowledge. For example, the provision unit checks when the knowledge was collected and prioritizes providing the latest knowledge. The provision unit can also prioritize providing knowledge related to important events or projects. For example, the provision unit checks the schedule of the event or project and prioritizes providing the related knowledge. Furthermore, the provision unit can also provide knowledge, including past knowledge, to grasp overall trends. For example, the provision unit collects past knowledge and provides it to grasp overall trends. In this way, by determining the priority of provision based on when the knowledge was collected, efficient knowledge provision is possible.

[0043] The providing unit adjusts the order of provision based on the relevance of the knowledge. For example, the providing unit provides directly related knowledge with priority. For example, the providing unit evaluates the relevance of the knowledge and provides directly related knowledge with priority. The providing unit can also provide indirectly related knowledge. For example, the providing unit collects and provides indirectly related knowledge. Furthermore, the providing unit can dynamically adjust the order of provision based on the relevance of the knowledge. For example, the providing unit evaluates the relevance of the knowledge in real time and dynamically adjusts the order of provision. This enables efficient knowledge provision by adjusting the order of provision based on the relevance of the knowledge.

[0044] The consulting department provides optimal advice based on the client's past performance or projects. For example, the consulting department provides advice based on the client's successful projects. For instance, the consulting department reviews the records of the client's successful projects and provides relevant advice. The consulting department can also propose improvements based on the client's failed projects. For example, the consulting department reviews the records of the client's failed projects and proposes improvements. Furthermore, the consulting department can provide advice based on periods when the client's performance has fluctuated significantly. For example, the consulting department reviews periods when the client's performance has fluctuated and provides advice based on those periods. In this way, by providing optimal advice based on past performance and projects, the quality of the client's operations can be improved.

[0045] The consulting department customizes advice based on the client's current business situation. For example, the consulting department provides advice based on the client's current market situation. For instance, the consulting department analyzes the client's market situation and provides optimal advice. The consulting department can also provide advice based on the client's current financial situation. For example, the consulting department analyzes the client's financial situation and provides optimal advice. Furthermore, the consulting department can also provide advice based on the client's current human resources situation. For example, the consulting department analyzes the client's human resources situation and provides optimal advice. This allows for the provision of more appropriate advice by customizing it based on the current business situation.

[0046] The consulting department provides optimal advice based on the client's geographic location information. The consulting department provides advice based on, for example, the market conditions in the region where the client is located. For example, the consulting department analyzes the client's local market and provides optimal advice. The consulting department can also provide advice based on the laws and regulations in the region where the client is located. For example, the consulting department checks local laws and regulations and provides optimal advice. Furthermore, the consulting department can also provide advice based on the culture and customs in the region where the client is located. For example, the consulting department understands the local culture and customs and provides advice based on them. This allows for more appropriate advice to be provided by taking geographic location information into consideration.

[0047] The consulting department analyzes the social media activity of clients and provides advice. For example, the consulting department analyzes the content posted on social media by clients and provides advice. For example, the consulting department monitors the social media accounts of clients and analyzes the content posted. The consulting department can also analyze the reactions on social media by clients and provide advice. For example, the consulting department analyzes comments and retweets on social media and provides related advice. Furthermore, the consulting department can also provide advice based on trends on social media by clients. For example, the consulting department monitors social media trends and provides related advice. This allows the consulting department to provide more appropriate advice by analyzing social media activity.

[0048] The Ministry of Education provides optimal content based on the student's past learning history. For example, the Ministry of Education provides relevant educational content based on what the student has previously studied. For example, the Ministry of Education checks the student's past learning history and provides relevant educational content. The Ministry of Education can also provide educational content tailored to the student's level of understanding based on their past learning history. For example, the Ministry of Education analyzes the student's past learning history and provides educational content tailored to their level of understanding. Furthermore, the Ministry of Education can analyze the student's past learning history and propose an optimal learning plan. For example, the Ministry of Education proposes an optimal learning plan based on the student's past learning history. This allows for the provision of more appropriate educational content by referring to past learning history.

[0049] The Ministry of Education customizes content based on the learner's current learning status. For example, it provides educational content with adjusted difficulty levels based on the learner's current level of understanding. For example, it assesses the learner's current level of understanding and provides educational content with adjusted difficulty levels. The Ministry of Education can also suggest what to learn next based on the learner's current learning progress. For example, it checks the learner's learning progress and suggests what to learn next. Furthermore, the Ministry of Education can analyze the learner's current learning situation and suggest the optimal learning method. For example, it analyzes the learner's learning situation and suggests the optimal learning method. In this way, by customizing content based on the current learning situation, more effective education can be provided.

