system

Abstract

The system according to this embodiment aims to efficiently facilitate the optimal matching of business owners with potential successors and the development of business succession plans. [Solution] The system according to the embodiment comprises an analysis unit, a matching unit, a support unit, a planning unit, and a risk unit. The analysis unit analyzes detailed profiles of the business owner and the successor candidate. The matching unit performs optimal matching based on the profiles analyzed by the analysis unit. The support unit assists in communication between the business owner and the successor candidate matched by the matching unit. The planning unit formulates a business succession plan based on the communication supported by the support unit. The risk unit analyzes risks based on the business succession plan formulated by the planning unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the matching between business operators and successor candidates and the formulation of business succession plans are not carried out efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently perform an optimal matching between a business operator and a successor candidate and the formulation of a business succession plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a matching unit, a support unit, a planning unit, and a risk unit. The analysis unit analyzes detailed profiles of business owners and successor candidates. The matching unit performs optimal matching based on the profiles analyzed by the analysis unit. The support unit assists in communication between business owners and successor candidates matched by the matching unit. The planning unit formulates a business succession plan based on the communication supported by the support unit. The risk unit analyzes risks based on the business succession plan formulated by the planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform optimal matching between business owners and potential successors, as well as develop business succession plans. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​business succession matching system according to an embodiment of the present invention is a system in which AI optimally matches business owners considering business succession with potential successors who wish to take over the business. This system analyzes the detailed profiles of business owners and potential successors, performs optimal matching, supports communication, formulates a business succession plan, and analyzes risks. For example, the AI ​​business succession matching system uses AI to analyze the careers, skills, and values ​​of business owners and potential successors. Next, it performs optimal matching using an AI algorithm and proposes compatible combinations. Furthermore, it provides initial dialogue support through an AI chatbot to support communication between business owners and potential successors. It also proposes an optimal business succession plan using AI. The AI ​​predicts risks related to business succession and proposes countermeasures. This makes it possible to formulate a business succession plan. Considering privacy, matching is performed anonymously in the initial stages, and detailed information is disclosed after mutual agreement. This system targets small and medium-sized business owners aged 60 and over, business owners without successors, business owners considering business succession, working professionals in their 30s and 40s interested in starting a business or business succession, and companies considering business expansion through M&A. The challenges faced by the target audience include not being able to find a suitable successor, not knowing how to proceed with business succession, issues with inheritance tax and stock valuation, business continuity risks due to the aging of business owners, lack of time for successor training, and difficulty in finding M&A partners. To address these challenges, the system provides optimal matching suggestions using AI, privacy protection through phased information disclosure, 24-hour support via AI chatbot, online consultation functions with experts such as tax accountants and lawyers, automatic generation of business succession plans using AI, provision of risk analysis reports, and online business succession seminars and study sessions. The use of generation AI includes profile analysis and communication support using natural language processing, construction of optimal matching algorithms using machine learning, prediction of business succession risks and proposal of countermeasures using predictive analysis, automatic creation of business succession plans through text generation, and understanding the user's psychological state and responding appropriately through sentiment analysis.This allows the AI ​​business succession matching system to analyze detailed profiles of business owners and potential successors, make optimal matches, support communication, develop business succession plans, and analyze risks.

[0029] The AI ​​business succession matching system according to this embodiment comprises an analysis unit, a matching unit, a support unit, a planning unit, and a risk unit. The analysis unit analyzes detailed profiles of business owners and successor candidates. These detailed profiles include, but are not limited to, career history, skills, values, and hobbies. For example, the analysis unit analyzes work history and educational background as career history. It evaluates specialized knowledge and skills as skills. It considers individual beliefs and goals as values. It analyzes individual interests and activities as hobbies. The matching unit performs optimal matching based on the profiles analyzed by the analysis unit. The matching unit proposes compatible combinations of business owners and successor candidates, for example, using an AI algorithm. The AI ​​algorithm evaluates similarities and differences between business owners and successor candidates, for example, using a machine learning model. The matching unit matches business owners and successor candidates who share common goals and visions, for example. The support unit supports communication between business owners and successor candidates matched by the matching unit. The support unit provides initial dialogue support, for example, using an AI chatbot. The AI ​​chatbot, for example, uses natural language processing technology to support dialogue between business owners and successor candidates. The support department, for example, uses online meeting tools to support communication between business owners and successor candidates. The planning department develops a business succession plan based on the communication supported by the support department. The planning department, for example, uses AI to propose an optimal business succession plan. The AI ​​automatically generates a step-by-step plan for business succession. The planning department proposes methods for risk management. The risk department analyzes risks based on the business succession plan developed by the planning department. The risk department predicts risks related to business succession using AI. The AI ​​generates risk scenarios and evaluates the types and impacts of risks. The risk department proposes risk countermeasures. Thus, the AI ​​business succession matching system according to this embodiment can analyze detailed profiles of business owners and successor candidates, perform optimal matching, support communication, develop business succession plans, and analyze risks.

[0030] The analysis department analyzes detailed profiles of business owners and successor candidates. These detailed profiles include, but are not limited to, career history, skills, values, and hobbies. Specifically, career history includes work experience and educational background. Work experience is evaluated in detail, including past employers, positions, responsibilities, and achievements. Educational background considers schools attended, majors, and qualifications and degrees obtained. Skills are evaluated, including specialized knowledge and technical skills. For example, the analysis details the business owner's knowledge of business strategy and leadership skills, and the successor candidate's technical expertise and problem-solving abilities. Values ​​are considered, including personal beliefs and goals. For example, the business owner's vision and mission, and the successor candidate's career goals and work style are evaluated. Hobbies are analyzed, including personal interests and activities. For example, the sports and cultural activities the business owner enjoys, and the fields and activities of interest to the successor candidate are considered. This allows the analysis department to comprehensively evaluate detailed profiles of business owners and successor candidates and provide foundational data for optimal matching. Furthermore, the analytics department uses AI to efficiently process this data and build algorithms to evaluate the compatibility between business owners and potential successors. For example, it uses natural language processing technology to extract important keywords and phrases from profile data and machine learning models to calculate compatibility scores. This allows the analytics department to quickly and accurately analyze detailed profiles of business owners and potential successors and provide data for optimal matching.

[0031] The matching department performs optimal matching based on profiles analyzed by the analysis department. Specifically, it uses an AI algorithm to propose compatible combinations of business owners and successor candidates. The AI ​​algorithm, for example, uses a machine learning model to evaluate the similarities and differences between business owners and successor candidates. Similarities include shared goals, visions, values, and skills. For example, it evaluates the shared corporate growth strategy, social contribution vision, and leadership style of both the business owner and the successor candidate. Differences include complementary skills and experiences, different perspectives, and approaches. For example, it evaluates the extensive management experience of the business owner and the latest technological knowledge and experience in different industries of the successor candidate. This allows the matching department to comprehensively evaluate the compatibility between business owners and successor candidates and propose the optimal combination. Furthermore, the matching department continuously improves the matching process between business owners and successor candidates using the AI ​​algorithm. For example, it learns to improve the accuracy of the algorithm based on past matching results and feedback. This allows the matching department to always provide optimal matching using the latest data and algorithms.

[0032] The support department assists in the communication between business owners and successor candidates matched by the matching department. Specifically, it provides initial dialogue support using an AI chatbot. The AI ​​chatbot supports the dialogue between business owners and successor candidates using, for example, natural language processing technology. For instance, when a business owner and successor candidate first communicate, the AI ​​chatbot provides self-introductions and common topics to facilitate smooth communication. The AI ​​chatbot also provides quick and appropriate answers to questions from business owners and successor candidates to streamline the dialogue. Furthermore, the support department supports communication between business owners and successor candidates using online meeting tools. For example, it utilizes video conferencing, voice calls, and chat functions to enable real-time communication between business owners and successor candidates. This allows the support department to provide support to facilitate smooth communication between business owners and successor candidates and help build trust. In addition, the support department monitors the progress of communication between business owners and successor candidates and provides additional support as needed. For example, it analyzes the frequency and content of conversations and suggests reminders and follow-ups if communication is insufficient. This allows the support department to continuously support communication between the business owner and potential successors, thereby facilitating the smooth progress of the business succession process.

[0033] The Planning Department develops business succession plans based on communication supported by the Support Department. Specifically, it uses AI to propose optimal business succession plans. For example, the AI ​​automatically generates step-by-step plans for business succession. For instance, it details the tasks, deadlines, and responsible parties required at each step of the business succession process and provides this information to the current manager and potential successors. This allows the Planning Department to provide a concrete plan for efficiently and effectively advancing the business succession process. Furthermore, the Planning Department proposes risk management methods. For example, it assesses the risks associated with business succession and proposes measures to mitigate them. This includes clarifying the roles and responsibilities of the current manager and potential successors, and establishing processes for important decision-making. This allows the Planning Department to minimize risks in the business succession process and support a smooth transition. In addition, the Planning Department monitors the progress of the business succession plan and makes revisions or improvements as needed. For example, it regularly evaluates the progress of the plan and takes prompt action if delays or problems occur. This allows the Planning Department to continuously support the progress of the business succession process and lead it to success.

[0034] The Risk Department analyzes risks based on the business succession plan formulated by the Planning Department. Specifically, it uses AI to predict risks related to business succession. For example, the AI ​​generates risk scenarios and evaluates the types and impacts of risks. For instance, it evaluates risks such as organizational disruption due to a change in management or a decline in performance during the successor candidate's adaptation period. This allows the Risk Department to identify potential risks in the business succession process in advance and take appropriate measures. Furthermore, the Risk Department proposes risk countermeasures. For example, it develops specific action plans for risk mitigation and provides them to the management and successor candidates. This includes response procedures in the event of a risk occurrence and preventive measures to avoid risks. This allows the Risk Department to minimize risks in the business succession process and support a smooth business succession. In addition, the Risk Department monitors the progress of risk management and modifies or improves risk countermeasures as needed. For example, it minimizes the impact of risks by detecting signs of risk occurrence early and taking prompt countermeasures. This allows the Risk Department to improve the safety and reliability of the business succession process and lead it to success.

[0035] The anonymous section can perform anonymous matching. The anonymous section protects the personal information of business owners and successor candidates by using, for example, anonymization technology. The anonymous section protects the privacy of business owners and successor candidates by using, for example, data protection techniques. The anonymous section matches business owners and successor candidates using, for example, anonymized profile information. This allows for optimal matching while protecting privacy by performing anonymous matching. Some or all of the above processing in the anonymous section may be performed using, for example, AI, or without AI. For example, the anonymous section can input the anonymized profile information of business owners and successor candidates into a generating AI and have the generating AI perform optimal matching.

[0036] The consultation department can provide online consultations with experts. For example, the consultation department can support business owners and successor candidates in consulting with experts using video calls. For example, the consultation department can support business owners and successor candidates in consulting with experts in real time using chat. For example, the consultation department can support business owners and successor candidates in consulting with experts using online meeting tools. This allows them to receive expert advice on business succession through online consultations with experts. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the consultation content of business owners and successor candidates into a generating AI and have the generating AI execute the optimal advice.

[0037] The reporting department can provide risk analysis reports. For example, the reporting department can assess risks related to business succession based on risk assessment criteria. For example, the reporting department can analyze the types and impacts of risks and propose risk countermeasures. For example, the reporting department can set the format of the risk analysis report and provide it to the manager and the successor candidate. By providing risk analysis reports, it is possible to predict risks related to business succession and propose countermeasures. Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input risk assessment criteria into a generating AI and have the generating AI create the risk analysis report.

