system

Abstract

The system according to this embodiment aims to collect and analyze corporate information quickly and with high quality, and to utilize it in sales activities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects company information. The analysis unit analyzes the company information collected by the collection unit. The evaluation unit evaluates the reliability and future potential of companies based on the analysis results obtained by the analysis unit. The provision unit provides the evaluation results obtained by the evaluation unit to sales activities.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the collection and analysis of corporate information are performed manually, and there is room for improvement in terms of quality and speed.

[0005] The system according to the embodiment aims to collect and analyze corporate information quickly and with high quality and utilize it in business activities.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects corporate information. The analysis unit analyzes the corporate information collected by the collection unit. The evaluation unit evaluates the reliability and future potential of the company based on the analysis results obtained by the analysis unit. The provision unit provides the evaluation results obtained by the evaluation unit for business activities. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze corporate information quickly and with high quality, and utilize it in sales activities. [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) An automated corporate credit assessment system according to an embodiment of the present invention is a mechanism that automates corporate credit assessment using AI. This automated corporate credit assessment system can be used in sales activities by collecting, analyzing, and evaluating corporate information, surrounding information, and social conditions. This mechanism improves the quality and speed of corporate credit assessment and streamlines sales activities. For example, the automated corporate credit assessment system collects corporate information. Corporate information includes the company's financial status, performance, and transaction history. Next, the automated corporate credit assessment system collects surrounding information. Surrounding information includes the economic conditions of the region where the company is located and the activities of competitors. Furthermore, the automated corporate credit assessment system collects social conditions. Social conditions include the current economic situation, political situation, and social trends. By analyzing this information, the automated corporate credit assessment system can evaluate the reliability and future potential of a company. For example, if a company's financial situation is sound and it has an advantage over competitors, it is recommended to enter into a contract with that company. Also, by evaluating the impact of the current economic situation and political situation on the company's performance, risks can be minimized. By utilizing the analysis results obtained in this way in sales activities, sales representatives can proceed with concluding contracts with companies more efficiently. For example, prioritizing contracts with highly reliable companies can improve the success rate of sales activities. Furthermore, avoiding contracts with high-risk companies can reduce business risks. This allows automated company screening systems to improve the quality and speed of company screening, thereby increasing the efficiency of sales activities. By utilizing AI, the effort previously required for manual company screening can be significantly reduced. Additionally, sales activities based on AI analysis results can accelerate company growth. In this way, automated company screening systems can efficiently collect, analyze, evaluate, and provide company information.

[0029] The automated corporate screening system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects corporate information. Corporate information includes, but is not limited to, financial information, performance data, and transaction history. The collection unit collects, for example, a company's financial status. For example, it can collect information such as revenue, liabilities, and assets. The collection unit can also collect a company's performance. For example, it can collect information such as sales, profit margins, and growth rates. Furthermore, the collection unit can collect a company's transaction history. For example, it can collect information such as trading partners, transaction amounts, and transaction frequency. The analysis unit analyzes the collected corporate information. The analysis is performed by, for example, statistical analysis, trend analysis, and risk assessment, but is not limited to these methods. For example, the analysis unit can use statistical analysis to evaluate a company's financial status. The analysis unit can also use trend analysis to evaluate fluctuations in a company's performance. Furthermore, the analysis unit can use risk assessment to evaluate a company's transaction history. The evaluation unit evaluates the reliability and future potential of a company based on the analysis results. Reliability assessments are conducted using criteria such as credit scores and past performance, but are not limited to these examples. For example, the evaluation department can assess a company's reliability using credit scores. The evaluation department can also assess a company's reliability using past performance. Future potential assessments are conducted using criteria such as growth forecasts and market trends, but are not limited to these examples. For example, the evaluation department can assess a company's future potential using growth forecasts. The evaluation department can also assess a company's future potential using market trends. The delivery department provides the evaluation results to sales representatives. Delivery is carried out by methods such as customer visits, presentations, and contract negotiations, but is not limited to these examples. For example, the delivery department can provide evaluation results through customer visits. The delivery department can also provide evaluation results through presentations. Furthermore, the delivery department can also provide evaluation results through contract negotiations. This enables the automated corporate screening system to efficiently collect, analyze, evaluate, and provide corporate information.

[0030] The data collection unit collects corporate information. Corporate information includes, but is not limited to, financial information, performance data, and transaction history. For example, the data collection unit collects information on a company's financial status. Specifically, it can collect information on revenue, liabilities, and assets. Revenue information includes sales, operating profit, and net profit, and this data is important for evaluating a company's profitability. Liability information includes short-term liabilities, long-term liabilities, and total liabilities, and is used to evaluate a company's financial health. Asset information includes current assets, fixed assets, and total assets, and is important for understanding a company's asset structure. The data collection unit can also collect information on a company's performance. For example, it can collect information on sales, profit margins, and growth rates. Sales indicate a company's market performance, and profit margins are indicators of profitability. Growth rates are used to evaluate a company's growth potential. Furthermore, the data collection unit can also collect a company's transaction history. For example, it can collect information on trading partners, transaction amounts, and transaction frequency. Information on trading partners is important for evaluating the reliability of a company's business partners and customers, and transaction amounts indicate the scale of a company's transactions. Transaction frequency is an indicator of the activity level of a company's trading activities. This allows the data collection unit to comprehensively gather diverse corporate information and grasp the overall picture of the company. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and evaluation departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes collected corporate information. Analysis is carried out using methods such as statistical analysis, trend analysis, and risk assessment, but is not limited to these examples. Specifically, statistical analysis can be used to evaluate a company's financial condition. Statistical analysis uses data such as revenue, liabilities, and assets to assess a company's financial health and profitability. For example, analyzing trends in revenue increases and decreases can assess a company's growth potential. Furthermore, debt ratios and equity ratios can be calculated to assess a company's financial health. Trend analysis can also be used to evaluate fluctuations in a company's performance. Trend analysis uses data such as sales, profit margins, and growth rates to understand trends in a company's performance. For example, analyzing sales trends over the past few years can assess a company's growth trend. It can also analyze fluctuations in profit margins to identify factors influencing a company's profitability. Finally, risk assessment can be used to evaluate a company's transaction history. Risk assessment uses data such as trading partners, transaction amounts, and transaction frequency to assess a company's transaction risks. For example, it can assess the credit risk of trading partners to ensure transaction safety. It can also analyze fluctuations in transaction amounts and frequency to identify factors influencing transaction risks. This allows the analytics department to quickly and accurately analyze collected data and comprehensively evaluate a company's financial condition, performance, and transaction risks. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical performance data, it can predict fluctuations in risks in specific industries or markets and formulate future countermeasures. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The evaluation department assesses a company's reliability and future potential based on the analysis results. Reliability assessment is conducted using criteria such as credit scores and past performance, but is not limited to these examples. Specifically, a company's reliability can be assessed using credit scores. Credit scores are calculated based on a company's financial condition and transaction history and are an indicator for assessing a company's credit risk. For example, a company's credit score is calculated considering factors such as the stability of earnings, the debt ratio, and the credit risk of trading partners. Furthermore, a company's reliability can also be assessed using past performance. Past performance includes trends in sales, fluctuations in profit margins, and the stability of transaction history, and a company's reliability is assessed based on this data. For example, the growth potential of a company can be assessed by analyzing the trend of sales increases and decreases over the past few years. Also, the stability of a company's profitability can be assessed by analyzing fluctuations in profit margins. Future potential assessment is conducted using criteria such as growth forecasts and market trends, but is not limited to these examples. Specifically, a company's future potential can be assessed using growth forecasts. Growth forecasts are calculated based on past performance data and market trends and are an indicator for assessing a company's future growth potential. For example, a company's growth forecast is calculated considering past sales trends and market growth trends. Furthermore, market trends can be used to evaluate a company's future prospects. Market trends include industry growth trends, competitor activity, and changes in the economic environment, and these data are used to assess a company's future potential. For example, industry growth trends can be analyzed to evaluate a company's market position. Competitor activity can also be analyzed to assess a company's competitiveness. This allows the evaluation department to comprehensively assess a company's reliability and future prospects based on the analysis results, and to clearly understand the company's risks and potential.

