system

Abstract

The system according to this embodiment aims to provide an appropriate assessment value in business evaluation. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, and a provision unit. The input unit inputs quantitative and qualitative data necessary for business evaluation. The analysis unit analyzes the data input by the input unit and calculates an appropriate appraisal amount. The provision unit provides the appraisal amount calculated by the analysis unit as a second opinion.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, an appropriate appraisal amount is not provided in business evaluation, and there is a risk that the seller may transfer the business under disadvantageous conditions.

[0005] The system according to the embodiment aims to provide an appropriate appraisal amount in business evaluation.

Means for Solving the Problems

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a provision unit. The input unit inputs quantitative and qualitative data necessary for business evaluation. The analysis unit analyzes the data input by the input unit and calculates an appropriate appraisal amount. The provision unit provides the appraisal amount calculated by the analysis unit as a second opinion.

Effects of the Invention

[0007] The system according to this embodiment can provide an appropriate assessment value in business evaluation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Business Valuation Second Opinion AI System according to an embodiment of the present invention is a system designed to address the current situation where business succession-type M&A is becoming more active, against the backdrop of a shortage of successors for small and medium-sized enterprises. The Business Valuation Second Opinion AI System works by having the seller input quantitative and qualitative data necessary for business valuation, and the AI ​​interviews managers and employees to acquire information that can be used for business valuation, and calculates an appropriate valuation amount by referring to past M&A data. This valuation amount is provided as a second opinion to be compared with the valuation amount of the intermediary. This allows the seller to confirm an appropriate valuation amount and make a decision on the business transfer at a price they are satisfied with. For example, the Business Valuation Second Opinion AI System has the seller input quantitative and qualitative data necessary for business valuation. For example, financial data such as sales, profits, assets, and liabilities, and non-financial data such as the number of employees, business content, and competitive situation are input. This data is analyzed by the AI. Next, the Business Valuation Second Opinion AI System has the AI ​​interview managers and employees to acquire information that can be used for business valuation. For example, it collects information that cannot be grasped from quantitative data alone, such as the manager's vision and strategy, and the skills and motivation of employees. This information is analyzed by AI. Furthermore, the business valuation second opinion AI system uses AI to reference past M&A data and calculate an appropriate valuation. For example, it refers to past M&A cases of companies in the same industry and of similar size to evaluate the value of the selling company. This valuation is provided as a second opinion to be compared with the valuation of the intermediary. This allows the seller to confirm an appropriate valuation and decide on a business transfer at a price they are satisfied with. For example, if the intermediary's valuation is too low, the seller can use the AI's valuation as a reference in negotiations. Also, if the AI's valuation matches the intermediary's valuation, the seller can proceed with the business transfer with peace of mind. In this way, the business valuation second opinion AI system enables sellers to receive an appropriate business valuation and prevent economic losses. It also strengthens the seller's position by providing a means to counter low-quality valuations from intermediaries. This is expected to facilitate business succession for small and medium-sized enterprises and contribute to economic revitalization.

[0029] The business evaluation second opinion AI system according to this embodiment comprises an input unit, an analysis unit, and a provision unit. The input unit inputs quantitative and qualitative data necessary for business evaluation. For example, the input unit inputs financial data such as sales, profits, assets, and liabilities. The input unit can also input non-financial data such as the number of employees, business content, and competitive situation. For example, the input unit inputs financial data such as sales and profits. The input unit can also input non-financial data such as the number of employees and business content. The analysis unit analyzes the data input by the input unit and calculates an appropriate valuation. For example, the analysis unit uses AI to analyze the input data and calculates an appropriate valuation. For example, the analysis unit uses AI to analyze financial data such as sales and profits and calculates an appropriate valuation. The analysis unit can also use AI to analyze non-financial data such as the number of employees and business content and calculate an appropriate valuation. The provision unit provides the valuation calculated by the analysis unit as a second opinion. For example, the provision unit provides the valuation calculated using AI as a second opinion. For example, the service provider can provide the appraisal amount calculated using AI in a report format. Alternatively, the service provider can provide the appraisal amount calculated using AI as a simplified evaluation result. This allows the business valuation second opinion AI system according to this embodiment to enable sellers to confirm a second opinion on a fair appraisal amount and make a decision on the business transfer at a price they are satisfied with.

[0030] The input section allows users to input quantitative and qualitative data necessary for business evaluation. For example, it inputs financial data such as sales, profits, assets, and liabilities. Specifically, it inputs monthly and annual sales data, and detailed profit data such as operating profit and net profit. For assets, it inputs a breakdown of fixed and current assets, and for liabilities, it inputs details of short-term and long-term liabilities. The input section can also input non-financial data such as the number of employees, business content, and competitive landscape. For example, the number of employees can be entered in detail by department and position, and the business content can include details of the type of business and services provided. For the competitive landscape, information on major competitors, market share, and competitive advantages can be entered. This allows the input section to comprehensively collect a wide range of data necessary for business evaluation and provide it to the analysis section. Furthermore, the input section improves user convenience by diversifying data input methods. For example, it supports not only manual input but also automatic input via CSV files and APIs to ensure data accuracy and efficiency. The input section also includes a data integrity check function, automatically detecting input errors and inconsistencies and prompting corrections. This allows the input unit to provide accurate and reliable data, improving the overall accuracy of the system.

[0031] The analysis unit analyzes the data entered by the input unit and calculates an appropriate valuation. For example, the analysis unit uses AI to analyze the entered data and calculate an appropriate valuation. Specifically, the AI ​​uses machine learning algorithms to learn from past business evaluation data and predict the optimal valuation based on the entered data. For example, it analyzes financial data such as sales and profits to evaluate the profitability and growth potential of the business. It can also analyze non-financial data such as the number of employees and business content to evaluate the operational efficiency and market competitiveness of the business. Furthermore, the analysis unit calculates a more accurate valuation by combining multiple evaluation models. For example, it uses deep learning models to detect complex patterns and statistical models to analyze the distribution of data. This allows the analysis unit to perform business evaluations from multiple perspectives and provide an appropriate valuation. In addition, the analysis unit can analyze data in real time and provide a valuation based on the latest information. For example, it can quickly update the valuation in response to market fluctuations and changes in the competitive landscape. The analysis unit can also consider past data and trends to evaluate future risks and opportunities. This allows the analysis department to conduct business evaluations not only from a short-term perspective but also from a long-term perspective, enabling them to provide more reliable valuations.