[0050] The Ministry of Education provides optimal content based on the geographical location of the learners. For example, it provides educational content based on the culture and customs of the region where the learners reside. For instance, it analyzes the regional culture of the learners and provides optimal educational content. It can also provide educational content based on the market conditions of the region where the learners reside. For example, it analyzes the local market conditions and provides optimal educational content. Furthermore, it can provide educational content based on the laws and regulations of the region where the learners reside. For example, it checks local laws and regulations and provides educational content based on them. By considering geographical location, it is possible to provide more appropriate educational content.

[0051] The Ministry of Education analyzes students' social media activity and provides content accordingly. For example, it analyzes students' social media posts and provides relevant educational content. For instance, it monitors students' social media accounts and analyzes their posts. The Ministry of Education can also analyze students' social media reactions and provide optimal educational content. For example, it analyzes social media comments and retweets and provides relevant educational content. Furthermore, the Ministry of Education can provide educational content based on students' social media trends. For example, it monitors social media trends and provides relevant educational content. This allows for the provision of more appropriate educational content by analyzing social media activity.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The data collection unit can collect health data from CEOs and founders and adjust the timing of data collection based on their health status. For example, the data collection unit can monitor the CEO's sleep patterns and food records and collect data when their health is good. The data collection unit can also collect data after a founder exercises while they are relaxed. Furthermore, if the CEO feels ill or fatigued, the data collection unit can temporarily suspend data collection and resume it after they have recovered. This allows for more accurate and useful data to be collected by adjusting the timing of data collection based on their health status.

[0054] The analytics department can also build predictive models to support future decision-making based on the CEO's and founder's past successes and failures. For example, the analytics department can analyze past project data to identify factors behind success and causes of failure. The analytics department can also analyze past market trends and competitor actions to forecast future market trends. Furthermore, the analytics department can forecast future financial conditions based on past financial data and provide advice on risk management. This can improve the quality of a company's operations by utilizing past data to support future decision-making.

[0055] The data provision department can also provide individually customized insights based on the employee's skills and experience. For example, the data provision department can analyze the employee's past work history and provide relevant insights. The data provision department can also adjust the difficulty of the insights according to the employee's skill level. Furthermore, the data provision department can provide insights that will be useful for future growth based on the employee's career goals. In this way, by providing individually customized insights, it is possible to improve the skills and work quality of employees.

[0056] The data collection unit can also filter data based on specific events or situations. For example, it can prioritize collecting statements and actions at the start and end of important projects. It can also collect statements and actions during company earnings announcements. Furthermore, it can filter and collect statements and actions related to internal company events and anniversaries. This allows for the priority collection of important data by filtering data based on specific events and situations.

[0057] The service provider can also apply different delivery methods depending on the category of knowledge. For example, financial knowledge can be provided in the form of a financial report. Human resources knowledge can be provided in the form of a human resources report. Furthermore, marketing knowledge can be provided in the form of a marketing report. By applying different delivery methods according to the category of knowledge, it becomes possible to provide more appropriate insights.

[0058] The consulting department can also provide optimal advice based on the client's geographical location. For example, they can provide advice based on the market conditions in the client's area. They can also provide advice based on the laws and regulations in the client's area. Furthermore, they can provide advice based on the culture and customs of the client's area. By considering geographical location, they can provide more appropriate advice.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The data collection unit collects past statements and actions of the CEO and founders. For example, it can collect statements made in meetings, email content, and social media posts. The data collection unit analyzes meeting recordings and saves the content of statements as text data. It can also automatically analyze email content and extract important information. Furthermore, it collects social media posts in real time and analyzes their content. Step 2: The analysis unit analyzes the collected data and reproduces specific insights. For example, it can use natural language processing techniques to analyze the content of statements and extract important insights. It can also use text mining techniques to extract important keywords from the content of statements. Furthermore, it can use machine learning algorithms to classify the content of statements and reproduce insights. It can learn from past statement data and reproduce insights based on new statement data. Step 3: The providing unit provides the reproduced knowledge to the employee. For example, the knowledge can be provided in text format. The providing unit sends the knowledge to the employee by email. The knowledge can also be provided through a web application. The knowledge can be made viewable on the web application.

[0061] (Example 2) A system according to an embodiment of the present invention dramatically improves the quality of employees' work by creating a personality model of a CEO, founder, or director (hereinafter "MASA") in a generative AI and utilizing the model as an advisor in daily operations and business processes. This system includes a data collection module for creating the MASA's personality, which collects data on the CEO's and founder's past statements and actions. Next, an analysis module analyzes the collected data, allowing the generative AI to recreate MASA's knowledge. Finally, a provision module provides the recreated knowledge to employees, enabling rapid decision-making and problem-solving. The system also includes an expansion function for services and commercialization, such as for business (external sales) and for the public (global audience). For example, consulting services utilizing MASA's knowledge could be provided to businesses, and educational content and advice based on MASA's ideas could be provided to the general public. For example, a data collection module for creating the MASA's personality could collect data on the CEO's and founder's past statements and actions. Next, an analysis module analyzes the collected data, allowing the generative AI to recreate MASA's knowledge. Furthermore, a provision department will be established to provide employees with the reproduced knowledge. This will enable quick decision-making and problem-solving. Furthermore, as an extension function, services and commercialization will be implemented for biz (external sales) and for the public (for people all over the world). For example, consulting services utilizing MASA's knowledge can be provided to companies, and educational content and advice based on MASA's ideas can be provided to the general public. This will enable the system to dramatically improve the quality of employees' work.