[0033] The service department provides evaluation results to sales representatives. This provision may be carried out through methods such as customer visits, presentations, and contract negotiations, but is not limited to these examples. Specifically, evaluation results can be provided through customer visits. Sales representatives explain the evaluation results during customer visits and provide information about the company's reliability and future prospects. Evaluation results can also be provided through presentations. Sales representatives use presentation materials to explain the evaluation results and provide information about the company's strengths and risks. Furthermore, evaluation results can also be provided through contract negotiations. Sales representatives make proposals based on the evaluation results during contract negotiations and provide information about the company's reliability and future prospects. This allows the service department to provide evaluation results to sales representatives quickly and effectively, supporting their sales activities. In addition, the service department can collect feedback based on the evaluation results and continuously improve the accuracy and effectiveness of the evaluation process. For example, evaluation criteria and evaluation methods can be revised based on feedback from sales representatives. The service department can also reliably transmit information using multiple communication methods. For example, evaluation results can be shared and information quickly transmitted using email and chat tools. Remote presentations and contract negotiations can also be conducted using online conferencing systems. This allows the service department to provide evaluation results to sales representatives quickly and reliably, effectively supporting their sales activities.

[0034] The data collection unit can collect corporate information such as a company's financial status, performance, and transaction history. For example, the data collection unit can collect a company's financial status, such as revenue, liabilities, and assets. The data collection unit can also collect a company's performance, such as sales, profit margins, and growth rates. Furthermore, the data collection unit can collect a company's transaction history, such as trading partners, transaction amounts, and transaction frequency. By collecting information such as a company's financial status and performance, it is possible to obtain basic data for evaluating a company's reliability and future potential. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze financial data and extract information such as revenue, liabilities, and assets in order to collect a company's financial status.

[0035] The data collection unit can collect peripheral information such as the economic conditions of the region where the company is located and the activities of its competitors. For example, the data collection unit can collect information on the economic conditions of the region where the company is located, such as GDP growth rate, unemployment rate, and inflation rate. The data collection unit can also collect information on the activities of its competitors, such as market share, product lineup, and pricing strategy. By collecting peripheral information about the company, supplementary data can be obtained to evaluate the company's reliability and future prospects. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to analyze economic data in order to collect information on the economic conditions of the region where the company is located, and extract information such as GDP growth rate, unemployment rate, and inflation rate.

[0036] The data collection unit can collect information on current economic conditions, political situations, and social trends. For example, it can collect information on current economic conditions, such as economic growth rates, unemployment rates, and inflation rates. It can also collect information on political situations, such as policy changes, changes in government, and international relations. Furthermore, it can collect information on social trends, such as changes in consumer behavior, technological innovations, and cultural shifts. By collecting this information on current conditions, background information can be obtained to evaluate the reliability and future potential of a company. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, to collect information on current economic conditions, the data collection unit can use AI to analyze economic data and extract information such as economic growth rates, unemployment rates, and inflation rates.

[0037] The analysis department can analyze collected company information, surrounding information, and current social trends. For example, the analysis department can analyze company information using data mining. For instance, it can analyze a company's financial data and extract information such as revenue, liabilities, and assets. The analysis department can also analyze surrounding information using machine learning. For example, it can analyze the trends of competitors and extract information such as market share, product lineup, and pricing strategies. Furthermore, the analysis department can analyze current social trends using statistical analysis. For example, it can analyze information such as economic growth rate, unemployment rate, and inflation rate to evaluate the economic situation. By analyzing the collected information, detailed data can be obtained to evaluate the reliability and future potential of companies. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to perform data mining in order to analyze company information and extract information such as revenue, liabilities, and assets.

[0038] The evaluation department can assess a company's reliability and future potential based on the analysis results. For example, the evaluation department can assess a company's reliability using a credit score. For example, it can calculate a credit score based on a company's financial data and assess its reliability. The evaluation department can also assess a company's reliability using past performance. For example, it can evaluate past performance based on a company's transaction history and assess its reliability. Furthermore, the evaluation department can assess a company's future potential using growth forecasts. For example, it can make growth forecasts based on a company's performance data and assess its future potential. The evaluation department can also assess a company's future potential using market trends. For example, it can evaluate market trends based on the actions of competitors and assess its future potential. This allows for increased efficiency in sales activities by evaluating a company's reliability and future potential based on the analysis results. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can use AI to calculate a credit score in order to assess a company's reliability and then assess the company's reliability.

[0039] The service provider can provide evaluation results to sales representatives. For example, the service provider can provide evaluation results through customer visits. For example, a sales representative can visit a customer and explain the evaluation results. The service provider can also provide evaluation results through presentations. For example, a sales representative can give a presentation and explain the evaluation results. Furthermore, the service provider can also provide evaluation results through contract negotiations. For example, a sales representative can conduct contract negotiations and explain the evaluation results. By providing evaluation results to sales representatives, the efficiency of sales activities can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to automatically generate evaluation results in order to provide them to sales representatives and then provide them to sales representatives.

[0040] The data collection unit can analyze a company's past financial condition and performance and select the optimal data collection method. For example, if a company's past financial condition is stable, the data collection unit can select a periodic collection method. For example, the data collection unit can analyze a company's financial data and select a periodic collection method if it is stable. The data collection unit can also select a method to collect information in real time if a company's performance is fluctuating. For example, the data collection unit can analyze a company's performance data and select a method to collect information in real time if it is fluctuating. Furthermore, if a company's financial condition is deteriorating, the data collection unit can select a method to collect detailed information. For example, the data collection unit can analyze a company's financial data and select a method to collect detailed information if it is deteriorating. This allows the optimal data collection method to be selected by analyzing a company's past financial condition and performance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze financial data in order to analyze a company's financial condition and select the optimal data collection method.

[0041] The data collection unit can filter company information based on the company's current projects and market trends. For example, if a company's current projects are successful, the data collection unit can prioritize collecting that information. For example, the data collection unit can analyze company project data and prioritize collecting information if the projects are successful. The data collection unit can also prioritize collecting information if market trends are favorable to the company. For example, the data collection unit can analyze market trend data and prioritize collecting information if the market trends are favorable. Furthermore, the data collection unit can filter and collect information if a company's projects are failing. For example, the data collection unit can analyze company project data and filter and collect information if the projects are failing. This allows for the collection of highly relevant information by filtering information based on the company's current projects and market trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to evaluate the success rate of projects and filter the information in order to analyze company project data.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies when collecting company information. For example, if a company is located near an important market, the data collection unit can prioritize the collection of that information. For example, the data collection unit can analyze a company's geographical location and prioritize the collection of that information if it is close to an important market. The data collection unit can also prioritize the collection of information if a company is located near a competitor. For example, the data collection unit can analyze a company's geographical location and prioritize the collection of that information if it is close to a competitor. Furthermore, the data collection unit can prioritize the collection of information if a company is located in an economically important region. For example, the data collection unit can analyze a company's geographical location and prioritize the collection of that information if it is located in an economically important region. By collecting information while considering the geographical location of companies, highly relevant information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze geographic data in order to analyze a company's geographical location and prioritize the collection of highly relevant information.

