System

Abstract

An object of the system according to the embodiment is to generate a new business or idea by utilizing knowledge of great people or experts from the past to the present.SOLUTION: A system includes an objective setting part, a database part, a selection part, a simulation part, and a generation part. The purpose setting unit sets a purpose of the user. The database unit holds data of great persons and experts from the past to the present. The selection unit selects an optimum sponsor based on the objective set by the objective setting unit. The simulation part simulates the discussion of the participants selected by the selection part. The generation unit generates a new business or idea from the discussion simulated by the simulation 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] Conventional technologies have faced the challenge of making it difficult to effectively utilize the knowledge of great people and experts from the past and present to generate new businesses and ideas.

[0005] The system according to the embodiment aims to generate new businesses and ideas by utilizing the knowledge of great people and experts from the past and present. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal setting unit, a database unit, a selection unit, a simulation unit, and a generation unit. The goal setting unit sets the user's goal. The database unit holds data on great people and experts from the past to the present. The selection unit selects the most suitable debater based on the goal set by the goal setting unit. The simulation unit simulates the debates of the debaters selected by the selection unit. The generation unit generates new businesses and ideas from the debates simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate new businesses and ideas by utilizing the knowledge of great people and experts from the past and present. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A brainstorming system according to an embodiment of the present invention is a system that selects great people and experts from the past and present according to the user's purpose, simulates a discussion, and generates new businesses and ideas. As a result, the brainstorming system can generate new businesses and ideas by selecting the most suitable debaters according to the user's purpose and simulating a discussion.

[0029] A brainstorming system according to an embodiment includes a goal setting unit, a database unit, a selection unit, a simulation unit, and a generation unit. The goal setting unit sets a user's goal. For example, a user can set a specific goal such as "I want to come up with a business idea for a new technology." The goal setting unit can also analyze the user's input and automatically suggest goals. The database unit stores data on great people and experts from the past to the present. For example, this data may include Steve Jobs' knowledge of innovation, Ryoma Sakamoto's knowledge of leadership, and Elon Musk's knowledge of technology. The selection unit selects the most suitable debater based on the goal set by the goal setting unit. For example, for the goal "I want to come up with a business idea for a new technology," technology experts such as Steve Jobs and Elon Musk are selected. The simulation unit simulates a discussion between the debaters selected by the selection unit. For example, the discussion may proceed in the form of Steve Jobs proposing a new business idea and Elon Musk discussing its technical feasibility. The generation unit generates new business ideas and ideas from the discussion simulated by the simulation unit. For example, a business model utilizing new technology emerges from a discussion between Jobs and Musk. As a result, the brainstorming system according to the embodiment can generate new businesses and ideas by selecting the most suitable debaters according to the user's purpose and simulating the discussion.

[0030] The goal setting unit can analyze a user's past behavioral history and interests and automatically make suggestions when setting a goal. The goal setting unit, for example, analyzes a user's past search history and browsing history and automatically suggests related goals. For example, for a user who has read many technology-related articles in the past, it can suggest new technology business ideas. The goal setting unit can also analyze the user's social media posts and the accounts they follow and suggest goals based on their interests. For example, for a user who is interested in environmental issues, it can suggest a goal to discuss environmental protection. The goal setting unit can also analyze a user's past projects and work history and automatically suggest related goals. For example, for a user who has taken leadership training in the past, it can suggest a goal to discuss leadership. In this way, by analyzing a user's past behavioral history and interests, it is possible to automatically make suggestions when setting a goal.

[0031] When the user sets a goal, the generation AI can present relevant past success stories and failure stories for reference. For example, when the user sets a goal, the generation AI can present past success stories for reference. For example, when thinking of a new business idea, the generation AI can present past examples of successful startups. Furthermore, when the user sets a goal, the generation AI can present past failure stories for reference. For example, when discussing solutions to social problems, the generation AI can present past examples of failed projects. Furthermore, when the user sets a goal, the generation AI can present both success stories and failure stories for comparison. For example, when thinking of solutions to environmental problems, the generation AI can present examples of successful and unsuccessful projects. This allows the user to refer to past success stories and failure stories when setting a goal, enabling more appropriate goal setting.

[0032] The database department adds unpublished letters and diaries of prominent figures and experts, allowing for deeper insights. For example, the database department adds unpublished letters and diaries of prominent figures and experts to the database and makes them accessible to users. For example, it adds unpublished letters from Steve Jobs. The database department also analyzes private information about prominent figures and experts to provide context for their thoughts and actions. For example, it analyzes the diary of Ryoma Sakamoto to provide context for his leadership. The database department also digitizes unpublished letters and diaries of prominent figures and experts and stores them in the database. For example, it digitizes and stores unpublished letters from Elon Musk. This allows for deeper insights by adding private information such as unpublished letters and diaries of prominent figures and experts.

[0033] The database section analyzes the ideas and knowledge of each great person or expert in relation to the historical background and social influence, enabling more comprehensive information to be provided. For example, the database section analyzes the ideas and knowledge of each great person or expert in relation to the historical background and social influence of the time period in which they lived. For example, it analyzes Steve Jobs's ideas on innovation in relation to the technological innovations of his time. The database section also analyzes the ideas and knowledge of each great person or expert in relation to the social events that influenced them. For example, it analyzes Sakamoto Ryoma's leadership ideas in relation to the social changes of the late Edo period. The database section also analyzes the ideas and knowledge of each great person or expert in relation to the social influence they had. For example, it analyzes Elon Musk's ideas on technology in relation to modern technological innovation. This allows the database section to provide more comprehensive information by analyzing the ideas and knowledge of each great person or expert in relation to the historical background and social influence of the time period in which they lived.

[0034] The database unit can add 3D models and audio data of famous people and experts, allowing users to experience them more realistically. For example, the database unit can add 3D models of famous people and experts to the database, allowing users to experience them visually. For example, it can add a 3D model of Steve Jobs. The database unit can also add audio data of famous people and experts to the database, allowing users to experience them auditorily. For example, it can add audio data of Elon Musk. The database unit can also combine 3D models of famous people and experts with audio data, allowing users to experience them more realistically. For example, it can combine a 3D model of Ryoma Sakamoto with audio data. In this way, adding 3D models and audio data of famous people and experts allows users to experience them more realistically.

