System

Abstract

An object of the system according to the embodiment is to efficiently collect and analyze information of a business partner company and propose appropriate business negotiation contents.SOLUTION: A system according to an embodiment includes a company information collection unit, a past log analysis unit, and a proposal generation unit. The company information collection unit collects company information. The past log analysis unit analyzes the company information collected by the company information collection unit. The proposal generation unit proposes the content of the business negotiation based on the information analyzed by the past log analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently collect and analyze information on business partners and propose appropriate business negotiation content.

[0005] The system according to the embodiment aims to efficiently collect and analyze information on business partners and propose appropriate business negotiation content. [Means for solving the problem]

[0006] The system according to the embodiment includes a company information collection unit, a past log analysis unit, and a proposal generation unit. The company information collection unit collects company information. The past log analysis unit analyzes the company information collected by the company information collection unit. The proposal generation unit proposes business negotiation content based on the information analyzed by the past log analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze information on business partners and propose appropriate business negotiation content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The business negotiation preparation support system according to the embodiment of the present invention is a system that efficiently grasps the business partner company's projects and makes appropriate business negotiation preparations. As a result, the business negotiation preparation support system can accurately grasp the current situation of the business partner company and make effective proposals based on past performance.

[0029] A business negotiation preparation support system according to an embodiment includes a company information collection unit, a past log analysis unit, and a proposal generation unit. The company information collection unit collects company information. For example, the company information collection unit collects information such as a company's annual sales, number of stores, addresses, and telephone numbers from the web. The company information collection unit can also collect company reputations and customer feedback from social media and online reviews. The company information collection unit can also collect company financial data and provide future financial risks and growth forecasts. For example, the company information collection unit uses web scraping technology to collect data on a company's annual sales and number of stores and graphs past trends. Social media analysis technology is used to collect company reputations and customer feedback and classify positive and negative feedback. Financial data analysis technology is used to analyze indicators such as a company's revenue, profits, and debt, and provide future financial risks and growth forecasts. The past log analysis unit analyzes the company information collected by the company information collection unit. For example, the past log analysis unit analyzes past logs and coupon stamp usage on Salesforce to extract commonalities and patterns among successful business negotiations. The log analysis unit can also analyze coupon users' purchasing history to visualize the behavioral patterns of repeat and new customers. Furthermore, the log analysis unit can integrate Salesforce data with other CRM systems to provide more comprehensive customer information. For example, the log analysis unit analyzes sales negotiation history on Salesforce to identify characteristics of sales negotiations with a high success rate. It analyzes coupon users' purchasing history to identify the purchasing frequency and purchased products of repeat customers. It integrates Salesforce data with other CRM systems to understand a complete customer picture. The proposal generation unit proposes sales negotiation content based on the information analyzed by the log analysis unit. For example, the proposal generation unit analyzes the ROI of a company's past promotional campaigns and extracts the characteristics of the most effective campaigns. The proposal generation unit can also perform a detailed analysis of a company's customer segments and propose sales negotiation content optimal for each segment. Furthermore, the proposal generation unit can analyze a company's industry and market trends to predict future sales negotiation content. For example, the proposal generation unit analyzes data from past promotional campaigns and calculates ROI.The system analyzes customer data, identifies customer segments, and proposes optimal sales negotiation content for each segment. It analyzes industry and market trends and predicts future sales negotiation content. This allows the sales negotiation preparation support system according to the embodiment to efficiently grasp client company projects and prepare for appropriate sales negotiations. For example, the output unit displays the proposal content to the sales team via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the sales team.

[0030] The Corporate Information Collection Department collects a company's social media activity or online reviews, allowing the visualization of the company's reputation and customer feedback. For example, the Corporate Information Collection Department uses a generation AI to analyze a company's social media accounts and collect post content and follower reactions. This visualizes the company's online reputation and customer feedback. The Corporate Information Collection Department also collects reviews about the company from online review sites, and the generation AI analyzes the content and classifies the feedback into positive and negative. This allows the company's reputation to be understood at a glance. The Corporate Information Collection Department also uses a generation AI to analyze a company's social media activity over time and graph changes in the number of followers and engagement rates. This visualizes changes in the company's online presence. By visualizing the company's reputation and customer feedback, the sales team can more effectively prepare for sales negotiations.

[0031] The Corporate Information Collection Department can analyze a company's financial data and provide future financial risks and growth forecasts. In this department, for example, the generation AI collects a company's financial reports and publicly available financial data and analyzes indicators such as revenue, profits, and debt. This visualizes the company's financial situation. In addition, the generation AI in the Corporate Information Collection Department predicts future revenue and growth rates based on past financial data. For example, it uses past sales data to graph future sales trends. In addition, the generation AI in the Corporate Information Collection Department analyzes a company's financial data and evaluates financial risks. For example, it analyzes debt ratios and cash flows to identify high-risk companies. This allows the sales team to more effectively prepare for sales negotiations by providing a company's financial risks and growth forecasts.

[0032] The Corporate Intelligence Collection Department can collect information on a company's competitors and conduct competitive analysis. For example, in the Corporate Intelligence Collection Department, the Generative AI collects financial data and market share of a company's competitors and conducts competitive analysis. This enables comparison with competitors. In addition, the Generative AI analyzes competitors' social media activities and online reviews to collect competitor reputations and customer feedback. This allows for an understanding of competitors' strengths and weaknesses. In addition, the Generative AI analyzes competitors' marketing campaigns and promotional activities and evaluates their effectiveness. This allows for an understanding of competitors' strategies. By collecting information on a company's competitors and conducting competitive analysis, sales teams can prepare for sales negotiations more effectively.

