system

Abstract

The system according to the embodiment aims to smoothly advance business succession by utilizing AI. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a matching unit, and a management agent unit. The collection unit collects management data of a company. The analysis unit analyzes the data collected by the collection unit. The matching unit searches for a transferee based on the data analyzed by the analysis unit. The management agent unit manages the company for a limited period of time based on the transferees listed by the matching unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was the issue that business succession did not proceed smoothly due to a lack of successors and the circumstances of the business takeover party.

[0005] The system according to the embodiment aims to smoothly advance business succession by utilizing AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a management agent unit. The collection unit collects business data of a company. The analysis unit analyzes the data collected by the collection unit. The matching unit searches for a transferee based on the data analyzed by the analysis unit. The management agent unit manages the company for a limited period of time based on the transferees listed by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment utilizes AI to facilitate business succession. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business succession support system according to an embodiment of the present invention utilizes AI to resolve business succession issues for small and medium-sized enterprises (SMEs). This system aims to facilitate business succession by collecting and analyzing a company's management data, finding a suitable transferee, and acting as a proxy for the company's management for a limited period of time. Specifically, AI first collects and analyzes the company's management data, garnering detailed information such as the company's financial status, performance, and employee skill sets. For example, data such as sales, profit margins, and number of employees is collected and analyzed by AI to understand the company's current situation. Next, AI searches for a suitable transferee based on the collected and analyzed data. AI then lists and matches potential transferees that meet certain criteria, such as the company's industry, size, and region. Potential transferees include companies seeking to expand in the same industry or locally-based companies. Furthermore, AI acts as a proxy for the company's management for a limited period of time. During this period, AI develops and implements the company's management strategy. For example, AI conducts market analysis, identifies new business opportunities, and develops business plans. AI also improves the company's competitiveness by streamlining employee management and operations. This system can resolve issues such as a lack of successors and time constraints that prevent deals from being concluded, facilitating smooth business succession. For example, by having AI take over management, a company's performance improves and it becomes easier to find a buyer. Furthermore, by having AI create a list of suitable buyers, the succession rate increases. In this way, AI-based business matching and engagement systems can solve business succession issues for small and medium-sized enterprises and support their survival and growth. As a result, the business succession support system can smoothly proceed with business succession by collecting and analyzing a company's management data, finding a suitable buyer, and acting as a proxy for company management for a limited period of time.

[0029] The business succession support system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a management agent unit. The collection unit collects management data of a company. The management data of a company includes, but is not limited to, financial data, performance data, employee data, etc. The collection unit collects, for example, financial data of a company. The financial data includes revenue, expenses, assets, liabilities, etc. The collection unit collects, for example, performance data of a company. The performance data includes sales, profit margins, growth rates, etc. The collection unit collects, for example, employee data of a company. The employee data includes the number of employees, average years of service, skill sets, etc. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected financial data to understand the financial situation of the company. For example, the analysis unit analyzes the collected performance data to evaluate the company's performance. For example, the analysis unit analyzes the collected employee data to evaluate the employee's skill sets. The matching unit searches for a transferee based on the data analyzed by the analysis unit. The matching unit, for example, lists transferees based on the company's industry, size, and region, and performs matching. The matching unit, for example, lists companies in the same industry that are aiming to expand their business. The matching unit, for example, lists community-based companies. The matching unit, for example, lists transferees according to the size of the company. The management agency unit acts as a company manager for a limited period of time based on the transferees listed by the matching unit. The management agency unit, for example, conducts market analysis, finds new business opportunities, and formulates a business plan. The management agency unit, for example, manages employees and improves business efficiency. The management agency unit, for example, plans and implements a company's management strategy. As a result, the business succession support system according to the embodiment is able to collect, analyze, match, and act as a management agency for companies' management data.

[0030] The collection unit can collect financial data, performance data, and employee data of a company. The collection unit, for example, collects financial data of a company. The financial data includes revenue, expenses, assets, liabilities, etc. The collection unit, for example, collects performance data of a company. The performance data includes sales, profit margins, growth rates, etc. The collection unit, for example, collects employee data of a company. The employee data includes the number of employees, average years of service, skill sets, etc. By collecting detailed data of a company, more accurate analysis becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's financial data into AI and have the AI ​​collect the financial data.

[0031] The analysis unit can analyze the data collected by the collection unit to grasp the current situation of the company. For example, the analysis unit analyzes collected financial data to grasp the financial situation of the company. For example, the analysis unit analyzes collected performance data to evaluate the performance of the company. For example, the analysis unit analyzes collected employee data to evaluate the skill sets of employees. This accurately grasps the current situation of the company, making it possible to select an appropriate transferee. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] The matching unit can list transferees based on the company's industry, size, and region, and perform matching. The matching unit can list transferees based on the company's industry, size, and region, for example, and perform matching. The matching unit can list companies in the same industry that are aiming to expand their business. The matching unit can list community-based companies. The matching unit can list transferees according to the company's size, for example. This makes it possible to efficiently list transferees that meet the company's conditions and perform matching. Some or all of the above-mentioned processing in the matching unit can be performed using AI, for example, or without AI. For example, the matching unit can input the company's conditions into AI and have the AI ​​perform the listing of transferees.