[0038] The seminar department can host online seminars and study sessions. For example, the seminar department can set seminar themes and provide them to business owners and potential successors. For example, the seminar department can host seminars using online platforms that business owners and potential successors can participate in. For example, the seminar department can set the content of study sessions and provide it to business owners and potential successors. In this way, by hosting online seminars and study sessions, knowledge about business succession can be provided and understanding can be deepened. Some or all of the above processes in the seminar department may be performed using AI, for example, or not using AI. For example, the seminar department can input seminar themes into a generating AI and have the generating AI execute the seminar content.

[0039] The analysis department can analyze the careers, skills, and values ​​of business owners and successor candidates. For example, the analysis department analyzes work history and educational background as part of career history. For example, the analysis department evaluates specialized knowledge and skills as part of skills. For example, the analysis department considers personal beliefs and goals as part of values. By analyzing the careers, skills, and values ​​of business owners and successor candidates, the optimal match can be made. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input the careers, skills, and values ​​of business owners and successor candidates into a generative AI and have the generative AI execute the analysis results.

[0040] The matching unit can propose compatible pairings using an AI algorithm. For example, the matching unit uses a machine learning model to evaluate the similarities and differences between business owners and successor candidates. The matching unit matches business owners and successor candidates who share common goals and visions. This allows for optimal matching by proposing compatible pairings using an AI algorithm. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input profile information of business owners and successor candidates into a generative AI and have the generative AI perform the optimal matching.

[0041] The support department can provide initial dialogue support using an AI chatbot. The support department can, for example, use natural language processing technology to support the dialogue between the manager and the successor candidate. The support department can, for example, use an AI chatbot to support the initial dialogue between the manager and the successor candidate. This allows for support of communication between the manager and the successor candidate by providing initial dialogue support using an AI chatbot. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input the content of the dialogue between the manager and the successor candidate into a generative AI and have the generative AI perform the dialogue support.

[0042] The planning department can propose an optimal business succession plan using AI. For example, the planning department can use AI to automatically generate a step-by-step plan for business succession. For example, the planning department can propose methods for risk management. In this way, by proposing an optimal business succession plan using AI, a business succession plan can be formulated. Some or all of the above processes in the planning department may be performed using, for example, a generating AI, or not using a generating AI. For example, the planning department can input the contents of the business succession plan into a generating AI and have the generating AI execute the optimal plan.

[0043] The risk management unit can predict and propose countermeasures for business succession risks using AI. For example, the risk management unit can use AI to predict risks related to business succession. For example, the risk management unit can generate risk scenarios and evaluate the types and impacts of risks. For example, the risk management unit can propose risk countermeasures. In this way, risks related to business succession can be managed by predicting and proposing countermeasures for business succession risks using AI. Some or all of the above processes in the risk management unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the risk management unit can input risk scenarios into a generating AI and have the generating AI perform risk prediction and countermeasures.

[0044] The analysis department can analyze the past performance and failures of the business owner and successor candidates to identify risk factors. For example, the analysis department can analyze the business owner's past performance to identify success and failure factors. For example, the analysis department can analyze the successor candidate's past performance to identify risk factors. For example, the analysis department can compare the past performance of the business owner and successor candidate to identify risk factors. By analyzing the past performance and failures of the business owner and successor candidate and identifying risk factors, the risks of business succession can be reduced. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis department can input past performance data of the business owner and successor candidate into a generative AI and have the generative AI identify risk factors.

[0045] The analysis department can analyze the networks and connections of business owners and successor candidates and evaluate their mutual trust. For example, the analysis department can analyze the business owner's network and evaluate the strength of their trust. For example, the analysis department can analyze the successor candidate's network and evaluate the strength of their trust. For example, the analysis department can compare the networks of business owners and successor candidates and evaluate their mutual trust. By analyzing the networks and connections of business owners and successor candidates and evaluating their mutual trust, highly reliable matching can be achieved. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can input network data of business owners and successor candidates into a generative AI and have the generative AI perform the trust evaluation.

[0046] The analysis department can analyze the health status and stress levels of business owners and successor candidates to assess their suitability for business succession. For example, the analysis department can analyze the health status of business owners and assess their suitability for business succession. For example, the analysis department can analyze the health status of successor candidates and assess their suitability for business succession. For example, the analysis department can analyze the stress levels of business owners and successor candidates and assess their suitability for business succession. In this way, by analyzing the health status and stress levels of business owners and successor candidates and assessing their suitability for business succession, an appropriate successor can be selected. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input health data of business owners and successor candidates into a generative AI and have the generative AI perform the suitability assessment for business succession.

[0047] The analysis department can analyze the hobbies and lifestyles of business owners and successor candidates and evaluate their compatibility. For example, the analysis department can analyze the hobbies of business owners and evaluate their compatibility with successor candidates. For example, the analysis department can analyze the lifestyles of successor candidates and evaluate their compatibility with business owners. For example, the analysis department can compare the hobbies and lifestyles of business owners and successor candidates and evaluate their compatibility. By analyzing the hobbies and lifestyles of business owners and successor candidates and evaluating their compatibility, better matching can be achieved. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input the hobbies and lifestyle data of business owners and successor candidates into a generative AI and have the generative AI perform the compatibility evaluation.

[0048] The matching unit can improve the accuracy of matching by considering the past collaborative relationship between the business owner and the successor candidate during the matching process. For example, the matching unit can analyze the past collaborative relationship between the business owner and the successor candidate to improve the accuracy of matching. For example, the matching unit can improve the accuracy of matching by considering the past collaborative projects between the business owner and the successor candidate. For example, the matching unit can improve the accuracy of matching by referring to the past collaborative history between the business owner and the successor candidate. By improving the accuracy of matching by considering the past collaborative relationship between the business owner and the successor candidate, more reliable matching can be achieved. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the matching unit can input data on the past collaborative relationship between the business owner and the successor candidate into a generative AI and have the generative AI perform the matching accuracy improvement.

[0049] The matching unit can perform matching by considering the future visions and goals of the business owner and the successor candidate. For example, the matching unit can analyze the business owner's future vision and match them with the successor candidate. For example, the matching unit can analyze the successor candidate's future goals and match them with the business owner. For example, the matching unit can compare the future visions and goals of the business owner and the successor candidate and perform the optimal match. By considering the future visions and goals of the business owner and the successor candidate when performing the match, it is possible to perform a match where the future directions align. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input the future vision and goal data of the business owner and the successor candidate into a generative AI and have the generative AI perform the optimal match.

[0050] The matching unit can perform matching by considering the geographical proximity of the business owner and the successor candidate. For example, the matching unit analyzes the geographical proximity of the business owner and the successor candidate and performs the optimal matching. For example, the matching unit performs matching by considering the geographical distance between the business owner and the successor candidate. For example, the matching unit compares the geographical locations of the business owner and the successor candidate and performs the optimal matching. By considering the geographical proximity of the business owner and the successor candidate when performing matching, it is possible to perform matching based on physical proximity. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the matching unit can input the geographical location data of the business owner and the successor candidate into a generative AI and have the generative AI perform the optimal matching.

[0051] The matching unit can perform matching by considering the industry experience and expertise of both the business owner and the successor candidate. For example, the matching unit can analyze the business owner's industry experience and match them with the successor candidate. For example, the matching unit can analyze the successor candidate's expertise and match them with the business owner. For example, the matching unit can compare the industry experience and expertise of both the business owner and the successor candidate to perform the optimal match. By considering the industry experience and expertise of both the business owner and the successor candidate when performing the matching, it is possible to perform matching that is well-versed in the industry. Some or all of the above processes in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input the industry experience and expertise data of the business owner and the successor candidate into a generative AI and have the generative AI perform the optimal match.

[0052] The support department can provide optimal support by referring to the past communication history between the business owner and the successor candidate during the support process. For example, the support department can analyze the past communication history between the business owner and the successor candidate to provide optimal support. For example, the support department can provide optimal support by referring to the content of past conversations between the business owner and the successor candidate. For example, the support department can analyze the past communication patterns between the business owner and the successor candidate to provide optimal support. By referring to the past communication history between the business owner and the successor candidate to provide optimal support, more effective support can be provided. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input past communication history data between the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal support.

[0053] The support department can provide dialogue support while considering the cultural backgrounds and values ​​of the business owner and the successor candidate. For example, the support department can analyze the business owner's cultural background and provide dialogue support with the successor candidate. For example, the support department can analyze the successor candidate's values ​​and provide dialogue support with the business owner. For example, the support department can compare the cultural backgrounds and values ​​of the business owner and the successor candidate and provide optimal dialogue support. By considering the cultural backgrounds and values ​​of the business owner and the successor candidate, the support department can provide more appropriate dialogue support. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input data on the cultural backgrounds and values ​​of the business owner and the successor candidate into a generative AI and have the generative AI perform the dialogue support.

[0054] The support department can analyze the communication styles of the business owner and the successor candidate during the support process and select the optimal support method. For example, the support department can analyze the business owner's communication style and select the optimal support method with the successor candidate. For example, the support department can analyze the successor candidate's communication style and select the optimal support method with the business owner. For example, the support department can compare the communication styles of the business owner and the successor candidate and select the optimal support method. By analyzing the communication styles of the business owner and the successor candidate and selecting the optimal support method, more effective support can be provided. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the communication style data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal support method.

[0055] The support department can provide dialogue support while considering the industry-specific terminology and customs of the business owner and successor candidate. For example, the support department can analyze the business owner's industry-specific terminology and provide dialogue support with the successor candidate. For example, the support department can analyze the successor candidate's industry-specific customs and provide dialogue support with the business owner. For example, the support department can compare the industry-specific terminology and customs of the business owner and successor candidate to provide optimal dialogue support. By considering the industry-specific terminology and customs of the business owner and successor candidate, the support department can provide more appropriate dialogue support. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input industry-specific terminology and customs data of the business owner and successor candidate into a generative AI and have the generative AI perform the dialogue support.

[0056] The planning department can propose the optimal plan by referring to the past business plans and strategies of the business owner and the successor candidate during the planning stage. For example, the planning department can analyze the business owner's past business plans and propose the optimal plan. For example, the planning department can analyze the successor candidate's past business strategies and propose the optimal plan. For example, the planning department can compare the past business plans and strategies of the business owner and the successor candidate and propose the optimal plan. By referring to the past business plans and strategies of the business owner and the successor candidate and proposing the optimal plan, a more effective business succession plan can be provided. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input the past business plan and strategy data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal plan.

[0057] The planning department can formulate a plan by analyzing the market environment and competitive landscape for both the business owner and the successor candidate. For example, the planning department can analyze the business owner's market environment and formulate the optimal plan. For example, the planning department can analyze the successor candidate's competitive landscape and formulate the optimal plan. For example, the planning department can compare the market environment and competitive landscape for both the business owner and the successor candidate and formulate the optimal plan. By analyzing the market environment and competitive landscape for both the business owner and the successor candidate and formulating a plan, a more realistic business succession plan can be provided. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input market environment and competitive landscape data for both the business owner and the successor candidate into a generative AI and have the generative AI formulate the plan.

[0058] The planning department can analyze the financial situation of the business owner and the successor candidate during the planning stage and propose the optimal plan. For example, the planning department can analyze the business owner's financial situation and propose the optimal plan. For example, the planning department can analyze the successor candidate's financial situation and propose the optimal plan. For example, the planning department can compare the financial situations of the business owner and the successor candidate and propose the optimal plan. By analyzing the financial situations of the business owner and the successor candidate and proposing the optimal plan, a more realistic business succession plan can be provided. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input the financial data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal plan.

[0059] The planning department can formulate a plan while considering the legal risks of the business owner and the successor candidate. For example, the planning department can analyze the legal risks of the business owner and formulate the optimal plan. For example, the planning department can analyze the legal risks of the successor candidate and formulate the optimal plan. For example, the planning department can compare the legal risks of the business owner and the successor candidate and formulate the optimal plan. By formulating a plan while considering the legal risks of the business owner and the successor candidate, it is possible to provide a business succession plan that reduces legal risks. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input legal risk data of the business owner and the successor candidate into a generative AI and have the generative AI formulate the plan.