[0033] The Corporate Information Collection Department collects information on a company's supply chain and can visualize supply risks and efficiency. In the Corporate Information Collection Department, for example, the Generative AI collects data on a company's supply chain and evaluates supply risks. For example, it analyzes the financial status and geographical risks of major suppliers. In addition, the Generative AI in the Corporate Information Collection Department analyzes logistics data and inventory data to evaluate supply chain efficiency. This identifies bottlenecks in the supply chain. In addition, the Generative AI in the Corporate Information Collection Department analyzes supply chain data over time and graphs fluctuations in supply risks and efficiency. This visualizes supply chain performance. In this way, by collecting information on a company's supply chain and visualizing supply risks and efficiency, sales teams can prepare for sales negotiations more effectively.

[0034] The past log analysis unit analyzes past sales negotiation data and can extract commonalities and patterns of successful negotiations. In this case, for example, the generation AI analyzes past sales negotiation data on Salesforce and extracts commonalities of successful negotiations. For example, it identifies patterns in the timing of sales negotiations and the content of proposals. In addition, the generation AI analyzes the characteristics of sales negotiations with a high success rate based on past sales negotiation data. For example, it identifies success patterns for specific industries and company sizes. In addition, the generation AI analyzes sales negotiation data chronologically and graphs the patterns of successful negotiations. This makes it possible to visualize the factors behind success and utilize them in future sales negotiations. In this way, by extracting commonalities and patterns of successful sales negotiations, the sales team can prepare for sales negotiations more effectively.

[0035] The past log analysis unit analyzes the purchasing history of coupon users and can visualize the behavioral patterns of repeat and new customers. In the past log analysis unit, for example, the generation AI analyzes the purchasing history of coupon users and identifies the behavioral patterns of repeat customers. For example, it analyzes the frequency of purchases and purchased items over a specific period. In the past log analysis unit, the generation AI analyzes the purchasing history of new customers and identifies their behavioral patterns. For example, it analyzes the coupon usage status at the time of their first purchase and their subsequent purchasing behavior. In the past log analysis unit, the generation AI analyzes the purchasing history of coupon users over time and graphs the behavioral patterns of repeat and new customers. This visualizes customer purchasing behavior. By visualizing the behavioral patterns of repeat and new customers, sales teams can prepare for sales negotiations more effectively.

[0036] The log analysis unit can integrate data on Salesforce with other CRM systems to provide comprehensive customer information. For example, the generation AI in the log analysis unit integrates data on Salesforce with other CRM systems to centralize customer information. This creates a comprehensive customer profile. The generation AI also collects and integrates data from different CRM systems to obtain a complete picture of the customer. For example, it integrates customer purchase history and inquiry history. The generation AI also analyzes the integrated data to identify customer behavior patterns and preferences. This provides more accurate customer information. This allows the data on Salesforce to be integrated with other CRM systems to provide more comprehensive customer information, enabling sales teams to more effectively prepare for sales negotiations.

[0037] The past log analysis unit can make proposals to optimize the timing and frequency of sales negotiations based on past sales negotiation data. In the past log analysis unit, for example, the generation AI analyzes past sales negotiation data and identifies the optimal timing for sales negotiations. For example, it makes proposals for sales negotiations that coincide with specific seasons or events. In addition, the past log analysis unit has the generation AI analyze the frequency of sales negotiations and propose the optimal frequency of sales negotiations. For example, it adjusts the interval between sales negotiations based on past data. In addition, the generation AI in the past log analysis unit analyzes sales negotiation data in chronological order and graphs the optimization of the timing and frequency of sales negotiations. This maximizes the effectiveness of sales negotiations. As a result, by making proposals to optimize the timing and frequency of sales negotiations, the sales team can prepare for sales negotiations more effectively.

[0038] The proposal generation unit can analyze the ROI of a company's past promotional campaigns and extract the characteristics of the most effective campaigns. For example, the generation AI in the proposal generation unit analyzes data from a company's past promotional campaigns and calculates the ROI. This identifies the most effective campaign. The generation AI in the proposal generation unit also analyzes the characteristics of promotional campaigns and extracts the factors for success. For example, it identifies that a specific target demographic or promotional method is effective. The generation AI in the proposal generation unit also analyzes promotional campaign data over time and graphs fluctuations in ROI. This makes the effectiveness of the campaign visible and can be used in future proposals. This allows the sales team to prepare for more effective sales negotiations by analyzing the ROI of a company's past promotional campaigns and extracting the characteristics of the most effective campaigns.

[0039] The proposal generation unit can analyze a company's customer segments in detail and propose the optimal sales negotiation content for each segment. In the proposal generation unit, for example, the generation AI analyzes the company's customer data and classifies customer segments in detail. For example, segments are identified based on data such as age, gender, and purchase history. In addition, the generation AI in the proposal generation unit proposes the optimal sales negotiation content for each customer segment. For example, it proposes promotions aimed at young people and services aimed at the elderly. In addition, the generation AI in the proposal generation unit analyzes customer segment data over time and graphs fluctuations in purchasing behavior for each segment. This allows it to propose effective sales negotiation content for each segment. In this way, by analyzing a company's customer segments in detail and proposing the optimal sales negotiation content for each segment, the sales team can prepare for sales negotiations more effectively.

[0040] The proposal generation unit can analyze a company's industry trends and market trends and predict the content of future sales negotiations. For example, the proposal generation unit uses a generation AI to analyze a company's industry trends and market trends and predict the content of future sales negotiations. For example, the proposal generation unit proposes sales negotiation content based on the latest technological trends and market needs. The proposal generation unit also uses a generation AI to collect market data and predict future market trends. This allows it to propose the optimal sales negotiation content for the company. The proposal generation unit also uses a generation AI to analyze industry trend data in chronological order and graph the content of future sales negotiations. This allows it to predict and propose the content of future sales negotiations. By analyzing a company's industry trends and market trends and predicting the content of future sales negotiations, the sales team can prepare for sales negotiations more effectively.