[0035] The database section can add a function to compare great figures and experts from different cultures and eras and analyze their similarities and differences. For example, compare the leadership of Steve Jobs and Ryoma Sakamoto. The database section can also add a function to compare great figures and experts from different eras and analyze their similarities and differences. For example, compare the innovations of Elon Musk and Thomas Edison. The database section can also add a function to compare great figures and experts from different cultures and eras and analyze their similarities and differences in their thoughts and actions. For example, compare the environmental protection ideas of Rachel Carson and contemporary environmental scientists. This allows for deeper insights by comparing great figures and experts from different cultures and eras and analyzing their similarities and differences.

[0036] The selection unit can propose combinations of multiple debaters based on the user's objectives and analyze the advantages and disadvantages of each combination. For example, the selection unit uses a generation AI to propose combinations of multiple debaters based on the user's objectives and analyze the advantages and disadvantages of each combination. For example, the selection unit analyzes the advantages and disadvantages of the combination of Steve Jobs and Elon Musk. The selection unit also proposes combinations of multiple debaters and simulates the type of discussion each combination will generate. For example, the selection unit simulates the type of discussion the combination of Ryoma Sakamoto and a modern sociologist will generate. The selection unit also proposes combinations of multiple debaters and evaluates how well each combination suits the user's objectives. For example, the selection unit evaluates how well the combination of Rachel Carson and a modern environmental scientist suits environmental problems. This allows the system to propose combinations of multiple debaters and select the optimal combination by analyzing the advantages and disadvantages of each combination.

[0037] When selecting debaters, the selection unit can analyze their past discussions and debate records to select the most effective combinations. For example, the selection unit can analyze the debaters' past discussions and debate records to select the most effective combinations. For example, the selection unit can analyze the past discussions of Steve Jobs and Elon Musk to select effective combinations. The selection unit can also analyze the debaters' past debate records to understand their discussion styles and strengths. For example, the selection unit can analyze Ryoma Sakamoto's leadership style to select effective combinations. The selection unit can also select the combinations that best suit the user's goals based on the debaters' past discussions and debate records. For example, the selection unit can select effective combinations based on Rachel Carson's environmental protection ideology. In this way, the selection unit can select the most effective combinations by analyzing the debaters' past discussions and debate records.

[0038] The selection unit can combine debaters from different fields and make proposals to draw out new perspectives and approaches. For example, the selection unit proposes a combination of Steve Jobs and Rachel Carson. By combining debaters from different fields, the selection unit can provide a multifaceted approach to the user's goals. For example, the selection unit proposes a combination of Sakamoto Ryoma and a modern environmental scientist. The selection unit also performs simulations to combine debaters from different fields and draw out new perspectives and approaches. For example, the selection unit simulates the combination of Elon Musk and a modern sociologist. This allows for the combination of debaters from different fields to draw out new perspectives and approaches.

[0039] When selecting debaters, the selection unit can refer to the history of debaters selected by the user in the past and support the optimal selection. For example, the selection unit analyzes the history of debaters selected by the user in the past and supports the optimal selection. For example, if a user previously selected Steve Jobs, the selection unit will suggest Elon Musk. The selection unit also proposes the optimal combination of debaters based on the user's past selection history. For example, if a user previously selected Sakamoto Ryoma, the selection unit will suggest a modern sociologist. The selection unit also builds a system that refers to the user's past selection history and supports the selection of the optimal debater. For example, if a user previously selected Rachel Carson, the selection unit will suggest a modern environmental scientist. In this way, the optimal selection can be supported by referring to the history of debaters selected by the user in the past.

[0040] The simulation unit can perform a detailed analysis of the background and rationale of each debater's statements in a debate simulation and provide the results to the user. For example, the simulation unit can analyze the background and rationale of each debater's statements in a debate simulation and provide the results to the user. For example, the simulation unit can analyze the background and rationale of Steve Jobs' statements. The simulation unit can also analyze the background and rationale of Elon Musk's statements to enable the user to gain a deeper understanding of the content of the debate. For example, the simulation unit can analyze the background and rationale of Elon Musk's statements. The simulation unit can also build a system that can perform a detailed analysis of the background and rationale of each debater's statements in a debate simulation and provide the results to the user. For example, the simulation unit can analyze the background and rationale of Sakamoto Ryoma's statements. This allows the user to gain a deeper understanding of the content of the debate by analyzing the background and rationale of each debater's statements in detail.

[0041] The simulation unit allows the generating AI to analyze the progress of the discussion in real time during the simulation and make suggestions to guide it in the optimal direction. For example, the simulation unit allows the generating AI to analyze the progress of the discussion in real time during the simulation and make suggestions to guide it in the optimal direction. For example, the simulation unit analyzes a discussion between Steve Jobs and Elon Musk and makes suggestions to guide it in the optimal direction. The simulation unit also allows the generating AI to analyze the progress of the discussion in real time and make suggestions to help users effectively advance the discussion. For example, the simulation unit analyzes a discussion between Ryoma Sakamoto and a modern sociologist and makes suggestions to guide it in the optimal direction. The simulation unit also builds a system where the generating AI analyzes the progress of the discussion in real time during the simulation and makes suggestions to guide it in the optimal direction. For example, the simulation unit analyzes a discussion between Rachel Carson and a modern environmental scientist and makes suggestions to guide it in the optimal direction. This allows the generating AI to analyze the progress of the discussion in real time and make suggestions to guide it in the optimal direction, maximizing the effectiveness of the discussion.

[0042] The simulation unit can re-simulate the results of a debate under different scenarios and conditions and compare multiple results. For example, the simulation unit re-simulates the results of a debate under different scenarios and conditions and compares multiple results. For example, it re-simulates a debate between Steve Jobs and Elon Musk under different scenarios. The simulation unit also simulates a debate under different conditions, allowing users to compare multiple results. For example, it re-simulates a debate between Ryoma Sakamoto and a modern sociologist under different conditions. The simulation unit also re-simulates the results of a debate under different scenarios and conditions, allowing users to select the optimal result. For example, it re-simulates a debate between Rachel Carson and a modern environmental scientist under different scenarios. This allows the optimal progress of the debate to be supported by re-simulating the results of a debate under different scenarios and conditions and comparing multiple results.