[0062] The system according to the embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit collects data on past statements and behavior of CEOs and founders. For example, the data collection unit can collect statements made in meetings, email content, and social media posts. For example, the data collection unit can analyze recordings of meetings and save the statements as text data. The data collection unit can also automatically analyze email content and extract important information. The data collection unit can also collect social media posts and analyze the posts. For example, the data collection unit collects social media posts in real time and analyzes the posts. The analysis unit analyzes the collected data and reproduces MASA's knowledge. For example, the analysis unit uses natural language processing technology to analyze the statements and extract important knowledge. The analysis unit uses text mining technology to extract important keywords from the statements. The analysis unit can also classify the statements and reproduce the knowledge using a machine learning algorithm. For example, the analysis unit learns from past statement data and reproduces knowledge based on new statement data. The providing unit provides the reproduced knowledge to the employee. For example, the providing unit can provide the knowledge in text format. For example, the providing unit can send the knowledge to the employee by email. The providing unit can also provide the knowledge through a web application. For example, the providing unit can make the knowledge viewable on the web application. In this way, the system according to the embodiment can dramatically improve the work quality of employees.

[0063] The service provider includes a consulting department that provides consulting services utilizing specific expertise. The consulting department provides consulting services that leverage MASA's expertise. For example, the consulting department can provide advice on a company's management strategy. For example, the consulting department can analyze a company's management strategy and propose the optimal strategy. The consulting department can also provide advice on marketing methods. For example, the consulting department can analyze a company's marketing strategy and propose effective marketing methods. Furthermore, the consulting department can also provide advice on technical know-how. For example, the consulting department can provide advice on a company's technology development. In this way, by providing consulting services that leverage MASA's expertise, the quality of business operations can be improved.

[0064] The provision department includes an education department that provides educational content based on specific ideas. The education department provides educational content based on MASA's ideas. For example, the education department can provide educational content on new product concepts. For example, the education department can create educational content explaining new product concepts and provide it to employees. The education department can also provide educational content on business improvement suggestions. For example, the education department can create educational content explaining business improvement suggestions and provide it to employees. Furthermore, the education department can also provide educational content on technical know-how. For example, the education department can create educational content explaining technical know-how and provide it to employees. In this way, by providing educational content based on MASA's ideas, it is possible to improve the skills of employees.

[0065] The data collection unit estimates the CEO's or founder's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the CEO is feeling stressed, the data collection unit avoids collection times and collects data when the CEO is relaxed. For example, the data collection unit monitors the CEO's heart rate and facial expression to determine whether the CEO is feeling stressed. Furthermore, if the founder is in an important meeting, the data collection unit can collect data after the meeting. For example, the data collection unit checks the meeting schedule and collects data after the meeting ends. Furthermore, if the CEO is emotionally charged, the data collection unit can prioritize collecting the CEO's words and actions at that moment. For example, the data collection unit analyzes the CEO's facial expressions and tone of voice to determine whether the CEO is emotionally charged. This allows for more accurate data collection by adjusting the timing of data collection based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0066] The data collection unit filters data based on specific events or situations. For example, the data collection unit prioritizes collecting comments and actions at the start and end of an important project. For example, the data collection unit checks the project schedule and collects comments and actions at the start and end. The data collection unit can also collect comments and actions at the time of a company's earnings announcement. For example, the data collection unit checks the schedule of the earnings announcement and collects comments and actions at the time of the announcement. Furthermore, the data collection unit can filter and collect comments and actions at company events or anniversaries. For example, the data collection unit checks the schedule of company events and collects comments and actions at the time of the event. In this way, by filtering data based on specific events or situations, important data can be collected preferentially.

[0067] The data collection department determines the priority of data to be collected based on past projects or performance. For example, the data collection department prioritizes the collection of comments and actions related to successful projects. For example, the data collection department checks the records of successful projects and collects related comments and actions. The data collection department can also collect comments about points to reflect on and areas for improvement related to failed projects. For example, the data collection department checks the records of failed projects and collects comments about points to reflect on and areas for improvement. Furthermore, the data collection department can prioritize the collection of comments and actions during periods when performance fluctuated significantly. For example, the data collection department checks periods when performance fluctuated and collects comments and actions during those periods. In this way, important data can be collected efficiently by determining the priority of data based on past projects and performance.