[0043] The data collection unit can analyze a company's social media activities and collect relevant information when collecting company information. For example, if a company is very active on social media, the data collection unit can prioritize collecting that information. For example, the data collection unit can analyze a company's social media data and prioritize collecting information if the activity is high. The data collection unit can also prioritize collecting information if a company has a good reputation on social media. For example, the data collection unit can analyze a company's social media data and prioritize collecting information if the reputation is good. Furthermore, if a company is inactive on social media, the data collection unit can filter and collect that information. For example, the data collection unit can analyze a company's social media data and filter and collect information if the activity is low. In this way, relevant information can be collected by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to extract information such as post content, engagement rate, and follower count in order to analyze a company's social media data and collect relevant information.

[0044] The analysis department can adjust the level of detail of its analysis based on the importance of the company information. For example, the analysis department can perform a detailed analysis of company information that is of high importance. For instance, it can analyze a company's financial data and perform a detailed analysis of information that is of high importance. The analysis department can also perform a simplified analysis of company information that is of low importance. For example, it can analyze a company's performance data and perform a simplified analysis of information that is of low importance. Furthermore, the analysis department can adjust the level of detail of its analysis in stages according to the importance of the company information. For example, it can analyze a company's transaction history and adjust the level of detail of its analysis in stages according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the company information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze data in order to evaluate the importance of company information and adjust the level of detail of the analysis based on its importance.

[0045] The analysis department can apply different analysis algorithms depending on the company category during analysis. For example, the analysis department can apply an analysis algorithm specialized in manufacturing processes to manufacturing companies. For example, the analysis department can analyze data from manufacturing companies and apply an analysis algorithm specialized in manufacturing processes. The analysis department can also apply an analysis algorithm specialized in customer satisfaction to service companies. For example, the analysis department can analyze data from service companies and apply an analysis algorithm specialized in customer satisfaction. Furthermore, the analysis department can apply an analysis algorithm specialized in technological innovation to IT companies. For example, the analysis department can analyze data from IT companies and apply an analysis algorithm specialized in technological innovation. By applying different analysis algorithms depending on the company category, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can use AI to analyze data and select the optimal algorithm in order to apply different analysis algorithms depending on the company category.

[0046] The analysis department can determine the priority of analysis based on the submission date of company information. For example, the analysis department can prioritize the analysis of the most recent company information. For example, the analysis department can evaluate the submission date of company information and prioritize the analysis of the most recent information. The analysis department can also postpone the analysis of older company information. For example, the analysis department can evaluate the submission date of company information and postpone the analysis of older information. Furthermore, the analysis department can adjust the priority of analysis in stages according to the submission date. For example, the analysis department can evaluate the submission date of company information and adjust the priority of analysis in stages according to the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of company information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze data in order to evaluate the submission date of company information and determine the priority of analysis based on the submission date.

[0047] The analysis department can adjust the order of analysis based on the relevance of company information during the analysis process. For example, the analysis department can prioritize the analysis of company information that is highly relevant. For example, the analysis department can evaluate the relevance of company information and prioritize the analysis of information that is highly relevant. The analysis department can also postpone the analysis of company information that is less relevant. For example, the analysis department can evaluate the relevance of company information and postpone the analysis of information that is less relevant. Furthermore, the analysis department can adjust the order of analysis in stages according to the relevance of company information. For example, the analysis department can evaluate the relevance of company information and adjust the order of analysis in stages according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of company information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze data in order to evaluate the relevance of company information and adjust the order of analysis based on the relevance.

[0048] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between companies. For example, the evaluation unit can improve the accuracy of its evaluations by considering the transaction history between companies. For example, the evaluation unit can analyze company transaction data and improve the accuracy of its evaluations by considering the transaction history. The evaluation unit can also improve the accuracy of its evaluations by considering the competitive situation between companies. For example, the evaluation unit can analyze company competition data and improve the accuracy of its evaluations by considering the competitive situation. Furthermore, the evaluation unit can also improve the accuracy of its evaluations by considering cooperative relationships between companies. For example, the evaluation unit can analyze company cooperation data and improve the accuracy of its evaluations by considering cooperative relationships. In this way, the accuracy of the evaluations can be improved by considering the interrelationships between companies. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use AI to analyze transaction data, competition data, and cooperation data in order to evaluate the interrelationships between companies and improve the accuracy of the evaluation.

[0049] The evaluation department can consider the attribute information of the company's applicants when conducting evaluations. For example, the evaluation department can consider the applicant's position and experience when conducting evaluations. For example, the evaluation department can analyze the applicant's attribute data and conduct evaluations considering their position and experience. The evaluation department can also consider the applicant's past performance when conducting evaluations. For example, the evaluation department can analyze the applicant's performance data and conduct evaluations considering their past performance. Furthermore, the evaluation department can also consider the applicant's expertise when conducting evaluations. For example, the evaluation department can analyze the applicant's knowledge data and conduct evaluations considering their expertise. This improves the accuracy of evaluations by considering the attribute information of the company's applicants. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can use AI to analyze position, experience, performance, and knowledge data in order to evaluate the applicant's attribute information and improve the accuracy of the evaluation.

[0050] The evaluation unit can consider the geographical distribution of companies when conducting evaluations. For example, if a company's location is close to an important market, the evaluation unit can consider that information when conducting evaluations. For example, the evaluation unit can analyze the company's geographical data and consider that information if it is close to an important market when conducting evaluations. The evaluation unit can also consider the information if a company's location is close to a competitor when conducting evaluations. For example, the evaluation unit can analyze the company's geographical data and consider that information if it is close to a competitor when conducting evaluations. Furthermore, the evaluation unit can also consider the information if a company's location is in an economically important region when conducting evaluations. For example, the evaluation unit can analyze the company's geographical data and consider that information if it is in an economically important region when conducting evaluations. By considering the geographical distribution of companies, the accuracy of the evaluation can be improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can use AI to analyze geographical data in order to evaluate the geographical distribution of companies and improve the accuracy of the evaluation.

[0051] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the company during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to literature on the company's past performance. For example, the evaluation unit can improve the accuracy of its evaluation by analyzing the company's performance data and referring to literature on past performance. The evaluation unit can also improve the accuracy of its evaluation by referring to literature on the company's technological innovations. For example, the evaluation unit can improve the accuracy of its evaluation by analyzing the company's technology data and referring to literature on technological innovations. Furthermore, the evaluation unit can improve the accuracy of its evaluation by referring to literature on the company's market trends. For example, the evaluation unit can improve the accuracy of its evaluation by analyzing the company's market data and referring to literature on market trends. In this way, the accuracy of the evaluation can be improved by referring to relevant literature on the company. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use AI to analyze literature data in order to refer to relevant literature on the company and improve the accuracy of its evaluation.

[0052] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has used in the past. For example, the service provider can analyze the user's operation history data and prioritize providing display methods that the user has used in the past. The service provider can also propose the optimal display method based on the user's past operation history. For example, the service provider can analyze the user's operation history data and propose the optimal display method. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. For example, the service provider can analyze the user's operation history data and provide the most efficient display method. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use AI to analyze data in order to analyze the user's operation history and select the optimal display method.

[0053] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, the service provider can analyze the user's device information and, if a smartphone is being used, provide a display method that matches the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. For example, the service provider can analyze the user's device information and, if a tablet is being used, provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. For example, the service provider can analyze the user's device information and, if a smartwatch is being used, provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use AI to analyze device data in order to analyze the user's device information and select the optimal display method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The automated corporate credit assessment system may also include a forecasting unit. This forecasting unit can predict future corporate performance and market trends based on collected corporate information, surrounding information, and current social conditions. For example, it can analyze a company's financial data to predict future revenue and growth rates. It can also predict fluctuations in a company's market share based on the actions of competitors. Furthermore, it can predict corporate risks by considering economic and political conditions. This allows the forecasting unit to help in the development of sales strategies by predicting future corporate performance and market trends. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For instance, the forecasting unit can use AI to analyze data and build a forecasting model to predict a company's future performance.

