system

Abstract

The system according to this embodiment aims to efficiently facilitate business matching between companies and propose the most suitable business partner. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects company data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes the most suitable business partner based on the analysis results obtained by the analysis unit. The generation unit generates new business proposals and collaboration plans that combine the strengths of the business partners proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that business matching between enterprises is not efficiently performed, and it is difficult to find an optimal business partner.

[0005] The system according to the embodiment aims to efficiently perform business matching between enterprises and propose an optimal business partner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a generation unit. The data collection unit collects company data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the most suitable business partners based on the analysis results obtained by the analysis unit. The generation unit generates new business proposals and collaboration plans that combine the strengths of the business partners proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform business matching between companies and propose the most suitable business partner. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business matching platform according to an embodiment of the present invention is a system that performs business matching between companies using generative AI. This system comprehensively analyzes information such as companies' financial data, business content, technological capabilities, and market trends, and proposes the most suitable business partner. Furthermore, the system automatically generates new business proposals and collaboration plans that combine the strengths of both companies, and shows a concrete path for cooperation. The platform is also equipped with online business negotiation functions and virtual factory tour functions, enabling business exchange that transcends geographical constraints. In addition, the system provides information on regional subsidies and regulations to support smooth business development. This aims to simultaneously revitalize local economies and alleviate the overconcentration of power in urban areas. As a result, the business matching platform can efficiently perform business matching between companies and promote the creation of new value.

[0029] The business matching platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a generation unit. The data collection unit collects company data. The data collection unit collects information such as company financial data, business content, technological capabilities, and market trends. For example, the data collection unit can collect company financial data to understand the company's management situation. The data collection unit can also collect company business content to understand the company's business areas and strengths. Furthermore, the data collection unit can collect company technological capabilities to understand the company's technological strengths and competitiveness. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can comprehensively analyze the collected data to understand the company's strengths and weaknesses. For example, the analysis unit can analyze company financial data to evaluate the company's management situation. Furthermore, the analysis unit can analyze company business content to evaluate the company's business areas and strengths. Furthermore, the analysis unit can analyze company technological capabilities to evaluate the company's technological strengths and competitiveness. The proposal unit proposes the most suitable business partner based on the analysis results obtained by the analysis unit. For example, the proposal unit can select the most suitable business partner by considering the company's strengths and weaknesses. The proposal unit can, for example, propose companies with stable management conditions based on their financial data. It can also propose companies with overlapping business areas based on their business activities. Furthermore, it can propose companies with technological strengths based on their technological capabilities. The generation unit generates new business proposals and collaboration plans by combining the strengths of the business partners proposed by the proposal unit. For example, the generation unit can generate new business proposals by combining the strengths of companies. For example, the generation unit can generate new product development plans by combining the technological capabilities of companies. Furthermore, the generation unit can generate new sales channel expansion plans by combining the sales networks of companies. As a result, the business matching platform according to this embodiment can efficiently facilitate business matching between companies and promote the creation of new value. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and generation unit may be performed using AI, or not using AI.For example, the data collection unit can use AI to automate data collection when gathering company data. The analysis unit can use AI to analyze the collected data and evaluate the company's strengths and weaknesses. The proposal unit can use AI to select and propose the most suitable business partners. The generation unit can use AI to generate new business proposals and collaboration plans.