[0032] The service provider offers the valuation calculated by the analysis department as a second opinion. For example, the service provider may offer a valuation calculated using AI as a second opinion. Specifically, the service provider provides the valuation calculated by the analysis department in report format. The report includes details of the basis for the valuation and the evaluation process, allowing users to verify the reliability of the valuation. The service provider can also provide the AI-calculated valuation as a simplified evaluation result. For example, it may use graphs and charts to display the information visually and intuitively, allowing users to understand it intuitively. Furthermore, the service provider can provide customized reports according to user needs. For example, it may use evaluation criteria specific to a particular industry or region to provide a more accurate valuation. The service provider can also provide the valuation quickly to users using multiple communication methods. For example, it may send reports via email or cloud storage, allowing users to access them anytime, anywhere. This enables the service provider to provide users with a quick and reliable second opinion, supporting their business transfer decisions. Additionally, the service provider can collect user feedback to continuously improve the accuracy and usefulness of the information it provides. This allows the service provider to always use the latest information and technology to offer users the most suitable second opinion.

[0033] The analysis unit includes an interview unit that conducts interviews with managers and employees to obtain information useful for business evaluation. The interview unit collects information that cannot be grasped by quantitative data alone, such as the vision and strategy of managers and the skills and motivation of employees. For example, the interview unit can interview managers to obtain information useful for business evaluation by listening to their vision and strategy. The interview unit can also interview managers to obtain information useful for business evaluation by listening to their skills and motivation. Furthermore, the interview unit can obtain information useful for business evaluation through interviews with managers and employees. For example, the interview unit can grasp the strengths and weaknesses of the business and future prospects through interviews with managers and employees. This allows for more accurate business evaluation by obtaining information from managers and employees. Some or all of the above processing in the interview unit may be performed using AI, for example, or not using AI. For example, the interview unit can input the content of interviews with managers and employees into AI, and the AI ​​can analyze the information useful for business evaluation.

[0034] The analysis unit includes a reference unit that calculates an appropriate valuation by referring to past M&A data. The reference unit, for example, refers to past M&A cases of companies in the same industry and of similar size to evaluate the value of the selling company. For example, the reference unit evaluates the value of the selling company based on past M&A data. The reference unit can also calculate an appropriate valuation based on past M&A data. Furthermore, the reference unit can evaluate the value of the selling company based on past M&A data. For example, the reference unit evaluates the value of the selling company based on past M&A data and calculates an appropriate valuation. In this way, an appropriate valuation can be calculated by referring to past M&A data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without using AI. For example, the reference unit can input past M&A data into AI, and the AI ​​can calculate an appropriate valuation.

[0035] The input unit inputs financial data such as sales, profits, assets, and liabilities. For example, the input unit inputs financial data such as sales and profits. For example, the input unit inputs sales. The input unit can also input profits. Furthermore, the input unit can also input financial data such as assets and liabilities. For example, the input unit inputs assets. The input unit can also input liabilities. This improves the accuracy of business evaluation by inputting financial data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input financial data such as sales and profits into AI, and the AI ​​can analyze the data.

[0036] The input unit accepts non-financial data such as the number of employees, business content, and competitive landscape. For example, the input unit accepts non-financial data such as the number of employees and business content. For example, the input unit accepts the number of employees. The input unit can also accept the business content. Furthermore, the input unit can accept non-financial data such as the competitive landscape. For example, the input unit accepts the competitive landscape. The input unit can also accept the business's growth potential. This improves the accuracy of business evaluation by accepting non-financial data. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input non-financial data such as the number of employees and business content into an AI, which can then analyze the data.

[0037] The service provider offers the calculated appraisal amount as a second opinion. The service provider offers, for example, an appraisal amount calculated using AI as a second opinion. For example, the service provider offers the appraisal amount calculated using AI in report format. The service provider can also offer the appraisal amount calculated using AI as a simplified evaluation result. By offering the appraisal amount as a second opinion, the seller can decide on a business transfer price that they are satisfied with. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can offer the appraisal amount calculated by AI in report format.

[0038] The input unit analyzes the seller's past data entry history and selects the optimal input method. For example, the input unit prioritizes suggesting input methods (voice, text, etc.) that the seller has frequently used in the past. The input unit can also analyze patterns in the data the seller has entered in the past and optimize the input order. The input unit can also customize the input interface based on the amount of data the seller has entered in the past. This allows the optimal input method to be selected by analyzing past data entry history. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input the seller's past data entry history into AI, which can then select the optimal input method.

[0039] The input unit filters data during data entry based on the seller's current business status and areas of interest. For example, the input unit prioritizes displaying data entry items that are highly relevant based on the seller's current business status. The input unit can also customize input items based on the seller's areas of interest. The input unit can also dynamically change the required data entry items according to the seller's business growth stage. This enables efficient data entry by filtering data based on the seller's business status and areas of interest. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input data on the seller's current business status and areas of interest into the AI, which can then filter the data entry.

[0040] The input unit prioritizes inputting highly relevant data, taking into account the seller's geographical location information during data entry. For example, the input unit prioritizes displaying region-specific data entry items based on the seller's location. The input unit can also prioritize inputting market data for the region where the seller's business operates. The input unit can also prioritize inputting relevant competitor information based on the seller's geographical location information. This enables efficient data entry by prioritizing the input of highly relevant data based on the seller's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the seller's geographical location information into AI, which can then prioritize inputting highly relevant data.

[0041] The input unit analyzes the seller's social media activity during data entry and inputs relevant data. For example, the input unit extracts business-related topics from the seller's social media activity and suggests them as input items. The input unit can also utilize the seller's social media follower count and engagement rate as input data. The input unit can also analyze the seller's social media activity history and input relevant data. This allows for efficient input of relevant data by analyzing the seller's social media activity. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input the seller's social media activity data into AI, which can then input relevant data.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0043] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a financial analysis algorithm to financial data. The analysis unit can also apply a text analysis algorithm to non-financial data. The analysis unit can also select the optimal analysis algorithm depending on the data category. This enables efficient analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0044] The analysis unit determines the priority of analysis based on the data submission date. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the data submission date into the AI, which can then determine the priority of analysis.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0046] The information provider adjusts the level of detail provided based on the importance of the information. For example, the provider provides detailed explanations for highly important information. The provider can also provide simplified explanations for less important information. The provider can also determine the priority of information provision based on its importance. This allows for efficient information provision by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the importance of the information into the AI, which can then adjust the level of detail.