[0043] The simulation unit can visualize the results of a simulation to allow users to intuitively understand it. For example, the simulation unit visualizes the results of a debate to allow users to intuitively understand it. For example, the results of a debate between Steve Jobs and Elon Musk are displayed in graphs and charts. The simulation unit also visualizes the results of a simulation to allow users to understand the content of the debate at a glance. For example, the simulation unit visualizes the results of a debate between Ryoma Sakamoto and a modern sociologist. The simulation unit also builds a system to visualize the results of a debate to allow users to intuitively understand it. For example, the simulation unit visualizes the results of a debate between Rachel Carson and a modern environmental scientist. In this way, by visualizing the simulation results, users can intuitively understand it.

[0044] The generation unit can perform detailed analysis to turn ideas generated from simulated discussions into concrete business plans. For example, the generation unit analyzes ideas generated from simulated discussions and turns them into concrete business plans. For example, an idea generated from a discussion between Steve Jobs and Elon Musk is converted into a business plan. The generation unit also analyzes generated ideas in detail to create feasible business plans. For example, an idea generated from a discussion between Ryoma Sakamoto and a modern sociologist is converted into a business plan. The generation unit also builds a system that analyzes ideas generated from simulated discussions and turns them into concrete business plans. For example, an idea generated from a discussion between Rachel Carson and a modern environmental scientist is converted into a business plan. This allows for the creation of feasible business plans by performing detailed analysis to turn ideas generated from simulated discussions into concrete business plans.

[0045] The generation unit can evaluate the feasibility of generated ideas by referring to past successes and failures. For example, the generation unit can compare ideas born from discussions between Steve Jobs and Elon Musk with past examples. The generation unit can also build a system to compare generated ideas with past successes and failures to evaluate their feasibility. For example, ideas born from discussions between Ryoma Sakamoto and modern sociologists can be compared with past examples. The generation unit can also automate the process of evaluating the feasibility of generated ideas by referring to past successes and failures. For example, ideas born from discussions between Rachel Carson and modern environmental scientists can be compared with past examples. This allows the feasibility of generated ideas to be evaluated by referring to past successes and failures.

[0046] The generation unit can apply the generated ideas to different industries and applications to discover new business opportunities. For example, the generation unit applies the generated ideas to different industries to discover new business opportunities. For example, ideas born from a discussion between Steve Jobs and Elon Musk could be applied to the medical field. The generation unit also builds a system to apply the generated ideas to different applications to discover new business opportunities. For example, ideas born from a discussion between Ryoma Sakamoto and modern sociologists could be applied to the education field. The generation unit also automates the process of applying the generated ideas to different industries and applications to discover new business opportunities. For example, ideas born from a discussion between Rachel Carson and modern environmental scientists could be applied to the energy field. This makes it possible to discover new business opportunities by applying the generated ideas to different industries and applications.

[0047] The generation unit can implement the generated ideas as prototypes and improve them based on user feedback. For example, the generation unit implements the generated ideas as prototypes and improves them based on user feedback. For example, an idea born from a discussion between Steve Jobs and Elon Musk could be implemented as a prototype. The generation unit also builds a system that implements the generated ideas as prototypes and collects user feedback in real time to improve them. For example, an idea born from a discussion between Ryoma Sakamoto and a modern sociologist could be implemented as a prototype. The generation unit also automates the process of implementing the generated ideas as prototypes and improving them based on user feedback. For example, an idea born from a discussion between Rachel Carson and a modern environmental scientist could be implemented as a prototype. This allows the generation unit to implement the generated ideas as prototypes and improve them based on user feedback to create feasible business plans.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The Selection Department can draw out new perspectives and approaches to a user's goals by combining debaters from different cultures and eras. For example, it proposes a pairing of Steve Jobs and Ryoma Sakamoto, combining technological innovation and leadership. The Selection Department also offers a multifaceted approach to a user's goals by combining experts from different fields. For example, it proposes a pairing of Elon Musk and Rachel Carson, combining the perspectives of technology and environmental protection. The Selection Department also combines debaters from different eras, fusing historical perspectives with contemporary knowledge. For example, it proposes a pairing of Thomas Edison with a modern AI expert, drawing out the synergy between past inventions and the latest technology. This allows for the combination of debaters from different cultures and eras to draw out new perspectives and approaches.

[0050] The simulation module can provide users with a detailed analysis of the background and rationale behind each debater's statements during a debate simulation. For example, it can analyze the background and rationale behind Steve Jobs' statements to clarify the underlying thinking behind his innovative philosophy. It can also analyze the background and rationale behind Elon Musk's statements to provide concrete evidence for his technological vision. It can also analyze the background and rationale behind Sakamoto Ryoma's statements to clarify his leadership style and strategy. By providing a detailed analysis of the background and rationale behind each debater's statements, users can gain a deeper understanding of the content of the debate.

[0051] The generation unit can perform detailed analysis to turn ideas generated from simulated discussions into concrete business plans. For example, it converts ideas generated from a discussion between Steve Jobs and Elon Musk into a business plan and clearly shows feasible steps. It also converts ideas generated from a discussion between Ryoma Sakamoto and a modern sociologist into a business plan and proposes a specific market strategy. It also converts ideas generated from a discussion between Rachel Carson and a modern environmental scientist into a business plan and evaluates their feasibility from an environmental protection perspective. This allows for detailed analysis to turn ideas generated from simulated discussions into concrete business plans, creating feasible business plans.

[0052] The database section can add 3D models and audio data of famous people and experts, allowing users to experience them more realistically. For example, adding a 3D model of Steve Jobs will recreate his presentation style. Adding audio data of Elon Musk will also recreate his speeches more realistically. Furthermore, combining a 3D model of Ryoma Sakamoto with audio data will allow users to experience his leadership style visually and audibly. Adding 3D models and audio data of famous people and experts allows users to experience them more realistically.

[0053] The database section adds unpublished letters and diaries of prominent figures and experts, providing deeper insights. For example, adding unpublished letters from Steve Jobs will shed light on the background of his thoughts and vision. Adding the diaries of Ryoma Sakamoto will also provide a deeper understanding of his leadership style and strategies. Adding unpublished letters from Elon Musk will reveal his technological vision and strategies. By adding private information such as unpublished letters and diaries of prominent figures and experts, the database section can provide deeper insights.