[0068] The data collection unit selects the type of data to collect based on emotions. For example, if the CEO is relaxed, the data collection unit collects his or her daily remarks and behaviors. For example, the data collection unit monitors the CEO's heart rate and facial expression to determine whether the CEO is relaxed. Furthermore, if the founder is stressed, the data collection unit can collect remarks and behaviors that cause stress. For example, the data collection unit analyzes the founder's facial expression and tone of voice to determine whether the founder is stressed. Furthermore, if the CEO is emotionally aroused, the data collection unit can collect motivational remarks and behaviors. For example, the data collection unit analyzes the CEO's facial expression and tone of voice to determine whether the CEO is emotionally aroused. This allows for more appropriate data collection by selecting the type of data based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The data collection department prioritizes collecting highly relevant data based on geographical location information. For example, if the CEO is on a business trip, the data collection department will collect statements and actions taken at that location. For instance, the data collection department will check the geographical location of the business trip destination and collect statements and actions taken at that location. The data collection department can also collect statements and actions taken by founders if they are attending an event in a specific region. For example, the data collection department will check the geographical location of the event and collect statements and actions taken at that location. Furthermore, if the CEO is at headquarters, the data collection department can prioritize collecting statements and actions taken at headquarters. For example, the data collection department will check the geographical location of headquarters and collect statements and actions taken at that location. This allows for the efficient collection of highly relevant data by considering geographical location information.

[0070] The data collection department analyzes social media activity and collects relevant data. For example, it collects content posted by the CEO on social media. For instance, it monitors the CEO's social media accounts and collects their posts. The data collection department can also analyze the founder's social media reactions and collect relevant statements and actions. For example, it analyzes social media comments and retweets and collects relevant statements and actions. Furthermore, the data collection department can collect the CEO's statements about social media trends. For example, it monitors social media trends and collects relevant statements. This allows for the efficient collection of relevant data by analyzing social media activity.

[0071] The analysis unit adjusts the analysis algorithm based on the emotion. For example, if the CEO is relaxed, the analysis unit applies an algorithm that performs a detailed analysis. For example, the analysis unit monitors the CEO's heart rate and facial expression to determine whether the CEO is relaxed and then performs a detailed analysis. The analysis unit can also apply an algorithm that performs a simplified analysis if the founder is stressed. For example, the analysis unit analyzes the founder's facial expression and tone of voice to determine whether the founder is stressed and then performs a simplified analysis. Furthermore, if the CEO is emotionally aroused, the analysis unit can also apply an algorithm that performs an analysis that emphasizes emotional fluctuations. For example, the analysis unit analyzes the CEO's facial expression and tone of voice to determine whether the CEO is emotionally aroused and then performs an analysis that emphasizes emotional fluctuations. This enables more accurate analysis by adjusting the analysis algorithm based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The analysis unit adjusts the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data related to important projects in detail. For example, the analysis unit evaluates the importance of the project and analyzes the related data in detail. The analysis unit can also simplify and analyze data related to daily operations. For example, the analysis unit simplifies and analyzes data of daily operations. Furthermore, the analysis unit can also analyze in detail data that has a major impact on the company's performance. For example, the analysis unit analyzes in detail data that has a major impact on performance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, efficient analysis is possible.

[0073] The analysis unit applies different analysis algorithms depending on the category of data. For example, the analysis unit applies a financial analysis algorithm to financial data. For example, the analysis unit applies a dedicated algorithm for analyzing financial data. The analysis unit can also apply a personnel analysis algorithm to personnel data. For example, the analysis unit applies a dedicated algorithm for analyzing personnel data. The analysis unit can also apply a marketing analysis algorithm to marketing data. For example, the analysis unit applies a dedicated algorithm for analyzing marketing data. In this way, applying an analysis algorithm depending on the category of data enables more appropriate analysis.

[0074] The analysis unit adjusts the display method of the analysis results based on the emotion. For example, if the CEO is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit monitors the CEO's heart rate and facial expression to determine whether the CEO is nervous and provides a simple, highly visible display method. Furthermore, if the founder is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit analyzes the founder's facial expression and tone of voice to determine whether the founder is relaxed and provides a display method that includes detailed information. Furthermore, if the CEO is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit checks the CEO's schedule, determines whether the CEO is in a hurry, and provides a display method that focuses on the main points. This allows for adjusting the display method based on the emotion, thereby providing more understandable analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The analysis unit determines the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. For example, the analysis unit checks the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also prioritize analyzing data of important events or projects. For example, the analysis unit checks the schedule of the event or project and prioritizes analyzing the related data. Furthermore, the analysis unit can also analyze data including past data to grasp overall trends. For example, the analysis unit collects past data and analyzes it to grasp overall trends. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the data was collected.