[0041] The proposal generation unit integrates a company's internal data with external data, allowing it to propose highly accurate sales negotiation content. For example, in the proposal generation unit, the generation AI integrates a company's internal data (e.g., sales data and customer data) with external data (e.g., market data and competitor data) to propose sales negotiation content. This enables more accurate proposals. In addition, in the proposal generation unit, the generation AI analyzes internal and external data to improve the accuracy of sales negotiation content. For example, it identifies customer needs based on internal data and grasps market trends based on external data. In addition, in the proposal generation unit, the generation AI analyzes the integrated data over time and graphs the accuracy of the sales negotiation content. This allows it to propose more accurate sales negotiation content. In this way, by integrating a company's internal data with external data and proposing more accurate sales negotiation content, the sales team can prepare for sales negotiations more effectively.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Corporate Information Collection Department can collect a company's environmental data and evaluate its environmental risk and sustainability. For example, the Corporate Information Collection Department's generation AI collects a company's environmental reports and publicly available environmental data and analyzes indicators such as CO2 emissions and energy consumption. This visualizes the company's environmental performance. The Corporate Information Collection Department's generation AI also predicts future environmental risk and sustainability based on past environmental data. For example, it uses past data to graph future trends in CO2 emissions. The Corporate Information Collection Department's generation AI also analyzes a company's environmental data and evaluates environmental risk. For example, it identifies companies with high compliance with environmental laws and regulations and high environmental risk. This allows sales teams to more effectively prepare for sales negotiations by providing information on a company's environmental risk and sustainability.

[0044] The Corporate Information Collection Department collects the skill sets and training histories of a company's employees, making it possible to visualize the strength of the company's human resources. For example, in the Corporate Information Collection Department, the Generative AI collects a company's personnel data and analyzes employees' skill sets and training histories. This visualizes the strength of the company's human resources. In addition, in the Corporate Information Collection Department, the Generative AI identifies a company's strengths and weaknesses based on employees' skill sets. For example, it evaluates the percentage of employees with specific skills and the effectiveness of training. In addition, in the Corporate Information Collection Department, the Generative AI analyzes employee skill data over time and graphs fluctuations in skill sets. This visualizes fluctuations in the strength of the company's human resources. By visualizing the strength of a company's human resources, sales teams can prepare for sales negotiations more effectively.

[0045] The Corporate Information Collection Department collects patent data from companies and can evaluate their technological capabilities and innovation. For example, in the Corporate Information Collection Department, the generation AI analyzes a company's patent database and evaluates the number and quality of patents. This visualizes the company's technological capabilities. In addition, in the Corporate Information Collection Department, the generation AI evaluates a company's innovation capabilities based on patent data. For example, it analyzes the number of patent citations and the breadth of technological fields. In addition, in the Corporate Information Collection Department, the generation AI analyzes patent data over time and graphs fluctuations in technological capabilities and innovation. This visualizes fluctuations in a company's technological capabilities and innovation. By visualizing a company's technological capabilities and innovation, sales teams can prepare for sales negotiations more effectively.

[0046] The Corporate Information Collection Department can collect data on a company's CSR activities and evaluate their social responsibility. For example, in the Corporate Information Collection Department, the Generation AI collects companies' CSR reports and publicly available data, and analyzes the content and results of their CSR activities. This visualizes the company's social responsibility. In addition, in the Corporate Information Collection Department, the Generation AI evaluates the company's social responsibility based on the CSR data. For example, it evaluates the impact and sustainability of CSR activities. In addition, in the Corporate Information Collection Department, the Generation AI analyzes the CSR data over time and graphs fluctuations in CSR activities. This visualizes fluctuations in corporate social responsibility. By visualizing a company's CSR activities, sales teams can prepare for business negotiations more effectively.

[0047] The Corporate Information Collection Department can evaluate the environmental impact of a company's supply chain and make proposals for sustainable supply chains. For example, in the Corporate Information Collection Department, the generation AI collects data related to the supply chain and evaluates the environmental impact. For example, it analyzes CO2 emissions and energy consumption in logistics. In addition, in the Corporate Information Collection Department, the generation AI makes proposals for sustainable supply chains based on the supply chain data. For example, it proposes eco-friendly logistics methods and the use of renewable energy. In addition, in the Corporate Information Collection Department, the generation AI analyzes supply chain data over time and graphs fluctuations in environmental impact. This makes it possible to visualize proposals for sustainable supply chains. In this way, by evaluating the environmental impact of a company's supply chain and making proposals for sustainable supply chains, the sales team can prepare for business negotiations more effectively.

[0048] The Corporate Information Collection Department can collect health data of a company's employees and assess their health risks. For example, the Corporate Information Collection Department's Generative AI collects health data of a company's employees and assesses their health risks. For example, it analyzes employees' medical histories and health checkup results. The Corporate Information Collection Department's Generative AI also assesses a company's health risks based on the health data. For example, it analyzes the risk of specific diseases and fluctuations in health status. The Corporate Information Collection Department's Generative AI also analyzes health data over time and graphs fluctuations in health risks. This visualizes the company's health risks. By collecting health data of a company's employees and assessing their health risks, the sales team can more effectively prepare for sales negotiations.