[0060] The risk department can propose optimal countermeasures by referring to the past risk response history of the manager and successor candidates during risk analysis. For example, the risk department can analyze the manager's past risk response history and propose optimal countermeasures. For example, the risk department can analyze the successor candidate's past risk response history and propose optimal countermeasures. For example, the risk department can compare the past risk response histories of the manager and successor candidate and propose optimal countermeasures. By referring to the past risk response histories of the manager and successor candidate and proposing optimal countermeasures, more effective risk management can be achieved. Some or all of the above processing in the risk department may be performed using, for example, a generative AI, or without a generative AI. For example, the risk department can input the past risk response history data of the manager and successor candidate into a generative AI and have the generative AI execute the optimal countermeasures.

[0061] The Risk Department can perform risk analysis while considering the industry-specific risks of the business owner and the successor candidate. For example, the Risk Department can analyze the industry-specific risks of the business owner and propose the optimal countermeasures. For example, the Risk Department can analyze the industry-specific risks of the successor candidate and propose the optimal countermeasures. For example, the Risk Department can compare the industry-specific risks of the business owner and the successor candidate and propose the optimal countermeasures. In this way, by performing analysis while considering the industry-specific risks of the business owner and the successor candidate, industry-specific risk countermeasures can be implemented. Some or all of the above processing in the Risk Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Risk Department can input industry-specific risk data of the business owner and the successor candidate into a generative AI and have the generative AI perform the risk analysis.

[0062] The risk department can perform risk analysis while considering the geographical risks of the business owner and the successor candidate. For example, the risk department can analyze the geographical risks of the business owner and propose the optimal countermeasures. For example, the risk department can analyze the geographical risks of the successor candidate and propose the optimal countermeasures. For example, the risk department can compare the geographical risks of the business owner and the successor candidate and propose the optimal countermeasures. In this way, by performing analysis while considering the geographical risks of the business owner and the successor candidate, risk countermeasures based on geographical factors can be implemented. Some or all of the above processing in the risk department may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk department can input geographical risk data of the business owner and the successor candidate into a generative AI and have the generative AI perform the risk analysis.

[0063] The risk department can propose countermeasures by considering the legal risks of the business owner and the successor candidate during risk analysis. For example, the risk department can analyze the legal risks of the business owner and propose the optimal countermeasures. For example, the risk department can analyze the legal risks of the successor candidate and propose the optimal countermeasures. For example, the risk department can compare the legal risks of the business owner and the successor candidate and propose the optimal countermeasures. By considering the legal risks of the business owner and the successor candidate and proposing countermeasures accordingly, legal risks can be reduced. Some or all of the above processing in the risk department may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk department can input legal risk data of the business owner and the successor candidate into a generative AI and have the generative AI execute the proposal of countermeasures.

[0064] The anonymity unit can select the optimal method during anonymous matching by referring to the past anonymous matching history of the business owner and the successor candidate. For example, the anonymity unit can analyze the business owner's past anonymous matching history and select the optimal method. For example, the anonymity unit can analyze the successor candidate's past anonymous matching history and select the optimal method. For example, the anonymity unit can compare the past anonymous matching history of the business owner and the successor candidate and select the optimal method. By selecting the optimal method by referring to the past anonymous matching history of the business owner and the successor candidate, more effective anonymous matching can be achieved. Some or all of the above processing in the anonymity unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the anonymity unit can input the past anonymous matching history data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal method.

[0065] The anonymity unit can propose additional measures to protect the privacy of business owners and successor candidates during anonymous matching. For example, the anonymity unit can propose additional measures to protect the privacy of business owners. For example, the anonymity unit can propose additional measures to protect the privacy of successor candidates. For example, the anonymity unit can propose additional measures to protect the privacy of business owners and successor candidates. This allows for safer anonymous matching by proposing additional measures to protect the privacy of business owners and successor candidates. Some or all of the above processing in the anonymity unit may be performed using, for example, a generative AI, or without a generative AI. For example, the anonymity unit can input privacy protection data of business owners and successor candidates into a generative AI and have the generative AI execute the proposal of additional measures.

[0066] The consultation department can provide optimal advice by referring to the past consultation history of the business owner and the successor candidate during the consultation. For example, the consultation department can analyze the business owner's past consultation history and provide optimal advice. For example, the consultation department can analyze the successor candidate's past consultation history and provide optimal advice. For example, the consultation department can compare the past consultation histories of the business owner and the successor candidate and provide optimal advice. This allows for more effective online consultations by providing optimal advice by referring to the past consultation histories of the business owner and the successor candidate. Some or all of the above processing in the consultation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the consultation department can input the past consultation history data of the business owner and the successor candidate into a generative AI and have the generative AI execute optimal advice.

[0067] The consulting department can analyze the selection criteria for business owners and successor candidates when providing consultations, and propose the most suitable experts. For example, the consulting department can analyze the selection criteria for business owners and propose the most suitable experts. For example, the consulting department can analyze the selection criteria for successor candidates and propose the most suitable experts. For example, the consulting department can compare the selection criteria for business owners and successor candidates and propose the most suitable experts. By analyzing the selection criteria for business owners and successor candidates and proposing the most suitable experts, a more appropriate expert can be selected. Some or all of the above processing in the consulting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the consulting department can input the selection criteria data for business owners and successor candidates into a generative AI and have the generative AI propose the most suitable experts.

[0068] The reporting unit can create an optimal report by referring to the past risk response history of the manager and the successor candidate when creating the report. For example, the reporting unit can analyze the manager's past risk response history and create an optimal report. For example, the reporting unit can analyze the successor candidate's past risk response history and create an optimal report. For example, the reporting unit can compare the past risk response history of the manager and the successor candidate and create an optimal report. In this way, by creating an optimal report by referring to the past risk response history of the manager and the successor candidate, a more effective risk analysis report can be provided. Some or all of the above processing in the reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting unit can input the past risk response history data of the manager and the successor candidate into a generative AI and have the generative AI create an optimal report.

[0069] The reporting department can create reports considering the industry-specific risks of both the business owner and the successor candidate. For example, the reporting department can analyze the industry-specific risks of the business owner and create an optimal report. For example, the reporting department can analyze the industry-specific risks of the successor candidate and create an optimal report. For example, the reporting department can compare the industry-specific risks of the business owner and the successor candidate and create an optimal report. By creating reports that consider the industry-specific risks of both the business owner and the successor candidate, the reporting department can provide industry-specific risk analysis reports. Some or all of the above processing in the reporting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting department can input industry-specific risk data for the business owner and the successor candidate into a generative AI and have the generative AI create the report.

[0070] The seminar department can provide the most suitable seminar by referring to the past participation history of business owners and successor candidates when holding a seminar. For example, the seminar department can analyze the past seminar participation history of business owners and provide the most suitable seminar. For example, the seminar department can analyze the past seminar participation history of successor candidates and provide the most suitable seminar. For example, the seminar department can compare the past seminar participation history of business owners and successor candidates and provide the most suitable seminar. By referring to the past participation history of business owners and successor candidates and providing the most suitable seminar, it is possible to hold more effective seminars. Some or all of the above processing in the seminar department may be performed using, for example, a generative AI, or not using a generative AI. For example, the seminar department can input the past seminar participation history data of business owners and successor candidates into a generative AI and have the generative AI perform the task of providing the most suitable seminar.

[0071] The seminar department can plan seminars by considering the industry-specific needs of business owners and successor candidates when holding seminars. For example, the seminar department can analyze the industry-specific needs of business owners and plan the optimal seminar. For example, the seminar department can analyze the industry-specific needs of successor candidates and plan the optimal seminar. For example, the seminar department can compare the industry-specific needs of business owners and successor candidates and plan the optimal seminar. By planning seminars while considering the industry-specific needs of business owners and successor candidates, more effective seminars can be held. Some or all of the above processes in the seminar department may be performed using, for example, generative AI, or not using generative AI. For example, the seminar department can input data on the industry-specific needs of business owners and successor candidates into generative AI and have the generative AI execute the seminar planning.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The anonymity function can select the optimal method during anonymous matching by referring to the past anonymous matching history of the business owner and the successor candidate. For example, it can analyze the business owner's past anonymous matching history and select the optimal method. It can also analyze the successor candidate's past anonymous matching history and select the optimal method. It can compare the past anonymous matching history of the business owner and the successor candidate and select the optimal method. This allows for more effective anonymous matching by selecting the optimal method by referring to the past anonymous matching history of the business owner and the successor candidate.

[0074] The consultation department can provide optimal advice by referring to the past consultation history of both the business owner and the successor candidate during the consultation. For example, it can analyze the business owner's past consultation history and provide optimal advice. It can analyze the successor candidate's past consultation history and provide optimal advice. It can compare the past consultation histories of the business owner and the successor candidate and provide optimal advice. This allows for more effective online consultations by providing optimal advice by referring to the past consultation histories of both the business owner and the successor candidate.

[0075] The reporting department can create optimal reports by referring to the past risk response history of both the business owner and the successor candidate. For example, it can analyze the business owner's past risk response history to create an optimal report. It can analyze the successor candidate's past risk response history to create an optimal report. It can compare the past risk response histories of the business owner and the successor candidate to create an optimal report. By referring to the past risk response histories of both the business owner and the successor candidate to create an optimal report, it is possible to provide more effective risk analysis reports.

[0076] The planning department can propose the optimal plan by referring to the past business plans and strategies of the current manager and potential successor during the planning stage. For example, it can analyze the current manager's past business plans and propose the optimal plan. It can analyze the potential successor's past business strategies and propose the optimal plan. It can compare the past business plans and strategies of the current manager and potential successor and propose the optimal plan. In this way, by referring to the past business plans and strategies of the current manager and potential successor and proposing the optimal plan, a more effective business succession plan can be provided.

[0077] The risk department can conduct risk analysis by considering the industry-specific risks of both the current manager and the successor candidate. For example, it can analyze the industry-specific risks of the current manager and propose optimal countermeasures. It can also analyze the industry-specific risks of the successor candidate and propose optimal countermeasures. Furthermore, it can compare the industry-specific risks of the current manager and the successor candidate and propose optimal countermeasures. By considering the industry-specific risks of both the current manager and the successor candidate during the analysis, industry-specific risk countermeasures can be implemented.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The analysis department analyzes detailed profiles of the business owner and potential successors. These detailed profiles include career history, skills, values, and hobbies. For example, career history includes work experience and educational background, skills include evaluating specialized knowledge and techniques, values ​​include considering personal beliefs and goals, and hobbies include analyzing personal interests and activities. Step 2: The matching department performs optimal matching based on the profiles analyzed by the analysis department. For example, it uses an AI algorithm to suggest compatible combinations of business owners and successor candidates. The AI ​​algorithm uses a machine learning model to evaluate commonalities and differences and matches business owners and successor candidates who share common goals and visions. Step 3: The support department assists communication between the business owner and successor candidate matched by the matching department. For example, it provides initial conversational support using an AI chatbot and supports the conversation using natural language processing technology. It also supports communication using online meeting tools. Step 4: The Planning Department develops a business succession plan based on the communication supported by the Support Department. For example, it uses AI to propose the optimal business succession plan and automatically generates a step-by-step plan for business succession. It also proposes methods for risk management. Step 5: The Risk Department analyzes risks based on the business succession plan formulated by the Planning Department. For example, they use AI to predict risks related to business succession, generate risk scenarios, and evaluate the types and impacts of risks. They also propose risk countermeasures.