[0056] The automated corporate review system may also include a notification unit. The notification unit can notify users of important information based on collected information and analysis results. For example, the notification unit can notify users if a company's financial situation changes rapidly. It can also notify users of important information regarding the activities of competitors. Furthermore, the notification unit can notify users of changes in economic and political conditions. This allows the notification unit to quickly communicate important information to users, enabling rapid responses in sales activities. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit may use AI to evaluate the importance of information and determine notification priorities in order to notify important information.

[0057] The automated corporate review system may also include a feedback unit. The feedback unit can collect user feedback and use it to improve the system. For example, the feedback unit can evaluate the information provided by the user. It can also receive user feedback on the system's usability. Furthermore, it can receive user requests regarding the system's functionality. In this way, the feedback unit can use user feedback to improve the system. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit may use AI to analyze text data and extract areas for improvement in order to analyze user feedback.

[0058] The automated corporate credit screening system may also include a learning unit. The learning unit can learn to improve the system's accuracy based on collected information and analysis results. For example, the learning unit can compare past corporate information with subsequent performance to improve the accuracy of the predictive model. The learning unit can also learn to improve the system's usability based on user feedback. Furthermore, the learning unit can incorporate new data to improve the system's analytical capabilities. This allows the learning unit to achieve more accurate corporate credit screening by learning to improve the system's accuracy. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use AI to analyze data and update the learning model to improve the system's accuracy.

[0059] The automated corporate screening system may also include a customization section. This customization section can customize system settings according to user needs. For example, the customization section can set the types of corporate information the user is interested in. It can also set the frequency of notifications the user desires. Furthermore, the customization section can adjust the display method depending on the device the user is using. This allows the customization section to provide a more user-friendly system by customizing system settings according to user needs. Some or all of the above-described processes in the customization section may be performed using AI, for example, or without AI. For example, the customization section may use AI to analyze the user's operation history and suggest optimal settings in order to understand user needs.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects company information. This information includes financial information, performance data, and transaction history. For example, it can collect information such as revenue, liabilities, assets, sales, profit margins, growth rates, trading partners, transaction amounts, and transaction frequency. Step 2: The analysis department analyzes the collected company information. The analysis is carried out using methods such as statistical analysis, trend analysis, and risk assessment. For example, statistical analysis can be used to evaluate the company's financial condition, trend analysis can be used to evaluate fluctuations in the company's performance, and risk assessment can be used to evaluate the company's transaction history. Step 3: The evaluation department assesses the reliability and future potential of the company based on the analysis results. Reliability is assessed using criteria such as credit scores and past performance, while future potential is assessed using criteria such as growth forecasts and market trends. For example, a company's reliability can be assessed using credit scores, and its future potential can be assessed using growth forecasts. Step 4: The delivery department provides the evaluation results to the sales representative. This delivery can be done through methods such as customer visits, presentations, or contract negotiations. For example, the evaluation results can be provided through customer visits, presentations, or contract negotiations.

[0062] (Example of form 2) An automated corporate credit assessment system according to an embodiment of the present invention is a mechanism that automates corporate credit assessment using AI. This automated corporate credit assessment system can be used in sales activities by collecting, analyzing, and evaluating corporate information, surrounding information, and social conditions. This mechanism improves the quality and speed of corporate credit assessment and streamlines sales activities. For example, the automated corporate credit assessment system collects corporate information. Corporate information includes the company's financial status, performance, and transaction history. Next, the automated corporate credit assessment system collects surrounding information. Surrounding information includes the economic conditions of the region where the company is located and the activities of competitors. Furthermore, the automated corporate credit assessment system collects social conditions. Social conditions include the current economic situation, political situation, and social trends. By analyzing this information, the automated corporate credit assessment system can evaluate the reliability and future potential of a company. For example, if a company's financial situation is sound and it has an advantage over competitors, it is recommended to enter into a contract with that company. Also, by evaluating the impact of the current economic situation and political situation on the company's performance, risks can be minimized. By utilizing the analysis results obtained in this way in sales activities, sales representatives can proceed with concluding contracts with companies more efficiently. For example, prioritizing contracts with highly reliable companies can improve the success rate of sales activities. Furthermore, avoiding contracts with high-risk companies can reduce business risks. This allows automated company screening systems to improve the quality and speed of company screening, thereby increasing the efficiency of sales activities. By utilizing AI, the effort previously required for manual company screening can be significantly reduced. Additionally, sales activities based on AI analysis results can accelerate company growth. In this way, automated company screening systems can efficiently collect, analyze, evaluate, and provide company information.

[0063] The automated corporate screening system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects corporate information. Corporate information includes, but is not limited to, financial information, performance data, and transaction history. The collection unit collects, for example, a company's financial status. For example, it can collect information such as revenue, liabilities, and assets. The collection unit can also collect a company's performance. For example, it can collect information such as sales, profit margins, and growth rates. Furthermore, the collection unit can collect a company's transaction history. For example, it can collect information such as trading partners, transaction amounts, and transaction frequency. The analysis unit analyzes the collected corporate information. The analysis is performed by, for example, statistical analysis, trend analysis, and risk assessment, but is not limited to these methods. For example, the analysis unit can use statistical analysis to evaluate a company's financial status. The analysis unit can also use trend analysis to evaluate fluctuations in a company's performance. Furthermore, the analysis unit can use risk assessment to evaluate a company's transaction history. The evaluation unit evaluates the reliability and future potential of a company based on the analysis results. Reliability assessments are conducted using criteria such as credit scores and past performance, but are not limited to these examples. For example, the evaluation department can assess a company's reliability using credit scores. The evaluation department can also assess a company's reliability using past performance. Future potential assessments are conducted using criteria such as growth forecasts and market trends, but are not limited to these examples. For example, the evaluation department can assess a company's future potential using growth forecasts. The evaluation department can also assess a company's future potential using market trends. The delivery department provides the evaluation results to sales representatives. Delivery is carried out by methods such as customer visits, presentations, and contract negotiations, but is not limited to these examples. For example, the delivery department can provide evaluation results through customer visits. The delivery department can also provide evaluation results through presentations. Furthermore, the delivery department can also provide evaluation results through contract negotiations. This enables the automated corporate screening system to efficiently collect, analyze, evaluate, and provide corporate information.