[0049] The Corporate Information Collection Department can evaluate a company's brand value and propose a brand strategy. For example, in the Corporate Information Collection Department, the generation AI collects data on a company's brand and evaluates the brand value. For example, it analyzes brand awareness and customer brand loyalty. In addition, in the Corporate Information Collection Department, the generation AI proposes a brand strategy based on the brand data. For example, it proposes a marketing strategy that makes use of the brand's strengths and measures to improve the brand image. In addition, in the Corporate Information Collection Department, the generation AI analyzes the brand data over time and graphs fluctuations in brand value. This makes it possible to visualize proposed brand strategies. In this way, by evaluating a company's brand value and proposing brand strategies, the sales team can prepare for sales negotiations more effectively.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The Corporate Information Collection Department collects corporate information. For example, the Corporate Information Collection Department collects information such as a company's annual sales, number of stores, addresses, and telephone numbers from the web. It can also collect corporate reputation and customer feedback from social media and online reviews. It can also collect corporate financial data to provide future financial risks and growth forecasts. Specifically, web scraping technology is used to collect data on a company's annual sales and number of stores, and past trends are graphed. Social media analysis technology is used to collect corporate reputation and customer feedback, and positive and negative feedback is classified. Financial data analysis technology is used to analyze indicators such as a company's revenue, profits, and debt, and provide future financial risks and growth forecasts. Step 2: The past log analysis unit analyzes the company information collected by the company information collection unit. For example, it analyzes past logs and coupon stamp usage on Salesforce to extract commonalities and patterns in successful sales negotiations. It can also analyze the purchasing history of coupon users to visualize the behavioral patterns of repeat and new customers. Furthermore, it can integrate data on Salesforce with other CRM systems to provide more comprehensive customer information. Specifically, it analyzes sales negotiation history on Salesforce to identify the characteristics of sales negotiations with a high success rate. It analyzes the purchasing history of coupon users to identify the purchasing frequency and purchased products of repeat customers. It integrates data on Salesforce with other CRM systems to get a complete picture of customers. Step 3: The proposal generation unit proposes sales negotiation content based on the information analyzed by the past log analysis unit. For example, it analyzes the ROI of a company's past promotional campaigns and extracts the characteristics of the most effective campaigns. It can also perform a detailed analysis of a company's customer segments and propose the optimal sales negotiation content for each segment. It can also analyze the company's industry trends and market trends and predict future sales negotiation content. Specifically, it analyzes data from past promotional campaigns and calculates ROI. It analyzes customer data, identifies customer segments, and proposes the optimal sales negotiation content for each segment. It analyzes industry trends and market trends and predicts future sales negotiation content.

[0052] (Example 2) The business negotiation preparation support system according to the embodiment of the present invention is a system that efficiently grasps the business partner company's projects and makes appropriate business negotiation preparations. As a result, the business negotiation preparation support system can accurately grasp the current situation of the business partner company and make effective proposals based on past performance.

[0053] A business negotiation preparation support system according to an embodiment includes a company information collection unit, a past log analysis unit, and a proposal generation unit. The company information collection unit collects company information. For example, the company information collection unit collects information such as a company's annual sales, number of stores, addresses, and telephone numbers from the web. The company information collection unit can also collect company reputations and customer feedback from social media and online reviews. The company information collection unit can also collect company financial data and provide future financial risks and growth forecasts. For example, the company information collection unit uses web scraping technology to collect data on a company's annual sales and number of stores and graphs past trends. Social media analysis technology is used to collect company reputations and customer feedback and classify positive and negative feedback. Financial data analysis technology is used to analyze indicators such as a company's revenue, profits, and debt, and provide future financial risks and growth forecasts. The past log analysis unit analyzes the company information collected by the company information collection unit. For example, the past log analysis unit analyzes past logs and coupon stamp usage on Salesforce to extract commonalities and patterns among successful business negotiations. The log analysis unit can also analyze coupon users' purchasing history to visualize the behavioral patterns of repeat and new customers. Furthermore, the log analysis unit can integrate Salesforce data with other CRM systems to provide more comprehensive customer information. For example, the log analysis unit analyzes sales negotiation history on Salesforce to identify characteristics of sales negotiations with a high success rate. It analyzes coupon users' purchasing history to identify the purchasing frequency and purchased products of repeat customers. It integrates Salesforce data with other CRM systems to understand a complete customer picture. The proposal generation unit proposes sales negotiation content based on the information analyzed by the log analysis unit. For example, the proposal generation unit analyzes the ROI of a company's past promotional campaigns and extracts the characteristics of the most effective campaigns. The proposal generation unit can also perform a detailed analysis of a company's customer segments and propose sales negotiation content optimal for each segment. Furthermore, the proposal generation unit can analyze a company's industry and market trends to predict future sales negotiation content. For example, the proposal generation unit analyzes data from past promotional campaigns and calculates ROI.The system analyzes customer data, identifies customer segments, and proposes optimal sales negotiation content for each segment. It analyzes industry and market trends and predicts future sales negotiation content. This allows the sales negotiation preparation support system according to the embodiment to efficiently grasp client company projects and prepare for appropriate sales negotiations. For example, the output unit displays the proposal content to the sales team via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the sales team.

[0054] The Corporate Information Collection Department collects a company's social media activity or online reviews, allowing the visualization of the company's reputation and customer feedback. For example, the Corporate Information Collection Department uses a generation AI to analyze a company's social media accounts and collect post content and follower reactions. This visualizes the company's online reputation and customer feedback. The Corporate Information Collection Department also collects reviews about the company from online review sites, and the generation AI analyzes the content and classifies the feedback into positive and negative. This allows the company's reputation to be understood at a glance. The Corporate Information Collection Department also uses a generation AI to analyze a company's social media activity over time and graph changes in the number of followers and engagement rates. This visualizes changes in the company's online presence. By visualizing the company's reputation and customer feedback, the sales team can more effectively prepare for sales negotiations.