[0080] (Example of form 2) The AI ​​business succession matching system according to an embodiment of the present invention is a system in which AI optimally matches business owners considering business succession with potential successors who wish to take over the business. This system analyzes the detailed profiles of business owners and potential successors, performs optimal matching, supports communication, formulates a business succession plan, and analyzes risks. For example, the AI ​​business succession matching system uses AI to analyze the careers, skills, and values ​​of business owners and potential successors. Next, it performs optimal matching using an AI algorithm and proposes compatible combinations. Furthermore, it provides initial dialogue support through an AI chatbot to support communication between business owners and potential successors. It also proposes an optimal business succession plan using AI. The AI ​​predicts risks related to business succession and proposes countermeasures. This makes it possible to formulate a business succession plan. Considering privacy, matching is performed anonymously in the initial stages, and detailed information is disclosed after mutual agreement. This system targets small and medium-sized business owners aged 60 and over, business owners without successors, business owners considering business succession, working professionals in their 30s and 40s interested in starting a business or business succession, and companies considering business expansion through M&A. The challenges faced by the target audience include not being able to find a suitable successor, not knowing how to proceed with business succession, issues with inheritance tax and stock valuation, business continuity risks due to the aging of business owners, lack of time for successor training, and difficulty in finding M&A partners. To address these challenges, the system provides optimal matching suggestions using AI, privacy protection through phased information disclosure, 24-hour support via AI chatbot, online consultation functions with experts such as tax accountants and lawyers, automatic generation of business succession plans using AI, provision of risk analysis reports, and online business succession seminars and study sessions. The use of generation AI includes profile analysis and communication support using natural language processing, construction of optimal matching algorithms using machine learning, prediction of business succession risks and proposal of countermeasures using predictive analysis, automatic creation of business succession plans through text generation, and understanding the user's psychological state and responding appropriately through sentiment analysis.This allows the AI ​​business succession matching system to analyze detailed profiles of business owners and potential successors, make optimal matches, support communication, develop business succession plans, and analyze risks.

[0081] The AI ​​business succession matching system according to this embodiment comprises an analysis unit, a matching unit, a support unit, a planning unit, and a risk unit. The analysis unit analyzes detailed profiles of business owners and successor candidates. These detailed profiles include, but are not limited to, career history, skills, values, and hobbies. For example, the analysis unit analyzes work history and educational background as career history. It evaluates specialized knowledge and skills as skills. It considers individual beliefs and goals as values. It analyzes individual interests and activities as hobbies. The matching unit performs optimal matching based on the profiles analyzed by the analysis unit. The matching unit proposes compatible combinations of business owners and successor candidates, for example, using an AI algorithm. The AI ​​algorithm evaluates similarities and differences between business owners and successor candidates, for example, using a machine learning model. The matching unit matches business owners and successor candidates who share common goals and visions, for example. The support unit supports communication between business owners and successor candidates matched by the matching unit. The support unit provides initial dialogue support, for example, using an AI chatbot. The AI ​​chatbot, for example, uses natural language processing technology to support dialogue between business owners and successor candidates. The support department, for example, uses online meeting tools to support communication between business owners and successor candidates. The planning department develops a business succession plan based on the communication supported by the support department. The planning department, for example, uses AI to propose an optimal business succession plan. The AI ​​automatically generates a step-by-step plan for business succession. The planning department proposes methods for risk management. The risk department analyzes risks based on the business succession plan developed by the planning department. The risk department predicts risks related to business succession using AI. The AI ​​generates risk scenarios and evaluates the types and impacts of risks. The risk department proposes risk countermeasures. Thus, the AI ​​business succession matching system according to this embodiment can analyze detailed profiles of business owners and successor candidates, perform optimal matching, support communication, develop business succession plans, and analyze risks.

[0082] The analysis department analyzes detailed profiles of business owners and successor candidates. These detailed profiles include, but are not limited to, career history, skills, values, and hobbies. Specifically, career history includes work experience and educational background. Work experience is evaluated in detail, including past employers, positions, responsibilities, and achievements. Educational background considers schools attended, majors, and qualifications and degrees obtained. Skills are evaluated, including specialized knowledge and technical skills. For example, the analysis details the business owner's knowledge of business strategy and leadership skills, and the successor candidate's technical expertise and problem-solving abilities. Values ​​are considered, including personal beliefs and goals. For example, the business owner's vision and mission, and the successor candidate's career goals and work style are evaluated. Hobbies are analyzed, including personal interests and activities. For example, the sports and cultural activities the business owner enjoys, and the fields and activities of interest to the successor candidate are considered. This allows the analysis department to comprehensively evaluate detailed profiles of business owners and successor candidates and provide foundational data for optimal matching. Furthermore, the analytics department uses AI to efficiently process this data and build algorithms to evaluate the compatibility between business owners and potential successors. For example, it uses natural language processing technology to extract important keywords and phrases from profile data and machine learning models to calculate compatibility scores. This allows the analytics department to quickly and accurately analyze detailed profiles of business owners and potential successors and provide data for optimal matching.

[0083] The matching department performs optimal matching based on profiles analyzed by the analysis department. Specifically, it uses an AI algorithm to propose compatible combinations of business owners and successor candidates. The AI ​​algorithm, for example, uses a machine learning model to evaluate the similarities and differences between business owners and successor candidates. Similarities include shared goals, visions, values, and skills. For example, it evaluates the shared corporate growth strategy, social contribution vision, and leadership style of both the business owner and the successor candidate. Differences include complementary skills and experiences, different perspectives, and approaches. For example, it evaluates the extensive management experience of the business owner and the latest technological knowledge and experience in different industries of the successor candidate. This allows the matching department to comprehensively evaluate the compatibility between business owners and successor candidates and propose the optimal combination. Furthermore, the matching department continuously improves the matching process between business owners and successor candidates using the AI ​​algorithm. For example, it learns to improve the accuracy of the algorithm based on past matching results and feedback. This allows the matching department to always provide optimal matching using the latest data and algorithms.

[0084] The support department assists in the communication between business owners and successor candidates matched by the matching department. Specifically, it provides initial dialogue support using an AI chatbot. The AI ​​chatbot supports the dialogue between business owners and successor candidates using, for example, natural language processing technology. For instance, when a business owner and successor candidate first communicate, the AI ​​chatbot provides self-introductions and common topics to facilitate smooth communication. The AI ​​chatbot also provides quick and appropriate answers to questions from business owners and successor candidates to streamline the dialogue. Furthermore, the support department supports communication between business owners and successor candidates using online meeting tools. For example, it utilizes video conferencing, voice calls, and chat functions to enable real-time communication between business owners and successor candidates. This allows the support department to provide support to facilitate smooth communication between business owners and successor candidates and help build trust. In addition, the support department monitors the progress of communication between business owners and successor candidates and provides additional support as needed. For example, it analyzes the frequency and content of conversations and suggests reminders and follow-ups if communication is insufficient. This allows the support department to continuously support communication between the business owner and potential successors, thereby facilitating the smooth progress of the business succession process.

[0085] The Planning Department develops business succession plans based on communication supported by the Support Department. Specifically, it uses AI to propose optimal business succession plans. For example, the AI ​​automatically generates step-by-step plans for business succession. For instance, it details the tasks, deadlines, and responsible parties required at each step of the business succession process and provides this information to the current manager and potential successors. This allows the Planning Department to provide a concrete plan for efficiently and effectively advancing the business succession process. Furthermore, the Planning Department proposes risk management methods. For example, it assesses the risks associated with business succession and proposes measures to mitigate them. This includes clarifying the roles and responsibilities of the current manager and potential successors, and establishing processes for important decision-making. This allows the Planning Department to minimize risks in the business succession process and support a smooth transition. In addition, the Planning Department monitors the progress of the business succession plan and makes revisions or improvements as needed. For example, it regularly evaluates the progress of the plan and takes prompt action if delays or problems occur. This allows the Planning Department to continuously support the progress of the business succession process and lead it to success.

[0086] The Risk Department analyzes risks based on the business succession plan formulated by the Planning Department. Specifically, it uses AI to predict risks related to business succession. For example, the AI ​​generates risk scenarios and evaluates the types and impacts of risks. For instance, it evaluates risks such as organizational disruption due to a change in management or a decline in performance during the successor candidate's adaptation period. This allows the Risk Department to identify potential risks in the business succession process in advance and take appropriate measures. Furthermore, the Risk Department proposes risk countermeasures. For example, it develops specific action plans for risk mitigation and provides them to the management and successor candidates. This includes response procedures in the event of a risk occurrence and preventive measures to avoid risks. This allows the Risk Department to minimize risks in the business succession process and support a smooth business succession. In addition, the Risk Department monitors the progress of risk management and modifies or improves risk countermeasures as needed. For example, it minimizes the impact of risks by detecting signs of risk occurrence early and taking prompt countermeasures. This allows the Risk Department to improve the safety and reliability of the business succession process and lead it to success.

[0087] The anonymous section can perform anonymous matching. The anonymous section protects the personal information of business owners and successor candidates by using, for example, anonymization technology. The anonymous section protects the privacy of business owners and successor candidates by using, for example, data protection techniques. The anonymous section matches business owners and successor candidates using, for example, anonymized profile information. This allows for optimal matching while protecting privacy by performing anonymous matching. Some or all of the above processing in the anonymous section may be performed using, for example, AI, or without AI. For example, the anonymous section can input the anonymized profile information of business owners and successor candidates into a generating AI and have the generating AI perform optimal matching.

[0088] The consultation department can provide online consultations with experts. For example, the consultation department can support business owners and successor candidates in consulting with experts using video calls. For example, the consultation department can support business owners and successor candidates in consulting with experts in real time using chat. For example, the consultation department can support business owners and successor candidates in consulting with experts using online meeting tools. This allows them to receive expert advice on business succession through online consultations with experts. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the consultation content of business owners and successor candidates into a generating AI and have the generating AI execute the optimal advice.

[0089] The reporting department can provide risk analysis reports. For example, the reporting department can assess risks related to business succession based on risk assessment criteria. For example, the reporting department can analyze the types and impacts of risks and propose risk countermeasures. For example, the reporting department can set the format of the risk analysis report and provide it to the manager and the successor candidate. By providing risk analysis reports, it is possible to predict risks related to business succession and propose countermeasures. Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input risk assessment criteria into a generating AI and have the generating AI create the risk analysis report.

[0090] The seminar department can host online seminars and study sessions. For example, the seminar department can set seminar themes and provide them to business owners and potential successors. For example, the seminar department can host seminars using online platforms that business owners and potential successors can participate in. For example, the seminar department can set the content of study sessions and provide it to business owners and potential successors. In this way, by hosting online seminars and study sessions, knowledge about business succession can be provided and understanding can be deepened. Some or all of the above processes in the seminar department may be performed using AI, for example, or not using AI. For example, the seminar department can input seminar themes into a generating AI and have the generating AI execute the seminar content.

[0091] The analysis department can analyze the careers, skills, and values ​​of business owners and successor candidates. For example, the analysis department analyzes work history and educational background as part of career history. For example, the analysis department evaluates specialized knowledge and skills as part of skills. For example, the analysis department considers personal beliefs and goals as part of values. By analyzing the careers, skills, and values ​​of business owners and successor candidates, the optimal match can be made. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input the careers, skills, and values ​​of business owners and successor candidates into a generative AI and have the generative AI execute the analysis results.

[0092] The matching unit can propose compatible pairings using an AI algorithm. For example, the matching unit uses a machine learning model to evaluate the similarities and differences between business owners and successor candidates. The matching unit matches business owners and successor candidates who share common goals and visions. This allows for optimal matching by proposing compatible pairings using an AI algorithm. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input profile information of business owners and successor candidates into a generative AI and have the generative AI perform the optimal matching.