[0064] The data collection unit collects corporate information. Corporate information includes, but is not limited to, financial information, performance data, and transaction history. For example, the data collection unit collects information on a company's financial status. Specifically, it can collect information on revenue, liabilities, and assets. Revenue information includes sales, operating profit, and net profit, and this data is important for evaluating a company's profitability. Liability information includes short-term liabilities, long-term liabilities, and total liabilities, and is used to evaluate a company's financial health. Asset information includes current assets, fixed assets, and total assets, and is important for understanding a company's asset structure. The data collection unit can also collect information on a company's performance. For example, it can collect information on sales, profit margins, and growth rates. Sales indicate a company's market performance, and profit margins are indicators of profitability. Growth rates are used to evaluate a company's growth potential. Furthermore, the data collection unit can also collect a company's transaction history. For example, it can collect information on trading partners, transaction amounts, and transaction frequency. Information on trading partners is important for evaluating the reliability of a company's business partners and customers, and transaction amounts indicate the scale of a company's transactions. Transaction frequency is an indicator of the activity level of a company's trading activities. This allows the data collection unit to comprehensively gather diverse corporate information and grasp the overall picture of the company. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and evaluation departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0065] The analysis department analyzes collected corporate information. Analysis is carried out using methods such as statistical analysis, trend analysis, and risk assessment, but is not limited to these examples. Specifically, statistical analysis can be used to evaluate a company's financial condition. Statistical analysis uses data such as revenue, liabilities, and assets to assess a company's financial health and profitability. For example, analyzing trends in revenue increases and decreases can assess a company's growth potential. Furthermore, debt ratios and equity ratios can be calculated to assess a company's financial health. Trend analysis can also be used to evaluate fluctuations in a company's performance. Trend analysis uses data such as sales, profit margins, and growth rates to understand trends in a company's performance. For example, analyzing sales trends over the past few years can assess a company's growth trend. It can also analyze fluctuations in profit margins to identify factors influencing a company's profitability. Finally, risk assessment can be used to evaluate a company's transaction history. Risk assessment uses data such as trading partners, transaction amounts, and transaction frequency to assess a company's transaction risks. For example, it can assess the credit risk of trading partners to ensure transaction safety. It can also analyze fluctuations in transaction amounts and frequency to identify factors influencing transaction risks. This allows the analytics department to quickly and accurately analyze collected data and comprehensively evaluate a company's financial condition, performance, and transaction risks. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical performance data, it can predict fluctuations in risks in specific industries or markets and formulate future countermeasures. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0066] The evaluation department assesses a company's reliability and future potential based on the analysis results. Reliability assessment is conducted using criteria such as credit scores and past performance, but is not limited to these examples. Specifically, a company's reliability can be assessed using credit scores. Credit scores are calculated based on a company's financial condition and transaction history and are an indicator for assessing a company's credit risk. For example, a company's credit score is calculated considering factors such as the stability of earnings, the debt ratio, and the credit risk of trading partners. Furthermore, a company's reliability can also be assessed using past performance. Past performance includes trends in sales, fluctuations in profit margins, and the stability of transaction history, and a company's reliability is assessed based on this data. For example, the growth potential of a company can be assessed by analyzing the trend of sales increases and decreases over the past few years. Also, the stability of a company's profitability can be assessed by analyzing fluctuations in profit margins. Future potential assessment is conducted using criteria such as growth forecasts and market trends, but is not limited to these examples. Specifically, a company's future potential can be assessed using growth forecasts. Growth forecasts are calculated based on past performance data and market trends and are an indicator for assessing a company's future growth potential. For example, a company's growth forecast is calculated considering past sales trends and market growth trends. Furthermore, market trends can be used to evaluate a company's future prospects. Market trends include industry growth trends, competitor activity, and changes in the economic environment, and these data are used to assess a company's future potential. For example, industry growth trends can be analyzed to evaluate a company's market position. Competitor activity can also be analyzed to assess a company's competitiveness. This allows the evaluation department to comprehensively assess a company's reliability and future prospects based on the analysis results, and to clearly understand the company's risks and potential.

[0067] The service department provides evaluation results to sales representatives. This provision may be carried out through methods such as customer visits, presentations, and contract negotiations, but is not limited to these examples. Specifically, evaluation results can be provided through customer visits. Sales representatives explain the evaluation results during customer visits and provide information about the company's reliability and future prospects. Evaluation results can also be provided through presentations. Sales representatives use presentation materials to explain the evaluation results and provide information about the company's strengths and risks. Furthermore, evaluation results can also be provided through contract negotiations. Sales representatives make proposals based on the evaluation results during contract negotiations and provide information about the company's reliability and future prospects. This allows the service department to provide evaluation results to sales representatives quickly and effectively, supporting their sales activities. In addition, the service department can collect feedback based on the evaluation results and continuously improve the accuracy and effectiveness of the evaluation process. For example, evaluation criteria and evaluation methods can be revised based on feedback from sales representatives. The service department can also reliably transmit information using multiple communication methods. For example, evaluation results can be shared and information quickly transmitted using email and chat tools. Remote presentations and contract negotiations can also be conducted using online conferencing systems. This allows the service department to provide evaluation results to sales representatives quickly and reliably, effectively supporting their sales activities.

[0068] The data collection unit can collect corporate information such as a company's financial status, performance, and transaction history. For example, the data collection unit can collect a company's financial status, such as revenue, liabilities, and assets. The data collection unit can also collect a company's performance, such as sales, profit margins, and growth rates. Furthermore, the data collection unit can collect a company's transaction history, such as trading partners, transaction amounts, and transaction frequency. By collecting information such as a company's financial status and performance, it is possible to obtain basic data for evaluating a company's reliability and future potential. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze financial data and extract information such as revenue, liabilities, and assets in order to collect a company's financial status.

[0069] The data collection unit can collect peripheral information such as the economic conditions of the region where the company is located and the activities of its competitors. For example, the data collection unit can collect information on the economic conditions of the region where the company is located, such as GDP growth rate, unemployment rate, and inflation rate. The data collection unit can also collect information on the activities of its competitors, such as market share, product lineup, and pricing strategy. By collecting peripheral information about the company, supplementary data can be obtained to evaluate the company's reliability and future prospects. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to analyze economic data in order to collect information on the economic conditions of the region where the company is located, and extract information such as GDP growth rate, unemployment rate, and inflation rate.

[0070] The data collection unit can collect information on current economic conditions, political situations, and social trends. For example, it can collect information on current economic conditions, such as economic growth rates, unemployment rates, and inflation rates. It can also collect information on political situations, such as policy changes, changes in government, and international relations. Furthermore, it can collect information on social trends, such as changes in consumer behavior, technological innovations, and cultural shifts. By collecting this information on current conditions, background information can be obtained to evaluate the reliability and future potential of a company. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, to collect information on current economic conditions, the data collection unit can use AI to analyze economic data and extract information such as economic growth rates, unemployment rates, and inflation rates.

[0071] The analysis department can analyze collected company information, surrounding information, and current social trends. For example, the analysis department can analyze company information using data mining. For instance, it can analyze a company's financial data and extract information such as revenue, liabilities, and assets. The analysis department can also analyze surrounding information using machine learning. For example, it can analyze the trends of competitors and extract information such as market share, product lineup, and pricing strategies. Furthermore, the analysis department can analyze current social trends using statistical analysis. For example, it can analyze information such as economic growth rate, unemployment rate, and inflation rate to evaluate the economic situation. By analyzing the collected information, detailed data can be obtained to evaluate the reliability and future potential of companies. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to perform data mining in order to analyze company information and extract information such as revenue, liabilities, and assets.

[0072] The evaluation department can assess a company's reliability and future potential based on the analysis results. For example, the evaluation department can assess a company's reliability using a credit score. For example, it can calculate a credit score based on a company's financial data and assess its reliability. The evaluation department can also assess a company's reliability using past performance. For example, it can evaluate past performance based on a company's transaction history and assess its reliability. Furthermore, the evaluation department can assess a company's future potential using growth forecasts. For example, it can make growth forecasts based on a company's performance data and assess its future potential. The evaluation department can also assess a company's future potential using market trends. For example, it can evaluate market trends based on the actions of competitors and assess its future potential. This allows for increased efficiency in sales activities by evaluating a company's reliability and future potential based on the analysis results. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can use AI to calculate a credit score in order to assess a company's reliability and then assess the company's reliability.

