system

Abstract

An object of the system according to the embodiment is to receive knowledge and ideas of a specific person in a timely manner.SOLUTION: A system according to an embodiment includes a personality forming part, a data analyzing part, and an advice providing part. The personality forming part forms a personality of the MASA. The data analyzing unit analyzes the past speech or work of the MASA based on the personality of the MASA formed by the personality forming unit. The advice providing unit provides advice with respect to a task on business based on the data analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to receive the knowledge and ideas of specific individuals in a timely manner, which limited the improvement in work quality and efficiency.

[0005] The system according to the embodiment aims to receive the knowledge and ideas of a specific person in a timely manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality development unit, a data analysis unit, and an advice provision unit. The personality development unit develops the personality of MASA. The data analysis unit analyzes MASA's past statements or writings based on the personality of MASA developed by the personality development unit. The advice provision unit provides advice on business issues based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can receive the knowledge and ideas of a specific person in a timely manner. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The generative AI system according to an embodiment of the present invention recreates the personality of Masayoshi Son, founder and director (hereinafter referred to as "MASA") and forms it within the generative AI. This generative AI system is used as an advisor in daily operations and business, and can receive MASA's knowledge and ideas in a timely manner. This allows the generative AI system to improve the quality and efficiency of operations and speed up decision-making.

[0029] The generative AI system according to the embodiment includes a personality development unit, a data analysis unit, and an advice provision unit. The personality development unit develops MASA's personality. For example, the generative AI analyzes MASA's past statements and writings and recreates his / her personality. The generative AI can also learn MASA's thought patterns and values ​​and develop a personality based on them. The generative AI can also learn MASA's business strategy and develop a personality based on them. The data analysis unit analyzes MASA's past statements and writings based on MASA's personality developed by the personality development unit. For example, the data analysis unit analyzes MASA's statements and writings using natural language processing technology to extract important information. The data analysis unit can also analyze MASA's statements and writings using machine learning algorithms to find patterns. The data analysis unit can also analyze MASA's statements and writings using text mining technology to find trends. The advice provision unit provides advice on business issues based on the data analyzed by the data analysis unit. For example, the advice providing unit proposes solutions to business issues based on the analysis results. The advice providing unit can also propose improvements to business issues based on the analysis results. The advice providing unit can also propose new ideas for business issues based on the analysis results. As a result, the generative AI system according to the embodiment can improve the quality and efficiency of business operations and speed up decision-making by receiving knowledge and ideas from MASA in a timely manner.

[0030] The data analysis unit can analyze MASA's past decision-making processes and reproduce them. For example, the data analysis unit allows the generative AI to analyze MASA's past decision-making processes and learn decision-making patterns and criteria. For example, it models decision-making trends based on data from past projects and business strategies. The data analysis unit also allows the generative AI to reproduce MASA's decision-making processes and provide advice on business issues. For example, the generative AI can propose solutions to current business issues based on past decision-making processes. The generative AI can also suggest improvements to current business issues based on past decision-making processes. The generative AI can also propose new ideas for current business issues based on past decision-making processes. In this way, by reproducing MASA's decision-making process, a more realistic personality can be formed.

[0031] The personality development unit can also incorporate data from other business leaders to create a new personality that combines the personalities of multiple leaders. For example, the generation AI can collect data on the statements and writings of other prominent business leaders and combine it with MASA's personality. For example, data on Steve Jobs and Jeff Bezos can be incorporated. The personality development unit can also create a new personality that combines the personalities of multiple leaders. For example, the generation AI can learn the thought patterns and values ​​of multiple leaders and use them to create a new personality. The generation AI can also learn the business strategies of multiple leaders and use them to create a new personality. The generation AI can also create a new personality based on the statements and behavioral data of multiple leaders. By combining the personalities of multiple leaders, a personality with a more multifaceted perspective can be created.

[0032] The personality development unit can learn business practices from different cultures or regions and develop a personality with a global perspective. For example, the generation AI can learn business practices from different cultures or regions and reflect them in MASA's personality. For example, it can incorporate business practices from Asia and Europe. The personality development unit also allows the generation AI to develop a personality with a global perspective. For example, the generation AI can learn business practices from different cultures or regions and develop a personality based on that. The generation AI can also learn business etiquette and negotiation styles from different cultures or regions and develop a personality based on that. The generation AI can also learn business strategies from different cultures or regions and develop a personality based on that. In this way, by learning business practices from different cultures and regions, a personality with a global perspective can be developed.

[0033] When analyzing MASA's statements and writings, the generation AI takes into account the background and circumstances of the statements, allowing it to recreate a personality that is more faithful to the context. For example, when analyzing MASA's statements and writings, the generation AI takes into account the background and circumstances of the statements. For example, it recreates the context based on the time, place, and target audience of the statements. The generation AI also recreates MASA's personality based on the background and circumstances of the statements. For example, it recreates the personality by taking into account the context before and after the statements and related events. The generation AI can also adjust the content of advice based on the background and circumstances of the statements. The generation AI can also adjust the tone of advice based on the background and circumstances of the statements. In this way, by taking into account the background and circumstances of the statements, it is possible to recreate a personality that is more faithful to the context.