[0093] The support department can provide initial dialogue support using an AI chatbot. The support department can, for example, use natural language processing technology to support the dialogue between the manager and the successor candidate. The support department can, for example, use an AI chatbot to support the initial dialogue between the manager and the successor candidate. This allows for support of communication between the manager and the successor candidate by providing initial dialogue support using an AI chatbot. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input the content of the dialogue between the manager and the successor candidate into a generative AI and have the generative AI perform the dialogue support.

[0094] The planning department can propose an optimal business succession plan using AI. For example, the planning department can use AI to automatically generate a step-by-step plan for business succession. For example, the planning department can propose methods for risk management. In this way, by proposing an optimal business succession plan using AI, a business succession plan can be formulated. Some or all of the above processes in the planning department may be performed using, for example, a generating AI, or not using a generating AI. For example, the planning department can input the contents of the business succession plan into a generating AI and have the generating AI execute the optimal plan.

[0095] The risk management unit can predict and propose countermeasures for business succession risks using AI. For example, the risk management unit can use AI to predict risks related to business succession. For example, the risk management unit can generate risk scenarios and evaluate the types and impacts of risks. For example, the risk management unit can propose risk countermeasures. In this way, risks related to business succession can be managed by predicting and proposing countermeasures for business succession risks using AI. Some or all of the above processes in the risk management unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the risk management unit can input risk scenarios into a generating AI and have the generating AI perform risk prediction and countermeasures.

[0096] The analysis department can estimate the emotions of business owners and successor candidates and improve the accuracy of profile analysis based on these estimated emotions. For example, if a business owner is stressed, the analysis department can suggest an interview in a relaxed environment and perform an emotion-based profile analysis. For example, if a successor candidate is nervous, the analysis department can use a relaxing question format and perform an emotion-based profile analysis. For example, if a business owner is relaxed, the analysis department can ask detailed questions and perform an emotion-based profile analysis. This allows for more accurate matching by estimating the emotions of business owners and successor candidates and improving the accuracy of profile analysis based on these estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input the emotional data of business owners and successor candidates into a generative AI and have the generative AI perform an emotion-based profile analysis.

[0097] The analysis department can analyze the past performance and failures of the business owner and successor candidates to identify risk factors. For example, the analysis department can analyze the business owner's past performance to identify success and failure factors. For example, the analysis department can analyze the successor candidate's past performance to identify risk factors. For example, the analysis department can compare the past performance of the business owner and successor candidate to identify risk factors. By analyzing the past performance and failures of the business owner and successor candidate and identifying risk factors, the risks of business succession can be reduced. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis department can input past performance data of the business owner and successor candidate into a generative AI and have the generative AI identify risk factors.

[0098] The analysis department can analyze the networks and connections of business owners and successor candidates and evaluate their mutual trust. For example, the analysis department can analyze the business owner's network and evaluate the strength of their trust. For example, the analysis department can analyze the successor candidate's network and evaluate the strength of their trust. For example, the analysis department can compare the networks of business owners and successor candidates and evaluate their mutual trust. By analyzing the networks and connections of business owners and successor candidates and evaluating their mutual trust, highly reliable matching can be achieved. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can input network data of business owners and successor candidates into a generative AI and have the generative AI perform the trust evaluation.

[0099] The analysis unit can estimate the emotions of the business owner and the successor candidate, and adjust the display method of the analysis results based on the estimated emotions. For example, if the business owner is stressed, the analysis unit provides a simple display method. For example, if the successor candidate is relaxed, the analysis unit provides a detailed display method. For example, if the business owner is tense, the analysis unit provides a visually calming display method. This enables a user-friendly display by estimating the emotions of the business owner and the successor candidate and adjusting the display method of the analysis results based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the emotion data of the business owner and the successor candidate into the generative AI and have the generative AI adjust the display method of the analysis results.

[0100] The analysis department can analyze the health status and stress levels of business owners and successor candidates to assess their suitability for business succession. For example, the analysis department can analyze the health status of business owners and assess their suitability for business succession. For example, the analysis department can analyze the health status of successor candidates and assess their suitability for business succession. For example, the analysis department can analyze the stress levels of business owners and successor candidates and assess their suitability for business succession. In this way, by analyzing the health status and stress levels of business owners and successor candidates and assessing their suitability for business succession, an appropriate successor can be selected. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input health data of business owners and successor candidates into a generative AI and have the generative AI perform the suitability assessment for business succession.

[0101] The analysis department can analyze the hobbies and lifestyles of business owners and successor candidates and evaluate their compatibility. For example, the analysis department can analyze the hobbies of business owners and evaluate their compatibility with successor candidates. For example, the analysis department can analyze the lifestyles of successor candidates and evaluate their compatibility with business owners. For example, the analysis department can compare the hobbies and lifestyles of business owners and successor candidates and evaluate their compatibility. By analyzing the hobbies and lifestyles of business owners and successor candidates and evaluating their compatibility, better matching can be achieved. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input the hobbies and lifestyle data of business owners and successor candidates into a generative AI and have the generative AI perform the compatibility evaluation.

[0102] The matching unit can estimate the emotions of the business owner and the successor candidate, and adjust the matching criteria based on the estimated emotions. For example, if the business owner is relaxed, the matching unit provides detailed matching criteria. For example, if the successor candidate is tense, the matching unit provides simple matching criteria. For example, if the business owner is stressed, the matching unit provides emotion-based matching criteria. This allows for more appropriate matching by estimating the emotions of the business owner and the successor candidate and adjusting the matching criteria based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the emotion data of the business owner and the successor candidate into the generative AI and have the generative AI perform the adjustment of the matching criteria.

[0103] The matching unit can improve the accuracy of matching by considering the past collaborative relationship between the business owner and the successor candidate during the matching process. For example, the matching unit can analyze the past collaborative relationship between the business owner and the successor candidate to improve the accuracy of matching. For example, the matching unit can improve the accuracy of matching by considering the past collaborative projects between the business owner and the successor candidate. For example, the matching unit can improve the accuracy of matching by referring to the past collaborative history between the business owner and the successor candidate. By improving the accuracy of matching by considering the past collaborative relationship between the business owner and the successor candidate, more reliable matching can be achieved. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the matching unit can input data on the past collaborative relationship between the business owner and the successor candidate into a generative AI and have the generative AI perform the matching accuracy improvement.

[0104] The matching unit can perform matching by considering the future visions and goals of the business owner and the successor candidate. For example, the matching unit can analyze the business owner's future vision and match them with the successor candidate. For example, the matching unit can analyze the successor candidate's future goals and match them with the business owner. For example, the matching unit can compare the future visions and goals of the business owner and the successor candidate and perform the optimal match. By considering the future visions and goals of the business owner and the successor candidate when performing the match, it is possible to perform a match where the future directions align. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input the future vision and goal data of the business owner and the successor candidate into a generative AI and have the generative AI perform the optimal match.

[0105] The matching unit can estimate the emotions of the business owner and the successor candidate, and adjust the display order of the matching results based on the estimated emotions. For example, if the business owner is relaxed, the matching unit will provide detailed matching results. For example, if the successor candidate is tense, the matching unit will provide simple matching results. For example, if the business owner is stressed, the matching unit will provide emotion-based matching results. This allows for a user-friendly display by estimating the emotions of the business owner and the successor candidate and adjusting the display order of the matching results based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the emotion data of the business owner and the successor candidate into the generative AI and have the generative AI adjust the display order of the matching results.

[0106] The matching unit can perform matching by considering the geographical proximity of the business owner and the successor candidate. For example, the matching unit analyzes the geographical proximity of the business owner and the successor candidate and performs the optimal matching. For example, the matching unit performs matching by considering the geographical distance between the business owner and the successor candidate. For example, the matching unit compares the geographical locations of the business owner and the successor candidate and performs the optimal matching. By considering the geographical proximity of the business owner and the successor candidate when performing matching, it is possible to perform matching based on physical proximity. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the matching unit can input the geographical location data of the business owner and the successor candidate into a generative AI and have the generative AI perform the optimal matching.

[0107] The matching unit can perform matching by considering the industry experience and expertise of both the business owner and the successor candidate. For example, the matching unit can analyze the business owner's industry experience and match them with the successor candidate. For example, the matching unit can analyze the successor candidate's expertise and match them with the business owner. For example, the matching unit can compare the industry experience and expertise of both the business owner and the successor candidate to perform the optimal match. By considering the industry experience and expertise of both the business owner and the successor candidate when performing the matching, it is possible to perform matching that is well-versed in the industry. Some or all of the above processes in the matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input the industry experience and expertise data of the business owner and the successor candidate into a generative AI and have the generative AI perform the optimal match.

[0108] The support unit can estimate the emotions of the business owner and the successor candidate, and adjust the content of the dialogue support based on the estimated emotions. For example, if the business owner is relaxed, the support unit will provide detailed dialogue support. For example, if the successor candidate is tense, the support unit will provide simple dialogue support. For example, if the business owner is stressed, the support unit will provide emotion-based dialogue support. In this way, by estimating the emotions of the business owner and the successor candidate, and adjusting the content of the dialogue support based on the estimated emotions, more appropriate dialogue support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the emotion data of the business owner and the successor candidate into the generative AI and have the generative AI adjust the content of the dialogue support.

[0109] The support department can provide optimal support by referring to the past communication history between the business owner and the successor candidate during the support process. For example, the support department can analyze the past communication history between the business owner and the successor candidate to provide optimal support. For example, the support department can provide optimal support by referring to the content of past conversations between the business owner and the successor candidate. For example, the support department can analyze the past communication patterns between the business owner and the successor candidate to provide optimal support. By referring to the past communication history between the business owner and the successor candidate to provide optimal support, more effective support can be provided. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input past communication history data between the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal support.

[0110] The support department can provide dialogue support while considering the cultural backgrounds and values ​​of the business owner and the successor candidate. For example, the support department can analyze the business owner's cultural background and provide dialogue support with the successor candidate. For example, the support department can analyze the successor candidate's values ​​and provide dialogue support with the business owner. For example, the support department can compare the cultural backgrounds and values ​​of the business owner and the successor candidate and provide optimal dialogue support. By considering the cultural backgrounds and values ​​of the business owner and the successor candidate, the support department can provide more appropriate dialogue support. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input data on the cultural backgrounds and values ​​of the business owner and the successor candidate into a generative AI and have the generative AI perform the dialogue support.

[0111] The support unit can estimate the emotions of the business owner and the successor candidate, and adjust the timing of dialogue support based on the estimated emotions. For example, if the business owner is relaxed, the support unit will provide detailed dialogue support. For example, if the successor candidate is tense, the support unit will provide simple dialogue support. For example, if the business owner is stressed, the support unit will provide emotion-based dialogue support. This allows for the provision of dialogue support at a more appropriate time by estimating the emotions of the business owner and the successor candidate and adjusting the timing of dialogue support based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the emotion data of the business owner and the successor candidate into the generative AI and have the generative AI adjust the timing of dialogue support.

[0112] The support department can analyze the communication styles of the business owner and the successor candidate during the support process and select the optimal support method. For example, the support department can analyze the business owner's communication style and select the optimal support method with the successor candidate. For example, the support department can analyze the successor candidate's communication style and select the optimal support method with the business owner. For example, the support department can compare the communication styles of the business owner and the successor candidate and select the optimal support method. By analyzing the communication styles of the business owner and the successor candidate and selecting the optimal support method, more effective support can be provided. Some or all of the above processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can input the communication style data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal support method.