[0047] The information provider applies different information provision algorithms depending on the information category at the time of provision. For example, the information provider applies a financial analysis algorithm to financial information. The information provider can also apply a text analysis algorithm to non-financial information. The information provider can also select the optimal information provision algorithm depending on the information category. This enables efficient information provision by applying the optimal information provision algorithm according to the information category. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into the AI, and the AI ​​can apply the optimal information provision algorithm.

[0048] The information provider determines the priority of information provision based on the submission date. For example, the provider may prioritize providing the most recent information. The provider may also postpone providing older information. The provider may also adjust the provision schedule based on the submission date. This allows for efficient information provision by determining the priority of information provision based on the submission date. Some or all of the above processing in the information provider may be performed using AI, or not using AI. For example, the information provider can input the submission dates of the information into the AI, and the AI ​​can determine the priority of information provision.

[0049] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the information provider prioritizes providing highly relevant information. The information provider can also postpone providing less relevant information. The information provider can also optimize the order of delivery based on the relevance of the information. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into AI, and the AI ​​can adjust the order of delivery.

[0050] The interview department selects the most appropriate questions during the interview by referring to the seller's past interview history. For example, the interview department asks related questions based on questions the seller has answered in the past. The interview department can also extract important points from the seller's past interview history and ask questions. The interview department can also analyze the seller's past interview history and select the most appropriate questions. This allows the interview department to select the most appropriate questions by referring to the seller's past interview history. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's past interview history into an AI, which can then select the most appropriate questions.

[0051] The interview department customizes the questions during the interview based on the seller's current business situation. For example, the interview department asks relevant questions based on the seller's current business situation. The interview department can also customize the questions according to the stage of growth of the seller's business. The interview department can also adjust the questions based on the characteristics of the seller's business. This allows for more efficient interviews by customizing the questions based on the seller's current business situation. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's current business situation into the AI, which can then customize the questions.

[0052] The interview department selects the most suitable interview method during an interview, taking into account the seller's geographical location. For example, if the seller is far away, the interview department will prioritize online interviews. The interview department can also prioritize in-person interviews if the seller is nearby. The interview department can also optimize the interview location based on the seller's geographical location. This allows for more efficient interviews by selecting the most suitable interview method based on the seller's geographical location. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's geographical location into an AI, which can then select the most suitable interview method.

[0053] The interview department analyzes the seller's social media activity during the interview and proposes interview questions. For example, the interview department extracts business-related topics from the seller's social media activity and proposes them as questions. The interview department can also adjust the questions based on the seller's number of followers and engagement rate on social media. The interview department can also analyze the seller's social media activity history and propose relevant questions. This allows for the efficient proposal of relevant questions by analyzing the seller's social media activity. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's social media activity data into an AI, which can then propose questions.

[0054] The reference unit optimizes the reference algorithm by referencing past reference data during the reference process. For example, the reference unit analyzes patterns in previously referenced data and selects the optimal reference algorithm. The reference unit can also prioritize referencing highly relevant data based on past reference data. The reference unit can also dynamically adjust the reference algorithm based on past reference data. This allows the optimal reference algorithm to be selected by referencing past reference data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past reference data into AI, which can then optimize the reference algorithm.

[0055] The reference unit applies different reference algorithms depending on the data category during the reference process. For example, the reference unit applies a financial analysis algorithm to financial data. The reference unit can also apply a text analysis algorithm to non-financial data. The reference unit can also select the optimal reference algorithm depending on the data category. This enables efficient data referencing by applying the most suitable reference algorithm according to the data category. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input the data category into the AI, which can then apply the most suitable reference algorithm.

[0056] The reference unit weights the referenced data based on the data submission date when it is referenced. For example, the reference unit prioritizes referencing the most recent data. The reference unit can also reference older data with lower weighting. The reference unit can also dynamically adjust the weighting of the referenced data based on the submission date. This enables efficient data retrieval by weighting the referenced data based on the data submission date. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input the data submission date into AI, and the AI ​​can weight the referenced data.

[0057] The reference unit adjusts the order of references based on the relevance of the data during the reference process. For example, the reference unit prioritizes referencing highly relevant data. The reference unit can also postpone referencing less relevant data. The reference unit can also optimize the order of references based on the relevance of the data. This allows for efficient data referencing by adjusting the order of references based on the relevance of the data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input the relevance of the data into AI, which can then adjust the order of references.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The input unit analyzes the seller's past data entry history and selects the optimal input method. For example, it can prioritize suggesting input methods that the seller has frequently used in the past (such as voice or text). It can also analyze patterns in the data the seller has entered in the past and optimize the input order. Furthermore, it can customize the input interface based on the amount of data the seller has entered in the past. This allows the optimal input method to be selected by analyzing past data entry history. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit can input the seller's past data entry history into an AI, which can then select the optimal input method.

[0060] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0061] The information delivery unit adjusts the level of detail provided based on the importance of the information. For example, it can provide detailed explanations for highly important information, and simplified explanations for less important information. Furthermore, it can determine the priority of information delivery based on its importance. This allows for efficient information delivery by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the importance of the information into the AI, and the AI ​​can adjust the level of detail provided.

[0062] The interview department selects the most appropriate questions during the interview by referring to the seller's past interview history. For example, it can ask related questions based on questions the seller has answered in the past. It can also extract important points from the seller's past interview history and ask questions based on those points. Furthermore, it can analyze the seller's past interview history to select the most appropriate questions. In this way, the most appropriate questions can be selected by referring to the seller's past interview history. Some or all of the above processes in the interview department may be performed using AI, for example, or not. For example, the interview department can input the seller's past interview history into an AI, which can then select the most appropriate questions.

[0063] The reference unit optimizes the reference algorithm by referencing past reference data during a reference operation. For example, it can analyze patterns in previously referenced data and select the optimal reference algorithm. It can also prioritize the reference of highly relevant data based on past reference data. Furthermore, it can dynamically adjust the reference algorithm based on past reference data. This allows for the selection of the optimal reference algorithm by referencing past reference data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past reference data into AI, which can then optimize the reference algorithm.