[0076] The analysis unit adjusts the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of directly related data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of directly related data. The analysis unit can also analyze indirectly related data. For example, the analysis unit collects and analyzes indirectly related data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data in real time and dynamically adjusts the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0077] The providing unit adjusts the way in which the insights are presented based on the emotion. For example, if the CEO is relaxed, the providing unit provides insights with detailed explanations. For example, the providing unit monitors the CEO's heart rate and facial expression to determine whether the CEO is relaxed and provides insights with detailed explanations. Furthermore, if the founder is stressed, the providing unit can provide concise and to-the-point insights. For example, the providing unit analyzes the founder's facial expression and tone of voice to determine whether the founder is stressed and provide concise and to-the-point insights. Furthermore, if the CEO is emotionally charged, the providing unit can provide motivational insights. For example, the providing unit analyzes the CEO's facial expression and tone of voice to determine whether the CEO is emotionally charged and provide motivational insights. This allows for more appropriate insights to be provided by adjusting the way the insights are presented based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The provision department adjusts the level of detail of the knowledge provided based on the importance of the knowledge. For example, the provision department provides detailed knowledge related to important projects. For example, the provision department evaluates the importance of a project and provides detailed related knowledge. The provision department can also provide simplified knowledge related to daily operations. For example, the provision department provides simplified knowledge of daily operations. Furthermore, the provision department can also provide detailed knowledge that will have a major impact on the company's performance. For example, the provision department provides detailed knowledge that will have a major impact on performance. In this way, efficient knowledge provision is possible by adjusting the level of detail of the knowledge provided based on the importance of the knowledge.

[0079] The provision unit applies different provision methods depending on the category of knowledge. For example, the provision unit provides financial knowledge in the form of a financial report. For example, the provision unit analyzes financial data and provides the knowledge in the form of a financial report. The provision unit can also provide human resources knowledge in the form of a human resources report. For example, the provision unit analyzes human resources data and provides the knowledge in the form of a human resources report. Furthermore, the provision unit can also provide marketing knowledge in the form of a marketing report. For example, the provision unit analyzes marketing data and provides the knowledge in the form of a marketing report. This makes it possible to provide more appropriate knowledge by applying a provision method depending on the category of knowledge.

[0080] The providing unit adjusts the length of the insight to be provided based on the emotion. For example, if the CEO is relaxed, the providing unit provides a long insight with detailed explanations. For example, the providing unit monitors the CEO's heart rate and facial expression to determine whether the CEO is relaxed and provides a long insight with detailed explanations. The providing unit can also provide a concise and short insight if the founder is stressed. For example, the providing unit analyzes the founder's facial expression and tone of voice to determine whether the founder is stressed and provides a concise and short insight. Furthermore, the providing unit can also provide a long insight to increase motivation if the CEO is emotionally aroused. For example, the providing unit analyzes the CEO's facial expression and tone of voice to determine whether the CEO is emotionally aroused and provides a long insight to increase motivation. This allows for adjusting the length of the insight based on emotion to provide more appropriate insights. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The provision unit determines the priority of provision based on the time when the knowledge was collected. The provision unit, for example, prioritizes providing the latest knowledge. For example, the provision unit checks when the knowledge was collected and prioritizes providing the latest knowledge. The provision unit can also prioritize providing knowledge related to important events or projects. For example, the provision unit checks the schedule of the event or project and prioritizes providing the related knowledge. Furthermore, the provision unit can also provide knowledge, including past knowledge, to grasp overall trends. For example, the provision unit collects past knowledge and provides it to grasp overall trends. In this way, by determining the priority of provision based on when the knowledge was collected, efficient knowledge provision is possible.

[0082] The providing unit adjusts the order of provision based on the relevance of the knowledge. For example, the providing unit provides directly related knowledge with priority. For example, the providing unit evaluates the relevance of the knowledge and provides directly related knowledge with priority. The providing unit can also provide indirectly related knowledge. For example, the providing unit collects and provides indirectly related knowledge. Furthermore, the providing unit can dynamically adjust the order of provision based on the relevance of the knowledge. For example, the providing unit evaluates the relevance of the knowledge in real time and dynamically adjusts the order of provision. This enables efficient knowledge provision by adjusting the order of provision based on the relevance of the knowledge.