[0073] The service provider can provide evaluation results to sales representatives. For example, the service provider can provide evaluation results through customer visits. For example, a sales representative can visit a customer and explain the evaluation results. The service provider can also provide evaluation results through presentations. For example, a sales representative can give a presentation and explain the evaluation results. Furthermore, the service provider can also provide evaluation results through contract negotiations. For example, a sales representative can conduct contract negotiations and explain the evaluation results. By providing evaluation results to sales representatives, the efficiency of sales activities can be improved. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to automatically generate evaluation results in order to provide them to sales representatives and then provide them to sales representatives.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the data collection to reduce the user's burden. For example, the data collection unit can monitor the user's emotions in real time and adjust the data collection timing if the user is stressed. The data collection unit can also accelerate the data collection timing to provide information quickly if the user is relaxed. For example, the data collection unit can monitor the user's emotions in real time and adjust the data collection timing if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can set the data collection timing immediately and collect information quickly. For example, the data collection unit can monitor the user's emotions in real time and adjust the data collection timing if the user is in a hurry. In this way, the user's burden can be reduced by adjusting the data collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0075] The data collection unit can analyze a company's past financial condition and performance and select the optimal data collection method. For example, if a company's past financial condition is stable, the data collection unit can select a periodic collection method. For example, the data collection unit can analyze a company's financial data and select a periodic collection method if it is stable. The data collection unit can also select a method to collect information in real time if a company's performance is fluctuating. For example, the data collection unit can analyze a company's performance data and select a method to collect information in real time if it is fluctuating. Furthermore, if a company's financial condition is deteriorating, the data collection unit can select a method to collect detailed information. For example, the data collection unit can analyze a company's financial data and select a method to collect detailed information if it is deteriorating. This allows the optimal data collection method to be selected by analyzing a company's past financial condition and performance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze financial data in order to analyze a company's financial condition and select the optimal data collection method.

[0076] The data collection unit can filter company information based on the company's current projects and market trends. For example, if a company's current projects are successful, the data collection unit can prioritize collecting that information. For example, the data collection unit can analyze company project data and prioritize collecting information if the projects are successful. The data collection unit can also prioritize collecting information if market trends are favorable to the company. For example, the data collection unit can analyze market trend data and prioritize collecting information if the market trends are favorable. Furthermore, the data collection unit can filter and collect information if a company's projects are failing. For example, the data collection unit can analyze company project data and filter and collect information if the projects are failing. This allows for the collection of highly relevant information by filtering information based on the company's current projects and market trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to evaluate the success rate of projects and filter the information in order to analyze company project data.

[0077] The data collection unit can estimate the user's emotions and prioritize the company information to collect based on those emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. For example, the data collection unit can monitor the user's emotions in real time and prioritize collecting information of high importance if the user is stressed. The data collection unit can also prioritize collecting detailed information if the user is relaxed. For example, the data collection unit can monitor the user's emotions in real time and prioritize collecting detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that can be collected quickly. For example, the data collection unit can monitor the user's emotions in real time and prioritize collecting information that can be collected quickly if the user is in a hurry. This allows for information collection that meets the user's needs by prioritizing the company information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies when collecting company information. For example, if a company is located near an important market, the data collection unit can prioritize the collection of that information. For example, the data collection unit can analyze a company's geographical location and prioritize the collection of that information if it is close to an important market. The data collection unit can also prioritize the collection of information if a company is located near a competitor. For example, the data collection unit can analyze a company's geographical location and prioritize the collection of that information if it is close to a competitor. Furthermore, the data collection unit can prioritize the collection of information if a company is located in an economically important region. For example, the data collection unit can analyze a company's geographical location and prioritize the collection of that information if it is located in an economically important region. By collecting information while considering the geographical location of companies, highly relevant information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze geographic data in order to analyze a company's geographical location and prioritize the collection of highly relevant information.

[0079] The data collection unit can analyze a company's social media activities and collect relevant information when collecting company information. For example, if a company is very active on social media, the data collection unit can prioritize collecting that information. For example, the data collection unit can analyze a company's social media data and prioritize collecting information if the activity is high. The data collection unit can also prioritize collecting information if a company has a good reputation on social media. For example, the data collection unit can analyze a company's social media data and prioritize collecting information if the reputation is good. Furthermore, if a company is inactive on social media, the data collection unit can filter and collect that information. For example, the data collection unit can analyze a company's social media data and filter and collect information if the activity is low. In this way, relevant information can be collected by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to extract information such as post content, engagement rate, and follower count in order to analyze a company's social media data and collect relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-understand presentation. For example, the analysis unit can monitor the user's emotions in real time and provide a simple and easy-to-understand presentation if the user is nervous. The analysis unit can also provide a presentation that includes detailed information if the user is relaxed. For example, the analysis unit can monitor the user's emotions in real time and provide a presentation that includes detailed information if the user is relaxed. Furthermore, the analysis unit can provide a concise presentation if the user is in a hurry. For example, the analysis unit can monitor the user's emotions in real time and provide a concise presentation if the user is in a hurry. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0081] The analysis department can adjust the level of detail of its analysis based on the importance of the company information. For example, the analysis department can perform a detailed analysis of company information that is of high importance. For instance, it can analyze a company's financial data and perform a detailed analysis of information that is of high importance. The analysis department can also perform a simplified analysis of company information that is of low importance. For example, it can analyze a company's performance data and perform a simplified analysis of information that is of low importance. Furthermore, the analysis department can adjust the level of detail of its analysis in stages according to the importance of the company information. For example, it can analyze a company's transaction history and adjust the level of detail of its analysis in stages according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the company information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze data in order to evaluate the importance of company information and adjust the level of detail of the analysis based on its importance.

[0082] The analysis department can apply different analysis algorithms depending on the company category during analysis. For example, the analysis department can apply an analysis algorithm specialized in manufacturing processes to manufacturing companies. For example, the analysis department can analyze data from manufacturing companies and apply an analysis algorithm specialized in manufacturing processes. The analysis department can also apply an analysis algorithm specialized in customer satisfaction to service companies. For example, the analysis department can analyze data from service companies and apply an analysis algorithm specialized in customer satisfaction. Furthermore, the analysis department can apply an analysis algorithm specialized in technological innovation to IT companies. For example, the analysis department can analyze data from IT companies and apply an analysis algorithm specialized in technological innovation. By applying different analysis algorithms depending on the company category, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can use AI to analyze data and select the optimal algorithm in order to apply different analysis algorithms depending on the company category.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. For example, the analysis unit can monitor the user's emotions in real time and provide a short, to-the-point analysis if the user is in a hurry. The analysis unit can also provide a longer analysis with detailed explanations if the user is relaxed. For example, the analysis unit can monitor the user's emotions in real time and provide a longer analysis with detailed explanations if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit can monitor the user's emotions in real time and provide an analysis with visually stimulating effects if the user is excited. By adjusting the length of the analysis according to the user's emotions, the analysis results can be provided that meet the user's needs. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0084] The analysis department can determine the priority of analysis based on the submission date of company information. For example, the analysis department can prioritize the analysis of the most recent company information. For example, the analysis department can evaluate the submission date of company information and prioritize the analysis of the most recent information. The analysis department can also postpone the analysis of older company information. For example, the analysis department can evaluate the submission date of company information and postpone the analysis of older information. Furthermore, the analysis department can adjust the priority of analysis in stages according to the submission date. For example, the analysis department can evaluate the submission date of company information and adjust the priority of analysis in stages according to the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of company information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze data in order to evaluate the submission date of company information and determine the priority of analysis based on the submission date.

[0085] The analysis department can adjust the order of analysis based on the relevance of company information during the analysis process. For example, the analysis department can prioritize the analysis of company information that is highly relevant. For example, the analysis department can evaluate the relevance of company information and prioritize the analysis of information that is highly relevant. The analysis department can also postpone the analysis of company information that is less relevant. For example, the analysis department can evaluate the relevance of company information and postpone the analysis of information that is less relevant. Furthermore, the analysis department can adjust the order of analysis in stages according to the relevance of company information. For example, the analysis department can evaluate the relevance of company information and adjust the order of analysis in stages according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of company information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze data in order to evaluate the relevance of company information and adjust the order of analysis based on the relevance.