[0064] The information delivery unit determines the priority of information delivery based on the submission date. For example, the most recent information can be provided first. Older information can be provided later. Furthermore, the delivery schedule can be adjusted based on the submission date. This enables efficient information delivery by determining the priority of information delivery based on the submission date. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not. For example, the information delivery unit can input the submission dates of the information into the AI, and the AI ​​can determine the priority of delivery.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The input section is used to enter quantitative and qualitative data necessary for business evaluation. Specifically, it can input financial data such as sales, profits, assets, and liabilities, as well as non-financial data such as the number of employees, business content, and competitive landscape. Step 2: The analysis unit analyzes the data entered by the input unit and calculates an appropriate valuation. The analysis unit uses AI to analyze financial data such as sales and profits, as well as non-financial data such as the number of employees and business details, and calculates an appropriate valuation. Step 3: The service provider provides the assessed value calculated by the analysis unit as a second opinion. The service provider can provide the assessed value calculated using AI in report format or as a simplified evaluation result.

[0067] (Example of form 2) The Business Valuation Second Opinion AI System according to an embodiment of the present invention is a system designed to address the current situation where business succession-type M&A is becoming more active, against the backdrop of a shortage of successors for small and medium-sized enterprises. The Business Valuation Second Opinion AI System works by having the seller input quantitative and qualitative data necessary for business valuation, and the AI ​​interviews managers and employees to acquire information that can be used for business valuation, and calculates an appropriate valuation amount by referring to past M&A data. This valuation amount is provided as a second opinion to be compared with the valuation amount of the intermediary. This allows the seller to confirm an appropriate valuation amount and make a decision on the business transfer at a price they are satisfied with. For example, the Business Valuation Second Opinion AI System has the seller input quantitative and qualitative data necessary for business valuation. For example, financial data such as sales, profits, assets, and liabilities, and non-financial data such as the number of employees, business content, and competitive situation are input. This data is analyzed by the AI. Next, the Business Valuation Second Opinion AI System has the AI ​​interview managers and employees to acquire information that can be used for business valuation. For example, it collects information that cannot be grasped from quantitative data alone, such as the manager's vision and strategy, and the skills and motivation of employees. This information is analyzed by AI. Furthermore, the business valuation second opinion AI system uses AI to reference past M&A data and calculate an appropriate valuation. For example, it refers to past M&A cases of companies in the same industry and of similar size to evaluate the value of the selling company. This valuation is provided as a second opinion to be compared with the valuation of the intermediary. This allows the seller to confirm an appropriate valuation and decide on a business transfer at a price they are satisfied with. For example, if the intermediary's valuation is too low, the seller can use the AI's valuation as a reference in negotiations. Also, if the AI's valuation matches the intermediary's valuation, the seller can proceed with the business transfer with peace of mind. In this way, the business valuation second opinion AI system enables sellers to receive an appropriate business valuation and prevent economic losses. It also strengthens the seller's position by providing a means to counter low-quality valuations from intermediaries. This is expected to facilitate business succession for small and medium-sized enterprises and contribute to economic revitalization.

[0068] The business evaluation second opinion AI system according to this embodiment comprises an input unit, an analysis unit, and a provision unit. The input unit inputs quantitative and qualitative data necessary for business evaluation. For example, the input unit inputs financial data such as sales, profits, assets, and liabilities. The input unit can also input non-financial data such as the number of employees, business content, and competitive situation. For example, the input unit inputs financial data such as sales and profits. The input unit can also input non-financial data such as the number of employees and business content. The analysis unit analyzes the data input by the input unit and calculates an appropriate valuation. For example, the analysis unit uses AI to analyze the input data and calculates an appropriate valuation. For example, the analysis unit uses AI to analyze financial data such as sales and profits and calculates an appropriate valuation. The analysis unit can also use AI to analyze non-financial data such as the number of employees and business content and calculate an appropriate valuation. The provision unit provides the valuation calculated by the analysis unit as a second opinion. For example, the provision unit provides the valuation calculated using AI as a second opinion. For example, the service provider can provide the appraisal amount calculated using AI in a report format. Alternatively, the service provider can provide the appraisal amount calculated using AI as a simplified evaluation result. This allows the business valuation second opinion AI system according to this embodiment to enable sellers to confirm a second opinion on a fair appraisal amount and make a decision on the business transfer at a price they are satisfied with.

[0069] The input section allows users to input quantitative and qualitative data necessary for business evaluation. For example, it inputs financial data such as sales, profits, assets, and liabilities. Specifically, it inputs monthly and annual sales data, and detailed profit data such as operating profit and net profit. For assets, it inputs a breakdown of fixed and current assets, and for liabilities, it inputs details of short-term and long-term liabilities. The input section can also input non-financial data such as the number of employees, business content, and competitive landscape. For example, the number of employees can be entered in detail by department and position, and the business content can include details of the type of business and services provided. For the competitive landscape, information on major competitors, market share, and competitive advantages can be entered. This allows the input section to comprehensively collect a wide range of data necessary for business evaluation and provide it to the analysis section. Furthermore, the input section improves user convenience by diversifying data input methods. For example, it supports not only manual input but also automatic input via CSV files and APIs to ensure data accuracy and efficiency. The input section also includes a data integrity check function, automatically detecting input errors and inconsistencies and prompting corrections. This allows the input unit to provide accurate and reliable data, improving the overall accuracy of the system.

[0070] The analysis unit analyzes the data entered by the input unit and calculates an appropriate valuation. For example, the analysis unit uses AI to analyze the entered data and calculate an appropriate valuation. Specifically, the AI ​​uses machine learning algorithms to learn from past business evaluation data and predict the optimal valuation based on the entered data. For example, it analyzes financial data such as sales and profits to evaluate the profitability and growth potential of the business. It can also analyze non-financial data such as the number of employees and business content to evaluate the operational efficiency and market competitiveness of the business. Furthermore, the analysis unit calculates a more accurate valuation by combining multiple evaluation models. For example, it uses deep learning models to detect complex patterns and statistical models to analyze the distribution of data. This allows the analysis unit to perform business evaluations from multiple perspectives and provide an appropriate valuation. In addition, the analysis unit can analyze data in real time and provide a valuation based on the latest information. For example, it can quickly update the valuation in response to market fluctuations and changes in the competitive landscape. The analysis unit can also consider past data and trends to evaluate future risks and opportunities. This allows the analysis department to conduct business evaluations not only from a short-term perspective but also from a long-term perspective, enabling them to provide more reliable valuations.