[0034] When analyzing MASA's statements and writings, the generation AI can also analyze the tone or nuance of the statements and create a personality that reflects that. For example, when analyzing MASA's statement data, the generation AI takes tone and nuance into consideration. For example, it analyzes the emotional emphasis and intonation of the statements and reflects that. The generation AI also creates MASA's personality based on the tone and nuance of the statements. For example, it adjusts the personality's reactions based on the emotional emphasis and intonation of the statements. The generation AI can also adjust the content of advice based on the tone and nuance of the statements. The generation AI can also adjust the tone of advice based on the tone and nuance of the statements. In this way, a more realistic personality can be created by analyzing the tone and nuance of the statements.

[0035] When analyzing MASA data, the generative AI can simultaneously analyze data from other business leaders to create a personality that combines the knowledge of multiple leaders. For example, the generative AI can simultaneously analyze MASA data and data from other business leaders to create a personality that combines their knowledge. For example, it can incorporate data from Steve Jobs and Jeff Bezos. The generative AI can also create a personality that combines the knowledge of multiple leaders. For example, the generative AI can create a personality that combines the knowledge of multiple leaders based on their statements and writings. The generative AI can also learn the thought patterns and values ​​of multiple leaders and create a personality based on that. The generative AI can also learn the business strategies of multiple leaders and create a personality based on that. This allows for the creation of a personality with a more multifaceted perspective by incorporating the knowledge of other business leaders.

[0036] When analyzing MASA data, the generative AI can incorporate data from different industries or fields to create a personality with a more multifaceted perspective. For example, the generative AI can simultaneously analyze MASA data and data from other industries or fields to create a personality that combines the knowledge. For example, it can incorporate data from the technical and marketing fields. The generative AI can also create a MASA personality based on data from other industries or fields. For example, the generative AI can create a personality that combines the knowledge based on statements and writings from other industries or fields. The generative AI can also learn the thought patterns and values ​​of other industries and fields and create a personality based on that. The generative AI can also learn the business strategies of other industries and fields and create a personality based on that. In this way, by incorporating data from different industries and fields, it is possible to create a personality with a more multifaceted perspective.

[0037] When used as an advisor, the generative AI can learn the user's past consultation details and solutions and provide more personalized advice. For example, the generative AI stores the user's past consultation details and solutions in a database and provides personalized advice based on them. For example, it refers to past consultation history to suggest solutions to similar problems. The generative AI also learns the user's past consultation details and solutions and provides personalized advice. For example, the generative AI can suggest solutions to current business issues based on the user's past consultation details. The generative AI can also suggest improvements to current business issues based on the user's past solutions. The generative AI can also suggest new ideas for current business issues based on the user's past consultation details and solutions. In this way, more personalized advice can be provided by learning the user's past consultation details and solutions.

[0038] When used as an advisor, the generative AI can monitor the user's work status or progress in real time and provide advice based on that. For example, the generative AI can monitor the user's work status and progress in real time and provide advice based on that. For example, the generative AI can analyze the progress of a project and propose the next step. The generative AI can also provide advice based on the user's work status and progress. For example, the generative AI can propose solutions to current work issues based on the work progress. The generative AI can also propose improvements to current work issues based on the work progress. The generative AI can also propose new ideas for current work issues based on the work progress. This allows the user's work status and progress to be monitored in real time, allowing for more appropriate advice to be provided.

[0039] When used as an advisor, generative AI can incorporate the knowledge of experts from different industries or fields to provide more multifaceted advice. For example, generative AI incorporates the knowledge of experts from different industries or fields to provide multifaceted advice to users. For example, it combines the opinions of experts in the fields of technology and marketing. Generative AI also provides advice based on the knowledge of experts from different industries or fields. For example, generative AI can propose solutions to current business challenges based on the opinions of experts from different industries or fields. Generative AI can also propose improvements to current business challenges based on the opinions of experts from different industries or fields. Generative AI can also propose new ideas for current business challenges based on the opinions of experts from different industries or fields. In this way, more multifaceted advice can be provided by incorporating the knowledge of experts from different industries and fields.

[0040] When used as an advisor, generative AI can provide more appropriate advice based on the user's work environment and cultural background. For example, generative AI can take into account the user's work environment and cultural background and provide advice based on that. For example, it can take into account business etiquette and communication styles in different cultures. Generative AI can also provide advice based on the user's work environment and cultural background. For example, generative AI can propose solutions to current work issues based on the user's work environment. Generative AI can also suggest improvements to current work issues based on the user's cultural background. Generative AI can also propose new ideas for current work issues based on the user's work environment and cultural background. This allows more appropriate advice to be provided by taking the user's work environment and cultural background into consideration.

[0041] When used as an advisor in daily work or business, generative AI can monitor the user's business goals or KPIs and provide advice based on them. For example, generative AI can monitor the user's business goals and KPIs and provide advice based on them. For example, it can suggest specific steps for achieving goals. Generative AI can also provide advice based on the user's business goals and KPIs. For example, generative AI can suggest solutions to current business challenges based on business goals. Generative AI can also suggest improvements to current business challenges based on KPIs. Generative AI can also suggest new ideas for current business challenges based on business goals and KPIs. This allows more appropriate advice to be provided by monitoring the user's business goals and KPIs.