[0055] The Corporate Information Collection Department can analyze a company's financial data and provide future financial risks and growth forecasts. In this department, for example, the generation AI collects a company's financial reports and publicly available financial data and analyzes indicators such as revenue, profits, and debt. This visualizes the company's financial situation. In addition, the generation AI in the Corporate Information Collection Department predicts future revenue and growth rates based on past financial data. For example, it uses past sales data to graph future sales trends. In addition, the generation AI in the Corporate Information Collection Department analyzes a company's financial data and evaluates financial risks. For example, it analyzes debt ratios and cash flows to identify high-risk companies. This allows the sales team to more effectively prepare for sales negotiations by providing a company's financial risks and growth forecasts.

[0056] The corporate information collection department can use the emotion estimation function to analyze the emotions of a company's employees or customers, and visualize the company's internal culture and customer satisfaction. For example, the corporate information collection department uses a generation AI to analyze the social media posts and internal surveys of company employees and estimate employee emotions, thereby visualizing the company's internal culture. The corporate information collection department also uses a generation AI to analyze customer reviews and feedback and estimate customer emotions, thereby visualizing customer satisfaction. The corporate information collection department also uses the emotion estimation function to analyze the emotion scores of a company's employees and customers over time and graph emotional fluctuations, thereby visualizing fluctuations in the company's internal culture and customer satisfaction. By visualizing the company's internal culture and customer satisfaction, the sales team can more effectively prepare for sales negotiations.

[0057] The Corporate Intelligence Collection Department can collect information on a company's competitors and conduct competitive analysis. For example, in the Corporate Intelligence Collection Department, the Generative AI collects financial data and market share of a company's competitors and conducts competitive analysis. This enables comparison with competitors. In addition, the Generative AI analyzes competitors' social media activities and online reviews to collect competitor reputations and customer feedback. This allows for an understanding of competitors' strengths and weaknesses. In addition, the Generative AI analyzes competitors' marketing campaigns and promotional activities and evaluates their effectiveness. This allows for an understanding of competitors' strategies. By collecting information on a company's competitors and conducting competitive analysis, sales teams can prepare for sales negotiations more effectively.

[0058] The Corporate Information Collection Department collects information on a company's supply chain and can visualize supply risks and efficiency. In the Corporate Information Collection Department, for example, the Generative AI collects data on a company's supply chain and evaluates supply risks. For example, it analyzes the financial status and geographical risks of major suppliers. In addition, the Generative AI in the Corporate Information Collection Department analyzes logistics data and inventory data to evaluate supply chain efficiency. This identifies bottlenecks in the supply chain. In addition, the Generative AI in the Corporate Information Collection Department analyzes supply chain data over time and graphs fluctuations in supply risks and efficiency. This visualizes supply chain performance. In this way, by collecting information on a company's supply chain and visualizing supply risks and efficiency, sales teams can prepare for sales negotiations more effectively.

[0059] The corporate information collection department can use the emotion estimation function to analyze the effectiveness of a company's marketing campaigns and make improvement proposals based on emotional responses. For example, the corporate information collection department uses a generation AI to analyze customers' emotional responses to a company's marketing campaigns and evaluate the effectiveness of the campaigns based on the results. For example, it identifies campaigns that have a high percentage of positive emotions. The corporate information collection department also uses the emotion estimation function to make improvement proposals for marketing campaigns. For example, it makes proposals to improve elements of campaigns that have a high percentage of negative emotions. The corporate information collection department also uses a generation AI to analyze marketing campaign data over time and graph fluctuations in emotional responses. This makes it possible to visualize the effectiveness of the campaigns and identify areas for improvement. This allows the sales team to more effectively prepare for sales negotiations by analyzing the effectiveness of a company's marketing campaigns and making improvement proposals based on emotional responses.

[0060] The past log analysis unit analyzes past sales negotiation data and can extract commonalities and patterns of successful negotiations. In this case, for example, the generation AI analyzes past sales negotiation data on Salesforce and extracts commonalities of successful negotiations. For example, it identifies patterns in the timing of sales negotiations and the content of proposals. In addition, the generation AI analyzes the characteristics of sales negotiations with a high success rate based on past sales negotiation data. For example, it identifies success patterns for specific industries and company sizes. In addition, the generation AI analyzes sales negotiation data chronologically and graphs the patterns of successful negotiations. This makes it possible to visualize the factors behind success and utilize them in future sales negotiations. In this way, by extracting commonalities and patterns of successful sales negotiations, the sales team can prepare for sales negotiations more effectively.

[0061] The past log analysis unit analyzes the purchasing history of coupon users and can visualize the behavioral patterns of repeat and new customers. In the past log analysis unit, for example, the generation AI analyzes the purchasing history of coupon users and identifies the behavioral patterns of repeat customers. For example, it analyzes the frequency of purchases and purchased items over a specific period. In the past log analysis unit, the generation AI analyzes the purchasing history of new customers and identifies their behavioral patterns. For example, it analyzes the coupon usage status at the time of their first purchase and their subsequent purchasing behavior. In the past log analysis unit, the generation AI analyzes the purchasing history of coupon users over time and graphs the behavioral patterns of repeat and new customers. This visualizes customer purchasing behavior. By visualizing the behavioral patterns of repeat and new customers, sales teams can prepare for sales negotiations more effectively.

[0062] The past log analysis unit uses the emotion estimation function to analyze customers' emotional reactions in past sales negotiations and identify the factors that led to their success. In the past log analysis unit, for example, the generation AI analyzes past sales negotiation data and estimates customers' emotional reactions. For example, it analyzes the customer's facial expressions and voice during sales negotiations and calculates an emotion score. The past log analysis unit also uses the emotion estimation function to identify customers' positive emotional reactions in successful sales negotiations. This clarifies the factors for success. In the past log analysis unit, the generation AI analyzes sales negotiation data chronologically and graphs fluctuations in customers' emotional reactions. This visualizes the factors that led to the success of sales negotiations and can be used for future sales negotiations. This allows the sales team to prepare for sales negotiations more effectively by analyzing customers' emotional reactions in past sales negotiations and identifying the factors that led to their success.