[0071] The service provider offers the valuation calculated by the analysis department as a second opinion. For example, the service provider may offer a valuation calculated using AI as a second opinion. Specifically, the service provider provides the valuation calculated by the analysis department in report format. The report includes details of the basis for the valuation and the evaluation process, allowing users to verify the reliability of the valuation. The service provider can also provide the AI-calculated valuation as a simplified evaluation result. For example, it may use graphs and charts to display the information visually and intuitively, allowing users to understand it intuitively. Furthermore, the service provider can provide customized reports according to user needs. For example, it may use evaluation criteria specific to a particular industry or region to provide a more accurate valuation. The service provider can also provide the valuation quickly to users using multiple communication methods. For example, it may send reports via email or cloud storage, allowing users to access them anytime, anywhere. This enables the service provider to provide users with a quick and reliable second opinion, supporting their business transfer decisions. Additionally, the service provider can collect user feedback to continuously improve the accuracy and usefulness of the information it provides. This allows the service provider to always use the latest information and technology to offer users the most suitable second opinion.

[0072] The analysis unit includes an interview unit that conducts interviews with managers and employees to obtain information useful for business evaluation. The interview unit collects information that cannot be grasped by quantitative data alone, such as the vision and strategy of managers and the skills and motivation of employees. For example, the interview unit can interview managers to obtain information useful for business evaluation by listening to their vision and strategy. The interview unit can also interview managers to obtain information useful for business evaluation by listening to their skills and motivation. Furthermore, the interview unit can obtain information useful for business evaluation through interviews with managers and employees. For example, the interview unit can grasp the strengths and weaknesses of the business and future prospects through interviews with managers and employees. This allows for more accurate business evaluation by obtaining information from managers and employees. Some or all of the above processing in the interview unit may be performed using AI, for example, or not using AI. For example, the interview unit can input the content of interviews with managers and employees into AI, and the AI ​​can analyze the information useful for business evaluation.

[0073] The analysis unit includes a reference unit that calculates an appropriate valuation by referring to past M&A data. The reference unit, for example, refers to past M&A cases of companies in the same industry and of similar size to evaluate the value of the selling company. For example, the reference unit evaluates the value of the selling company based on past M&A data. The reference unit can also calculate an appropriate valuation based on past M&A data. Furthermore, the reference unit can evaluate the value of the selling company based on past M&A data. For example, the reference unit evaluates the value of the selling company based on past M&A data and calculates an appropriate valuation. In this way, an appropriate valuation can be calculated by referring to past M&A data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without using AI. For example, the reference unit can input past M&A data into AI, and the AI ​​can calculate an appropriate valuation.

[0074] The input unit inputs financial data such as sales, profits, assets, and liabilities. For example, the input unit inputs financial data such as sales and profits. For example, the input unit inputs sales. The input unit can also input profits. Furthermore, the input unit can also input financial data such as assets and liabilities. For example, the input unit inputs assets. The input unit can also input liabilities. This improves the accuracy of business evaluation by inputting financial data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input financial data such as sales and profits into AI, and the AI ​​can analyze the data.

[0075] The input unit accepts non-financial data such as the number of employees, business content, and competitive landscape. For example, the input unit accepts non-financial data such as the number of employees and business content. For example, the input unit accepts the number of employees. The input unit can also accept the business content. Furthermore, the input unit can accept non-financial data such as the competitive landscape. For example, the input unit accepts the competitive landscape. The input unit can also accept the business's growth potential. This improves the accuracy of business evaluation by accepting non-financial data. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input non-financial data such as the number of employees and business content into an AI, which can then analyze the data.

[0076] The service provider offers the calculated appraisal amount as a second opinion. The service provider offers, for example, an appraisal amount calculated using AI as a second opinion. For example, the service provider offers the appraisal amount calculated using AI in report format. The service provider can also offer the appraisal amount calculated using AI as a simplified evaluation result. By offering the appraisal amount as a second opinion, the seller can decide on a business transfer price that they are satisfied with. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can offer the appraisal amount calculated by AI in report format.

[0077] The input unit estimates the seller's emotions and adjusts the timing of data entry based on the estimated emotions. For example, if the seller is stressed, the input unit may divide the data entry into shorter segments to allow for quicker processing. The input unit may also allow detailed data entry to occur all at once if the seller is relaxed. The input unit may also prioritize inputting only the most important data if the seller is in a hurry. This allows for efficient data entry by adjusting the timing of data entry according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input seller's emotional data into the AI, which can then adjust the timing of data input.

[0078] The input unit analyzes the seller's past data entry history and selects the optimal input method. For example, the input unit prioritizes suggesting input methods (voice, text, etc.) that the seller has frequently used in the past. The input unit can also analyze patterns in the data the seller has entered in the past and optimize the input order. The input unit can also customize the input interface based on the amount of data the seller has entered in the past. This allows the optimal input method to be selected by analyzing past data entry history. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input the seller's past data entry history into AI, which can then select the optimal input method.

[0079] The input unit filters data during data entry based on the seller's current business status and areas of interest. For example, the input unit prioritizes displaying data entry items that are highly relevant based on the seller's current business status. The input unit can also customize input items based on the seller's areas of interest. The input unit can also dynamically change the required data entry items according to the seller's business growth stage. This enables efficient data entry by filtering data based on the seller's business status and areas of interest. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input data on the seller's current business status and areas of interest into the AI, which can then filter the data entry.

[0080] The input unit estimates the seller's emotions and determines the priority of the data to be entered based on the estimated emotions. For example, if the seller is stressed, the input unit prioritizes inputting only the most important data. For example, if the seller is stressed, the input unit prioritizes inputting only the most important data. For example, if the seller is relaxed, the input unit may allow detailed data to be entered all at once. For example, if the seller is relaxed, the input unit may allow detailed data to be entered all at once. For example, if the seller is in a hurry, the input unit may prioritize inputting only the most important data. For example, if the seller is in a hurry, the input unit may prioritize inputting only the most important data. This enables efficient data entry by prioritizing data according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input seller sentiment data into the AI, which can then determine the priority of the data.

[0081] The input unit prioritizes inputting highly relevant data, taking into account the seller's geographical location information during data entry. For example, the input unit prioritizes displaying region-specific data entry items based on the seller's location. The input unit can also prioritize inputting market data for the region where the seller's business operates. The input unit can also prioritize inputting relevant competitor information based on the seller's geographical location information. This enables efficient data entry by prioritizing the input of highly relevant data based on the seller's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the seller's geographical location information into AI, which can then prioritize inputting highly relevant data.