[0042] When used as an advisor in daily work or business, generative AI can incorporate the knowledge of experts from different industries or fields to provide more multifaceted advice. For example, generative AI incorporates the knowledge of experts from different industries and fields to provide multifaceted advice to users. For example, it combines the opinions of experts in the fields of technology and marketing. Generative AI also provides advice based on the knowledge of experts from different industries and fields. For example, generative AI can propose solutions to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose improvements to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose new ideas for current business challenges based on the opinions of experts from different industries and fields. In this way, more multifaceted advice can be provided by incorporating the knowledge of experts from different industries and fields.

[0043] When used as an advisor in daily work or business, generative AI can provide more appropriate advice based on the user's work environment and cultural background. For example, generative AI can take into account the user's work environment and cultural background and provide advice based on that. For example, it can take into account business etiquette and communication styles in different cultures. Generative AI can also provide advice based on the user's work environment and cultural background. For example, generative AI can propose solutions to current work challenges based on the user's work environment. Generative AI can also suggest improvements to current work challenges based on the user's cultural background. Generative AI can also propose new ideas for current work challenges based on the user's work environment and cultural background. This allows more appropriate advice to be provided by taking into account the user's work environment and cultural background.

[0044] When providing insights and ideas in a timely manner, the generative AI can learn the user's past consultation content or solutions and provide more personalized advice. For example, the generative AI stores the user's past consultation content and solutions in a database and provides personalized advice based on that. For example, it refers to past consultation history to suggest solutions to similar problems. The generative AI also learns the user's past consultation content and solutions and provides personalized advice. For example, the generative AI can suggest solutions to current business issues based on the user's past consultation content. The generative AI can also suggest improvements to current business issues based on the user's past solutions. The generative AI can also suggest new ideas for current business issues based on the user's past consultation content and solutions. In this way, more personalized advice can be provided by learning the user's past consultation content and solutions.

[0045] When providing knowledge and ideas in a timely manner, generative AI can monitor the user's work status or progress in real time and provide advice based on that. For example, generative AI can monitor the user's work status and progress in real time and provide advice based on that. For example, it can analyze the progress of a project and propose the next step. Generative AI can also provide advice based on the user's work status and progress. For example, generative AI can propose solutions to current work issues based on the progress of work. Generative AI can also propose improvements to current work issues based on the progress of work. Generative AI can also propose new ideas for current work issues based on the progress of work. This allows more appropriate advice to be provided by monitoring the user's work status and progress in real time.

[0046] When providing insights and ideas in a timely manner, generative AI can incorporate the knowledge of experts from different industries or fields to provide more multifaceted advice. For example, generative AI incorporates the knowledge of experts from different industries and fields to provide multifaceted advice to users. For example, it combines the opinions of experts in the fields of technology and marketing. Generative AI also provides advice based on the knowledge of experts from different industries and fields. For example, generative AI can propose solutions to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose improvements to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose new ideas for current business challenges based on the opinions of experts from different industries and fields. In this way, more multifaceted advice can be provided by incorporating the knowledge of experts from different industries and fields.

[0047] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

[0048] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

[0049] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

[0050] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

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

[0052] Generative AI systems can also learn from a user's past work history and provide personalized advice. For example, they can analyze data from projects the user has worked on in the past and suggest solutions to similar issues. They can also suggest improvements to current work issues based on the user's past successes and failures. They can also suggest new ideas based on the user's past work history. This allows the system to provide more personalized advice by learning from the user's past work history.

[0053] Generative AI systems can also incorporate data from different industries and fields to provide advice from a more multifaceted perspective. For example, they can analyze data from the technical or marketing fields to propose solutions to the user's business challenges. They can also suggest improvements to current business challenges based on success stories and failures from other industries. They can also propose new ideas based on data from other industries and fields. In this way, by incorporating data from different industries and fields, advice can be provided from a more multifaceted perspective.

[0054] The generative AI system can also monitor the user's business goals and KPIs and provide advice based on them. For example, it can suggest specific steps to achieve the goal. It can also suggest solutions to current business challenges based on the user's business goals and KPIs. It can also suggest new ideas based on the user's business goals and KPIs. This allows the system to provide more appropriate advice by monitoring the user's business goals and KPIs.

[0055] Generative AI systems can also provide advice based on a user's work environment and cultural background. For example, they can take into account business etiquette and communication styles in different cultures. They can also propose solutions to current business challenges based on the user's work environment and cultural background. They can also propose new ideas based on the user's work environment and cultural background. This allows them to provide more appropriate advice by taking into account the user's work environment and cultural background.

[0056] The generative AI system can also monitor the user's work status and progress in real time and provide advice based on that. For example, it can analyze the progress of a project and suggest the next step. It can also propose solutions to current business issues based on the user's work status and progress. It can also propose new ideas based on the user's work status and progress. This allows the system to provide more appropriate advice by monitoring the user's work status and progress in real time.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The personality formation unit forms MASA's personality. For example, the generation AI analyzes MASA's past statements and writings and recreates his personality. The generation AI can also learn MASA's thought patterns and values ​​and form a personality based on them. Furthermore, the generation AI can learn MASA's business strategy and form a personality based on them. Step 2: The Data Analysis Department analyzes MASA's past statements and writings based on MASA's personality formed by the Personality Development Department. For example, the Data Analysis Department may analyze MASA's statements and writings using natural language processing technology to extract important information. The Data Analysis Department may also analyze MASA's statements and writings using machine learning algorithms to find patterns. Furthermore, the Data Analysis Department may analyze MASA's statements and writings using text mining technology to find trends. Step 3: The advice providing unit provides advice for business issues based on the data analyzed by the data analysis unit. For example, the advice providing unit proposes solutions to business issues based on the analysis results. The advice providing unit can also propose improvements to business issues based on the analysis results. Furthermore, the advice providing unit can also propose new ideas for business issues based on the analysis results.