[0113] The support department can provide dialogue support while considering the industry-specific terminology and customs of the business owner and successor candidate. For example, the support department can analyze the business owner's industry-specific terminology and provide dialogue support with the successor candidate. For example, the support department can analyze the successor candidate's industry-specific customs and provide dialogue support with the business owner. For example, the support department can compare the industry-specific terminology and customs of the business owner and successor candidate to provide optimal dialogue support. By considering the industry-specific terminology and customs of the business owner and successor candidate, the support department can provide more appropriate dialogue support. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input industry-specific terminology and customs data of the business owner and successor candidate into a generative AI and have the generative AI perform the dialogue support.

[0114] The planning department can estimate the emotions of the business owner and the successor candidate, and adjust the content of the business succession plan based on the estimated emotions. For example, if the business owner is relaxed, the planning department will provide a detailed business succession plan. For example, if the successor candidate is tense, the planning department will provide a simple business succession plan. For example, if the business owner is stressed, the planning department will provide an emotion-based business succession plan. In this way, by estimating the emotions of the business owner and the successor candidate, and adjusting the content of the business succession plan based on the estimated emotions, a more appropriate business succession plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input emotion data of the business owner and the successor candidate into the generative AI and have the generative AI perform the adjustment of the content of the business succession plan.

[0115] The planning department can propose the optimal plan by referring to the past business plans and strategies of the business owner and the successor candidate during the planning stage. For example, the planning department can analyze the business owner's past business plans and propose the optimal plan. For example, the planning department can analyze the successor candidate's past business strategies and propose the optimal plan. For example, the planning department can compare the past business plans and strategies of the business owner and the successor candidate and propose the optimal plan. By referring to the past business plans and strategies of the business owner and the successor candidate and proposing the optimal plan, a more effective business succession plan can be provided. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input the past business plan and strategy data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal plan.

[0116] The planning department can formulate a plan by analyzing the market environment and competitive landscape for both the business owner and the successor candidate. For example, the planning department can analyze the business owner's market environment and formulate the optimal plan. For example, the planning department can analyze the successor candidate's competitive landscape and formulate the optimal plan. For example, the planning department can compare the market environment and competitive landscape for both the business owner and the successor candidate and formulate the optimal plan. By analyzing the market environment and competitive landscape for both the business owner and the successor candidate and formulating a plan, a more realistic business succession plan can be provided. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input market environment and competitive landscape data for both the business owner and the successor candidate into a generative AI and have the generative AI formulate the plan.

[0117] The planning department can estimate the emotions of the business owner and potential successor and determine the priorities of the business succession plan based on these estimated emotions. For example, if the business owner is relaxed, the planning department can provide detailed priorities for the business succession plan. For example, if the potential successor is tense, the planning department can provide simple priorities for the business succession plan. For example, if the business owner is stressed, the planning department can provide emotion-based priorities for the business succession plan. This allows for the setting of more appropriate priorities by estimating the emotions of the business owner and potential successor and determining the priorities of the business succession plan based on these estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning department may be performed using AI or not. For example, the planning department can input emotion data of the business owner and potential successor into a generative AI and have the generative AI perform the determination of priorities for the business succession plan.

[0118] The planning department can analyze the financial situation of the business owner and the successor candidate during the planning stage and propose the optimal plan. For example, the planning department can analyze the business owner's financial situation and propose the optimal plan. For example, the planning department can analyze the successor candidate's financial situation and propose the optimal plan. For example, the planning department can compare the financial situations of the business owner and the successor candidate and propose the optimal plan. By analyzing the financial situations of the business owner and the successor candidate and proposing the optimal plan, a more realistic business succession plan can be provided. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input the financial data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal plan.

[0119] The planning department can formulate a plan while considering the legal risks of the business owner and the successor candidate. For example, the planning department can analyze the legal risks of the business owner and formulate the optimal plan. For example, the planning department can analyze the legal risks of the successor candidate and formulate the optimal plan. For example, the planning department can compare the legal risks of the business owner and the successor candidate and formulate the optimal plan. By formulating a plan while considering the legal risks of the business owner and the successor candidate, it is possible to provide a business succession plan that reduces legal risks. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input legal risk data of the business owner and the successor candidate into a generative AI and have the generative AI formulate the plan.

[0120] The risk assessment unit can estimate the emotions of the manager and successor candidates and adjust the content of the risk analysis based on the estimated emotions. For example, if the manager is relaxed, the risk assessment unit will provide a detailed risk analysis. For example, if the successor candidate is tense, the risk assessment unit will provide a simple risk analysis. For example, if the manager is stressed, the risk assessment unit will provide an emotion-based risk analysis. This allows for a more appropriate risk analysis by estimating the emotions of the manager and successor candidates and adjusting the content of the risk analysis based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input emotion data of the manager and successor candidates into the generative AI and have the generative AI adjust the content of the risk analysis.

[0121] The risk department can propose optimal countermeasures by referring to the past risk response history of the manager and successor candidates during risk analysis. For example, the risk department can analyze the manager's past risk response history and propose optimal countermeasures. For example, the risk department can analyze the successor candidate's past risk response history and propose optimal countermeasures. For example, the risk department can compare the past risk response histories of the manager and successor candidate and propose optimal countermeasures. By referring to the past risk response histories of the manager and successor candidate and proposing optimal countermeasures, more effective risk management can be achieved. Some or all of the above processing in the risk department may be performed using, for example, a generative AI, or without a generative AI. For example, the risk department can input the past risk response history data of the manager and successor candidate into a generative AI and have the generative AI execute the optimal countermeasures.

[0122] The Risk Department can perform risk analysis while considering the industry-specific risks of the business owner and the successor candidate. For example, the Risk Department can analyze the industry-specific risks of the business owner and propose the optimal countermeasures. For example, the Risk Department can analyze the industry-specific risks of the successor candidate and propose the optimal countermeasures. For example, the Risk Department can compare the industry-specific risks of the business owner and the successor candidate and propose the optimal countermeasures. In this way, by performing analysis while considering the industry-specific risks of the business owner and the successor candidate, industry-specific risk countermeasures can be implemented. Some or all of the above processing in the Risk Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Risk Department can input industry-specific risk data of the business owner and the successor candidate into a generative AI and have the generative AI perform the risk analysis.

[0123] The risk assessment unit can estimate the emotions of the manager and successor candidates and determine the priority of risk countermeasures based on the estimated emotions. For example, if the manager is relaxed, the risk assessment unit will provide detailed risk countermeasure priorities. For example, if the successor candidate is tense, the risk assessment unit will provide simple risk countermeasure priorities. For example, if the manager is stressed, the risk assessment unit will provide emotion-based risk countermeasure priorities. This allows for the setting of more appropriate priorities by estimating the emotions of the manager and successor candidates and determining the priority of risk countermeasures based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk assessment unit may be performed using AI or not using AI. For example, the risk assessment unit can input emotion data of the manager and successor candidates into a generative AI and have the generative AI perform the determination of risk countermeasure priorities.

[0124] The risk department can perform risk analysis while considering the geographical risks of the business owner and the successor candidate. For example, the risk department can analyze the geographical risks of the business owner and propose the optimal countermeasures. For example, the risk department can analyze the geographical risks of the successor candidate and propose the optimal countermeasures. For example, the risk department can compare the geographical risks of the business owner and the successor candidate and propose the optimal countermeasures. In this way, by performing analysis while considering the geographical risks of the business owner and the successor candidate, risk countermeasures based on geographical factors can be implemented. Some or all of the above processing in the risk department may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk department can input geographical risk data of the business owner and the successor candidate into a generative AI and have the generative AI perform the risk analysis.

[0125] The risk department can propose countermeasures by considering the legal risks of the business owner and the successor candidate during risk analysis. For example, the risk department can analyze the legal risks of the business owner and propose the optimal countermeasures. For example, the risk department can analyze the legal risks of the successor candidate and propose the optimal countermeasures. For example, the risk department can compare the legal risks of the business owner and the successor candidate and propose the optimal countermeasures. By considering the legal risks of the business owner and the successor candidate and proposing countermeasures accordingly, legal risks can be reduced. Some or all of the above processing in the risk department may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk department can input legal risk data of the business owner and the successor candidate into a generative AI and have the generative AI execute the proposal of countermeasures.

[0126] The anonymous unit can estimate the emotions of the business owner and the successor candidate, and adjust the timing of anonymous matching based on the estimated emotions. For example, if the business owner is relaxed, the anonymous unit provides detailed anonymous matching timing. For example, if the successor candidate is tense, the anonymous unit provides simple anonymous matching timing. For example, if the business owner is stressed, the anonymous unit provides emotion-based anonymous matching timing. This allows anonymous matching to be performed at a more appropriate time by estimating the emotions of the business owner and the successor candidate and adjusting the timing of anonymous matching based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the anonymous unit may be performed using AI, for example, or not using AI. For example, the anonymous unit can input the emotion data of the business owner and the successor candidate into a generative AI and have the generative AI perform the adjustment of the anonymous matching timing.

[0127] The anonymity unit can select the optimal method during anonymous matching by referring to the past anonymous matching history of the business owner and the successor candidate. For example, the anonymity unit can analyze the business owner's past anonymous matching history and select the optimal method. For example, the anonymity unit can analyze the successor candidate's past anonymous matching history and select the optimal method. For example, the anonymity unit can compare the past anonymous matching history of the business owner and the successor candidate and select the optimal method. By selecting the optimal method by referring to the past anonymous matching history of the business owner and the successor candidate, more effective anonymous matching can be achieved. Some or all of the above processing in the anonymity unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the anonymity unit can input the past anonymous matching history data of the business owner and the successor candidate into a generative AI and have the generative AI execute the optimal method.

[0128] The anonymous unit can estimate the emotions of the business owner and the successor candidate, and determine the priority of anonymous matching based on the estimated emotions. For example, if the business owner is relaxed, the anonymous unit will provide a detailed priority for anonymous matching. For example, if the successor candidate is tense, the anonymous unit will provide a simpler priority for anonymous matching. For example, if the business owner is stressed, the anonymous unit will provide an emotion-based priority for anonymous matching. This allows for the setting of more appropriate priorities by estimating the emotions of the business owner and the successor candidate and determining the priority of anonymous matching based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anonymous unit may be performed using AI, for example, or not using AI. For example, the anonymous unit can input the emotion data of the business owner and the successor candidate into the generative AI and have the generative AI perform the determination of the priority for anonymous matching.

[0129] The anonymity unit can propose additional measures to protect the privacy of business owners and successor candidates during anonymous matching. For example, the anonymity unit can propose additional measures to protect the privacy of business owners. For example, the anonymity unit can propose additional measures to protect the privacy of successor candidates. For example, the anonymity unit can propose additional measures to protect the privacy of business owners and successor candidates. This allows for safer anonymous matching by proposing additional measures to protect the privacy of business owners and successor candidates. Some or all of the above processing in the anonymity unit may be performed using, for example, a generative AI, or without a generative AI. For example, the anonymity unit can input privacy protection data of business owners and successor candidates into a generative AI and have the generative AI execute the proposal of additional measures.

[0130] The consultation department can estimate the emotions of the business owner and the successor candidate, and adjust the content of the online consultation based on the estimated emotions. For example, if the business owner is relaxed, the consultation department will provide detailed online consultation content. For example, if the successor candidate is nervous, the consultation department will provide simple online consultation content. For example, if the business owner is stressed, the consultation department will provide emotionally-based online consultation content. In this way, by estimating the emotions of the business owner and the successor candidate and adjusting the content of the online consultation based on the estimated emotions, a more appropriate online consultation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the emotional data of the business owner and the successor candidate into the generative AI and have the generative AI perform the adjustment of the online consultation content.