[0054] The simulation unit can re-simulate the results of a debate under different scenarios and conditions and compare multiple results. For example, it can re-simulate a debate between Steve Jobs and Elon Musk under different scenarios and select the optimal result. It can also re-simulate a debate between Ryoma Sakamoto and a modern sociologist under different conditions and compare multiple results. It can also re-simulate a debate between Rachel Carson and a modern environmental scientist under different scenarios and select the optimal result. This allows it to support optimal debate progression by re-simulating the results of a debate under different scenarios and conditions and comparing multiple results.

[0055] The Generation Department can apply the generated ideas to different industries and applications to discover new business opportunities. For example, ideas born from a discussion between Steve Jobs and Elon Musk can be applied to the medical field to promote the development of new medical technologies. Ideas born from a discussion between Ryoma Sakamoto and modern sociologists can be applied to the education field to develop new educational programs. Furthermore, ideas born from a discussion between Rachel Carson and modern environmental scientists can be applied to the energy field to discover new energy solutions. By applying the generated ideas to different industries and applications, new business opportunities can be discovered.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The goal setting unit sets the user's goal. For example, the user can set a specific goal such as "I want to think of a business idea for a new technology." The goal setting unit can also analyze the user's input and automatically suggest goals. Step 2: The database section holds data on great people and experts from the past and present, such as Steve Jobs' knowledge of innovation, Ryoma Sakamoto's knowledge of leadership, and Elon Musk's knowledge of technology. Step 3: The selection department selects the most suitable debaters based on the objectives set by the objective setting department. For example, for the objective of "composing business ideas for new technology," experts in the field of technology such as Steve Jobs or Elon Musk are selected. Step 4: The simulation department simulates a debate between the debaters selected by the selection department. For example, Steve Jobs might propose a new business idea, and Elon Musk might discuss its technical feasibility. Step 5: The generation unit generates new businesses and ideas from the discussions simulated by the simulation unit. For example, a discussion between Jobs and Musk could lead to a business model that utilizes a new technology.

[0058] (Example 2) A brainstorming system according to an embodiment of the present invention is a system that selects great people and experts from the past and present according to the user's purpose, simulates a discussion, and generates new businesses and ideas. As a result, the brainstorming system can generate new businesses and ideas by selecting the most suitable debaters according to the user's purpose and simulating a discussion.

[0059] A brainstorming system according to an embodiment includes a goal setting unit, a database unit, a selection unit, a simulation unit, and a generation unit. The goal setting unit sets a user's goal. For example, a user can set a specific goal such as "I want to come up with a business idea for a new technology." The goal setting unit can also analyze the user's input and automatically suggest goals. The database unit stores data on great people and experts from the past to the present. For example, this data may include Steve Jobs' knowledge of innovation, Ryoma Sakamoto's knowledge of leadership, and Elon Musk's knowledge of technology. The selection unit selects the most suitable debater based on the goal set by the goal setting unit. For example, for the goal "I want to come up with a business idea for a new technology," technology experts such as Steve Jobs and Elon Musk are selected. The simulation unit simulates a discussion between the debaters selected by the selection unit. For example, the discussion may proceed in the form of Steve Jobs proposing a new business idea and Elon Musk discussing its technical feasibility. The generation unit generates new business ideas and ideas from the discussion simulated by the simulation unit. For example, a business model utilizing new technology emerges from a discussion between Jobs and Musk. As a result, the brainstorming system according to the embodiment can generate new businesses and ideas by selecting the most suitable debaters according to the user's purpose and simulating the discussion.

[0060] The goal setting unit can analyze a user's past behavioral history and interests and automatically make suggestions when setting a goal. The goal setting unit, for example, analyzes a user's past search history and browsing history and automatically suggests related goals. For example, for a user who has read many technology-related articles in the past, it can suggest new technology business ideas. The goal setting unit can also analyze the user's social media posts and the accounts they follow and suggest goals based on their interests. For example, for a user who is interested in environmental issues, it can suggest a goal to discuss environmental protection. The goal setting unit can also analyze a user's past projects and work history and automatically suggest related goals. For example, for a user who has taken leadership training in the past, it can suggest a goal to discuss leadership. In this way, by analyzing a user's past behavioral history and interests, it is possible to automatically make suggestions when setting a goal.

[0061] When the user sets a goal, the generation AI can present relevant past success stories and failure stories for reference. For example, when the user sets a goal, the generation AI can present past success stories for reference. For example, when thinking of a new business idea, the generation AI can present past examples of successful startups. Furthermore, when the user sets a goal, the generation AI can present past failure stories for reference. For example, when discussing solutions to social problems, the generation AI can present past examples of failed projects. Furthermore, when the user sets a goal, the generation AI can present both success stories and failure stories for comparison. For example, when thinking of solutions to environmental problems, the generation AI can present examples of successful and unsuccessful projects. This allows the user to refer to past success stories and failure stories when setting a goal, enabling more appropriate goal setting.

[0062] The goal setting unit can use the emotion estimation function to analyze the user's current emotional state and support optimal goal setting. The goal setting unit, for example, analyzes the user's facial expressions and voice to estimate the user's current emotional state. For example, if the user is relaxed, it suggests a creative discussion goal. The goal setting unit also analyzes the user's input content and typing speed to estimate the user's current emotional state. For example, if the user is feeling stressed, it suggests a relaxing discussion goal. The goal setting unit also analyzes the user's biometric data (heart rate and electrodermal activity) to estimate the user's current emotional state. For example, if the user is excited, it suggests an energetic discussion goal. In this way, optimal goal setting can be supported by analyzing the user's current emotional state.

[0063] The database department adds unpublished letters and diaries of prominent figures and experts, allowing for deeper insights. For example, the database department adds unpublished letters and diaries of prominent figures and experts to the database and makes them accessible to users. For example, it adds unpublished letters from Steve Jobs. The database department also analyzes private information about prominent figures and experts to provide context for their thoughts and actions. For example, it analyzes the diary of Ryoma Sakamoto to provide context for his leadership. The database department also digitizes unpublished letters and diaries of prominent figures and experts and stores them in the database. For example, it digitizes and stores unpublished letters from Elon Musk. This allows for deeper insights by adding private information such as unpublished letters and diaries of prominent figures and experts.