[0030] The data collection department collects corporate data. For example, it collects information such as corporate financial data, business activities, technological capabilities, and market trends. Specifically, it collects detailed financial data such as revenue, profit, liabilities, and assets to understand the company's management situation. Regarding corporate business activities, it collects information such as the types of products and services offered, the scale of the business, and target markets to understand the company's business domain and strengths. Furthermore, regarding corporate technological capabilities, it collects patent information, research and development results, and technical know-how to understand the company's technological strengths and competitiveness. Regarding market trends, it collects information such as industry trends, competitor activities, and consumer needs to understand the company's market environment. This data is collected from various sources, including publicly available information on the internet, official corporate websites, industry reports, news articles, and information provided directly by companies. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can comprehensively analyze the collected data to understand a company's strengths and weaknesses. Specifically, it analyzes a company's financial data to evaluate its management status from the perspectives of profitability, stability, and growth. It also analyzes a company's business activities to evaluate its business areas and strengths from the perspectives of business diversification, competitive advantage, and market share. Furthermore, it analyzes a company's technological capabilities to evaluate its technological strengths and competitiveness from the perspectives of technological originality, innovativeness, and applicability. It also analyzes market trends to evaluate the company's market environment, taking into account industry growth potential, competitor strategies, and consumer trends. These analyses are often automated using AI, which can process large amounts of data quickly and accurately and extract patterns and trends. For example, machine learning algorithms can be used to predict future earnings from a company's financial data, and natural language processing technology can be used to analyze text data related to a company's business activities. As a result, the analysis department can quickly and accurately analyze the collected data and comprehensively evaluate a company's strengths and weaknesses. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific industries or regions based on past performance data and formulate future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The Proposal Department proposes the most suitable business partners based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can select the most suitable business partners by considering a company's strengths and weaknesses. Specifically, based on a company's financial data, it can propose companies with stable management conditions. For example, it can select and propose companies with high profitability and a solid financial foundation. It can also propose companies with overlapping business areas based on a company's business content. For example, it can select and propose companies operating in the same market segment or companies offering complementary products or services. Furthermore, based on a company's technological capabilities, it can propose companies with technological strengths. For example, it can select and propose companies with superior technology in a specific technological field or companies developing innovative technologies. The Proposal Department uses AI to analyze this data and select the most suitable business partners. The AI ​​comprehensively evaluates a company's strengths and weaknesses and compares multiple candidate companies to identify the most suitable business partner. For example, it can use machine learning algorithms to predict the optimal partnership under similar conditions based on past success stories. This allows the proposal department to provide companies with highly accurate business partner suggestions and promote collaboration between companies. Furthermore, the proposal department can collect user feedback and optimize its suggestion algorithm to continuously improve the accuracy and effectiveness of its suggestions. As a result, the proposal department can always provide highly accurate suggestions based on the latest information and efficiently facilitate business matching between companies.

[0033] The generation unit generates new business proposals and collaboration plans by combining the strengths of business partners proposed by the proposal unit. For example, the generation unit can combine the strengths of companies to generate new business proposals. Specifically, it can combine the technological capabilities of companies to generate new product development plans. For example, it can generate plans for companies with superior technologies in different technological fields to collaborate and develop new products or services. It can also combine the sales networks of companies to generate plans for expanding sales channels. For example, it can generate plans for companies with strong sales networks in different regions or markets to collaborate and enter new markets. Furthermore, it can combine the financial strengths of companies to generate funding plans for realizing large-scale projects. The generation unit generates these plans using AI. The AI ​​can comprehensively evaluate the strengths and weaknesses of companies and find the optimal combination. For example, it can use machine learning algorithms to predict the optimal collaboration plan under similar conditions based on past success stories. As a result, the generation unit can provide companies with highly accurate new business proposals and collaboration plans, promoting collaboration between companies. Furthermore, the generation unit can evaluate the feasibility and effectiveness of the generated plans and modify them as needed. For example, it evaluates the resources and risks required to execute a plan and develops an optimal execution plan. Furthermore, the generation unit collects user feedback and optimizes its generation algorithm, enabling it to consistently provide highly accurate plans based on the latest information. This allows the generation unit to efficiently facilitate collaboration between companies and realize the creation of new value.