[0131] The consultation department can provide optimal advice by referring to the past consultation history of the business owner and the successor candidate during the consultation. For example, the consultation department can analyze the business owner's past consultation history and provide optimal advice. For example, the consultation department can analyze the successor candidate's past consultation history and provide optimal advice. For example, the consultation department can compare the past consultation histories of the business owner and the successor candidate and provide optimal advice. This allows for more effective online consultations by providing optimal advice by referring to the past consultation histories of the business owner and the successor candidate. Some or all of the above processing in the consultation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the consultation department can input the past consultation history data of the business owner and the successor candidate into a generative AI and have the generative AI execute optimal advice.

[0132] The consultation department can estimate the emotions of the business owner and the successor candidate and adjust the timing of online consultations based on the estimated emotions. For example, if the business owner is relaxed, the consultation department can provide detailed online consultation timing. For example, if the successor candidate is nervous, the consultation department can provide simple online consultation timing. For example, if the business owner is stressed, the consultation department can provide emotion-based online consultation timing. In this way, by estimating the emotions of the business owner and the successor candidate and adjusting the timing of online consultations based on the estimated emotions, online consultations can be conducted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the emotion data of the business owner and the successor candidate into a generative AI and have the generative AI perform the adjustment of the timing of online consultations.

[0133] The consulting department can analyze the selection criteria for business owners and successor candidates when providing consultations, and propose the most suitable experts. For example, the consulting department can analyze the selection criteria for business owners and propose the most suitable experts. For example, the consulting department can analyze the selection criteria for successor candidates and propose the most suitable experts. For example, the consulting department can compare the selection criteria for business owners and successor candidates and propose the most suitable experts. By analyzing the selection criteria for business owners and successor candidates and proposing the most suitable experts, a more appropriate expert can be selected. Some or all of the above processing in the consulting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the consulting department can input the selection criteria data for business owners and successor candidates into a generative AI and have the generative AI propose the most suitable experts.

[0134] The reporting unit can estimate the emotions of the manager and successor candidates and adjust the content of the risk analysis report based on the estimated emotions. For example, if the manager is relaxed, the reporting unit will provide a detailed risk analysis report. For example, if the successor candidate is tense, the reporting unit will provide a simple risk analysis report. For example, if the manager is stressed, the reporting unit will provide an emotion-based risk analysis report. In this way, by estimating the emotions of the manager and successor candidates and adjusting the content of the risk analysis report based on the estimated emotions, a more appropriate risk analysis report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the emotion data of the manager and successor candidates into the generative AI and have the generative AI perform the adjustment of the content of the risk analysis report.

[0135] The reporting unit can create an optimal report by referring to the past risk response history of the manager and the successor candidate when creating the report. For example, the reporting unit can analyze the manager's past risk response history and create an optimal report. For example, the reporting unit can analyze the successor candidate's past risk response history and create an optimal report. For example, the reporting unit can compare the past risk response history of the manager and the successor candidate and create an optimal report. In this way, by creating an optimal report by referring to the past risk response history of the manager and the successor candidate, a more effective risk analysis report can be provided. Some or all of the above processing in the reporting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting unit can input the past risk response history data of the manager and the successor candidate into a generative AI and have the generative AI create an optimal report.

[0136] The reporting unit can estimate the emotions of the manager and successor candidates and determine the priority of risk analysis reports based on the estimated emotions. For example, if the manager is relaxed, the reporting unit will provide a priority for detailed risk analysis reports. For example, if the successor candidate is tense, the reporting unit will provide a priority for simple risk analysis reports. For example, if the manager is stressed, the reporting unit will provide an emotion-based priority for risk analysis reports. This allows for more appropriate prioritization by estimating the emotions of the manager and successor candidates and determining the priority of risk analysis reports based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input emotion data of the manager and successor candidates into a generative AI and have the generative AI perform the determination of the priority of risk analysis reports.

[0137] The reporting department can create reports considering the industry-specific risks of both the business owner and the successor candidate. For example, the reporting department can analyze the industry-specific risks of the business owner and create an optimal report. For example, the reporting department can analyze the industry-specific risks of the successor candidate and create an optimal report. For example, the reporting department can compare the industry-specific risks of the business owner and the successor candidate and create an optimal report. By creating reports that consider the industry-specific risks of both the business owner and the successor candidate, the reporting department can provide industry-specific risk analysis reports. Some or all of the above processing in the reporting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reporting department can input industry-specific risk data for the business owner and the successor candidate into a generative AI and have the generative AI create the report.

[0138] The seminar department can estimate the emotions of business owners and successor candidates and adjust the seminar content based on these estimated emotions. For example, if the business owner is relaxed, the seminar department can provide detailed seminar content. For example, if the successor candidate is nervous, the seminar department can provide simple seminar content. For example, if the business owner is stressed, the seminar department can provide emotion-based seminar content. In this way, by estimating the emotions of business owners and successor candidates and adjusting the seminar content based on these estimated emotions, a more appropriate seminar can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the seminar department may be performed using AI, for example, or not using AI. For example, the seminar department can input emotion data of business owners and successor candidates into a generative AI and have the generative AI adjust the seminar content.

[0139] The seminar department can provide the most suitable seminar by referring to the past participation history of business owners and successor candidates when holding a seminar. For example, the seminar department can analyze the past seminar participation history of business owners and provide the most suitable seminar. For example, the seminar department can analyze the past seminar participation history of successor candidates and provide the most suitable seminar. For example, the seminar department can compare the past seminar participation history of business owners and successor candidates and provide the most suitable seminar. By referring to the past participation history of business owners and successor candidates and providing the most suitable seminar, it is possible to hold more effective seminars. Some or all of the above processing in the seminar department may be performed using, for example, a generative AI, or not using a generative AI. For example, the seminar department can input the past seminar participation history data of business owners and successor candidates into a generative AI and have the generative AI perform the task of providing the most suitable seminar.

[0140] The seminar department can estimate the emotions of business owners and successor candidates and adjust the timing of seminars based on these estimated emotions. For example, if the business owner is relaxed, the seminar department can provide detailed seminar timing. For example, if the successor candidate is nervous, the seminar department can provide simple seminar timing. For example, if the business owner is stressed, the seminar department can provide emotion-based seminar timing. This allows seminars to be held at more appropriate times by estimating the emotions of business owners and successor candidates and adjusting the timing based on these estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the seminar department may be performed using AI or not. For example, the seminar department can input emotion data of business owners and successor candidates into a generative AI and have the generative AI adjust the timing of seminars.

[0141] The seminar department can plan seminars by considering the industry-specific needs of business owners and successor candidates when holding seminars. For example, the seminar department can analyze the industry-specific needs of business owners and plan the optimal seminar. For example, the seminar department can analyze the industry-specific needs of successor candidates and plan the optimal seminar. For example, the seminar department can compare the industry-specific needs of business owners and successor candidates and plan the optimal seminar. By planning seminars while considering the industry-specific needs of business owners and successor candidates, more effective seminars can be held. Some or all of the above processes in the seminar department may be performed using, for example, generative AI, or not using generative AI. For example, the seminar department can input data on the industry-specific needs of business owners and successor candidates into generative AI and have the generative AI execute the seminar planning.

[0142] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0143] The matching unit can estimate the emotions of the business owner and the successor candidate, and adjust the matching criteria based on these estimated emotions. For example, if the business owner is relaxed, detailed matching criteria can be provided. If the successor candidate is tense, simple matching criteria can be provided. If the business owner is stressed, emotion-based matching criteria can be provided. This allows for more appropriate matching by estimating the emotions of the business owner and the successor candidate and adjusting the matching criteria based on these estimated emotions.

[0144] The support department can estimate the emotions of the business owner and the successor candidate, and adjust the content of the dialogue support based on those estimated emotions. For example, if the business owner is relaxed, detailed dialogue support can be provided. If the successor candidate is tense, simpler dialogue support can be provided. If the business owner is stressed, emotion-based dialogue support can be provided. In this way, by estimating the emotions of the business owner and the successor candidate and adjusting the content of the dialogue support based on those estimated emotions, more appropriate dialogue support can be provided.

[0145] The planning department can estimate the emotions of the business owner and the successor candidate, and adjust the content of the business succession plan based on these estimates. For example, if the business owner is relaxed, a detailed business succession plan can be provided. If the successor candidate is tense, a simpler business succession plan can be provided. If the business owner is stressed, an emotion-based business succession plan can be provided. In this way, by estimating the emotions of the business owner and the successor candidate and adjusting the content of the business succession plan based on these estimates, a more appropriate business succession plan can be provided.

[0146] The risk assessment department can estimate the emotions of the business owner and potential successor, and adjust the risk analysis based on these estimates. For example, if the business owner is relaxed, a detailed risk analysis can be provided. If the potential successor is tense, a simpler risk analysis can be provided. If the business owner is stressed, an emotion-based risk analysis can be provided. This allows for a more appropriate risk analysis by estimating the emotions of the business owner and potential successor and adjusting the risk analysis based on these estimates.

[0147] The seminar department can estimate the emotions of business owners and successor candidates and adjust the seminar content based on those estimates. For example, if the business owner is relaxed, a detailed seminar can be provided. If the successor candidate is nervous, a simpler seminar can be provided. If the business owner is stressed, an emotion-based seminar can be provided. In this way, by estimating the emotions of business owners and successor candidates and adjusting the seminar content based on those estimates, a more appropriate seminar can be provided.

[0148] The anonymity function can select the optimal method during anonymous matching by referring to the past anonymous matching history of the business owner and the successor candidate. For example, it can analyze the business owner's past anonymous matching history and select the optimal method. It can also analyze the successor candidate's past anonymous matching history and select the optimal method. It can compare the past anonymous matching history of the business owner and the successor candidate and select the optimal method. This allows for more effective anonymous matching by selecting the optimal method by referring to the past anonymous matching history of the business owner and the successor candidate.

[0149] The consultation department can provide optimal advice by referring to the past consultation history of both the business owner and the successor candidate during the consultation. For example, it can analyze the business owner's past consultation history and provide optimal advice. It can analyze the successor candidate's past consultation history and provide optimal advice. It can compare the past consultation histories of the business owner and the successor candidate and provide optimal advice. This allows for more effective online consultations by providing optimal advice by referring to the past consultation histories of both the business owner and the successor candidate.

[0150] The reporting department can create optimal reports by referring to the past risk response history of both the business owner and the successor candidate. For example, it can analyze the business owner's past risk response history to create an optimal report. It can analyze the successor candidate's past risk response history to create an optimal report. It can compare the past risk response histories of the business owner and the successor candidate to create an optimal report. By referring to the past risk response histories of both the business owner and the successor candidate to create an optimal report, it is possible to provide more effective risk analysis reports.

[0151] The planning department can propose the optimal plan by referring to the past business plans and strategies of the current manager and potential successor during the planning stage. For example, it can analyze the current manager's past business plans and propose the optimal plan. It can analyze the potential successor's past business strategies and propose the optimal plan. It can compare the past business plans and strategies of the current manager and potential successor and propose the optimal plan. In this way, by referring to the past business plans and strategies of the current manager and potential successor and proposing the optimal plan, a more effective business succession plan can be provided.

[0152] The risk department can conduct risk analysis by considering the industry-specific risks of both the current manager and the successor candidate. For example, it can analyze the industry-specific risks of the current manager and propose optimal countermeasures. It can also analyze the industry-specific risks of the successor candidate and propose optimal countermeasures. Furthermore, it can compare the industry-specific risks of the current manager and the successor candidate and propose optimal countermeasures. By considering the industry-specific risks of both the current manager and the successor candidate during the analysis, industry-specific risk countermeasures can be implemented.

[0153] The following briefly describes the processing flow for example form 2.