[0033] The management agency department can conduct market analysis, identify new business opportunities, and formulate a business plan. The management agency department, for example, conducts market analysis, identify new business opportunities, and formulate a business plan. Market analysis includes, for example, market research techniques and data collection methods. New business opportunities include, for example, developing new markets and new products. Business plans include, for example, goal setting, strategy development, and resource allocation. In this way, by conducting market analysis, new business opportunities can be identified and a business plan can be formulated. Some or all of the above-mentioned processing in the management agency department may be performed, for example, using AI, or may be performed without using AI. For example, the management agency department can input market analysis data into AI and have the AI ​​identify business opportunities and formulate a business plan.

[0034] The management agency can manage employees and improve business efficiency. The management agency, for example, manages employees. Employee management includes performance evaluation, attendance management, and skill development. The management agency, for example, improves business efficiency. Business efficiency includes process optimization and tool implementation. This improves employee management and business efficiency, thereby improving the competitiveness of the company. Some or all of the above-mentioned processing in the management agency can be performed using, or without, AI, for example. For example, the management agency can input employee management data into AI and have the AI ​​execute the management and efficiency improvement processes.

[0035] The collection unit can analyze the company's past data collection history and select a collection method. For example, the collection unit identifies the most efficient collection method from the past data collection history and applies the same method. For example, the collection unit analyzes the past data collection history and prioritizes the collection of data that takes a long time to collect. For example, the collection unit optimizes the collection frequency based on the past data collection history to achieve efficient data collection. In this way, efficient data collection is possible by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and have the AI ​​select the optimal collection method.

[0036] When collecting data, the collection unit can filter the data based on the company's current business situation and market trends. For example, the collection unit prioritizes the collection of important data based on the company's current financial situation. For example, the collection unit takes market trends into consideration and collects data on competitors to perform comparative analysis. For example, the collection unit adjusts the type of data to be collected according to the company's business situation and acquires necessary information. This allows for efficient collection of necessary information by filtering data based on the company's business situation and market trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the company's business situation and market trends into AI and have the AI ​​perform the filtering.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. The collection unit, for example, collects region-specific market data based on the location of the company. The collection unit, for example, prioritizes collecting data on geographically nearby competitors and performs comparative analysis. The collection unit, for example, collects data on regional economic trends by taking into account the geographical location information of the company. This makes it possible to efficiently collect region-specific data by taking into account the geographical location information of the company. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the geographical location information of the company into AI and have the AI ​​collect highly relevant data.

[0038] During data collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit analyzes the company's social media posts and collects related market data. For example, the collection unit collects customer responses on social media and evaluates the company's reputation. For example, the collection unit collects data on competitors based on the company's social media activities and performs comparative analysis. This allows for efficient collection of related data by analyzing the company's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's social media activity data into AI and have the AI ​​collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data and provides comprehensive information. For example, the analysis unit performs a concise analysis on less important data and proceeds with efficient processing. For example, the analysis unit determines the priority of the analysis according to the importance of the data and proceeds with efficient processing. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a financial analysis algorithm to financial data to perform a detailed analysis. For example, the analysis unit applies a performance evaluation algorithm to performance data to perform a comprehensive analysis. For example, the analysis unit applies an employee evaluation algorithm to employee data to perform an efficient analysis. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI and have the AI ​​apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of data collection. The analysis unit, for example, prioritizes analysis of the most recent data and provides real-time information. The analysis unit, for example, performs trend analysis based on past data and provides future predictions. The analysis unit, for example, determines the priority of analysis according to the time of data collection and efficiently proceeds with processing. In this way, by determining the priority of analysis based on the time of data collection, real-time information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection into AI and have the AI ​​determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and provides comprehensive information. For example, the analysis unit postpones less relevant data to efficiently proceed with processing. For example, the analysis unit adjusts the order of analysis according to the relevance of the data and efficiently proceeds with processing. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0043] The matching unit can improve the accuracy of matching based on the interrelationships between companies during matching. For example, the matching unit analyzes the transaction history of companies and prioritizes matching of companies with strong interrelationships. For example, the matching unit considers the industry and size of the companies to list companies with strong interrelationships. For example, the matching unit considers the regional relationships of the companies to prioritize matching of companies with strong interrelationships. This enables highly accurate matching by taking into account the interrelationships between companies. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input interrelationship data between companies into AI and have the AI ​​improve the accuracy of matching.