[0082] The input unit analyzes the seller's social media activity during data entry and inputs relevant data. For example, the input unit extracts business-related topics from the seller's social media activity and suggests them as input items. The input unit can also utilize the seller's social media follower count and engagement rate as input data. The input unit can also analyze the seller's social media activity history and input relevant data. This allows for efficient input of relevant data by analyzing the seller's social media activity. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input the seller's social media activity data into AI, which can then input relevant data.

[0083] The analysis unit estimates the seller's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the seller is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the seller is stressed, the analysis unit provides a simple and easy-to-understand analysis result. The analysis unit can also provide a detailed analysis result if the seller is relaxed. For example, if the seller is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a concise analysis result if the seller is in a hurry. For example, if the seller is in a hurry, the analysis unit provides a concise analysis result. By adjusting the presentation of the analysis according to the seller's emotions, efficient analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seller sentiment data into the AI, which can then adjust how the analysis is presented.

[0084] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0085] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a financial analysis algorithm to financial data. The analysis unit can also apply a text analysis algorithm to non-financial data. The analysis unit can also select the optimal analysis algorithm depending on the data category. This enables efficient analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0086] The analysis unit estimates the seller's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the seller is stressed, the analysis unit provides a short, concise analysis result. For example, if the seller is stressed, the analysis unit provides a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the seller is relaxed. For example, if the seller is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a short, concise analysis result if the seller is in a hurry. For example, if the seller is in a hurry, the analysis unit provides a short, concise analysis result. This allows for efficient analysis results by adjusting the length of the analysis according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seller sentiment data into the AI, which can then adjust the length of the analysis.

[0087] The analysis unit determines the priority of analysis based on the data submission date. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the data submission date into the AI, which can then determine the priority of analysis.

[0088] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0089] The information provider estimates the seller's emotions and adjusts the presentation of the information based on the estimated emotions. For example, if the seller is stressed, the provider provides simple and easily understandable information. For example, if the seller is stressed, the provider provides simple and easily understandable information. The provider can also provide detailed information if the seller is relaxed. For example, if the seller is relaxed, the provider provides detailed information. The provider can also provide concise information if the seller is in a hurry. For example, if the seller is in a hurry, the provider provides concise information. This allows for efficient information delivery by adjusting the presentation of information according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input seller emotional data into the AI, which can then adjust how the information is presented.

[0090] The information provider adjusts the level of detail provided based on the importance of the information. For example, the provider provides detailed explanations for highly important information. The provider can also provide simplified explanations for less important information. The provider can also determine the priority of information provision based on its importance. This allows for efficient information provision by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the importance of the information into the AI, which can then adjust the level of detail.

[0091] The information provider applies different information provision algorithms depending on the information category at the time of provision. For example, the information provider applies a financial analysis algorithm to financial information. The information provider can also apply a text analysis algorithm to non-financial information. The information provider can also select the optimal information provision algorithm depending on the information category. This enables efficient information provision by applying the optimal information provision algorithm according to the information category. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into the AI, and the AI ​​can apply the optimal information provision algorithm.

[0092] The information provider estimates the seller's emotions and adjusts the length of the information provided based on the estimated emotions. For example, if the seller is stressed, the provider provides short, concise information. The provider can also provide detailed information if the seller is relaxed. The provider can also provide short, concise information if the seller is in a hurry. This allows for efficient information provision by adjusting the length of information according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input seller's emotional data into the AI, which can then adjust the length of the information.

[0093] The information provider determines the priority of information provision based on the submission date. For example, the provider may prioritize providing the most recent information. The provider may also postpone providing older information. The provider may also adjust the provision schedule based on the submission date. This allows for efficient information provision by determining the priority of information provision based on the submission date. Some or all of the above processing in the information provider may be performed using AI, or not using AI. For example, the information provider can input the submission dates of the information into the AI, and the AI ​​can determine the priority of information provision.

[0094] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the information provider prioritizes providing highly relevant information. The information provider can also postpone providing less relevant information. The information provider can also optimize the order of delivery based on the relevance of the information. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into AI, and the AI ​​can adjust the order of delivery.

[0095] The interviewer estimates the seller's emotions and adjusts the interview process based on the estimated emotions. For example, if the seller is stressed, the interviewer prioritizes questions that help them relax. If the seller is relaxed, the interviewer may ask more detailed questions. If the seller is in a hurry, the interviewer may ask concise questions. By adjusting the interview process according to the seller's emotions, an efficient interview becomes possible. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewer may be performed using AI, for example, or without AI. For example, the interview department can input seller emotional data into the AI, which can then adjust how the interview proceeds.

[0096] The interview department selects the most appropriate questions during the interview by referring to the seller's past interview history. For example, the interview department asks related questions based on questions the seller has answered in the past. The interview department can also extract important points from the seller's past interview history and ask questions. The interview department can also analyze the seller's past interview history and select the most appropriate questions. This allows the interview department to select the most appropriate questions by referring to the seller's past interview history. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's past interview history into an AI, which can then select the most appropriate questions.

[0097] The interview department customizes the questions during the interview based on the seller's current business situation. For example, the interview department asks relevant questions based on the seller's current business situation. The interview department can also customize the questions according to the stage of growth of the seller's business. The interview department can also adjust the questions based on the characteristics of the seller's business. This allows for more efficient interviews by customizing the questions based on the seller's current business situation. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's current business situation into the AI, which can then customize the questions.

[0098] The interview department estimates the seller's emotions and prioritizes interviews based on the estimated emotions. For example, if the seller is stressed, the interview department will conduct the interview as soon as possible. The interview department can also flexibly adjust the interview schedule if the seller is relaxed. The interview department can also prioritize the most important interviews if the seller is in a hurry. This allows for efficient interviews by prioritizing interviews according to the seller's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the interview department may be performed using AI, for example, or without AI. For example, the interview department can input seller sentiment data into AI, which can then determine the priority of interviews.

[0099] The interview department selects the most suitable interview method during an interview, taking into account the seller's geographical location. For example, if the seller is far away, the interview department will prioritize online interviews. The interview department can also prioritize in-person interviews if the seller is nearby. The interview department can also optimize the interview location based on the seller's geographical location. This allows for more efficient interviews by selecting the most suitable interview method based on the seller's geographical location. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's geographical location into an AI, which can then select the most suitable interview method.