[0064] The database section analyzes the ideas and knowledge of each great person or expert in relation to the historical background and social influence, enabling more comprehensive information to be provided. For example, the database section analyzes the ideas and knowledge of each great person or expert in relation to the historical background and social influence of the time period in which they lived. For example, it analyzes Steve Jobs's ideas on innovation in relation to the technological innovations of his time. The database section also analyzes the ideas and knowledge of each great person or expert in relation to the social events that influenced them. For example, it analyzes Sakamoto Ryoma's leadership ideas in relation to the social changes of the late Edo period. The database section also analyzes the ideas and knowledge of each great person or expert in relation to the social influence they had. For example, it analyzes Elon Musk's ideas on technology in relation to modern technological innovation. This allows the database section to provide more comprehensive information by analyzing the ideas and knowledge of each great person or expert in relation to the historical background and social influence of the time period in which they lived.

[0065] The database unit uses the emotion estimation function to analyze the emotional aspects of famous people and experts and understand the background to their actions and decisions. For example, the database unit analyzes the letters and diaries of famous people and experts and uses the emotion estimation function to understand their emotional aspects. For example, it analyzes Steve Jobs' letters to understand his emotional background. The database unit also analyzes the performances and interviews of famous people and experts and uses the emotion estimation function to understand their emotional aspects. For example, it analyzes Elon Musk's interviews to understand his emotional background. The database unit also analyzes the actions and decisions of famous people and experts and uses the emotion estimation function to understand their emotional aspects. For example, it analyzes the actions of Ryoma Sakamoto to understand his emotional background. This makes it possible to understand the background to their actions and decisions by analyzing the emotional aspects of famous people and experts.

[0066] The database unit can add 3D models and audio data of famous people and experts, allowing users to experience them more realistically. For example, the database unit can add 3D models of famous people and experts to the database, allowing users to experience them visually. For example, it can add a 3D model of Steve Jobs. The database unit can also add audio data of famous people and experts to the database, allowing users to experience them auditorily. For example, it can add audio data of Elon Musk. The database unit can also combine 3D models of famous people and experts with audio data, allowing users to experience them more realistically. For example, it can combine a 3D model of Ryoma Sakamoto with audio data. In this way, adding 3D models and audio data of famous people and experts allows users to experience them more realistically.

[0067] The database section can add a function to compare great figures and experts from different cultures and eras and analyze their similarities and differences. For example, compare the leadership of Steve Jobs and Ryoma Sakamoto. The database section can also add a function to compare great figures and experts from different eras and analyze their similarities and differences. For example, compare the innovations of Elon Musk and Thomas Edison. The database section can also add a function to compare great figures and experts from different cultures and eras and analyze their similarities and differences in their thoughts and actions. For example, compare the environmental protection ideas of Rachel Carson and contemporary environmental scientists. This allows for deeper insights by comparing great figures and experts from different cultures and eras and analyzing their similarities and differences.

[0068] The database unit can use the emotion estimation function to simulate the emotional reactions of famous people and experts, allowing users to understand their emotions. For example, the database unit simulates the emotional reactions of famous people and experts, allowing users to understand their emotions. For example, the database unit simulates the emotional reactions of Steve Jobs. The database unit also simulates the emotional reactions of famous people and experts, allowing users to understand the background to their actions and decisions. For example, the database unit simulates the emotional reactions of Ryoma Sakamoto. The database unit also simulates the emotional reactions of famous people and experts, allowing users to gain a deeper understanding of their thoughts and knowledge. For example, the database unit simulates the emotional reactions of Elon Musk. In this way, the database unit can simulate the emotional reactions of famous people and experts, allowing users to understand their emotions.

[0069] The selection unit can propose combinations of multiple debaters based on the user's objectives and analyze the advantages and disadvantages of each combination. For example, the selection unit uses a generation AI to propose combinations of multiple debaters based on the user's objectives and analyze the advantages and disadvantages of each combination. For example, the selection unit analyzes the advantages and disadvantages of the combination of Steve Jobs and Elon Musk. The selection unit also proposes combinations of multiple debaters and simulates the type of discussion each combination will generate. For example, the selection unit simulates the type of discussion the combination of Ryoma Sakamoto and a modern sociologist will generate. The selection unit also proposes combinations of multiple debaters and evaluates how well each combination suits the user's objectives. For example, the selection unit evaluates how well the combination of Rachel Carson and a modern environmental scientist suits environmental problems. This allows the system to propose combinations of multiple debaters and select the optimal combination by analyzing the advantages and disadvantages of each combination.

[0070] When selecting debaters, the selection unit can analyze their past discussions and debate records to select the most effective combinations. For example, the selection unit can analyze the debaters' past discussions and debate records to select the most effective combinations. For example, the selection unit can analyze the past discussions of Steve Jobs and Elon Musk to select effective combinations. The selection unit can also analyze the debaters' past debate records to understand their discussion styles and strengths. For example, the selection unit can analyze Ryoma Sakamoto's leadership style to select effective combinations. The selection unit can also select the combinations that best suit the user's goals based on the debaters' past discussions and debate records. For example, the selection unit can select effective combinations based on Rachel Carson's environmental protection ideology. In this way, the selection unit can select the most effective combinations by analyzing the debaters' past discussions and debate records.

[0071] The selection unit can use the emotion estimation function to analyze the emotional compatibility between debaters and select a pairing that is likely to result in the most positive discussion. For example, the selection unit uses the emotion estimation function to analyze the emotional compatibility between debaters and select a pairing that is likely to result in the most positive discussion. For example, the selection unit analyzes the emotional compatibility between Steve Jobs and Elon Musk. The selection unit also analyzes the emotional compatibility between debaters and simulates pairings that are likely to result in positive discussion. For example, the selection unit analyzes the emotional compatibility between Ryoma Sakamoto and a modern sociologist. The selection unit also uses the emotion estimation function to select a pairing that is most suitable for the user's purpose based on the emotional compatibility between debaters. For example, the selection unit analyzes the emotional compatibility between Rachel Carson and a modern environmental scientist. This makes it possible to select a pairing that is likely to result in the most positive discussion by analyzing the emotional compatibility between debaters using the emotion estimation function.

[0072] The selection unit can combine debaters from different fields and make proposals to draw out new perspectives and approaches. For example, the selection unit proposes a combination of Steve Jobs and Rachel Carson. By combining debaters from different fields, the selection unit can provide a multifaceted approach to the user's goals. For example, the selection unit proposes a combination of Sakamoto Ryoma and a modern environmental scientist. The selection unit also performs simulations to combine debaters from different fields and draw out new perspectives and approaches. For example, the selection unit simulates the combination of Elon Musk and a modern sociologist. This allows for the combination of debaters from different fields to draw out new perspectives and approaches.