[0044] When matching, the matching unit can perform matching by taking into account attribute information of the company's transferee. The matching unit performs appropriate matching by taking into account, for example, the financial situation of the company's transferee. The matching unit performs appropriate matching by taking into account, for example, the performance of the company's transferee. The matching unit performs appropriate matching by taking into account, for example, the employee skill set of the company's transferee. This enables appropriate matching by taking into account the attribute information of the company's transferee. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input attribute information of the transferee into AI and have the AI ​​perform the matching.

[0045] The matching unit can perform matching taking into account the geographic distribution of companies. For example, the matching unit prioritizes matching of geographically close companies based on the location of the companies. For example, the matching unit performs matching based on market data specific to the region, taking into account the geographic distribution. For example, the matching unit performs matching based on economic trends in the region, taking into account the geographic distribution of companies. In this way, by taking into account the geographic distribution of companies, matching that meets the conditions specific to the region becomes possible. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input geographic distribution data of companies into AI and have the AI ​​perform matching.

[0046] The matching unit can improve the accuracy of matching by referring to literature related to the company during matching. The matching unit, for example, analyzes literature related to the company and performs appropriate matching. The matching unit, for example, improves the accuracy of matching by referring to literature related to the company's industry and size. The matching unit, for example, improves the accuracy of matching by referring to literature related to the company's region. This enables highly accurate matching by referring to literature related to the company. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input literature data related to the company into AI and have the AI ​​improve the accuracy of matching.

[0047] When acting as a management agent, the management agency department can analyze the company's past management data to select a management agency method. The management agency department, for example, analyzes the company's past financial data and plans an optimal management strategy. The management agency department, for example, selects an effective management agency method based on the company's past performance data. The management agency department, for example, analyzes the company's past employee data and proposes an efficient human resources management method. In this way, by analyzing the company's past management data, the optimal management agency method can be selected. Some or all of the above-mentioned processing in the management agency department may be performed, for example, using AI, or may be performed without using AI. For example, the management agency department can input the company's past management data into AI and have the AI ​​select a management agency method.

[0048] During management agency, the management agency department can customize management agency methods based on the company's current market conditions. For example, the management agency department analyzes current market trends and develops optimal management strategies. For example, the management agency department considers the trends of competitors and selects effective management agency methods. For example, the management agency department customizes management agency methods according to market demand, thereby improving the company's competitiveness. In this way, customizing management agency methods based on the company's current market conditions can improve the company's competitiveness. Some or all of the above-mentioned processing in the management agency department may be performed using, or without, AI. For example, the management agency department can input current market condition data into AI and have the AI ​​customize the management agency methods.

[0049] When performing management agency, the management agency department can select the optimal management agency method by taking into account the geographical location information of the company. The management agency department, for example, plans a region-specific management strategy based on the location of the company. The management agency department, for example, considers geographical factors and selects an effective management agency method. The management agency department, for example, proposes management agency measures that correspond to regional economic trends based on the geographical location information of the company. In this way, by taking into account the geographical location information of the company, it is possible to plan a region-specific management strategy. Some or all of the above-mentioned processing in the management agency department may be performed using, for example, AI, or may be performed without using AI. For example, the management agency department can input the geographical location information of the company into AI and have the AI ​​select the optimal management agency method.

[0050] During management agency, the management agency department can analyze the company's social media activity and propose management agency methods. For example, the management agency department analyzes the company's social media posts and proposes effective marketing strategies. For example, the management agency department customizes management agency methods based on customer responses on social media. For example, the management agency department analyzes the company's social media activity and proposes management agency methods that will differentiate the company from its competitors. In this way, by analyzing the company's social media activity, it is possible to propose effective management agency methods. Some or all of the above-mentioned processing in the management agency department may be performed using, for example, AI, or may be performed without using AI. For example, the management agency department can input the company's social media activity data into AI and have the AI ​​execute the proposed management agency methods.

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

[0052] The business succession support system can further include a risk assessment unit. The risk assessment unit assesses the risk of a company based on the data collected by the collection unit. For example, it can analyze financial data to assess the risk of a company going bankrupt. It can also predict the risk of future declines in performance based on performance data. It can also analyze employee data to assess the risk of employee turnover. In this way, the risk assessment unit can comprehensively assess the risk of a company and support risk management during business succession.

[0053] The collection department can collect a company's supply chain data. For example, it can collect information on a company's major suppliers and customers to evaluate the robustness of the supply chain. It can also collect supplier financial status and performance data to evaluate the risk of supply chain interruptions. Furthermore, it can collect logistics data and inventory data to evaluate the efficiency of the supply chain. This allows the collection department to evaluate the health of a company's supply chain and support risk management during business succession.

[0054] The analysis department can evaluate a company's brand value. For example, it can analyze a company's market share and customer satisfaction data to evaluate brand value. It can also analyze customer responses on social media to evaluate brand reputation. It can also analyze comparative data with competitors to evaluate the brand's competitiveness. This allows the analysis department to comprehensively evaluate a company's brand value and support brand management during business succession.