[0059] (Example 2) The generative AI system according to an embodiment of the present invention recreates the personality of Masayoshi Son, founder and director (hereinafter referred to as "MASA") and forms it within the generative AI. This generative AI system is used as an advisor in daily operations and business, and can receive MASA's knowledge and ideas in a timely manner. This allows the generative AI system to improve the quality and efficiency of operations and speed up decision-making.

[0060] The generative AI system according to the embodiment includes a personality development unit, a data analysis unit, and an advice provision unit. The personality development unit develops MASA's personality. For example, the generative AI analyzes MASA's past statements and writings and recreates his / her personality. The generative AI can also learn MASA's thought patterns and values ​​and develop a personality based on them. The generative AI can also learn MASA's business strategy and develop a personality based on them. The data analysis unit analyzes MASA's past statements and writings based on MASA's personality developed by the personality development unit. For example, the data analysis unit analyzes MASA's statements and writings using natural language processing technology to extract important information. The data analysis unit can also analyze MASA's statements and writings using machine learning algorithms to find patterns. The data analysis unit can also analyze MASA's statements and writings using text mining technology to find trends. The advice provision unit provides advice on business issues based on the data analyzed by the data analysis unit. For example, the advice providing unit proposes solutions to business issues based on the analysis results. The advice providing unit can also propose improvements to business issues based on the analysis results. The advice providing unit can also propose new ideas for business issues based on the analysis results. As a result, the generative AI system according to the embodiment can improve the quality and efficiency of business operations and speed up decision-making by receiving knowledge and ideas from MASA in a timely manner.

[0061] The personality development unit can reproduce MASA's emotions or stress level and generate emotional responses appropriate to the situation. For example, the generation AI analyzes MASA's past speech and behavioral data to estimate his emotions and stress level. For example, it models emotional changes based on speech and behavior patterns in specific situations. The personality development unit also reproduces MASA's emotions and stress level and generates emotional responses appropriate to the situation. For example, the generation AI reproduces MASA's speech and behavior in situations where MASA is feeling stressed to generate an emotional response. The generation AI can also reproduce MASA's speech and behavior in situations where MASA is feeling happy to generate an emotional response. The generation AI can also reproduce MASA's speech and behavior in situations where MASA is feeling angry to generate an emotional response. By reproducing MASA's emotions and stress level, more realistic advice can be provided.

[0062] The data analysis unit can analyze MASA's past decision-making processes and reproduce them. For example, the data analysis unit allows the generative AI to analyze MASA's past decision-making processes and learn decision-making patterns and criteria. For example, it models decision-making trends based on data from past projects and business strategies. The data analysis unit also allows the generative AI to reproduce MASA's decision-making processes and provide advice on business issues. For example, the generative AI can propose solutions to current business issues based on past decision-making processes. The generative AI can also suggest improvements to current business issues based on past decision-making processes. The generative AI can also propose new ideas for current business issues based on past decision-making processes. In this way, by reproducing MASA's decision-making process, a more realistic personality can be formed.

[0063] The data analysis unit can use the emotion estimation function to infer emotions from MASA's past statements or actions and create a personality that reflects those emotions. In the data analysis unit, for example, the generation AI analyzes MASA's past statements and behavioral data and estimates emotions using an emotion estimation algorithm. For example, the data analysis unit calculates an emotion score based on the content and tone of the statements. Furthermore, the data analysis unit allows the generation AI to create a personality that reflects MASA's emotions. For example, the generation AI reproduces emotional reactions based on MASA's statements and behavioral data. Furthermore, the generation AI can create a personality that reflects MASA's emotions and provide advice on work-related issues. Furthermore, the generation AI can create a personality that reflects MASA's emotions and suggest improvements to work-related issues. By creating a personality that reflects MASA's emotions, more realistic advice can be provided.

[0064] The personality development unit can also incorporate data from other business leaders to create a new personality that combines the personalities of multiple leaders. For example, the generation AI can collect data on the statements and writings of other prominent business leaders and combine it with MASA's personality. For example, data on Steve Jobs and Jeff Bezos can be incorporated. The personality development unit can also create a new personality that combines the personalities of multiple leaders. For example, the generation AI can learn the thought patterns and values ​​of multiple leaders and use them to create a new personality. The generation AI can also learn the business strategies of multiple leaders and use them to create a new personality. The generation AI can also create a new personality based on the statements and behavioral data of multiple leaders. By combining the personalities of multiple leaders, a personality with a more multifaceted perspective can be created.

[0065] The personality development unit can learn business practices from different cultures or regions and develop a personality with a global perspective. For example, the generation AI can learn business practices from different cultures or regions and reflect them in MASA's personality. For example, it can incorporate business practices from Asia and Europe. The personality development unit also allows the generation AI to develop a personality with a global perspective. For example, the generation AI can learn business practices from different cultures or regions and develop a personality based on that. The generation AI can also learn business etiquette and negotiation styles from different cultures or regions and develop a personality based on that. The generation AI can also learn business strategies from different cultures or regions and develop a personality based on that. In this way, by learning business practices from different cultures and regions, a personality with a global perspective can be developed.