[0083] The consulting department adjusts its consulting approach based on the CEO's emotions. For example, if the CEO appears relaxed, the consulting department provides detailed consulting. For example, the consulting department monitors the CEO's heart rate and facial expression to determine whether the CEO is relaxed and provides detailed consulting. Furthermore, if the founder appears stressed, the consulting department can provide concise, to-the-point consulting. For example, the consulting department analyzes the founder's facial expression and tone of voice to determine whether the founder is stressed and provides concise, to-the-point consulting. Furthermore, if the CEO appears emotionally charged, the consulting department can provide motivational consulting. For example, the consulting department analyzes the CEO's facial expression and tone of voice to determine whether the CEO is emotionally charged and provides motivational consulting. This allows for more appropriate consulting by adjusting the consulting approach based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The consulting department provides optimal advice based on the client's past performance or projects. For example, the consulting department provides advice based on the client's successful projects. For instance, the consulting department reviews the records of the client's successful projects and provides relevant advice. The consulting department can also propose improvements based on the client's failed projects. For example, the consulting department reviews the records of the client's failed projects and proposes improvements. Furthermore, the consulting department can provide advice based on periods when the client's performance has fluctuated significantly. For example, the consulting department reviews periods when the client's performance has fluctuated and provides advice based on those periods. In this way, by providing optimal advice based on past performance and projects, the quality of the client's operations can be improved.

[0085] The consulting department customizes advice based on the client's current business situation. For example, the consulting department provides advice based on the client's current market situation. For instance, the consulting department analyzes the client's market situation and provides optimal advice. The consulting department can also provide advice based on the client's current financial situation. For example, the consulting department analyzes the client's financial situation and provides optimal advice. Furthermore, the consulting department can also provide advice based on the client's current human resources situation. For example, the consulting department analyzes the client's human resources situation and provides optimal advice. This allows for the provision of more appropriate advice by customizing it based on the current business situation.

[0086] The consulting department prioritizes consulting based on emotions. For example, if the CEO is relaxed, the consulting department will prioritize providing detailed consulting. For instance, the consulting department may monitor the CEO's heart rate and facial expressions to determine if they are relaxed and then prioritize providing detailed consulting. Similarly, if the founder is stressed, the consulting department may prioritize providing concise and to-the-point consulting. For example, the consulting department may analyze the founder's facial expressions and tone of voice to determine if they are stressed and then prioritize providing concise and to-the-point consulting. Furthermore, if the CEO is emotionally aroused, the consulting department may prioritize providing motivational consulting. For example, the consulting department may analyze the CEO's facial expressions and tone of voice to determine if they are emotionally aroused and then prioritize providing motivational consulting. By prioritizing consulting based on emotions, more effective consulting can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The consulting department provides optimal advice based on the client's geographic location information. The consulting department provides advice based on, for example, the market conditions in the region where the client is located. For example, the consulting department analyzes the client's local market and provides optimal advice. The consulting department can also provide advice based on the laws and regulations in the region where the client is located. For example, the consulting department checks local laws and regulations and provides optimal advice. Furthermore, the consulting department can also provide advice based on the culture and customs in the region where the client is located. For example, the consulting department understands the local culture and customs and provides advice based on them. This allows for more appropriate advice to be provided by taking geographic location information into consideration.

[0088] The consulting department analyzes the social media activity of clients and provides advice. For example, the consulting department analyzes the content posted on social media by clients and provides advice. For example, the consulting department monitors the social media accounts of clients and analyzes the content posted. The consulting department can also analyze the reactions on social media by clients and provide advice. For example, the consulting department analyzes comments and retweets on social media and provides related advice. Furthermore, the consulting department can also provide advice based on trends on social media by clients. For example, the consulting department monitors social media trends and provides related advice. This allows the consulting department to provide more appropriate advice by analyzing social media activity.

[0089] The education department adjusts the way educational content is presented based on emotions. For example, if the CEO is relaxed, the education department provides educational content with detailed explanations. For example, the education department monitors the CEO's heart rate and facial expression to determine whether the CEO is relaxed and provides educational content with detailed explanations. Furthermore, if a founder is stressed, the education department can provide concise and to-the-point educational content. For example, the education department analyzes the founder's facial expression and tone of voice to determine whether the founder is stressed and provides concise and to-the-point educational content. Furthermore, if the CEO is emotionally charged, the education department can provide motivational educational content. For example, the education department analyzes the CEO's facial expression and tone of voice to determine whether the CEO is emotionally charged and provides motivational educational content. This allows for more effective education by adjusting the way educational content is presented based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The Ministry of Education provides optimal content based on the student's past learning history. For example, the Ministry of Education provides relevant educational content based on what the student has previously studied. For example, the Ministry of Education checks the student's past learning history and provides relevant educational content. The Ministry of Education can also provide educational content tailored to the student's level of understanding based on their past learning history. For example, the Ministry of Education analyzes the student's past learning history and provides educational content tailored to their level of understanding. Furthermore, the Ministry of Education can analyze the student's past learning history and propose an optimal learning plan. For example, the Ministry of Education proposes an optimal learning plan based on the student's past learning history. This allows for the provision of more appropriate educational content by referring to past learning history.