[0055] The Matching Department can create a list of potential transferees by taking into account the company's culture and values. For example, they can analyze a company's mission statement and vision to create a list of potential transferees with similar values. They can also analyze employee opinion survey data to create a list of potential transferees that are culturally compatible. Furthermore, they can take into account the company's history and traditions to create a list of potential transferees that empathize with them. This allows the Matching Department to find a potential transferee that is a good fit with the company's culture and values.

[0056] The agency can support a company's CSR (Corporate Social Responsibility) activities. For example, it can evaluate a company's environmental protection and social contribution activities and develop a plan to continue supporting these activities. It can also develop strategies to strengthen relationships with the company's stakeholders. Furthermore, it can evaluate the results of a company's CSR activities and prepare reports. In this way, the agency can support a company's CSR activities and promote efforts to fulfill its social responsibility.

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

[0058] Step 1: The collection department collects the company's management data. The company's management data includes financial data, performance data, employee data, etc. For example, the collection department collects the company's financial data (revenue, expenses, assets, liabilities, etc.), performance data (sales, profit margin, growth rate, etc.), and employee data (number of employees, average length of service, skill set, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected financial data to understand the financial status of the company, the performance data to evaluate the performance of the company, and the employee data to evaluate the skill sets of the employees. Step 3: The matching department searches for a potential buyer based on the data analyzed by the analysis department. For example, it may create a list of potential buyers based on the company's industry, size, and region, and may create a list of potential buyers based on companies in the same industry that are looking to expand, companies that are locally based, and companies of a certain size. Step 4: The Management Agency Department will act as a temporary agent for the company based on the transferees listed by the Matching Department. For example, they will conduct market analysis to identify new business opportunities, develop business plans, manage employees and streamline operations, and develop and implement corporate management strategies.

[0059] (Example 2) A business succession support system according to an embodiment of the present invention utilizes AI to resolve business succession issues for small and medium-sized enterprises (SMEs). This system aims to facilitate business succession by collecting and analyzing a company's management data, finding a suitable transferee, and acting as a proxy for the company's management for a limited period of time. Specifically, AI first collects and analyzes the company's management data, garnering detailed information such as the company's financial status, performance, and employee skill sets. For example, data such as sales, profit margins, and number of employees is collected and analyzed by AI to understand the company's current situation. Next, AI searches for a suitable transferee based on the collected and analyzed data. AI then lists and matches potential transferees that meet certain criteria, such as the company's industry, size, and region. Potential transferees include companies seeking to expand in the same industry or locally-based companies. Furthermore, AI acts as a proxy for the company's management for a limited period of time. During this period, AI develops and implements the company's management strategy. For example, AI conducts market analysis, identifies new business opportunities, and develops business plans. AI also improves the company's competitiveness by streamlining employee management and operations. This system can resolve issues such as a lack of successors and time constraints that prevent deals from being concluded, facilitating smooth business succession. For example, by having AI take over management, a company's performance improves and it becomes easier to find a buyer. Furthermore, by having AI create a list of suitable buyers, the succession rate increases. In this way, AI-based business matching and engagement systems can solve business succession issues for small and medium-sized enterprises and support their survival and growth. As a result, the business succession support system can smoothly proceed with business succession by collecting and analyzing a company's management data, finding a suitable buyer, and acting as a proxy for company management for a limited period of time.

[0060] The business succession support system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a management agent unit. The collection unit collects management data of a company. The management data of a company includes, but is not limited to, financial data, performance data, employee data, etc. The collection unit collects, for example, financial data of a company. The financial data includes revenue, expenses, assets, liabilities, etc. The collection unit collects, for example, performance data of a company. The performance data includes sales, profit margins, growth rates, etc. The collection unit collects, for example, employee data of a company. The employee data includes the number of employees, average years of service, skill sets, etc. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected financial data to understand the financial situation of the company. For example, the analysis unit analyzes the collected performance data to evaluate the company's performance. For example, the analysis unit analyzes the collected employee data to evaluate the employee's skill sets. The matching unit searches for a transferee based on the data analyzed by the analysis unit. The matching unit, for example, lists transferees based on the company's industry, size, and region, and performs matching. The matching unit, for example, lists companies in the same industry that are aiming to expand their business. The matching unit, for example, lists community-based companies. The matching unit, for example, lists transferees according to the size of the company. The management agency unit acts as a company manager for a limited period of time based on the transferees listed by the matching unit. The management agency unit, for example, conducts market analysis, finds new business opportunities, and formulates a business plan. The management agency unit, for example, manages employees and improves business efficiency. The management agency unit, for example, plans and implements a company's management strategy. As a result, the business succession support system according to the embodiment is able to collect, analyze, match, and act as a management agency for companies' management data.