[0066] The data analysis unit uses the emotion estimation function to monitor in real time what emotions the user has toward the generating AI and can adjust the personality based on that feedback. The data analysis unit, for example, uses the emotion estimation function to monitor in real time the emotions the user has toward the generating AI. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The data analysis unit also allows the generating AI to adjust the personality based on the user's emotions. For example, the generating AI adjusts the personality's reactions based on the user's emotion score. The generating AI can also adjust the content of advice based on the user's emotions. The generating AI can also adjust the tone of the advice based on the user's emotions. In this way, by monitoring the user's emotions in real time and adjusting the personality based on feedback, more appropriate advice can be provided.

[0067] When analyzing MASA's statements and writings, the generation AI takes into account the background and circumstances of the statements, allowing it to recreate a personality that is more faithful to the context. For example, when analyzing MASA's statements and writings, the generation AI takes into account the background and circumstances of the statements. For example, it recreates the context based on the time, place, and target audience of the statements. The generation AI also recreates MASA's personality based on the background and circumstances of the statements. For example, it recreates the personality by taking into account the context before and after the statements and related events. The generation AI can also adjust the content of advice based on the background and circumstances of the statements. The generation AI can also adjust the tone of advice based on the background and circumstances of the statements. In this way, by taking into account the background and circumstances of the statements, it is possible to recreate a personality that is more faithful to the context.

[0068] When analyzing MASA's statements and writings, the generation AI can also analyze the tone or nuance of the statements and create a personality that reflects that. For example, when analyzing MASA's statement data, the generation AI takes tone and nuance into consideration. For example, it analyzes the emotional emphasis and intonation of the statements and reflects that. The generation AI also creates MASA's personality based on the tone and nuance of the statements. For example, it adjusts the personality's reactions based on the emotional emphasis and intonation of the statements. The generation AI can also adjust the content of advice based on the tone and nuance of the statements. The generation AI can also adjust the tone of advice based on the tone and nuance of the statements. In this way, a more realistic personality can be created by analyzing the tone and nuance of the statements.

[0069] When analyzing MASA data, the generative AI can simultaneously analyze data from other business leaders to create a personality that combines the knowledge of multiple leaders. For example, the generative AI can simultaneously analyze MASA data and data from other business leaders to create a personality that combines their knowledge. For example, it can incorporate data from Steve Jobs and Jeff Bezos. The generative AI can also create a personality that combines the knowledge of multiple leaders. For example, the generative AI can create a personality that combines the knowledge of multiple leaders based on their statements and writings. The generative AI can also learn the thought patterns and values ​​of multiple leaders and create a personality based on that. The generative AI can also learn the business strategies of multiple leaders and create a personality based on that. This allows for the creation of a personality with a more multifaceted perspective by incorporating the knowledge of other business leaders.

[0070] When analyzing MASA data, the generative AI can incorporate data from different industries or fields to create a personality with a more multifaceted perspective. For example, the generative AI can simultaneously analyze MASA data and data from other industries or fields to create a personality that combines the knowledge. For example, it can incorporate data from the technical and marketing fields. The generative AI can also create a MASA personality based on data from other industries or fields. For example, the generative AI can create a personality that combines the knowledge based on statements and writings from other industries or fields. The generative AI can also learn the thought patterns and values ​​of other industries and fields and create a personality based on that. The generative AI can also learn the business strategies of other industries and fields and create a personality based on that. In this way, by incorporating data from different industries and fields, it is possible to create a personality with a more multifaceted perspective.

[0071] The generation AI can use the emotion estimation function to monitor in real time what emotions the user has toward the generation AI and adjust its personality based on that feedback. The generation AI, for example, uses the emotion estimation function to monitor in real time the emotions the user has toward the generation AI. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI also adjusts its personality based on the user's emotions. For example, the generation AI adjusts the personality's reactions based on the user's emotion score. The generation AI can also adjust the content of advice based on the user's emotions. The generation AI can also adjust the tone of advice based on the user's emotions. In this way, by monitoring the user's emotions in real time and adjusting the personality based on feedback, more appropriate advice can be provided.

[0072] When used as an advisor, the generative AI can learn the user's past consultation details and solutions and provide more personalized advice. For example, the generative AI stores the user's past consultation details and solutions in a database and provides personalized advice based on them. For example, it refers to past consultation history to suggest solutions to similar problems. The generative AI also learns the user's past consultation details and solutions and provides personalized advice. For example, the generative AI can suggest solutions to current business issues based on the user's past consultation details. The generative AI can also suggest improvements to current business issues based on the user's past solutions. The generative AI can also suggest new ideas for current business issues based on the user's past consultation details and solutions. In this way, more personalized advice can be provided by learning the user's past consultation details and solutions.

[0073] When used as an advisor, the generative AI can monitor the user's work status or progress in real time and provide advice based on that. For example, the generative AI can monitor the user's work status and progress in real time and provide advice based on that. For example, the generative AI can analyze the progress of a project and propose the next step. The generative AI can also provide advice based on the user's work status and progress. For example, the generative AI can propose solutions to current work issues based on the work progress. The generative AI can also propose improvements to current work issues based on the work progress. The generative AI can also propose new ideas for current work issues based on the work progress. This allows the user's work status and progress to be monitored in real time, allowing for more appropriate advice to be provided.