[0063] The log analysis unit can integrate data on Salesforce with other CRM systems to provide comprehensive customer information. For example, the generation AI in the log analysis unit integrates data on Salesforce with other CRM systems to centralize customer information. This creates a comprehensive customer profile. The generation AI also collects and integrates data from different CRM systems to obtain a complete picture of the customer. For example, it integrates customer purchase history and inquiry history. The generation AI also analyzes the integrated data to identify customer behavior patterns and preferences. This provides more accurate customer information. This allows the data on Salesforce to be integrated with other CRM systems to provide more comprehensive customer information, enabling sales teams to more effectively prepare for sales negotiations.

[0064] The past log analysis unit can make proposals to optimize the timing and frequency of sales negotiations based on past sales negotiation data. In the past log analysis unit, for example, the generation AI analyzes past sales negotiation data and identifies the optimal timing for sales negotiations. For example, it makes proposals for sales negotiations that coincide with specific seasons or events. In addition, the past log analysis unit has the generation AI analyze the frequency of sales negotiations and propose the optimal frequency of sales negotiations. For example, it adjusts the interval between sales negotiations based on past data. In addition, the generation AI in the past log analysis unit analyzes sales negotiation data in chronological order and graphs the optimization of the timing and frequency of sales negotiations. This maximizes the effectiveness of sales negotiations. As a result, by making proposals to optimize the timing and frequency of sales negotiations, the sales team can prepare for sales negotiations more effectively.

[0065] The past log analysis unit uses the emotion estimation function to analyze the emotional reactions of coupon users and optimize coupons based on their emotions. In the past log analysis unit, for example, the generation AI analyzes the emotional reactions of coupon users and identifies coupons with strong positive emotions. This allows for the provision of effective coupons. The past log analysis unit also uses the emotion estimation function to identify negative emotional reactions of coupon users and make suggestions to improve the factors behind these reactions. For example, the content and conditions of the coupon are adjusted. In addition, the generation AI in the past log analysis unit analyzes the emotional reactions of coupon users over time and graphs emotional fluctuations. This allows for the visualization and optimization of coupon effectiveness. This allows the sales team to more effectively prepare for sales negotiations by analyzing the emotional reactions of coupon users and optimizing coupons based on their emotions.

[0066] The proposal generation unit can analyze the ROI of a company's past promotional campaigns and extract the characteristics of the most effective campaigns. For example, the generation AI in the proposal generation unit analyzes data from a company's past promotional campaigns and calculates the ROI. This identifies the most effective campaign. The generation AI in the proposal generation unit also analyzes the characteristics of promotional campaigns and extracts the factors for success. For example, it identifies that a specific target demographic or promotional method is effective. The generation AI in the proposal generation unit also analyzes promotional campaign data over time and graphs fluctuations in ROI. This makes the effectiveness of the campaign visible and can be used in future proposals. This allows the sales team to prepare for more effective sales negotiations by analyzing the ROI of a company's past promotional campaigns and extracting the characteristics of the most effective campaigns.

[0067] The proposal generation unit can analyze a company's customer segments in detail and propose the optimal sales negotiation content for each segment. In the proposal generation unit, for example, the generation AI analyzes the company's customer data and classifies customer segments in detail. For example, segments are identified based on data such as age, gender, and purchase history. In addition, the generation AI in the proposal generation unit proposes the optimal sales negotiation content for each customer segment. For example, it proposes promotions aimed at young people and services aimed at the elderly. In addition, the generation AI in the proposal generation unit analyzes customer segment data over time and graphs fluctuations in purchasing behavior for each segment. This allows it to propose effective sales negotiation content for each segment. In this way, by analyzing a company's customer segments in detail and proposing the optimal sales negotiation content for each segment, the sales team can prepare for sales negotiations more effectively.

[0068] The proposal generation unit uses the emotion estimation function to analyze customers' emotional responses to past promotional campaigns and can propose sales negotiation content based on their emotions. For example, the proposal generation unit uses a generation AI to analyze customers' emotional responses to past promotional campaigns and identify campaigns that generated strong positive emotions. This allows the proposal generation unit to propose effective sales negotiation content. The proposal generation unit also uses the emotion estimation function to identify the factors behind campaigns that generate a lot of negative emotions and propose areas for improvement. For example, the proposal generation unit adjusts the content and timing of the campaign. The proposal generation unit also uses a generation AI to analyze sales promotion campaign data over time and graph fluctuations in customers' emotional responses. This allows the proposal generation unit to propose sales negotiation content based on their emotions. This allows the sales team to prepare for sales negotiations more effectively by analyzing customers' emotional responses to past promotional campaigns and proposing sales negotiation content based on their emotions.

[0069] The proposal generation unit can analyze a company's industry trends and market trends and predict the content of future sales negotiations. For example, the proposal generation unit uses a generation AI to analyze a company's industry trends and market trends and predict the content of future sales negotiations. For example, the proposal generation unit proposes sales negotiation content based on the latest technological trends and market needs. The proposal generation unit also uses a generation AI to collect market data and predict future market trends. This allows it to propose the optimal sales negotiation content for the company. The proposal generation unit also uses a generation AI to analyze industry trend data in chronological order and graph the content of future sales negotiations. This allows it to predict and propose the content of future sales negotiations. By analyzing a company's industry trends and market trends and predicting the content of future sales negotiations, the sales team can prepare for sales negotiations more effectively.