[0061] The collection unit can collect financial data, performance data, and employee data of a company. The collection unit, for example, collects financial data of a company. The financial data includes revenue, expenses, assets, liabilities, etc. The collection unit, for example, collects performance data of a company. The performance data includes sales, profit margins, growth rates, etc. The collection unit, for example, collects employee data of a company. The employee data includes the number of employees, average years of service, skill sets, etc. By collecting detailed data of a company, more accurate analysis becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's financial data into AI and have the AI ​​collect the financial data.

[0062] The analysis unit can analyze the data collected by the collection unit to grasp the current situation of the company. For example, the analysis unit analyzes collected financial data to grasp the financial situation of the company. For example, the analysis unit analyzes collected performance data to evaluate the performance of the company. For example, the analysis unit analyzes collected employee data to evaluate the skill sets of employees. This accurately grasps the current situation of the company, making it possible to select an appropriate transferee. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0063] The matching unit can list transferees based on the company's industry, size, and region, and perform matching. The matching unit can list transferees based on the company's industry, size, and region, for example, and perform matching. The matching unit can list companies in the same industry that are aiming to expand their business. The matching unit can list community-based companies. The matching unit can list transferees according to the company's size, for example. This makes it possible to efficiently list transferees that meet the company's conditions and perform matching. Some or all of the above-mentioned processing in the matching unit can be performed using AI, for example, or without AI. For example, the matching unit can input the company's conditions into AI and have the AI ​​perform the listing of transferees.

[0064] The management agency department can conduct market analysis, identify new business opportunities, and formulate a business plan. The management agency department, for example, conducts market analysis, identify new business opportunities, and formulate a business plan. Market analysis includes, for example, market research techniques and data collection methods. New business opportunities include, for example, developing new markets and new products. Business plans include, for example, goal setting, strategy development, and resource allocation. In this way, by conducting market analysis, new business opportunities can be identified and a business plan can be formulated. Some or all of the above-mentioned processing in the management agency department may be performed, for example, using AI, or may be performed without using AI. For example, the management agency department can input market analysis data into AI and have the AI ​​identify business opportunities and formulate a business plan.

[0065] The management agency can manage employees and improve business efficiency. The management agency, for example, manages employees. Employee management includes performance evaluation, attendance management, and skill development. The management agency, for example, improves business efficiency. Business efficiency includes process optimization and tool implementation. This improves employee management and business efficiency, thereby improving the competitiveness of the company. Some or all of the above-mentioned processing in the management agency can be performed using, or without, AI, for example. For example, the management agency can input employee management data into AI and have the AI ​​execute the management and efficiency improvement processes.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection, thereby reducing the user's burden. For example, if the user is relaxed, the collection unit collects detailed data to obtain more information. For example, if the user is in a hurry, the collection unit prioritizes collecting only important data and quickly proceeds with processing. This adjusts the timing of data collection according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0067] The collection unit can analyze the company's past data collection history and select a collection method. For example, the collection unit identifies the most efficient collection method from the past data collection history and applies the same method. For example, the collection unit analyzes the past data collection history and prioritizes the collection of data that takes a long time to collect. For example, the collection unit optimizes the collection frequency based on the past data collection history to achieve efficient data collection. In this way, efficient data collection is possible by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and have the AI ​​select the optimal collection method.

[0068] When collecting data, the collection unit can filter the data based on the company's current business situation and market trends. For example, the collection unit prioritizes the collection of important data based on the company's current financial situation. For example, the collection unit takes market trends into consideration and collects data on competitors to perform comparative analysis. For example, the collection unit adjusts the type of data to be collected according to the company's business situation and acquires necessary information. This allows for efficient collection of necessary information by filtering data based on the company's business situation and market trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the company's business situation and market trends into AI and have the AI ​​perform the filtering.

[0069] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting only important data, thereby reducing the user's burden. For example, when the user is relaxed, the collection unit collects detailed data and provides comprehensive information. For example, when the user is in a hurry, the collection unit prioritizes data that can be collected quickly, thereby efficiently proceeding with processing. This enables efficient data collection by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data.

[0070] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. The collection unit, for example, collects region-specific market data based on the location of the company. The collection unit, for example, prioritizes collecting data on geographically nearby competitors and performs comparative analysis. The collection unit, for example, collects data on regional economic trends by taking into account the geographical location information of the company. This makes it possible to efficiently collect region-specific data by taking into account the geographical location information of the company. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the geographical location information of the company into AI and have the AI ​​collect highly relevant data.