[0100] The interview department analyzes the seller's social media activity during the interview and proposes interview questions. For example, the interview department extracts business-related topics from the seller's social media activity and proposes them as questions. The interview department can also adjust the questions based on the seller's number of followers and engagement rate on social media. The interview department can also analyze the seller's social media activity history and propose relevant questions. This allows for the efficient proposal of relevant questions by analyzing the seller's social media activity. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the seller's social media activity data into an AI, which can then propose questions.

[0101] The reference unit estimates the seller's emotions and selects the data to reference based on the estimated emotions. For example, if the seller is stressed, the reference unit prioritizes referencing simple and easily viewable data. The reference unit can also referencing detailed data if the seller is relaxed. The reference unit can also prioritize referencing concise data if the seller is in a hurry. This allows for efficient data retrieval by selecting the data to reference according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input seller sentiment data into the AI ​​and select the data that the AI ​​will refer to.

[0102] The reference unit optimizes the reference algorithm by referencing past reference data during the reference process. For example, the reference unit analyzes patterns in previously referenced data and selects the optimal reference algorithm. The reference unit can also prioritize referencing highly relevant data based on past reference data. The reference unit can also dynamically adjust the reference algorithm based on past reference data. This allows the optimal reference algorithm to be selected by referencing past reference data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past reference data into AI, which can then optimize the reference algorithm.

[0103] The reference unit applies different reference algorithms depending on the data category during the reference process. For example, the reference unit applies a financial analysis algorithm to financial data. The reference unit can also apply a text analysis algorithm to non-financial data. The reference unit can also select the optimal reference algorithm depending on the data category. This enables efficient data referencing by applying the most suitable reference algorithm according to the data category. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input the data category into the AI, which can then apply the most suitable reference algorithm.

[0104] The reference unit estimates the seller's emotions and adjusts the frequency of references based on the estimated emotions. For example, if the seller is stressed, the reference unit reduces the frequency of references to alleviate the burden. The reference unit can also increase the frequency of references to provide more detailed information if the seller is relaxed. The reference unit can also prioritize referencing the most important data if the seller is in a hurry. This allows for efficient data retrieval by adjusting the frequency of references according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference function can input seller sentiment data into the AI, which can then adjust the frequency of the references.

[0105] The reference unit weights the referenced data based on the data submission date when it is referenced. For example, the reference unit prioritizes referencing the most recent data. The reference unit can also reference older data with lower weighting. The reference unit can also dynamically adjust the weighting of the referenced data based on the submission date. This enables efficient data retrieval by weighting the referenced data based on the data submission date. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input the data submission date into AI, and the AI ​​can weight the referenced data.

[0106] The reference unit adjusts the order of references based on the relevance of the data during the reference process. For example, the reference unit prioritizes referencing highly relevant data. The reference unit can also postpone referencing less relevant data. The reference unit can also optimize the order of references based on the relevance of the data. This allows for efficient data referencing by adjusting the order of references based on the relevance of the data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input the relevance of the data into AI, which can then adjust the order of references.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The analysis unit estimates the seller's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the seller is stressed, it can provide a simple and easy-to-understand analysis result. If the seller is relaxed, it can provide a detailed analysis result. Furthermore, if the seller is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the seller's emotions, efficient analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input seller emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0109] The input unit analyzes the seller's past data entry history and selects the optimal input method. For example, it can prioritize suggesting input methods that the seller has frequently used in the past (such as voice or text). It can also analyze patterns in the data the seller has entered in the past and optimize the input order. Furthermore, it can customize the input interface based on the amount of data the seller has entered in the past. This allows the optimal input method to be selected by analyzing past data entry history. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit can input the seller's past data entry history into an AI, which can then select the optimal input method.

[0110] The information provider estimates the seller's emotions and adjusts the presentation of the information based on the estimated emotions. For example, if the seller is stressed, simple and easily understandable information can be provided. If the seller is relaxed, detailed information can be provided. Furthermore, if the seller is in a hurry, concise information can be provided. This allows for efficient information delivery by adjusting the presentation of information according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input seller emotion data into an AI, which can then adjust the presentation of the information.

[0111] The interview department estimates the seller's emotions and adjusts the interview process based on the estimated emotions. For example, if the seller is stressed, questions that help them relax can be prioritized. If the seller is relaxed, more detailed questions can be asked. Furthermore, if the seller is in a hurry, concise questions can be asked. By adjusting the interview process according to the seller's emotions, an efficient interview becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview department may be performed using AI or not. For example, the interview department can input seller emotion data into an AI, which can then adjust the interview process.

[0112] The reference unit estimates the seller's emotions and selects data to reference based on the estimated emotions. For example, if the seller is stressed, simple and highly visual data can be prioritized. If the seller is relaxed, detailed data can be referenced. Furthermore, if the seller is in a hurry, concise data can be prioritized. This allows for efficient data retrieval by selecting data according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using AI or not. For example, the reference unit can input seller emotion data into an AI, which can then select the data to reference.

[0113] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0114] The information delivery unit adjusts the level of detail provided based on the importance of the information. For example, it can provide detailed explanations for highly important information, and simplified explanations for less important information. Furthermore, it can determine the priority of information delivery based on its importance. This allows for efficient information delivery by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the importance of the information into the AI, and the AI ​​can adjust the level of detail provided.

[0115] The interview department selects the most appropriate questions during the interview by referring to the seller's past interview history. For example, it can ask related questions based on questions the seller has answered in the past. It can also extract important points from the seller's past interview history and ask questions based on those points. Furthermore, it can analyze the seller's past interview history to select the most appropriate questions. In this way, the most appropriate questions can be selected by referring to the seller's past interview history. Some or all of the above processes in the interview department may be performed using AI, for example, or not. For example, the interview department can input the seller's past interview history into an AI, which can then select the most appropriate questions.

[0116] The reference unit optimizes the reference algorithm by referencing past reference data during a reference operation. For example, it can analyze patterns in previously referenced data and select the optimal reference algorithm. It can also prioritize the reference of highly relevant data based on past reference data. Furthermore, it can dynamically adjust the reference algorithm based on past reference data. This allows for the selection of the optimal reference algorithm by referencing past reference data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past reference data into AI, which can then optimize the reference algorithm.