[0034] The business matching platform includes an online business negotiation function. This function allows companies to conduct business negotiations online, for example, using video conferencing tools. It enables real-time business negotiations between companies. Furthermore, it includes a chat function, allowing text messages to be sent and received during negotiations. Additionally, it includes a file sharing function, enabling the sharing of documents and data during negotiations. This online business negotiation function makes business exchange possible, transcending geographical limitations. Some or all of the processes described above in the online business negotiation function may be performed using AI, or not. For example, the online business negotiation function can use AI to support the progress of negotiations. The AI ​​can analyze conversations during negotiations in real time and provide appropriate advice and suggestions. The AI ​​can also automatically organize documents and data during negotiations, ensuring a smoother process.

[0035] The business matching platform includes a virtual factory tour function. This function allows users to virtually tour a company's factory, for example, using VR technology. It also allows real-time tours of the factory. Furthermore, the virtual factory tour function includes a guide that provides detailed explanations of the factory's manufacturing process. Additionally, it can display factory equipment and products in 3D models, providing detailed information. This enables business exchange that transcends geographical limitations. Some or all of the above-described processes in the virtual factory tour function may be performed using AI, for example, or without AI. For example, the virtual factory tour function can use AI to support the tour's progress. The AI ​​can answer questions during the tour in real time, deepening the visitor's understanding. The AI ​​can also automatically organize data and information during the tour, ensuring a smooth tour.

[0036] The business matching platform includes a provision unit that provides regional subsidy and regulatory information. This unit can, for example, collect subsidy information provided by local governments and provide it to companies. It can also automatically collect and provide subsidy information provided by local governments. Furthermore, it can collect and provide regional regulatory information to companies. In addition, the unit can provide companies with the information they need in real time, supporting smooth business development. This supports smooth business development by providing regional subsidy and regulatory information. Some or all of the above processing in the provision unit may be performed using AI, for example, or without AI. For example, the unit can use AI to automatically collect subsidy and regulatory information and provide it to companies. AI can collect the latest information in real time and provide it to companies. Furthermore, AI can select and provide the most suitable information according to the needs of the companies.

[0037] The data collection unit can collect information such as a company's financial data, business activities, technological capabilities, and market trends. For example, the data collection unit can collect a company's financial data to understand its management situation. For example, the data collection unit can collect a company's financial data to evaluate its profitability and financial soundness. The data collection unit can also collect a company's business activities to understand its business domain and strengths. For example, the data collection unit can collect a company's business activities to evaluate the characteristics of a company's products and services. Furthermore, the data collection unit can collect a company's technological capabilities to understand its technological strengths and competitiveness. For example, the data collection unit can collect a company's technological capabilities to evaluate its technological superiority. This enables comprehensive data analysis by collecting information such as a company's financial data, business activities, technological capabilities, and market trends. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, when collecting company data, the data collection unit can use AI to automate data collection. AI can automatically collect a company's financial data and business activities and store them in a database. Furthermore, AI can collect information on companies' technological capabilities and market trends in real time, and provide the latest information.

[0038] The analysis department can comprehensively analyze the collected data. For example, the analysis department can comprehensively analyze the collected data to understand a company's strengths and weaknesses. For example, the analysis department can analyze a company's financial data to evaluate its management status. For example, the analysis department can analyze a company's financial data to evaluate its profitability and financial soundness. The analysis department can also analyze a company's business operations to evaluate its business areas and strengths. For example, the analysis department can analyze a company's business operations to evaluate the characteristics of its products and services. Furthermore, the analysis department can analyze a company's technological capabilities to evaluate its technological strengths and competitiveness. For example, the analysis department can analyze a company's technological capabilities to evaluate its technological advantages. By comprehensively analyzing the collected data, the analysis department can obtain basic data to propose the most suitable business partners. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze the collected data and evaluate a company's strengths and weaknesses. AI can automatically analyze a company's financial data and business operations to evaluate its management status and business areas. Furthermore, AI can analyze a company's technological capabilities and market trends in real time, providing the latest information.