[0071] During data collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit analyzes the company's social media posts and collects related market data. For example, the collection unit collects customer responses on social media and evaluates the company's reputation. For example, the collection unit collects data on competitors based on the company's social media activities and performs comparative analysis. This allows for efficient collection of related data by analyzing the company's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's social media activity data into AI and have the AI ​​collect related data.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results and comprehensive information. For example, if the user is in a hurry, the analysis unit provides concise analysis results that focus on the main points. This allows the analysis results to be easily understood by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the way the analysis is presented.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data and provides comprehensive information. For example, the analysis unit performs a concise analysis on less important data and proceeds with efficient processing. For example, the analysis unit determines the priority of the analysis according to the importance of the data and proceeds with efficient processing. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a financial analysis algorithm to financial data to perform a detailed analysis. For example, the analysis unit applies a performance evaluation algorithm to performance data to perform a comprehensive analysis. For example, the analysis unit applies an employee evaluation algorithm to employee data to perform an efficient analysis. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI and have the AI ​​apply an appropriate analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result and provides comprehensive information. For example, if the user is excited, the analysis unit provides an analysis result with a visually stimulating effect. This allows the analysis length to be adjusted according to the user's emotions, thereby providing the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0076] During analysis, the analysis unit can determine the priority of analysis based on the time of data collection. The analysis unit, for example, prioritizes analysis of the most recent data and provides real-time information. The analysis unit, for example, performs trend analysis based on past data and provides future predictions. The analysis unit, for example, determines the priority of analysis according to the time of data collection and efficiently proceeds with processing. In this way, by determining the priority of analysis based on the time of data collection, real-time information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection into AI and have the AI ​​determine the priority of analysis.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and provides comprehensive information. For example, the analysis unit postpones less relevant data to efficiently proceed with processing. For example, the analysis unit adjusts the order of analysis according to the relevance of the data and efficiently proceeds with processing. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0078] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, if the user is nervous, the matching unit provides simple, highly visible matching criteria. For example, if the user is relaxed, the matching unit provides detailed matching criteria and comprehensive information. For example, if the user is in a hurry, the matching unit provides concise matching criteria that focus on the main points. This allows the matching criteria to be adjusted according to the user's emotions, thereby providing an optimal matching result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data into an AI and have the AI ​​adjust the matching criteria.

[0079] The matching unit can improve the accuracy of matching based on the interrelationships between companies during matching. For example, the matching unit analyzes the transaction history of companies and prioritizes matching of companies with strong interrelationships. For example, the matching unit considers the industry and size of the companies to list companies with strong interrelationships. For example, the matching unit considers the regional relationships of the companies to prioritize matching of companies with strong interrelationships. This enables highly accurate matching by taking into account the interrelationships between companies. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input interrelationship data between companies into AI and have the AI ​​improve the accuracy of matching.

[0080] When matching, the matching unit can perform matching by taking into account attribute information of the company's transferee. The matching unit performs appropriate matching by taking into account, for example, the financial situation of the company's transferee. The matching unit performs appropriate matching by taking into account, for example, the performance of the company's transferee. The matching unit performs appropriate matching by taking into account, for example, the employee skill set of the company's transferee. This enables appropriate matching by taking into account the attribute information of the company's transferee. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input attribute information of the transferee into AI and have the AI ​​perform the matching.

[0081] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated user emotions. For example, if the user is nervous, the matching unit displays the results in a simple, highly visible order. For example, if the user is relaxed, the matching unit displays the results in an order that includes detailed information. For example, if the user is in a hurry, the matching unit displays the results in a concise order that focuses on the main points. This allows the display order of the matching results to be adjusted according to the user's emotions, thereby providing results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data into an AI and have the AI ​​adjust the display order.

[0082] The matching unit can perform matching taking into account the geographic distribution of companies. For example, the matching unit prioritizes matching of geographically close companies based on the location of the companies. For example, the matching unit performs matching based on market data specific to the region, taking into account the geographic distribution. For example, the matching unit performs matching based on economic trends in the region, taking into account the geographic distribution of companies. In this way, by taking into account the geographic distribution of companies, matching that meets the conditions specific to the region becomes possible. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input geographic distribution data of companies into AI and have the AI ​​perform matching.

[0083] The matching unit can improve the accuracy of matching by referring to literature related to the company during matching. The matching unit, for example, analyzes literature related to the company and performs appropriate matching. The matching unit, for example, improves the accuracy of matching by referring to literature related to the company's industry and size. The matching unit, for example, improves the accuracy of matching by referring to literature related to the company's region. This enables highly accurate matching by referring to literature related to the company. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input literature data related to the company into AI and have the AI ​​improve the accuracy of matching.

[0084] The management agent unit can estimate the user's emotions and adjust the management agent method based on the estimated user emotions. For example, if the user is nervous, the management agent unit provides a simple and highly visible management agent method. For example, if the user is relaxed, the management agent unit provides a detailed management agent method and comprehensive information. For example, if the user is in a hurry, the management agent unit provides a concise management agent method that focuses on the main points. This enables the optimal management agent for the user by adjusting the management agent method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management agent unit may be performed using an AI, for example, or without an AI. For example, the management agent unit can input the user's emotion data into an AI and have the AI ​​adjust the management agent method.