[0074] The generation AI can use the emotion estimation function to estimate the user's emotional state and provide advice based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and provide advice based on that emotion. For example, if stress is high, the generation AI can suggest relaxation methods. The generation AI also provides advice based on the user's emotional state. For example, the generation AI can suggest solutions to current work issues based on the user's emotional state. The generation AI can also suggest improvements to current work issues based on the user's emotional state. The generation AI can also suggest new ideas for current work issues based on the user's emotional state. In this way, more appropriate advice can be provided by estimating the user's emotional state.

[0075] When used as an advisor, generative AI can incorporate the knowledge of experts from different industries or fields to provide more multifaceted advice. For example, generative AI incorporates the knowledge of experts from different industries or fields to provide multifaceted advice to users. For example, it combines the opinions of experts in the fields of technology and marketing. Generative AI also provides advice based on the knowledge of experts from different industries or fields. For example, generative AI can propose solutions to current business challenges based on the opinions of experts from different industries or fields. Generative AI can also propose improvements to current business challenges based on the opinions of experts from different industries or fields. Generative AI can also propose new ideas for current business challenges based on the opinions of experts from different industries or fields. In this way, more multifaceted advice can be provided by incorporating the knowledge of experts from different industries and fields.

[0076] When used as an advisor, generative AI can provide more appropriate advice based on the user's work environment and cultural background. For example, generative AI can take into account the user's work environment and cultural background and provide advice based on that. For example, it can take into account business etiquette and communication styles in different cultures. Generative AI can also provide advice based on the user's work environment and cultural background. For example, generative AI can propose solutions to current work issues based on the user's work environment. Generative AI can also suggest improvements to current work issues based on the user's cultural background. Generative AI can also propose new ideas for current work issues based on the user's work environment and cultural background. This allows more appropriate advice to be provided by taking the user's work environment and cultural background into consideration.

[0077] The generation AI can use the emotion estimation function to monitor in real time what emotions the user has toward the generation AI and adjust its advice based on that feedback. The generation AI, for example, uses the emotion estimation function to monitor in real time the emotions the user has toward the generation AI. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI also adjusts its advice based on the user's emotions. For example, the generation AI adjusts the content of the advice based on the user's emotion score. The generation AI can also adjust the tone of the advice based on the user's emotions. The generation AI can also adjust the timing of the advice based on the user's emotions. In this way, by monitoring the user's emotions in real time and adjusting the advice based on feedback, more appropriate advice can be provided.

[0078] When used as an advisor in daily work or business, generative AI can monitor the user's business goals or KPIs and provide advice based on them. For example, generative AI can monitor the user's business goals and KPIs and provide advice based on them. For example, it can suggest specific steps for achieving goals. Generative AI can also provide advice based on the user's business goals and KPIs. For example, generative AI can suggest solutions to current business challenges based on business goals. Generative AI can also suggest improvements to current business challenges based on KPIs. Generative AI can also suggest new ideas for current business challenges based on business goals and KPIs. This allows more appropriate advice to be provided by monitoring the user's business goals and KPIs.

[0079] The generation AI can use the emotion estimation function to estimate the user's emotional state and provide advice based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and provide advice based on that emotion. For example, if stress is high, the generation AI can suggest relaxation methods. The generation AI also provides advice based on the user's emotional state. For example, the generation AI can suggest solutions to current work issues based on the user's emotional state. The generation AI can also suggest improvements to current work issues based on the user's emotional state. The generation AI can also suggest new ideas for current work issues based on the user's emotional state. In this way, more appropriate advice can be provided by estimating the user's emotional state.

[0080] When used as an advisor in daily work or business, generative AI can incorporate the knowledge of experts from different industries or fields to provide more multifaceted advice. For example, generative AI incorporates the knowledge of experts from different industries and fields to provide multifaceted advice to users. For example, it combines the opinions of experts in the fields of technology and marketing. Generative AI also provides advice based on the knowledge of experts from different industries and fields. For example, generative AI can propose solutions to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose improvements to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose new ideas for current business challenges based on the opinions of experts from different industries and fields. In this way, more multifaceted advice can be provided by incorporating the knowledge of experts from different industries and fields.

[0081] When used as an advisor in daily work or business, generative AI can provide more appropriate advice based on the user's work environment and cultural background. For example, generative AI can take into account the user's work environment and cultural background and provide advice based on that. For example, it can take into account business etiquette and communication styles in different cultures. Generative AI can also provide advice based on the user's work environment and cultural background. For example, generative AI can propose solutions to current work challenges based on the user's work environment. Generative AI can also suggest improvements to current work challenges based on the user's cultural background. Generative AI can also propose new ideas for current work challenges based on the user's work environment and cultural background. This allows more appropriate advice to be provided by taking into account the user's work environment and cultural background.

[0082] The generation AI can use the emotion estimation function to monitor in real time what emotions the user has toward the generation AI and adjust its advice based on that feedback. The generation AI, for example, uses the emotion estimation function to monitor in real time the emotions the user has toward the generation AI. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI also adjusts its advice based on the user's emotions. For example, the generation AI adjusts the content of the advice based on the user's emotion score. The generation AI can also adjust the tone of the advice based on the user's emotions. The generation AI can also adjust the timing of the advice based on the user's emotions. In this way, by monitoring the user's emotions in real time and adjusting the advice based on feedback, more appropriate advice can be provided.