[0073] When selecting debaters, the selection unit can refer to the history of debaters selected by the user in the past and support the optimal selection. For example, the selection unit analyzes the history of debaters selected by the user in the past and supports the optimal selection. For example, if a user previously selected Steve Jobs, the selection unit will suggest Elon Musk. The selection unit also proposes the optimal combination of debaters based on the user's past selection history. For example, if a user previously selected Sakamoto Ryoma, the selection unit will suggest a modern sociologist. The selection unit also builds a system that refers to the user's past selection history and supports the selection of the optimal debater. For example, if a user previously selected Rachel Carson, the selection unit will suggest a modern environmental scientist. In this way, the optimal selection can be supported by referring to the history of debaters selected by the user in the past.

[0074] The selection unit can use the emotion estimation function to prioritize the debater in which the user is most interested. For example, the selection unit uses the emotion estimation function to prioritize the debater in which the user is most interested. For example, if the user shows a strong interest in Steve Jobs, he is selected preferentially. The selection unit also analyzes the user's emotional reactions and builds a system to select the debater in which the user is most interested. For example, if the user shows a strong interest in Ryoma Sakamoto, he is selected preferentially. The selection unit also uses the emotion estimation function to select the most suitable debater based on the user's interests. For example, if the user shows a strong interest in Rachel Carson, she is selected preferentially. In this way, by using the emotion estimation function to prioritize the debater in which the user is most interested, it is possible to provide discussions that match the user's interests.

[0075] The simulation unit can perform a detailed analysis of the background and rationale of each debater's statements in a debate simulation and provide the results to the user. For example, the simulation unit can analyze the background and rationale of each debater's statements in a debate simulation and provide the results to the user. For example, the simulation unit can analyze the background and rationale of Steve Jobs' statements. The simulation unit can also analyze the background and rationale of Elon Musk's statements to enable the user to gain a deeper understanding of the content of the debate. For example, the simulation unit can analyze the background and rationale of Elon Musk's statements. The simulation unit can also build a system that can perform a detailed analysis of the background and rationale of each debater's statements in a debate simulation and provide the results to the user. For example, the simulation unit can analyze the background and rationale of Sakamoto Ryoma's statements. This allows the user to gain a deeper understanding of the content of the debate by analyzing the background and rationale of each debater's statements in detail.

[0076] The simulation unit allows the generating AI to analyze the progress of the discussion in real time during the simulation and make suggestions to guide it in the optimal direction. For example, the simulation unit allows the generating AI to analyze the progress of the discussion in real time during the simulation and make suggestions to guide it in the optimal direction. For example, the simulation unit analyzes a discussion between Steve Jobs and Elon Musk and makes suggestions to guide it in the optimal direction. The simulation unit also allows the generating AI to analyze the progress of the discussion in real time and make suggestions to help users effectively advance the discussion. For example, the simulation unit analyzes a discussion between Ryoma Sakamoto and a modern sociologist and makes suggestions to guide it in the optimal direction. The simulation unit also builds a system where the generating AI analyzes the progress of the discussion in real time during the simulation and makes suggestions to guide it in the optimal direction. For example, the simulation unit analyzes a discussion between Rachel Carson and a modern environmental scientist and makes suggestions to guide it in the optimal direction. This allows the generating AI to analyze the progress of the discussion in real time and make suggestions to guide it in the optimal direction, maximizing the effectiveness of the discussion.

[0077] The simulation unit can use the emotion estimation function to analyze the emotional reactions of each debater during a discussion and optimize the flow of the discussion. For example, the simulation unit uses the emotion estimation function to analyze the emotional reactions of each debater during a discussion and optimize the flow of the discussion. For example, it analyzes the emotional reactions of Steve Jobs and optimizes the flow of the discussion. The simulation unit also builds a system that analyzes the emotional reactions of each debater and optimizes the progress of the discussion. For example, it analyzes the emotional reactions of Elon Musk and optimizes the flow of the discussion. The simulation unit also uses the emotion estimation function to analyze the emotional reactions of each debater during a discussion and makes suggestions to help the user effectively advance the discussion. For example, it analyzes the emotional reactions of Ryoma Sakamoto and optimizes the flow of the discussion. In this way, the flow of the discussion can be optimized by using the emotion estimation function to analyze the emotional reactions of each debater during a discussion.

[0078] The simulation unit can re-simulate the results of a debate under different scenarios and conditions and compare multiple results. For example, the simulation unit re-simulates the results of a debate under different scenarios and conditions and compares multiple results. For example, it re-simulates a debate between Steve Jobs and Elon Musk under different scenarios. The simulation unit also simulates a debate under different conditions, allowing users to compare multiple results. For example, it re-simulates a debate between Ryoma Sakamoto and a modern sociologist under different conditions. The simulation unit also re-simulates the results of a debate under different scenarios and conditions, allowing users to select the optimal result. For example, it re-simulates a debate between Rachel Carson and a modern environmental scientist under different scenarios. This allows the optimal progress of the debate to be supported by re-simulating the results of a debate under different scenarios and conditions and comparing multiple results.

[0079] The simulation unit can visualize the results of a simulation to allow users to intuitively understand it. For example, the simulation unit visualizes the results of a debate to allow users to intuitively understand it. For example, the results of a debate between Steve Jobs and Elon Musk are displayed in graphs and charts. The simulation unit also visualizes the results of a simulation to allow users to understand the content of the debate at a glance. For example, the simulation unit visualizes the results of a debate between Ryoma Sakamoto and a modern sociologist. The simulation unit also builds a system to visualize the results of a debate to allow users to intuitively understand it. For example, the simulation unit visualizes the results of a debate between Rachel Carson and a modern environmental scientist. In this way, by visualizing the simulation results, users can intuitively understand it.

[0080] The simulation unit uses the emotion estimation function to monitor the user's emotional reactions during the discussion simulation and support optimal discussion progression. For example, the simulation unit uses the emotion estimation function to monitor the user's emotional reactions during the discussion simulation and support optimal discussion progression. For example, if a user has a positive reaction to a statement by Steve Jobs, the discussion will proceed in that direction. The simulation unit also builds a system that monitors the user's emotional reactions in real time and optimizes the discussion progression. For example, if a user has a negative reaction to a statement by Elon Musk, the direction of the discussion will be adjusted. The simulation unit also uses the emotion estimation function to analyze the user's emotional reactions during the discussion simulation and support optimal discussion progression. For example, if a user has a positive reaction to a statement by Ryoma Sakamoto, the discussion will proceed in that direction. In this way, by using the emotion estimation function to monitor the user's emotional reactions during the discussion simulation, optimal discussion progression can be supported.