[0086] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide simple and easy-to-understand evaluation criteria. For example, the evaluation unit can monitor the user's emotions in real time and provide simple and easy-to-understand evaluation criteria if the user is nervous. The evaluation unit can also provide evaluation criteria that include detailed information if the user is relaxed. For example, the evaluation unit can monitor the user's emotions in real time and provide evaluation criteria that include detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can provide concise evaluation criteria. For example, the evaluation unit can monitor the user's emotions in real time and provide concise evaluation criteria if the user is in a hurry. By adjusting the evaluation criteria according to the user's emotions, it is possible to provide evaluation results that are easy for the user to understand. 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-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0087] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between companies. For example, the evaluation unit can improve the accuracy of its evaluations by considering the transaction history between companies. For example, the evaluation unit can analyze company transaction data and improve the accuracy of its evaluations by considering the transaction history. The evaluation unit can also improve the accuracy of its evaluations by considering the competitive situation between companies. For example, the evaluation unit can analyze company competition data and improve the accuracy of its evaluations by considering the competitive situation. Furthermore, the evaluation unit can also improve the accuracy of its evaluations by considering cooperative relationships between companies. For example, the evaluation unit can analyze company cooperation data and improve the accuracy of its evaluations by considering cooperative relationships. In this way, the accuracy of the evaluations can be improved by considering the interrelationships between companies. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use AI to analyze transaction data, competition data, and cooperation data in order to evaluate the interrelationships between companies and improve the accuracy of the evaluation.

[0088] The evaluation department can consider the attribute information of the company's applicants when conducting evaluations. For example, the evaluation department can consider the applicant's position and experience when conducting evaluations. For example, the evaluation department can analyze the applicant's attribute data and conduct evaluations considering their position and experience. The evaluation department can also consider the applicant's past performance when conducting evaluations. For example, the evaluation department can analyze the applicant's performance data and conduct evaluations considering their past performance. Furthermore, the evaluation department can also consider the applicant's expertise when conducting evaluations. For example, the evaluation department can analyze the applicant's knowledge data and conduct evaluations considering their expertise. This improves the accuracy of evaluations by considering the attribute information of the company's applicants. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can use AI to analyze position, experience, performance, and knowledge data in order to evaluate the applicant's attribute information and improve the accuracy of the evaluation.

[0089] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated emotions. For example, if the user is nervous, the evaluation unit can display important results first. For example, the evaluation unit can monitor the user's emotions in real time and display important results first if the user is nervous. The evaluation unit can also display detailed results first if the user is relaxed. For example, the evaluation unit can monitor the user's emotions in real time and display detailed results first if the user is relaxed. Furthermore, if the evaluation unit is in a hurry, it can display concise results first. For example, the evaluation unit can monitor the user's emotions in real time and display concise results first if the user is in a hurry. By adjusting the order in which the evaluation results are displayed according to the user's emotions, the evaluation results can be provided that are easy for the user to understand. 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-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0090] The evaluation unit can consider the geographical distribution of companies when conducting evaluations. For example, if a company's location is close to an important market, the evaluation unit can consider that information when conducting evaluations. For example, the evaluation unit can analyze the company's geographical data and consider that information if it is close to an important market when conducting evaluations. The evaluation unit can also consider the information if a company's location is close to a competitor when conducting evaluations. For example, the evaluation unit can analyze the company's geographical data and consider that information if it is close to a competitor when conducting evaluations. Furthermore, the evaluation unit can also consider the information if a company's location is in an economically important region when conducting evaluations. For example, the evaluation unit can analyze the company's geographical data and consider that information if it is in an economically important region when conducting evaluations. By considering the geographical distribution of companies, the accuracy of the evaluation can be improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can use AI to analyze geographical data in order to evaluate the geographical distribution of companies and improve the accuracy of the evaluation.

[0091] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the company during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to literature on the company's past performance. For example, the evaluation unit can improve the accuracy of its evaluation by analyzing the company's performance data and referring to literature on past performance. The evaluation unit can also improve the accuracy of its evaluation by referring to literature on the company's technological innovations. For example, the evaluation unit can improve the accuracy of its evaluation by analyzing the company's technology data and referring to literature on technological innovations. Furthermore, the evaluation unit can improve the accuracy of its evaluation by referring to literature on the company's market trends. For example, the evaluation unit can improve the accuracy of its evaluation by analyzing the company's market data and referring to literature on market trends. In this way, the accuracy of the evaluation can be improved by referring to relevant literature on the company. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use AI to analyze literature data in order to refer to relevant literature on the company and improve the accuracy of its evaluation.

[0092] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and easy-to-read display. For example, the service provider can monitor the user's emotions in real time and provide a simple and easy-to-read display if the user is nervous. The service provider can also provide a display that includes detailed information if the user is relaxed. For example, the service provider can monitor the user's emotions in real time and provide a display that includes detailed information if the user is relaxed. Furthermore, the service provider can provide a concise display if the user is in a hurry. For example, the service provider can monitor the user's emotions in real time and provide a concise display if the user is in a hurry. By adjusting the way information is displayed according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has used in the past. For example, the service provider can analyze the user's operation history data and prioritize providing display methods that the user has used in the past. The service provider can also propose the optimal display method based on the user's past operation history. For example, the service provider can analyze the user's operation history data and propose the optimal display method. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. For example, the service provider can analyze the user's operation history data and provide the most efficient display method. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use AI to analyze data in order to analyze the user's operation history and select the optimal display method.

[0094] The service provider can estimate the user's emotions and adjust the operation procedures for the information provided based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and easy-to-understand operation procedures. For example, the service provider can monitor the user's emotions in real time and provide simple and easy-to-understand operation procedures if the user is nervous. The service provider can also provide detailed operation procedures if the user is relaxed. For example, the service provider can monitor the user's emotions in real time and provide detailed operation procedures if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can provide procedures that can be operated quickly. For example, the service provider can monitor the user's emotions in real time and provide procedures that can be operated quickly if the user is in a hurry. In this way, by adjusting the operation procedures for information according to the user's emotions, it is possible to provide user-friendly operation procedures. 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 processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0095] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, the service provider can analyze the user's device information and, if a smartphone is being used, provide a display method that matches the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. For example, the service provider can analyze the user's device information and, if a tablet is being used, provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. For example, the service provider can analyze the user's device information and, if a smartwatch is being used, provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use AI to analyze device data in order to analyze the user's device information and select the optimal display method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The automated corporate credit assessment system may also include a forecasting unit. This forecasting unit can predict future corporate performance and market trends based on collected corporate information, surrounding information, and current social conditions. For example, it can analyze a company's financial data to predict future revenue and growth rates. It can also predict fluctuations in a company's market share based on the actions of competitors. Furthermore, it can predict corporate risks by considering economic and political conditions. This allows the forecasting unit to help in the development of sales strategies by predicting future corporate performance and market trends. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For instance, the forecasting unit can use AI to analyze data and build a forecasting model to predict a company's future performance.

[0098] The automated corporate review system may also include a notification unit. The notification unit can notify users of important information based on collected information and analysis results. For example, the notification unit can notify users if a company's financial situation changes rapidly. It can also notify users of important information regarding the activities of competitors. Furthermore, the notification unit can notify users of changes in economic and political conditions. This allows the notification unit to quickly communicate important information to users, enabling rapid responses in sales activities. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit may use AI to evaluate the importance of information and determine notification priorities in order to notify important information.

[0099] The automated corporate review system may also include a feedback unit. The feedback unit can collect user feedback and use it to improve the system. For example, the feedback unit can evaluate the information provided by the user. It can also receive user feedback on the system's usability. Furthermore, it can receive user requests regarding the system's functionality. In this way, the feedback unit can use user feedback to improve the system. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit may use AI to analyze text data and extract areas for improvement in order to analyze user feedback.

[0100] The automated corporate credit screening system may also include a learning unit. The learning unit can learn to improve the system's accuracy based on collected information and analysis results. For example, the learning unit can compare past corporate information with subsequent performance to improve the accuracy of the predictive model. The learning unit can also learn to improve the system's usability based on user feedback. Furthermore, the learning unit can incorporate new data to improve the system's analytical capabilities. This allows the learning unit to achieve more accurate corporate credit screening by learning to improve the system's accuracy. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can use AI to analyze data and update the learning model to improve the system's accuracy.