[0083] When providing insights and ideas in a timely manner, the generative AI can learn the user's past consultation content or solutions and provide more personalized advice. For example, the generative AI stores the user's past consultation content and solutions in a database and provides personalized advice based on that. For example, it refers to past consultation history to suggest solutions to similar problems. The generative AI also learns the user's past consultation content and solutions and provides personalized advice. For example, the generative AI can suggest solutions to current business issues based on the user's past consultation content. The generative AI can also suggest improvements to current business issues based on the user's past solutions. The generative AI can also suggest new ideas for current business issues based on the user's past consultation content and solutions. In this way, more personalized advice can be provided by learning the user's past consultation content and solutions.

[0084] When providing knowledge and ideas in a timely manner, generative AI can monitor the user's work status or progress in real time and provide advice based on that. For example, generative AI can monitor the user's work status and progress in real time and provide advice based on that. For example, it can analyze the progress of a project and propose the next step. Generative AI can also provide advice based on the user's work status and progress. For example, generative AI can propose solutions to current work issues based on the progress of work. Generative AI can also propose improvements to current work issues based on the progress of work. Generative AI can also propose new ideas for current work issues based on the progress of work. This allows more appropriate advice to be provided by monitoring the user's work status and progress in real time.

[0085] The generation AI can use the emotion estimation function to estimate the user's emotional state and provide advice based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and provide advice based on that emotion. For example, if stress is high, the generation AI can suggest relaxation methods. The generation AI also provides advice based on the user's emotional state. For example, the generation AI can suggest solutions to current work issues based on the user's emotional state. The generation AI can also suggest improvements to current work issues based on the user's emotional state. The generation AI can also suggest new ideas for current work issues based on the user's emotional state. In this way, more appropriate advice can be provided by estimating the user's emotional state.

[0086] When providing insights and ideas in a timely manner, generative AI can incorporate the knowledge of experts from different industries or fields to provide more multifaceted advice. For example, generative AI incorporates the knowledge of experts from different industries and fields to provide multifaceted advice to users. For example, it combines the opinions of experts in the fields of technology and marketing. Generative AI also provides advice based on the knowledge of experts from different industries and fields. For example, generative AI can propose solutions to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose improvements to current business challenges based on the opinions of experts from different industries and fields. Generative AI can also propose new ideas for current business challenges based on the opinions of experts from different industries and fields. In this way, more multifaceted advice can be provided by incorporating the knowledge of experts from different industries and fields.

[0087] The generation AI can use the emotion estimation function to monitor in real time what emotions the user has toward the generation AI and adjust its advice based on that feedback. The generation AI, for example, uses the emotion estimation function to monitor in real time the emotions the user has toward the generation AI. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI also adjusts its advice based on the user's emotions. For example, the generation AI adjusts the content of the advice based on the user's emotion score. The generation AI can also adjust the tone of the advice based on the user's emotions. The generation AI can also adjust the timing of the advice based on the user's emotions. In this way, by monitoring the user's emotions in real time and adjusting the advice based on feedback, more appropriate advice can be provided.

[0088] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

[0089] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

[0090] The generation AI can use the emotion estimation function to estimate the user's emotional state and provide advice based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and provide advice based on that emotion. For example, if stress is high, the generation AI can suggest relaxation methods. The generation AI also provides advice based on the user's emotional state. For example, the generation AI can suggest solutions to current work issues based on the user's emotional state. The generation AI can also suggest improvements to current work issues based on the user's emotional state. The generation AI can also suggest new ideas for current work issues based on the user's emotional state. In this way, more appropriate advice can be provided by estimating the user's emotional state.

[0091] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

[0092] When employees consult the generative AI about their work-related issues, the generative AI can obtain solutions based on MASA's knowledge, which is expected to improve the quality of work. For example, when an employee consults the generative AI about their work-related issues, the generative AI can provide solutions based on MASA's knowledge. For example, the generative AI can suggest improvements to specific work processes. The generative AI can also provide solutions based on the employee's work-related issues. For example, the generative AI can suggest solutions to current work-related issues based on the progress of work. The generative AI can also suggest improvements to current work-related issues based on the progress of work. The generative AI can also suggest new ideas for current work-related issues based on the progress of work. This is expected to improve the quality of work.

[0093] The generation AI can use the emotion estimation function to estimate the user's emotional state and provide advice based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and provide advice based on that emotion. For example, if stress is high, the generation AI can suggest relaxation methods. The generation AI also provides advice based on the user's emotional state. For example, the generation AI can suggest solutions to current work issues based on the user's emotional state. The generation AI can also suggest improvements to current work issues based on the user's emotional state. The generation AI can also suggest new ideas for current work issues based on the user's emotional state. In this way, more appropriate advice can be provided by estimating the user's emotional state.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] Generative AI systems can also learn from a user's past work history and provide personalized advice. For example, they can analyze data from projects the user has worked on in the past and suggest solutions to similar issues. They can also suggest improvements to current work issues based on the user's past successes and failures. They can also suggest new ideas based on the user's past work history. This allows the system to provide more personalized advice by learning from the user's past work history.