[0154] Step 1: The analysis department analyzes detailed profiles of the business owner and potential successors. These detailed profiles include career history, skills, values, and hobbies. For example, career history includes work experience and educational background, skills include evaluating specialized knowledge and techniques, values ​​include considering personal beliefs and goals, and hobbies include analyzing personal interests and activities. Step 2: The matching department performs optimal matching based on the profiles analyzed by the analysis department. For example, it uses an AI algorithm to suggest compatible combinations of business owners and successor candidates. The AI ​​algorithm uses a machine learning model to evaluate commonalities and differences and matches business owners and successor candidates who share common goals and visions. Step 3: The support department assists communication between the business owner and successor candidate matched by the matching department. For example, it provides initial conversational support using an AI chatbot and supports the conversation using natural language processing technology. It also supports communication using online meeting tools. Step 4: The Planning Department develops a business succession plan based on the communication supported by the Support Department. For example, it uses AI to propose the optimal business succession plan and automatically generates a step-by-step plan for business succession. It also proposes methods for risk management. Step 5: The Risk Department analyzes risks based on the business succession plan formulated by the Planning Department. For example, they use AI to predict risks related to business succession, generate risk scenarios, and evaluate the types and impacts of risks. They also propose risk countermeasures.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0157] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the analysis unit, matching unit, support unit, planning unit, risk unit, anonymity unit, consultation unit, reporting unit, and seminar unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes detailed profiles of managers and successor candidates. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs optimal matching. The support unit is implemented by, for example, the control unit 46A of the smart device 14 and supports communication using an AI chatbot. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and formulates a business succession plan. The risk unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes risks. The anonymity unit is implemented by, for example, the control unit 46A of the smart device 14 and performs anonymous matching. The consultation unit is implemented by, for example, the control unit 46A of the smart device 14 and supports online consultations with experts. The reporting section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides risk analysis reports. The seminar section is implemented, for example, by the control unit 46A of the smart device 14, and holds online seminars and study sessions. The correspondence between each section and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0159] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0160] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0162] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0166] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements mentioned above, including the analysis unit, matching unit, support unit, planning unit, risk unit, anonymity unit, consultation unit, reporting unit, and seminar unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes detailed profiles of managers and successor candidates. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs optimal matching. The support unit is implemented by, for example, the control unit 46A of the smart glasses 214 and supports communication using an AI chatbot. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and formulates a business succession plan. The risk unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes risks. The anonymity unit is implemented by, for example, the control unit 46A of the smart glasses 214 and performs anonymous matching. The consultation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and supports online consultations with experts. The reporting section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides risk analysis reports. The seminar section is implemented, for example, by the control unit 46A of the smart glasses 214, and holds online seminars and study sessions. The correspondence between each section and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0175] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0176] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0177] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0178] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0179] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0181] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0182] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the analysis unit, matching unit, support unit, planning unit, risk unit, anonymity unit, consultation unit, reporting unit, and seminar unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes detailed profiles of managers and successor candidates. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs optimal matching. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and supports communication using an AI chatbot. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and formulates a business succession plan. The risk unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes risks. The anonymity unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs anonymous matching. The consultation function is implemented, for example, by the control unit 46A of the headset terminal 314, and supports online consultations with experts. The reporting function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides risk analysis reports. The seminar function is implemented, for example, by the control unit 46A of the headset terminal 314, and holds online seminars and study sessions. The correspondence between each function and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0191] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0192] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0193] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0194] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0195] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0197] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0198] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0199] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0200] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0201] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0202] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0203] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0204] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0205] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0206] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0207] Each of the multiple elements described above, including the analysis unit, matching unit, support unit, planning unit, risk unit, anonymity unit, consultation unit, reporting unit, and seminar unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes detailed profiles of managers and successor candidates. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs optimal matching. The support unit is implemented by, for example, the control unit 46A of the robot 414 and supports communication using an AI chatbot. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and formulates a business succession plan. The risk unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes risks. The anonymity unit is implemented by, for example, the control unit 46A of the robot 414 and performs anonymous matching. The consultation unit is implemented by, for example, the control unit 46A of the robot 414 and supports online consultations with experts. The reporting section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides risk analysis reports. The seminar section is implemented, for example, by the control unit 46A of the robot 414, and holds online seminars and study sessions. The correspondence between each section and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0208] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0209] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0210] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0211] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0212] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0213] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0214] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0215] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0216] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0217] 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.

[0218] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0219] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0220] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0221] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0222] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0223] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0224] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0225] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0226] (Note 1) The analysis department analyzes detailed profiles of business owners and potential successors, A matching unit performs optimal matching based on the profile analyzed by the aforementioned analysis unit, A support department that assists in communication between business owners and successor candidates matched by the aforementioned matching department, The Planning Department, which formulates a business succession plan based on the communication supported by the aforementioned Support Department, The system comprises a risk management unit that analyzes risks based on the business succession plan formulated by the aforementioned planning unit. A system characterized by the following features. (Note 2) It features an anonymous matching section. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility includes a consultation department that provides online consultations with experts. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a reporting section that provides risk analysis reports. The system described in Appendix 1, characterized by the features described herein. (Note 5) The company has a seminar department that organizes online seminars and study sessions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyzing the background, skills, and values ​​of business owners and potential successors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The matching unit is AI algorithms suggest compatible combinations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned support unit, Provides initial conversational support via AI chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned planning department, AI proposes the optimal business succession plan. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned risk section is We propose AI-based risk prediction and countermeasures for business succession. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is This system estimates the emotions of business owners and potential successors, and improves the accuracy of profile analysis based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyze the past performance and failures of the current manager and potential successors to identify risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is Analyze the networks and connections of the business owner and potential successors, and evaluate their mutual trust. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is The system estimates the emotions of the business owner and potential successors, and adjusts how the analysis results are displayed based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The health status and stress levels of the current manager and potential successors are analyzed to assess their suitability for business succession. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is We analyze the hobbies and lifestyles of the business owner and the potential successor to assess their compatibility. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is We estimate the emotions of the business owner and the successor candidate, and adjust the matching criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is To improve the accuracy of matching, we take into account the past collaborative relationship between the business owner and the successor candidate. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is During the matching process, we take into account the future vision and goals of both the business owner and the potential successor. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is The system estimates the emotions of business owners and potential successors, and adjusts the display order of matching results based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is During the matching process, the geographical proximity between the business owner and the potential successor is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is During the matching process, the industry experience and expertise of both the business owner and the successor candidate will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit, The system estimates the emotions of the business owner and the successor candidate, and adjusts the content of the dialogue support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit, When providing support, we refer to the past communication history between the business owner and the successor candidate to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, During support, we provide dialogue support while considering the cultural background and values ​​of both the business owner and the potential successor. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, The system estimates the emotions of the business owner and the successor candidate, and adjusts the timing of dialogue support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, During the support process, we analyze the communication styles of the business owner and the successor candidate to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, During support, we provide dialogue support that takes into account the industry-specific terminology and customs of both the business owner and the successor candidate. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned planning department, The system estimates the emotions of the business owner and the potential successor, and adjusts the content of the business succession plan based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned planning department, During the planning stage, we propose the optimal plan by referring to the past business plans and strategies of the current manager and potential successors. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned planning department, During the planning stage, the market environment and competitive landscape for both the current manager and potential successors are analyzed to develop a plan. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned planning department, The system estimates the emotions of the current business owner and potential successors, and then prioritizes the business succession plan based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned planning department, During the planning stage, we analyze the financial situation of the current manager and potential successors to propose the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned planning department, When planning, the plan should be developed taking into account the legal risks for both the current manager and potential successors. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned risk section is We estimate the emotions of the current manager and potential successors, and adjust the risk analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned risk section is During risk analysis, we propose optimal countermeasures by referring to the past risk response history of the current manager and potential successors. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned risk section is When conducting a risk analysis, the analysis should take into account the industry-specific risks faced by both the current manager and potential successors. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned risk section is The system estimates the emotions of the current manager and potential successors, and prioritizes risk management measures based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned risk section is When conducting a risk analysis, the geographical risks of the current manager and potential successors should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned risk section is When conducting a risk analysis, we propose countermeasures that take into account the legal risks of the business owner and potential successors. The system described in Appendix 1, characterized by the features described herein. (Note 41) The anonymized portion is, Estimate the feelings of the operator and the successor candidates, and adjust the timing of anonymous matching based on the estimated feelings The system according to Supplementary Note 2, characterized by the above. (Supplementary Note 42) The anonymous part When performing anonymous matching, select the optimal method by referring to the past anonymous matching history of the operator and the successor candidates The system according to Supplementary Note 2, characterized by the above. (Supplementary Note 43) The anonymous part Estimate the feelings of the operator and the successor candidates, and determine the priority of anonymous matching based on the estimated feelings The system according to Supplementary Note 2, characterized by the above. (Supplementary Note 44) The anonymous part When performing anonymous matching, propose additional measures for protecting the privacy of the operator and the successor candidates The system according to Supplementary Note 2, characterized by the above. (Supplementary Note 45) The consultation part Estimate the feelings of the operator and the successor candidates, and adjust the content of the online consultation based on the estimated feelings The system according to Supplementary Note 3, characterized by the above. (Supplementary Note 46) The consultation part When consulting, provide the optimal advice by referring to the past consultation history of the operator and the successor candidates The system according to Supplementary Note 3, characterized by the above. (Supplementary Note 47) The consultation part Estimate the feelings of the operator and the successor candidates, and adjust the timing of the online consultation based on the estimated feelings The system according to Supplementary Note 3, characterized by the above. (Supplementary Note 48) The consultation part When consulting, analyze the selection criteria of experts for the operator and the successor candidates, and propose the optimal experts The system according to Supplementary Note 3, characterized by the above. (Note 49) The aforementioned report section is, We estimate the emotions of the current manager and potential successors, and adjust the content of the risk analysis report based on these estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 50) The aforementioned report section is, When creating the report, we refer to the past risk response history of the manager and the successor candidate to create the most optimal report. The system described in Appendix 4, characterized by the features described herein. (Note 51) The aforementioned report section is, We estimate the sentiments of the current manager and potential successors, and prioritize risk analysis reports based on these estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 52) The aforementioned report section is, When creating the report, we will take into account the industry-specific risks faced by both the current manager and the potential successor. The system described in Appendix 4, characterized by the features described herein. (Note 53) The aforementioned seminar section, We estimate the emotions of business owners and potential successors, and adjust the seminar content based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 54) The aforementioned seminar section, When organizing seminars, we refer to the past participation history of business owners and potential successors to provide the most suitable seminars. The system described in Appendix 5, characterized by the features described herein. (Note 55) The aforementioned seminar section, We estimate the emotions of business owners and potential successors, and adjust the timing of seminars based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 56) The aforementioned seminar section, When organizing seminars, we plan them taking into account the industry-specific needs of business owners and potential successors. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes detailed profiles of business owners and potential successors, A matching unit performs optimal matching based on the profile analyzed by the aforementioned analysis unit, A support department that assists in communication between business owners and successor candidates matched by the aforementioned matching department, The Planning Department, which formulates a business succession plan based on the communication supported by the aforementioned Support Department, The system comprises a risk management unit that analyzes risks based on the business succession plan formulated by the aforementioned planning unit. A system characterized by the following features.

2. It features an anonymous matching section. The system according to feature 1.

3. The facility includes a consultation department that provides online consultations with experts. The system according to feature 1.

4. It has a reporting section that provides risk analysis reports. The system according to feature 1.

5. The company has a seminar department that organizes online seminars and study sessions. The system according to feature 1.

6. The aforementioned analysis unit is Analyzing the background, skills, and values ​​of business owners and potential successors. The system according to feature 1.

7. The matching unit is We propose compatible combinations using an AI algorithm. The system according to feature 1.

8. The aforementioned support unit, Provides initial conversational support using an AI chatbot. The system according to feature 1.

9. The aforementioned planning department, We propose the optimal business succession plan using AI. The system according to feature 1.

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