[0070] The proposal generation unit integrates a company's internal data with external data, allowing it to propose highly accurate sales negotiation content. For example, in the proposal generation unit, the generation AI integrates a company's internal data (e.g., sales data and customer data) with external data (e.g., market data and competitor data) to propose sales negotiation content. This enables more accurate proposals. In addition, in the proposal generation unit, the generation AI analyzes internal and external data to improve the accuracy of sales negotiation content. For example, it identifies customer needs based on internal data and grasps market trends based on external data. In addition, in the proposal generation unit, the generation AI analyzes the integrated data over time and graphs the accuracy of the sales negotiation content. This allows it to propose more accurate sales negotiation content. In this way, by integrating a company's internal data with external data and proposing more accurate sales negotiation content, the sales team can prepare for sales negotiations more effectively.

[0071] The proposal generation unit can use the emotion estimation function to generate proposals in real time that will evoke the most positive emotions in customers during sales negotiations. For example, the proposal generation unit uses a generation AI to analyze the customer's emotional reactions in real time during sales negotiations and make proposals that will elicit the most positive emotions. For example, the proposal generation unit analyzes the customer's facial expressions and voice and adjusts the proposal content based on the emotion score. The proposal generation unit also uses the emotion estimation function to generate proposals in real time that will positively change the customer's emotions during sales negotiations. For example, the proposal generation unit dynamically changes the proposal content based on the customer's reactions. The proposal generation unit also uses a generation AI to analyze the customer's emotional data during sales negotiations in chronological order and graph the proposal that elicits the most positive emotions. This maximizes the effectiveness of sales negotiations. By generating proposals in real time that will evoke the most positive emotions in customers during sales negotiations, the sales team can prepare for sales negotiations more effectively.

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

[0073] The Corporate Information Collection Department can collect a company's environmental data and evaluate its environmental risk and sustainability. For example, the Corporate Information Collection Department's generation AI collects a company's environmental reports and publicly available environmental data and analyzes indicators such as CO2 emissions and energy consumption. This visualizes the company's environmental performance. The Corporate Information Collection Department's generation AI also predicts future environmental risk and sustainability based on past environmental data. For example, it uses past data to graph future trends in CO2 emissions. The Corporate Information Collection Department's generation AI also analyzes a company's environmental data and evaluates environmental risk. For example, it identifies companies with high compliance with environmental laws and regulations and high environmental risk. This allows sales teams to more effectively prepare for sales negotiations by providing information on a company's environmental risk and sustainability.

[0074] The Corporate Information Collection Department collects the skill sets and training histories of a company's employees, making it possible to visualize the strength of the company's human resources. For example, in the Corporate Information Collection Department, the Generative AI collects a company's personnel data and analyzes employees' skill sets and training histories. This visualizes the strength of the company's human resources. In addition, in the Corporate Information Collection Department, the Generative AI identifies a company's strengths and weaknesses based on employees' skill sets. For example, it evaluates the percentage of employees with specific skills and the effectiveness of training. In addition, in the Corporate Information Collection Department, the Generative AI analyzes employee skill data over time and graphs fluctuations in skill sets. This visualizes fluctuations in the strength of the company's human resources. By visualizing the strength of a company's human resources, sales teams can prepare for sales negotiations more effectively.

[0075] The Corporate Information Collection Department collects patent data from companies and can evaluate their technological capabilities and innovation. For example, in the Corporate Information Collection Department, the generation AI analyzes a company's patent database and evaluates the number and quality of patents. This visualizes the company's technological capabilities. In addition, in the Corporate Information Collection Department, the generation AI evaluates a company's innovation capabilities based on patent data. For example, it analyzes the number of patent citations and the breadth of technological fields. In addition, in the Corporate Information Collection Department, the generation AI analyzes patent data over time and graphs fluctuations in technological capabilities and innovation. This visualizes fluctuations in a company's technological capabilities and innovation. By visualizing a company's technological capabilities and innovation, sales teams can prepare for sales negotiations more effectively.

[0076] The Corporate Information Collection Department can use the emotion estimation function to analyze the stress levels of company employees and visualize the company's working environment. For example, the Corporate Information Collection Department uses the generative AI to analyze the social media posts and internal surveys of company employees to estimate their stress levels. This visualizes the company's working environment. The Corporate Information Collection Department also uses the emotion estimation function to analyze employee stress levels over time and graph fluctuations in stress. This visualizes fluctuations in the company's working environment. By visualizing the company's working environment, the sales team can prepare for sales negotiations more effectively.

[0077] The Corporate Information Collection Department can collect data on a company's CSR activities and evaluate their social responsibility. For example, in the Corporate Information Collection Department, the Generation AI collects companies' CSR reports and publicly available data, and analyzes the content and results of their CSR activities. This visualizes the company's social responsibility. In addition, in the Corporate Information Collection Department, the Generation AI evaluates the company's social responsibility based on the CSR data. For example, it evaluates the impact and sustainability of CSR activities. In addition, in the Corporate Information Collection Department, the Generation AI analyzes the CSR data over time and graphs fluctuations in CSR activities. This visualizes fluctuations in corporate social responsibility. By visualizing a company's CSR activities, sales teams can prepare for business negotiations more effectively.

[0078] The Corporate Information Collection Department can analyze customers' emotional reactions to a company's products and services and make suggestions for improving them. For example, the Corporate Information Collection Department's generation AI analyzes customer reviews and feedback to estimate their emotional reactions to the product or service. This allows the department to identify the product's strengths and weaknesses. The Corporate Information Collection Department also uses the emotion estimation function to identify customers' negative emotional reactions and make suggestions to improve the factors behind them. For example, adjusting the product's functions or design. The Corporate Information Collection Department's generation AI also analyzes customers' emotional reactions over time and graphs emotional fluctuations. This allows the department to visualize and suggest areas for improvement in the product or service. This allows the sales team to more effectively prepare for sales negotiations by analyzing customers' emotional reactions and making suggestions for product improvements.