[0096] The generative AI system can also estimate the user's emotional state and provide advice based on that emotion. For example, if the user is feeling stressed, it can provide advice on relaxation methods and stress management. If the user is feeling happy, it can provide positive advice that utilizes that emotion. Furthermore, if the user is feeling angry, it can provide advice on how to stay calm. In this way, by estimating the user's emotional state, more appropriate advice can be provided.

[0097] Generative AI systems can also incorporate data from different industries and fields to provide advice from a more multifaceted perspective. For example, they can analyze data from the technical or marketing fields to propose solutions to the user's business challenges. They can also suggest improvements to current business challenges based on success stories and failures from other industries. They can also propose new ideas based on data from other industries and fields. In this way, by incorporating data from different industries and fields, advice can be provided from a more multifaceted perspective.

[0098] The generative AI system can also monitor the user's emotional state in real time and provide advice based on that emotion. For example, if the user is tense, it can provide advice on how to relax and relieve tension. If the user is concentrating, it can suggest an efficient way to work that makes use of that concentration. Furthermore, if the user is tired, it can provide advice on taking a rest. In this way, by monitoring the user's emotional state in real time, more appropriate advice can be provided.

[0099] The generative AI system can also monitor the user's business goals and KPIs and provide advice based on them. For example, it can suggest specific steps to achieve the goal. It can also suggest solutions to current business challenges based on the user's business goals and KPIs. It can also suggest new ideas based on the user's business goals and KPIs. This allows the system to provide more appropriate advice by monitoring the user's business goals and KPIs.

[0100] The generative AI system can also estimate the user's emotional state and provide feedback according to that emotion. For example, if the user is feeling anxious, it can provide feedback to give a sense of security. If the user is excited, it can provide positive feedback that makes use of that excitement. Furthermore, if the user is feeling depressed, it can provide encouraging feedback. In this way, by estimating the user's emotional state, it is possible to provide more appropriate feedback.

[0101] Generative AI systems can also provide advice based on a user's work environment and cultural background. For example, they can take into account business etiquette and communication styles in different cultures. They can also propose solutions to current business challenges based on the user's work environment and cultural background. They can also propose new ideas based on the user's work environment and cultural background. This allows them to provide more appropriate advice by taking into account the user's work environment and cultural background.

[0102] The generative AI system can also estimate the user's emotional state and adjust the communication style according to that emotion. For example, if the user is relaxed, it can adopt a casual communication style. If the user is nervous, it can adopt a formal communication style. Furthermore, if the user is focused, it can adopt an efficient communication style. In this way, by estimating the user's emotional state, it can provide a more appropriate communication style.

[0103] The generative AI system can also monitor the user's work status and progress in real time and provide advice based on that. For example, it can analyze the progress of a project and suggest the next step. It can also propose solutions to current business issues based on the user's work status and progress. It can also propose new ideas based on the user's work status and progress. This allows the system to provide more appropriate advice by monitoring the user's work status and progress in real time.

[0104] The generative AI system can also estimate the user's emotional state and provide feedback according to that emotion. For example, if the user is feeling anxious, it can provide feedback to give a sense of security. If the user is excited, it can provide positive feedback that makes use of that excitement. Furthermore, if the user is feeling depressed, it can provide encouraging feedback. In this way, by estimating the user's emotional state, it is possible to provide more appropriate feedback.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The personality formation unit forms MASA's personality. For example, the generation AI analyzes MASA's past statements and writings and recreates his personality. The generation AI can also learn MASA's thought patterns and values ​​and form a personality based on them. Furthermore, the generation AI can learn MASA's business strategy and form a personality based on them. Step 2: The Data Analysis Department analyzes MASA's past statements and writings based on MASA's personality formed by the Personality Development Department. For example, the Data Analysis Department may analyze MASA's statements and writings using natural language processing technology to extract important information. The Data Analysis Department may also analyze MASA's statements and writings using machine learning algorithms to find patterns. Furthermore, the Data Analysis Department may analyze MASA's statements and writings using text mining technology to find trends. Step 3: The advice providing unit provides advice for business issues based on the data analyzed by the data analysis unit. For example, the advice providing unit proposes solutions to business issues based on the analysis results. The advice providing unit can also propose improvements to business issues based on the analysis results. Furthermore, the advice providing unit can also propose new ideas for business issues based on the analysis results.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

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

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

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

[0132] Fig. 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.

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

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

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

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

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

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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. [Explanation of symbols]

[0174] 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. The Personality Development Department, which shapes the personalities of MASA members, a data analysis unit that analyzes past statements or writings of the MASA based on the personality of the MASA formed by the personality formation unit; an advice providing unit that provides advice on business issues based on the data analyzed by the data analysis unit. A system characterized by:

2. The personality development department Incorporating data from other business leaders to create a new personality that combines the personalities of multiple leaders 2. The system of claim 1.

3. The generating AI is When analyzing MASA's statements and writings, we will also consider the background and circumstances of the statements to recreate a more contextually accurate personality.

2. The system of claim 1.

4. The generating AI is When used as a consultant, it learns from the user's past consultations and solutions to provide more personalized advice.

2. The system of claim 1.

5. The generating AI is When used as an advisor in the daily operations and business, periodically evaluate the user's work performance and provide feedback based on that evaluation.

2. The system of claim 1.

6. The personality development department Reproduce the emotions or stress levels of the MASA and generate situation-specific emotional responses 2. The system of claim 1.

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