[0091] The Ministry of Education customizes content based on the learner's current learning status. For example, it provides educational content with adjusted difficulty levels based on the learner's current level of understanding. For example, it assesses the learner's current level of understanding and provides educational content with adjusted difficulty levels. The Ministry of Education can also suggest what to learn next based on the learner's current learning progress. For example, it checks the learner's learning progress and suggests what to learn next. Furthermore, the Ministry of Education can analyze the learner's current learning situation and suggest the optimal learning method. For example, it analyzes the learner's learning situation and suggests the optimal learning method. In this way, by customizing content based on the current learning situation, more effective education can be provided.

[0092] The education department prioritizes educational content based on emotions. For example, if the CEO is relaxed, the education department prioritizes providing detailed educational content. For example, the education department monitors the CEO's heart rate and facial expression to determine whether the CEO is relaxed and prioritizes providing detailed educational content. Furthermore, if the founder is stressed, the education department can prioritize providing concise and to-the-point educational content. For example, the education department analyzes the founder's facial expression and tone of voice to determine whether the founder is stressed and prioritizes providing concise and to-the-point educational content. Furthermore, if the CEO is emotionally charged, the education department can prioritize providing motivational educational content. For example, the education department analyzes the CEO's facial expression and tone of voice to determine whether the CEO is emotionally charged and prioritizes providing motivational educational content. This allows for more effective education by prioritizing educational content based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The Ministry of Education provides optimal content based on the geographical location of the learners. For example, it provides educational content based on the culture and customs of the region where the learners reside. For instance, it analyzes the regional culture of the learners and provides optimal educational content. It can also provide educational content based on the market conditions of the region where the learners reside. For example, it analyzes the local market conditions and provides optimal educational content. Furthermore, it can provide educational content based on the laws and regulations of the region where the learners reside. For example, it checks local laws and regulations and provides educational content based on them. By considering geographical location, it is possible to provide more appropriate educational content.

[0094] The Ministry of Education analyzes students' social media activity and provides content accordingly. For example, it analyzes students' social media posts and provides relevant educational content. For instance, it monitors students' social media accounts and analyzes their posts. The Ministry of Education can also analyze students' social media reactions and provide optimal educational content. For example, it analyzes social media comments and retweets and provides relevant educational content. Furthermore, the Ministry of Education can provide educational content based on students' social media trends. For example, it monitors social media trends and provides relevant educational content. This allows for the provision of more appropriate educational content by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects data on the statements and actions of the CEO and founder using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the analysis device via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and reproduces the knowledge of MASA. The provision unit provides the reproduced knowledge to employees, for example, via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects statements and behavioral data of the CEO and founder using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data to the analysis device via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and reproduces the knowledge of MASA. The provision unit provides the reproduced knowledge to employees, for example, through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit collects data on the statements and actions of the CEO and founder using the camera 42 and microphone 238 of the headset terminal 314, and transmits the data to the analysis device via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to reproduce MASA's findings. The provision unit provides the reproduced findings to employees, for example, via the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects data on the statements and actions of the CEO and founder using the camera 42 and microphone 238 of the robot 414, and transmits the data to the analysis device via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and reproduces the knowledge of MASA. The provision unit provides the reproduced knowledge to employees, for example, through the speaker 240 of the robot 414.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The data collection unit can collect health data from CEOs and founders and adjust the timing of data collection based on their health status. For example, the data collection unit can monitor the CEO's sleep patterns and food records and collect data when their health is good. The data collection unit can also collect data after a founder exercises while they are relaxed. Furthermore, if the CEO feels ill or fatigued, the data collection unit can temporarily suspend data collection and resume it after they have recovered. This allows for more accurate and useful data to be collected by adjusting the timing of data collection based on their health status.

[0097] The analytics department can also build predictive models to support future decision-making based on the CEO's and founder's past successes and failures. For example, the analytics department can analyze past project data to identify factors behind success and causes of failure. The analytics department can also analyze past market trends and competitor actions to forecast future market trends. Furthermore, the analytics department can forecast future financial conditions based on past financial data and provide advice on risk management. This can improve the quality of a company's operations by utilizing past data to support future decision-making.

[0098] The data provision department can also provide individually customized insights based on the employee's skills and experience. For example, the data provision department can analyze the employee's past work history and provide relevant insights. The data provision department can also adjust the difficulty of the insights according to the employee's skill level. Furthermore, the data provision department can provide insights that will be useful for future growth based on the employee's career goals. In this way, by providing individually customized insights, it is possible to improve the skills and work quality of employees.