[0079] The Corporate Information Collection Department can evaluate the environmental impact of a company's supply chain and make proposals for sustainable supply chains. For example, in the Corporate Information Collection Department, the generation AI collects data related to the supply chain and evaluates the environmental impact. For example, it analyzes CO2 emissions and energy consumption in logistics. In addition, in the Corporate Information Collection Department, the generation AI makes proposals for sustainable supply chains based on the supply chain data. For example, it proposes eco-friendly logistics methods and the use of renewable energy. In addition, in the Corporate Information Collection Department, the generation AI analyzes supply chain data over time and graphs fluctuations in environmental impact. This makes it possible to visualize proposals for sustainable supply chains. In this way, by evaluating the environmental impact of a company's supply chain and making proposals for sustainable supply chains, the sales team can prepare for business negotiations more effectively.

[0080] The Corporate Information Collection Department can collect health data of a company's employees and assess their health risks. For example, the Corporate Information Collection Department's Generative AI collects health data of a company's employees and assesses their health risks. For example, it analyzes employees' medical histories and health checkup results. The Corporate Information Collection Department's Generative AI also assesses a company's health risks based on the health data. For example, it analyzes the risk of specific diseases and fluctuations in health status. The Corporate Information Collection Department's Generative AI also analyzes health data over time and graphs fluctuations in health risks. This visualizes the company's health risks. By collecting health data of a company's employees and assessing their health risks, the sales team can more effectively prepare for sales negotiations.

[0081] The Corporate Information Collection Department can use the emotion estimation function to evaluate the quality of a company's customer support and make suggestions for improvement. For example, the Corporate Information Collection Department uses the generation AI to analyze customer support interactions and estimate the customer's emotional reactions. This allows the quality of customer support to be evaluated. The Corporate Information Collection Department also uses the emotion estimation function to identify negative emotional reactions in customers and make suggestions to improve the causes of those reactions. For example, adjusting support response times or methods. The Corporate Information Collection Department also uses the generation AI to analyze customer support data over time and graph fluctuations in emotional reactions. This allows the quality of customer support to be visualized and areas for improvement to be identified. This allows the sales team to more effectively prepare for sales negotiations by evaluating the quality of customer support and making suggestions for improvement.

[0082] The Corporate Information Collection Department can evaluate a company's brand value and propose a brand strategy. For example, in the Corporate Information Collection Department, the generation AI collects data on a company's brand and evaluates the brand value. For example, it analyzes brand awareness and customer brand loyalty. In addition, in the Corporate Information Collection Department, the generation AI proposes a brand strategy based on the brand data. For example, it proposes a marketing strategy that makes use of the brand's strengths and measures to improve the brand image. In addition, in the Corporate Information Collection Department, the generation AI analyzes the brand data over time and graphs fluctuations in brand value. This makes it possible to visualize proposed brand strategies. In this way, by evaluating a company's brand value and proposing brand strategies, the sales team can prepare for sales negotiations more effectively.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The Corporate Information Collection Department collects corporate information. For example, the Corporate Information Collection Department collects information such as a company's annual sales, number of stores, addresses, and telephone numbers from the web. It can also collect corporate reputation and customer feedback from social media and online reviews. It can also collect corporate financial data to provide future financial risks and growth forecasts. Specifically, web scraping technology is used to collect data on a company's annual sales and number of stores, and past trends are graphed. Social media analysis technology is used to collect corporate reputation and customer feedback, and positive and negative feedback is classified. Financial data analysis technology is used to analyze indicators such as a company's revenue, profits, and debt, and provide future financial risks and growth forecasts. Step 2: The past log analysis unit analyzes the company information collected by the company information collection unit. For example, it analyzes past logs and coupon stamp usage on Salesforce to extract commonalities and patterns in successful sales negotiations. It can also analyze the purchasing history of coupon users to visualize the behavioral patterns of repeat and new customers. Furthermore, it can integrate data on Salesforce with other CRM systems to provide more comprehensive customer information. Specifically, it analyzes sales negotiation history on Salesforce to identify the characteristics of sales negotiations with a high success rate. It analyzes the purchasing history of coupon users to identify the purchasing frequency and purchased products of repeat customers. It integrates data on Salesforce with other CRM systems to get a complete picture of customers. Step 3: The proposal generation unit proposes sales negotiation content based on the information analyzed by the past log analysis unit. For example, it analyzes the ROI of a company's past promotional campaigns and extracts the characteristics of the most effective campaigns. It can also perform a detailed analysis of a company's customer segments and propose the optimal sales negotiation content for each segment. It can also analyze the company's industry trends and market trends and predict future sales negotiation content. Specifically, it analyzes data from past promotional campaigns and calculates ROI. It analyzes customer data, identifies customer segments, and proposes the optimal sales negotiation content for each segment. It analyzes industry trends and market trends and predicts future sales negotiation content.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a corporate information collection department that collects corporate information; a past log analysis unit that analyzes the company information collected by the company information collection unit; a proposal generation unit that proposes business negotiation details based on the information analyzed by the past log analysis unit. A system characterized by:

2. The company information collection unit Gathering social media activity or online reviews of a company to visualize its reputation and customer feedback 2. The system of claim 1.

3. The company information collection unit Analyzes corporate financial data and provides future financial risk and growth forecasts 2. The system of claim 1.

4. The company information collection unit Analyze the sentiment of a company's employees or customers to visualize the company's internal culture and customer satisfaction 2. The system of claim 1.

5. The company information collection unit Collect information on a company's competitors and conduct competitive analysis 2. The system of claim 1.

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