[0117] The information delivery unit determines the priority of information delivery based on the submission date. For example, the most recent information can be provided first. Older information can be provided later. Furthermore, the delivery schedule can be adjusted based on the submission date. This enables efficient information delivery by determining the priority of information delivery based on the submission date. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not. For example, the information delivery unit can input the submission dates of the information into the AI, and the AI ​​can determine the priority of delivery.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The input section is used to enter quantitative and qualitative data necessary for business evaluation. Specifically, it can input financial data such as sales, profits, assets, and liabilities, as well as non-financial data such as the number of employees, business content, and competitive landscape. Step 2: The analysis unit analyzes the data entered by the input unit and calculates an appropriate valuation. The analysis unit uses AI to analyze financial data such as sales and profits, as well as non-financial data such as the number of employees and business details, and calculates an appropriate valuation. Step 3: The service provider provides the assessed value calculated by the analysis unit as a second opinion. The service provider can provide the assessed value calculated using AI in report format or as a simplified evaluation result.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the input unit, analysis unit, provision unit, interview unit, and reference unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart device 14 and inputs financial data such as sales and profits. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input data to calculate an appropriate appraisal value. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the calculated appraisal value as a second opinion. The interview unit uses the camera 42 and microphone 38B of the smart device 14 to interview managers and employees and acquire information that will be useful for business evaluation. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to past M&A data to calculate an appropriate appraisal value. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the input unit, analysis unit, provision unit, interview unit, and reference unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart glasses 214 and inputs financial data such as sales and profits. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input data to calculate an appropriate appraisal value. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the calculated appraisal value as a second opinion. The interview unit uses the camera 42 and microphone 238 of the smart glasses 214 to interview managers and employees and acquire information that contributes to business evaluation. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to past M&A data to calculate an appropriate appraisal value. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the input unit, analysis unit, provision unit, interview unit, and reference unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the headset terminal 314 and inputs financial data such as sales and profits. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input data to calculate an appropriate appraisal amount. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the calculated appraisal amount as a second opinion. The interview unit uses the camera 42 and microphone 238 of the headset terminal 314 to interview managers and employees and acquire information that will contribute to the business evaluation. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to past M&A data to calculate an appropriate appraisal amount. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the input unit, analysis unit, provision unit, interview unit, and reference unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the robot 414 and inputs financial data such as sales and profits. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input data to calculate an appropriate appraisal value. The provision unit is implemented by the control unit 46A of the robot 414 and provides the calculated appraisal value as a second opinion. The interview unit uses the camera 42 and microphone 238 of the robot 414 to interview managers and employees and acquire information that will be useful for business evaluation. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to past M&A data to calculate an appropriate appraisal value. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] 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.

[0183] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) An input section for entering quantitative and qualitative data necessary for business evaluation, An analysis unit analyzes the data input by the aforementioned input unit and calculates an appropriate appraisal amount, The system includes a provision unit that provides the assessed value calculated by the analysis unit as a second opinion. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The company has an interview department that conducts interviews with managers and employees to obtain information that can be used for business evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes a reference section that calculates an appropriate valuation amount by referring to past M&A data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned input unit is Enter financial data such as sales revenue, profit, assets, and liabilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned input unit is Enter non-financial data such as the number of employees, business activities, and competitive landscape. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We will provide the calculated appraisal amount as a second opinion. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is The system estimates the seller's sentiment and adjusts the timing of data entry based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is Analyze the seller's past data entry history and select the optimal data entry method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is During data entry, filtering is performed based on the seller's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is The system estimates the seller's sentiment and prioritizes the data to be entered based on the estimated seller's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is When entering data, the system prioritizes inputting highly relevant data, taking into account the seller's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is During data entry, analyze the seller's social media activity and enter relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the seller's emotions and adjust the representation of the analysis based on the estimated seller's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The seller's sentiment is estimated, and the length of the analysis is adjusted based on the estimated seller's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the seller's sentiment and adjust the way we present information based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Estimate the seller's sentiment and adjust the length of the information provided based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned interview department, We estimate the seller's emotions and adjust the interview process based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned interview department, During the interview, we select the most appropriate questions by referring to the seller's past interview history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned interview department, During the interview, customize the questions based on the seller's current business situation. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned interview department, Estimate the seller's emotions and prioritize interviews based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned interview department, During the interview, the most suitable interview method will be selected, taking into account the seller's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned interview department, During the interview, we analyze the seller's social media activity and propose interview questions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned reference section is, The system estimates the seller's sentiment and selects data to reference based on the estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned reference section is, When referencing data, the reference algorithm is optimized by referring to past reference data. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned reference section is, When referencing data, different referencing algorithms are applied depending on the data category. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned reference section is, Estimate the seller's sentiment and adjust the frequency of references based on the estimated seller's sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned reference section is, When referencing data, weight the referenced data based on when it was submitted. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned reference section is, When referencing data, adjust the order of references based on the relevance of the data. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering quantitative and qualitative data necessary for business evaluation, An analysis unit analyzes the data input by the aforementioned input unit and calculates an appropriate appraisal amount, The system includes a provision unit that provides the assessed value calculated by the analysis unit as a second opinion. A system characterized by the following features.

2. The aforementioned analysis unit, The company has an interview department that conducts interviews with managers and employees to obtain information that can be used for business evaluation. The system according to feature 1.

3. The aforementioned analysis unit, It includes a reference section that calculates an appropriate valuation amount by referring to past M&A data. The system according to feature 1.

4. The aforementioned input unit is Enter financial data such as sales revenue, profit, assets, and liabilities. The system according to feature 1.

5. The aforementioned input unit is Enter non-financial data such as the number of employees, business activities, and competitive landscape. The system according to feature 1.

6. The aforementioned supply unit is, We will provide the calculated appraisal amount as a second opinion. The system according to feature 1.

7. The aforementioned input unit is The system estimates the seller's sentiment and adjusts the timing of data entry based on the estimated sentiment. The system according to feature 1.

8. The aforementioned input unit is Analyze the seller's past data entry history and select the optimal data entry method. The system according to feature 1.

9. The aforementioned input unit is During data entry, filtering is performed based on the seller's current business situation and areas of interest. The system according to feature 1.

10. The aforementioned input unit is The system estimates the seller's sentiment and prioritizes the data to be entered based on the estimated seller's sentiment. The system according to feature 1.

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