[0085] When acting as a management agent, the management agency department can analyze the company's past management data to select a management agency method. The management agency department, for example, analyzes the company's past financial data and plans an optimal management strategy. The management agency department, for example, selects an effective management agency method based on the company's past performance data. The management agency department, for example, analyzes the company's past employee data and proposes an efficient human resources management method. In this way, by analyzing the company's past management data, the optimal management agency method can be selected. Some or all of the above-mentioned processing in the management agency department may be performed, for example, using AI, or may be performed without using AI. For example, the management agency department can input the company's past management data into AI and have the AI ​​select a management agency method.

[0086] During management agency, the management agency department can customize management agency methods based on the company's current market conditions. For example, the management agency department analyzes current market trends and develops optimal management strategies. For example, the management agency department considers the trends of competitors and selects effective management agency methods. For example, the management agency department customizes management agency methods according to market demand, thereby improving the company's competitiveness. In this way, customizing management agency methods based on the company's current market conditions can improve the company's competitiveness. Some or all of the above-mentioned processing in the management agency department may be performed using, or without, AI. For example, the management agency department can input current market condition data into AI and have the AI ​​customize the management agency methods.

[0087] The management agent unit can estimate the user's emotions and determine management agent priorities based on the estimated user emotions. For example, if the user is nervous, the management agent unit prioritizes important management agent tasks. For example, if the user is relaxed, the management agent unit comprehensively processes detailed management agent tasks. For example, if the user is in a hurry, the management agent unit prioritizes management agent tasks that can be completed quickly. This enables efficient management agent by determining management agent priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management agent unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management agent unit can input the user's emotion data into an AI and have the AI ​​determine the management agent priorities.

[0088] When performing management agency, the management agency department can select the optimal management agency method by taking into account the geographical location information of the company. The management agency department, for example, plans a region-specific management strategy based on the location of the company. The management agency department, for example, considers geographical factors and selects an effective management agency method. The management agency department, for example, proposes management agency measures that correspond to regional economic trends based on the geographical location information of the company. In this way, by taking into account the geographical location information of the company, it is possible to plan a region-specific management strategy. Some or all of the above-mentioned processing in the management agency department may be performed using, for example, AI, or may be performed without using AI. For example, the management agency department can input the geographical location information of the company into AI and have the AI ​​select the optimal management agency method.

[0089] During management agency, the management agency department can analyze the company's social media activity and propose management agency methods. For example, the management agency department analyzes the company's social media posts and proposes effective marketing strategies. For example, the management agency department customizes management agency methods based on customer responses on social media. For example, the management agency department analyzes the company's social media activity and proposes management agency methods that will differentiate the company from its competitors. In this way, by analyzing the company's social media activity, it is possible to propose effective management agency methods. Some or all of the above-mentioned processing in the management agency department may be performed using, for example, AI, or may be performed without using AI. For example, the management agency department can input the company's social media activity data into AI and have the AI ​​execute the proposed management agency methods. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, and management agent unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects corporate management data using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The matching unit, realized, for example, by the specific processing unit 290 of the data processing device 12, searches for a transferee based on the analyzed data. The management agent unit, realized, for example, by the specific processing unit 290 of the data processing device 12, acts as a proxy for company management for a limited period of time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, and management agent unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects corporate management data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The matching unit, realized, for example, by the specific processing unit 290 of the data processing device 12, searches for a transferee based on the analyzed data. The management agent unit, realized, for example, by the specific processing unit 290 of the data processing device 12, acts as a proxy for company management for a limited period of time. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and management agent unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects corporate management data using the camera 42 and microphone 238 of the headset-type terminal 314 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for a transferee based on the analyzed data. The management agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acts as a proxy for company management for a limited period of time. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and management agent unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects corporate management data using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for a transferee based on the analyzed data. The management agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acts as a proxy for company management for a limited period of time.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The business succession support system can further include a risk assessment unit. The risk assessment unit assesses the risk of a company based on the data collected by the collection unit. For example, it can analyze financial data to assess the risk of a company going bankrupt. It can also predict the risk of future declines in performance based on performance data. It can also analyze employee data to assess the risk of employee turnover. In this way, the risk assessment unit can comprehensively assess the risk of a company and support risk management during business succession.

[0092] The collection department can collect a company's supply chain data. For example, it can collect information on a company's major suppliers and customers to evaluate the robustness of the supply chain. It can also collect supplier financial status and performance data to evaluate the risk of supply chain interruptions. Furthermore, it can collect logistics data and inventory data to evaluate the efficiency of the supply chain. This allows the collection department to evaluate the health of a company's supply chain and support risk management during business succession.