[0099] The data collection department can also estimate the emotions of the CEO or founder and evaluate the importance of data based on the estimated emotions. For example, if the CEO is emotionally charged, the data collection department will prioritize collecting his or her words and actions as important data. Also, if the founder is feeling stressed, the data collection department can carefully evaluate his or her words and actions and refrain from collecting them if necessary. Furthermore, if the CEO is relaxed, the data collection department can collect his or her words and actions as everyday data. This allows more useful data to be collected by evaluating the importance of data based on emotions.

[0100] The data collection unit can also filter data based on specific events or situations. For example, it can prioritize collecting statements and actions at the start and end of important projects. It can also collect statements and actions during company earnings announcements. Furthermore, it can filter and collect statements and actions related to internal company events and anniversaries. This allows for the priority collection of important data by filtering data based on specific events and situations.

[0101] The analysis unit can also adjust the analysis algorithm based on the emotions of the CEO or founder. For example, if the CEO is relaxed, the analysis unit can apply an algorithm that performs a detailed analysis. Alternatively, if the founder is feeling stressed, the analysis unit can apply an algorithm that performs a simplified analysis. Furthermore, if the CEO is emotionally excited, the analysis unit can apply an algorithm that performs an analysis that emphasizes emotional fluctuations. In this way, adjusting the analysis algorithm based on emotions enables more accurate analysis.

[0102] The delivery department can also adjust the way it delivers insights based on the employee's emotions. For example, if the employee is relaxed, the delivery department can deliver insights that include detailed explanations. If the employee is feeling stressed, the delivery department can deliver insights that are concise and to the point. Furthermore, if the employee is emotionally charged, the delivery department can deliver insights that increase motivation. This allows for more effective insight delivery by adjusting the way it delivers insights based on the employee's emotions.

[0103] The service provider can also apply different delivery methods depending on the category of knowledge. For example, financial knowledge can be provided in the form of a financial report. Human resources knowledge can be provided in the form of a human resources report. Furthermore, marketing knowledge can be provided in the form of a marketing report. By applying different delivery methods according to the category of knowledge, it becomes possible to provide more appropriate insights.

[0104] The consulting department can also adjust its consulting approach based on the client's emotions. For example, if the CEO is relaxed, it can provide detailed consulting. If the founder is stressed, it can provide concise, to-the-point consulting. Furthermore, if the CEO is emotionally charged, it can provide motivational consulting. By adjusting its consulting approach based on emotions, it can provide more appropriate consulting.

[0105] The consulting department can also provide optimal advice based on the client's geographical location. For example, they can provide advice based on the market conditions in the client's area. They can also provide advice based on the laws and regulations in the client's area. Furthermore, they can provide advice based on the culture and customs of the client's area. By considering geographical location, they can provide more appropriate advice.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The data collection unit collects past statements and actions of the CEO and founders. For example, it can collect statements made in meetings, email content, and social media posts. The data collection unit analyzes meeting recordings and saves the content of statements as text data. It can also automatically analyze email content and extract important information. Furthermore, it collects social media posts in real time and analyzes their content. Step 2: The analysis unit analyzes the collected data and reproduces specific insights. For example, it can use natural language processing techniques to analyze the content of statements and extract important insights. It can also use text mining techniques to extract important keywords from the content of statements. Furthermore, it can use machine learning algorithms to classify the content of statements and reproduce insights. It can learn from past statement data and reproduce insights based on new statement data. Step 3: The providing unit provides the reproduced knowledge to the employee. For example, the knowledge can be provided in text format. The providing unit sends the knowledge to the employee by email. The knowledge can also be provided through a web application. The knowledge can be made viewable on the web application.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0164] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0169] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0170] 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.

[0171] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0172] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0173] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0174] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0177] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects data on past statements or actions of the CEO or founder, An analysis unit analyzes the data collected by the aforementioned data collection unit and reproduces specific findings, The system includes a provisioning unit that provides employees with the insights reproduced by the aforementioned analysis unit. A system characterized by:

2. The providing unit The company has a consulting department that provides consulting services utilizing specific expertise. The system of claim 1 .

3. The providing unit It has an education department that provides educational content based on specific ideas. The system of claim 1 .

4. The aforementioned data acquisition unit is The system estimates the emotions of the CEO or founder and adjusts the timing of collecting data on their statements or actions based on those estimated emotions. The system of claim 1 .

5. The aforementioned data acquisition unit is Filter data based on specific events or circumstances. The system of claim 1 .

6. The aforementioned data acquisition unit is Prioritize the data to collect based on past projects or achievements. The system of claim 1 .

7. The aforementioned data acquisition unit is Select the types of data to collect based on emotions. The system of claim 1 .

8. The aforementioned data acquisition unit is Prioritize the collection of highly relevant data based on geographical location information. The system of claim 1 .

9. The aforementioned data acquisition unit is Analyze social media activity and collect relevant data. The system of claim 1 .

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