[0081] The generation unit can perform detailed analysis to turn ideas generated from simulated discussions into concrete business plans. For example, the generation unit analyzes ideas generated from simulated discussions and turns them into concrete business plans. For example, an idea generated from a discussion between Steve Jobs and Elon Musk is converted into a business plan. The generation unit also analyzes generated ideas in detail to create feasible business plans. For example, an idea generated from a discussion between Ryoma Sakamoto and a modern sociologist is converted into a business plan. The generation unit also builds a system that analyzes ideas generated from simulated discussions and turns them into concrete business plans. For example, an idea generated from a discussion between Rachel Carson and a modern environmental scientist is converted into a business plan. This allows for the creation of feasible business plans by performing detailed analysis to turn ideas generated from simulated discussions into concrete business plans.

[0082] The generation unit can evaluate the feasibility of generated ideas by referring to past successes and failures. For example, the generation unit can compare ideas born from discussions between Steve Jobs and Elon Musk with past examples. The generation unit can also build a system to compare generated ideas with past successes and failures to evaluate their feasibility. For example, ideas born from discussions between Ryoma Sakamoto and modern sociologists can be compared with past examples. The generation unit can also automate the process of evaluating the feasibility of generated ideas by referring to past successes and failures. For example, ideas born from discussions between Rachel Carson and modern environmental scientists can be compared with past examples. This allows the feasibility of generated ideas to be evaluated by referring to past successes and failures.

[0083] The generation unit can use the emotion estimation function to analyze the user's emotional response to the generated ideas and select the most positive idea. For example, the generation unit can use the emotion estimation function to analyze the user's emotional response to the generated ideas and select the most positive idea. For example, the generation unit can analyze the user's response to an idea born from a discussion between Steve Jobs and Elon Musk. The generation unit can also build a system that analyzes the user's emotional response to the generated ideas in real time and selects the most positive idea. For example, the generation unit can analyze the user's response to an idea born from a discussion between Ryoma Sakamoto and a modern sociologist. The generation unit can also use the emotion estimation function to analyze the user's emotional response to the generated ideas and automate the process of selecting the most positive idea. For example, the generation unit can analyze the user's response to an idea born from a discussion between Rachel Carson and a modern environmental scientist. This allows the generation unit to select the most positive idea by analyzing the user's emotional response to the generated ideas using the emotion estimation function.

[0084] The generation unit can apply the generated ideas to different industries and applications to discover new business opportunities. For example, the generation unit applies the generated ideas to different industries to discover new business opportunities. For example, ideas born from a discussion between Steve Jobs and Elon Musk could be applied to the medical field. The generation unit also builds a system to apply the generated ideas to different applications to discover new business opportunities. For example, ideas born from a discussion between Ryoma Sakamoto and modern sociologists could be applied to the education field. The generation unit also automates the process of applying the generated ideas to different industries and applications to discover new business opportunities. For example, ideas born from a discussion between Rachel Carson and modern environmental scientists could be applied to the energy field. This makes it possible to discover new business opportunities by applying the generated ideas to different industries and applications.

[0085] The generation unit can implement the generated ideas as prototypes and improve them based on user feedback. For example, the generation unit implements the generated ideas as prototypes and improves them based on user feedback. For example, an idea born from a discussion between Steve Jobs and Elon Musk could be implemented as a prototype. The generation unit also builds a system that implements the generated ideas as prototypes and collects user feedback in real time to improve them. For example, an idea born from a discussion between Ryoma Sakamoto and a modern sociologist could be implemented as a prototype. The generation unit also automates the process of implementing the generated ideas as prototypes and improving them based on user feedback. For example, an idea born from a discussion between Rachel Carson and a modern environmental scientist could be implemented as a prototype. This allows the generation unit to implement the generated ideas as prototypes and improve them based on user feedback to create feasible business plans.

[0086] The generation unit can use the emotion estimation function to predict the market's emotional reaction to the generated ideas and propose an optimal marketing strategy. For example, the generation unit uses the emotion estimation function to predict the market's emotional reaction to the generated ideas and propose an optimal marketing strategy. For example, the generation unit predicts the market's reaction to an idea born from a discussion between Steve Jobs and Elon Musk. The generation unit also builds a system that predicts the market's emotional reaction to generated ideas in real time and proposes an optimal marketing strategy. For example, the generation unit predicts the market's reaction to an idea born from a discussion between Ryoma Sakamoto and modern sociologists. The generation unit also automates the process of predicting the market's emotional reaction to generated ideas and proposing an optimal marketing strategy using the emotion estimation function. For example, the generation unit predicts the market's reaction to an idea born from a discussion between Rachel Carson and modern environmental scientists. This makes it possible to propose an optimal marketing strategy by predicting the market's emotional reaction to generated ideas using the emotion estimation function.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The Selection Department can draw out new perspectives and approaches to a user's goals by combining debaters from different cultures and eras. For example, it proposes a pairing of Steve Jobs and Ryoma Sakamoto, combining technological innovation and leadership. The Selection Department also offers a multifaceted approach to a user's goals by combining experts from different fields. For example, it proposes a pairing of Elon Musk and Rachel Carson, combining the perspectives of technology and environmental protection. The Selection Department also combines debaters from different eras, fusing historical perspectives with contemporary knowledge. For example, it proposes a pairing of Thomas Edison with a modern AI expert, drawing out the synergy between past inventions and the latest technology. This allows for the combination of debaters from different cultures and eras to draw out new perspectives and approaches.

[0089] The simulation module can provide users with a detailed analysis of the background and rationale behind each debater's statements during a debate simulation. For example, it can analyze the background and rationale behind Steve Jobs' statements to clarify the underlying thinking behind his innovative philosophy. It can also analyze the background and rationale behind Elon Musk's statements to provide concrete evidence for his technological vision. It can also analyze the background and rationale behind Sakamoto Ryoma's statements to clarify his leadership style and strategy. By providing a detailed analysis of the background and rationale behind each debater's statements, users can gain a deeper understanding of the content of the debate.