[0093] The analysis department can evaluate a company's brand value. For example, it can analyze a company's market share and customer satisfaction data to evaluate brand value. It can also analyze customer responses on social media to evaluate brand reputation. It can also analyze comparative data with competitors to evaluate the brand's competitiveness. This allows the analysis department to comprehensively evaluate a company's brand value and support brand management during business succession.

[0094] The Matching Department can create a list of potential transferees by taking into account the company's culture and values. For example, they can analyze a company's mission statement and vision to create a list of potential transferees with similar values. They can also analyze employee opinion survey data to create a list of potential transferees that are culturally compatible. Furthermore, they can take into account the company's history and traditions to create a list of potential transferees that empathize with them. This allows the Matching Department to find a potential transferee that is a good fit with the company's culture and values.

[0095] The agency can support a company's CSR (Corporate Social Responsibility) activities. For example, it can evaluate a company's environmental protection and social contribution activities and develop a plan to continue supporting these activities. It can also develop strategies to strengthen relationships with the company's stakeholders. Furthermore, it can evaluate the results of a company's CSR activities and prepare reports. In this way, the agency can support a company's CSR activities and promote efforts to fulfill its social responsibility.

[0096] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, data can be collected in the form of simple questions, reducing the burden on the user. Alternatively, if the user is relaxed, data can be collected in the form of a detailed questionnaire to obtain more information. Furthermore, if the user is in a hurry, data can be collected in a format that can be answered in a short amount of time, allowing for quick processing. This allows the collection unit to adjust the data collection method according to the user's emotions and achieve efficient data collection.

[0097] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis results can be presented using simple, highly visible graphs and charts. If the user is relaxed, the analysis results can be presented in a detailed report format to provide comprehensive information. Furthermore, if the user is in a hurry, the analysis results can be presented in a concise summary format that focuses on the main points. In this way, the analysis unit can adjust the presentation method of the analysis results according to the user's emotions and provide information that is easy for the user to understand.

[0098] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user's emotions. For example, if the user is nervous, simple and highly visible matching criteria can be provided. If the user is relaxed, detailed matching criteria can be provided to provide comprehensive information. Furthermore, if the user is in a hurry, concise matching criteria that focus on the main points can be provided. This allows the matching unit to adjust the matching criteria according to the user's emotions and provide the optimal matching result for the user.

[0099] The management agent unit can estimate the user's emotions and adjust the management agent method based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible management agent method can be provided. If the user is relaxed, a detailed management agent method can be provided, providing comprehensive information. Furthermore, if the user is in a hurry, a concise management agent method that focuses on the main points can be provided. This allows the management agent unit to adjust the management agent method according to the user's emotions, enabling the optimal management agent for the user.

[0100] The management agent unit can estimate the user's emotions and determine the priority of management agents based on the estimated user emotions. For example, if the user is nervous, important management agent tasks can be processed with priority. If the user is relaxed, detailed management agent tasks can be processed comprehensively. Furthermore, if the user is in a hurry, management agent tasks that can be processed quickly can be prioritized. This allows the management agent unit to determine the priority of management agents according to the user's emotions, enabling efficient management agent operations.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection department collects the company's management data. The company's management data includes financial data, performance data, employee data, etc. For example, the collection department collects the company's financial data (revenue, expenses, assets, liabilities, etc.), performance data (sales, profit margin, growth rate, etc.), and employee data (number of employees, average length of service, skill set, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected financial data to understand the financial status of the company, the performance data to evaluate the performance of the company, and the employee data to evaluate the skill sets of the employees. Step 3: The matching department searches for a potential buyer based on the data analyzed by the analysis department. For example, it may create a list of potential buyers based on the company's industry, size, and region, and may create a list of potential buyers based on companies in the same industry that are looking to expand, companies that are locally based, and companies of a certain size. Step 4: The Management Agency Department will act as a temporary agent for the company based on the transferees listed by the Matching Department. For example, they will conduct market analysis to identify new business opportunities, develop business plans, manage employees and streamline operations, and develop and implement corporate management strategies.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

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

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects management data from companies; an analysis unit that analyzes the data collected by the collection unit; a matching unit that searches for a transferee based on the data analyzed by the analysis unit; a management agency that acts as a proxy for company management for a limited period of time based on the transferees listed by the matching agency; A system characterized by:

2. The collecting unit Collecting company financial, performance, and employee data 2. The system of claim 1.

3. The analysis unit Analyze the data collected by the collection unit to understand the current situation of the company.

2. The system of claim 1.

4. The matching unit We will create a list of potential buyers based on the company's industry, size, and region, and then match them with potential buyers.

2. The system of claim 1.

5. The management agency department: Conduct market analysis, identify new business opportunities, and develop business plans 2. The system of claim 1.

6. The management agency department: Achieve employee management and operational efficiency 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the company's past data collection history and select a collection method 2. The system of claim 1.

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