[0101] The automated corporate screening system may also include a customization section. This customization section can customize system settings according to user needs. For example, the customization section can set the types of corporate information the user is interested in. It can also set the frequency of notifications the user desires. Furthermore, the customization section can adjust the display method depending on the device the user is using. This allows the customization section to provide a more user-friendly system by customizing system settings according to user needs. Some or all of the above-described processes in the customization section may be performed using AI, for example, or without AI. For example, the customization section may use AI to analyze the user's operation history and suggest optimal settings in order to understand user needs.

[0102] The automated corporate review system may further include an emotion estimation unit. The emotion estimation unit can estimate the user's emotions and adjust the system's operation based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can slow down the system's operation to reduce the user's burden. Conversely, if the user is relaxed, the emotion estimation unit can speed up the system's operation to provide information efficiently. Furthermore, if the user is in a hurry, the emotion estimation unit can instantly set the system's operation to provide information quickly. In this way, the emotion estimation unit can reduce the user's burden and achieve efficient information delivery by adjusting the system's operation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described processing in the emotion estimation unit may be performed using AI, or not. For example, the emotion estimation unit can use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0103] The automated corporate review system may further include an emotional feedback unit. This emotional feedback unit can estimate the user's emotions and provide feedback based on those emotions. For example, if the user is stressed, it can offer advice on how to relax. If the user is relaxed, it can also suggest more efficient work methods. Furthermore, if the user is in a hurry, it can provide hints for speeding up the work. In this way, the emotional feedback unit can improve the user's work efficiency by providing feedback according to their 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 above-described processes in the emotional feedback unit may be performed using AI, or not. For example, the emotional feedback unit can use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0104] The automated corporate review system may also include an emotion monitoring unit. The emotion monitoring unit can monitor the user's emotions in real time and adjust the system's operation accordingly. For example, if the user is stressed, the emotion monitoring unit can slow down the system's operation to reduce the user's burden. Conversely, if the user is relaxed, the emotion monitoring unit can speed up the system's operation to provide information efficiently. Furthermore, if the user is in a hurry, the emotion monitoring unit can instantly adjust the system's operation to provide information quickly. In this way, the emotion monitoring unit can reduce the user's burden and achieve efficient information delivery by adjusting the system's operation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 above-described processes in the emotion monitoring unit may be performed using AI, or not. For example, the emotion monitoring unit can use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0105] The automated corporate review system may also include a sentiment analysis unit. The sentiment analysis unit can analyze the user's emotions and optimize the system's operation. For example, if the user is stressed, the sentiment analysis unit can slow down the system's operation to reduce the user's burden. Conversely, if the user is relaxed, the sentiment analysis unit can speed up the system's operation to provide information efficiently. Furthermore, if the user is in a hurry, the sentiment analysis unit can instantly configure the system's operation to provide information quickly. In this way, the sentiment analysis unit can reduce the user's burden and achieve efficient information delivery by optimizing the system's operation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the sentiment analysis unit may be performed using AI, or not. For example, the sentiment analysis unit can use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0106] The automated corporate review system may further include an emotion adaptation unit. This unit can estimate the user's emotions and adjust the system interface based on the estimated emotions. For example, if the user is stressed, the emotion adaptation unit can provide a simple and highly visible interface. If the user is relaxed, it can also provide an interface with detailed information. Furthermore, if the user is in a hurry, it can provide a quickly operable interface. Thus, by adjusting the system interface according to the user's emotions, the emotion adaptation unit can provide a user-friendly interface. Emotion estimation is achieved using an emotion estimation function, for example, 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 above-described processing in the emotion adaptation unit may be performed using AI, or not. For example, the emotion adaptation unit can use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects company information. This information includes financial information, performance data, and transaction history. For example, it can collect information such as revenue, liabilities, assets, sales, profit margins, growth rates, trading partners, transaction amounts, and transaction frequency. Step 2: The analysis department analyzes the collected company information. The analysis is carried out using methods such as statistical analysis, trend analysis, and risk assessment. For example, statistical analysis can be used to evaluate the company's financial condition, trend analysis can be used to evaluate fluctuations in the company's performance, and risk assessment can be used to evaluate the company's transaction history. Step 3: The evaluation department assesses the reliability and future potential of the company based on the analysis results. Reliability is assessed using criteria such as credit scores and past performance, while future potential is assessed using criteria such as growth forecasts and market trends. For example, a company's reliability can be assessed using credit scores, and its future potential can be assessed using growth forecasts. Step 4: The delivery department provides the evaluation results to the sales representative. This delivery can be done through methods such as customer visits, presentations, or contract negotiations. For example, the evaluation results can be provided through customer visits, presentations, or contract negotiations.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects corporate information using the camera 42 and microphone 38B of the smart device 14 and adjusts the collection timing with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected corporate information. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability and future potential of the company based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the evaluation results to the sales representative. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects company information using the camera 42 and microphone 238 of the smart glasses 214 and adjusts the collection timing with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected company information. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and evaluates the reliability and future potential of the company based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides the evaluation results to the sales representative. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects company information using the camera 42 and microphone 238 of the headset terminal 314 and adjusts the collection timing by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected company information. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability and future potential of the company based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the evaluation results to the sales representative. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects corporate information using the camera 42 and microphone 238 of the robot 414 and adjusts the collection timing by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected corporate information. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability and future potential of the company based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the evaluation results to the sales representative. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The collection department collects corporate information, An analysis unit analyzes the corporate information collected by the aforementioned collection unit, An evaluation unit that evaluates the reliability and future potential of a company based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides the evaluation results obtained by the evaluation unit to sales activities. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect company information such as the company's financial status, performance, and transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The company gathers information about the surrounding area, such as the economic conditions of the region where it is located and the activities of its competitors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Gather information on current economic conditions, political situations, social trends, and other current social issues. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyzing collected company information, surrounding information, and social trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, The reliability and future prospects of a company are evaluated based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide the evaluation results to the sales representative. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user sentiment and adjust the timing of data collection based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze a company's past financial performance and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting company information, filter it based on the company's current projects and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and prioritizes the collection of company information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting company information, we prioritize collecting highly relevant information by considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting company information, we analyze the company's social media activities and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the company information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the company category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the company information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of company information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, When conducting evaluations, consider the interrelationships between companies to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation process, the attribute information of the company submitting the information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation process, the geographical distribution of companies will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During the evaluation process, we refer to relevant literature on the company to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the instructions for interacting with the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects corporate information, An analysis unit analyzes the corporate information collected by the aforementioned collection unit, An evaluation unit that evaluates the reliability and future potential of a company based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides the evaluation results obtained by the evaluation unit to sales activities. A system characterized by the following features.

2. The aforementioned collection unit is Collect company information such as the company's financial status, performance, and transaction history. The system according to feature 1.

3. The aforementioned collection unit is The company gathers information about the surrounding area, such as the economic conditions of the region where it is located and the activities of its competitors. The system according to feature 1.

4. The aforementioned collection unit is Gather information on current economic conditions, political situations, social trends, and other current social issues. The system according to feature 1.

5. The aforementioned analysis unit is Analyzing collected company information, surrounding information, and social trends. The system according to feature 1.

6. The evaluation unit described above, The reliability and future prospects of a company are evaluated based on the analysis results. The system according to feature 1.

7. The aforementioned supply unit is, Provide the evaluation results to the sales representative. The system according to feature 1.

8. The aforementioned collection unit is We estimate user sentiment and adjust the timing of data collection based on that estimated sentiment. The system according to feature 1.

9. The aforementioned collection unit is Analyze a company's past financial performance and select the most suitable data collection method. The system according to feature 1.

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