[0090] The generation unit can perform detailed analysis to turn ideas generated from simulated discussions into concrete business plans. For example, it converts ideas generated from a discussion between Steve Jobs and Elon Musk into a business plan and clearly shows feasible steps. It also converts ideas generated from a discussion between Ryoma Sakamoto and a modern sociologist into a business plan and proposes a specific market strategy. It also converts ideas generated from a discussion between Rachel Carson and a modern environmental scientist into a business plan and evaluates their feasibility from an environmental protection perspective. This allows for detailed analysis to turn ideas generated from simulated discussions into concrete business plans, creating feasible business plans.

[0091] The database section can add 3D models and audio data of famous people and experts, allowing users to experience them more realistically. For example, adding a 3D model of Steve Jobs will recreate his presentation style. Adding audio data of Elon Musk will also recreate his speeches more realistically. Furthermore, combining a 3D model of Ryoma Sakamoto with audio data will allow users to experience his leadership style visually and audibly. Adding 3D models and audio data of famous people and experts allows users to experience them more realistically.

[0092] The selection unit uses the emotion estimation function to analyze the emotional compatibility between debaters and select the pairing that is expected to produce the most positive discussion. For example, it analyzes the emotional compatibility between Steve Jobs and Elon Musk and predicts how their discussion will proceed. It also analyzes the emotional compatibility between Ryoma Sakamoto and a modern sociologist and simulates how their discussion will unfold. It also analyzes the emotional compatibility between Rachel Carson and a modern environmental scientist and evaluates how their discussion will proceed. In this way, by analyzing the emotional compatibility between debaters using the emotion estimation function, it is possible to select the pairing that is expected to produce the most positive discussion.

[0093] The simulation unit uses the emotion estimation function to analyze the emotional reactions of each debater during a discussion and optimize the flow of the discussion. For example, it analyzes the emotional reactions of Steve Jobs and evaluates the impact his comments have on the discussion. It also analyzes the emotional reactions of Elon Musk and predicts how his comments will affect the progress of the discussion. It also analyzes the emotional reactions of Ryoma Sakamoto and evaluates the impact his comments have on the flow of the discussion. This makes it possible to optimize the flow of the discussion by using the emotion estimation function to analyze the emotional reactions of each debater during a discussion.

[0094] The generator uses the emotion estimation function to analyze the user's emotional response to the generated ideas and select the most positive one. For example, it analyzes user responses to ideas generated from a discussion between Steve Jobs and Elon Musk and selects the most popular idea. It also analyzes user responses to ideas generated from a discussion between Ryoma Sakamoto and modern sociologists and selects the idea with the most positive response. It also analyzes user responses to ideas generated from a discussion between Rachel Carson and modern environmental scientists and selects the most positive idea. This allows the generator to select the most positive idea by analyzing the user's emotional response to the generated ideas using the emotion estimation function.

[0095] The database section adds unpublished letters and diaries of prominent figures and experts, providing deeper insights. For example, adding unpublished letters from Steve Jobs will shed light on the background of his thoughts and vision. Adding the diaries of Ryoma Sakamoto will also provide a deeper understanding of his leadership style and strategies. Adding unpublished letters from Elon Musk will reveal his technological vision and strategies. By adding private information such as unpublished letters and diaries of prominent figures and experts, the database section can provide deeper insights.

[0096] The simulation unit can re-simulate the results of a debate under different scenarios and conditions and compare multiple results. For example, it can re-simulate a debate between Steve Jobs and Elon Musk under different scenarios and select the optimal result. It can also re-simulate a debate between Ryoma Sakamoto and a modern sociologist under different conditions and compare multiple results. It can also re-simulate a debate between Rachel Carson and a modern environmental scientist under different scenarios and select the optimal result. This allows it to support optimal debate progression by re-simulating the results of a debate under different scenarios and conditions and comparing multiple results.

[0097] The Generation Department can apply the generated ideas to different industries and applications to discover new business opportunities. For example, ideas born from a discussion between Steve Jobs and Elon Musk can be applied to the medical field to promote the development of new medical technologies. Ideas born from a discussion between Ryoma Sakamoto and modern sociologists can be applied to the education field to develop new educational programs. Furthermore, ideas born from a discussion between Rachel Carson and modern environmental scientists can be applied to the energy field to discover new energy solutions. By applying the generated ideas to different industries and applications, new business opportunities can be discovered.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The goal setting unit sets the user's goal. For example, the user can set a specific goal such as "I want to think of a business idea for a new technology." The goal setting unit can also analyze the user's input and automatically suggest goals. Step 2: The database section holds data on great people and experts from the past and present, such as Steve Jobs' knowledge of innovation, Ryoma Sakamoto's knowledge of leadership, and Elon Musk's knowledge of technology. Step 3: The selection department selects the most suitable debaters based on the objectives set by the objective setting department. For example, for the objective of "composing business ideas for new technology," experts in the field of technology such as Steve Jobs or Elon Musk are selected. Step 4: The simulation department simulates a debate between the debaters selected by the selection department. For example, Steve Jobs might propose a new business idea, and Elon Musk might discuss its technical feasibility. Step 5: The generation unit generates new businesses and ideas from the discussions simulated by the simulation unit. For example, a discussion between Jobs and Musk could lead to a business model that utilizes a new technology.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a goal setting unit for setting a goal of a user; A database section that holds data on great people and experts from the past to the present, a selection unit that selects an optimal debater based on the objective set by the objective setting unit; a simulation unit that simulates the debate of the debaters selected by the selection unit; a generation unit that generates new businesses and ideas from the discussions simulated by the simulation unit. A system characterized by:

2. The purpose setting unit Analyze the user's past behavioral history and interests and automatically make suggestions when setting goals.

2. The system of claim 1.

3. The purpose setting unit When setting the user's goal, the generation AI presents relevant past success and failure cases for reference.

2. The system of claim 1.

4. The purpose setting unit Analyzing the user's current emotional state and helping them set optimal goals 2. The system of claim 1.

5. The database unit Adding information from unpublished letters and diaries of prominent figures and experts to provide deeper insights 2. The system of claim 1.

6. The database unit Analyzing the ideas and knowledge of each great person or expert in relation to the historical background and social impact to provide more comprehensive information 2. The system of claim 1.

Images