[0039] The proposal department can propose the most suitable business partners. For example, the proposal department can select the most suitable business partners by considering the strengths and weaknesses of a company. For example, the proposal department can propose companies with stable management based on a company's financial data. For example, the proposal department can propose companies with high profitability based on a company's financial data. Furthermore, the proposal department can propose companies with overlapping business areas based on a company's business content. For example, the proposal department can propose companies with complementary products and services based on a company's business content. In addition, the proposal department can propose companies with technological strengths based on a company's technological capabilities. For example, the proposal department can propose companies with technological advantages based on a company's technological capabilities. In this way, by proposing the most suitable business partners, collaboration between companies can be promoted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to select and propose the most suitable business partners. AI can automatically analyze a company's financial data and business content to select the most suitable business partners. Furthermore, AI can analyze a company's technological capabilities and market trends in real time, and propose the most suitable business partners based on the latest information.

[0040] The generation unit can generate new business proposals and collaboration plans by combining the strengths of business partners. For example, the generation unit can generate new business proposals by combining the strengths of companies. For example, the generation unit can generate new product development plans by combining the technological capabilities of companies. For example, the generation unit can generate new technology development plans by combining the technological capabilities of companies. Furthermore, the generation unit can generate new sales channel expansion plans by combining the sales networks of companies. For example, the generation unit can generate new market development plans by combining the sales networks of companies. In addition, the generation unit can generate new business development plans by combining the resources of companies. For example, the generation unit can generate new business expansion plans by combining the resources of companies. This allows for the generation of concrete collaboration paths by generating new business proposals and collaboration plans that combine the strengths of business partners. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate new business proposals and collaboration plans using a generation AI. The generation AI can automatically analyze the strengths and resources of companies and generate optimal business proposals and plans. Furthermore, the generation AI can analyze a company's technological capabilities and market trends in real time, and generate optimal business proposals and plans based on the latest information.

[0041] The data collection unit can analyze a company's past data collection history and select the optimal collection method. For example, the data collection unit can apply similar methods based on past successful data collection methods. Furthermore, the data collection unit can eliminate ineffective methods from past data collection history and select efficient methods. In addition, the data collection unit can analyze past data collection history and select the optimal collection method under specific conditions. This allows for the selection of effective data collection methods by analyzing a company's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can analyze past data collection history using AI to select the optimal collection method. AI can automatically analyze past data collection history and select effective collection methods. Furthermore, AI can select the optimal collection method in real time under specific conditions, enabling efficient data collection.

[0042] The data collection unit can filter data based on the company's current business status and areas of interest during data collection. For example, the data collection unit can collect only highly relevant data based on the company's current business status. The data collection unit can also prioritize the collection of data related to specific areas based on the company's areas of interest. Furthermore, the data collection unit can efficiently collect data by eliminating unnecessary data, taking into account the company's business status and areas of interest. This allows for the efficient collection of highly relevant data by filtering based on the company's current business status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the company's business status and areas of interest using AI and perform filtering. AI can analyze the company's business status and areas of interest in real time and select highly relevant data. Furthermore, AI can automatically eliminate unnecessary data, enabling efficient data collection.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can prioritize the collection of region-related data based on the company's location. The data collection unit can prioritize the collection of region-related data based on the company's location. Furthermore, the data collection unit can prioritize the collection of nearby market trend data by considering the geographical location information of companies. In addition, the data collection unit can prioritize the collection of region-specific regulatory information based on the company's geographical location information. The data collection unit can prioritize the collection of region-specific regulatory information based on the company's geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the company's geographical location information using AI and select highly relevant data. AI can analyze the company's geographical location information in real time and select highly relevant data. Furthermore, AI can automatically collect region-specific regulatory information, enabling efficient data collection.

[0044] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media activities and collect relevant market trend data. The data collection unit can analyze a company's social media activities and collect relevant market trend data. The data collection unit can also collect customer feedback data based on a company's social media activities. The data collection unit can also collect competitor activity data, taking into account a company's social media activities. In addition, the data collection unit can collect competitor activity data, taking into account a company's social media activities. This allows for the collection of relevant market trend data and customer feedback data by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze a company's social media activities using AI and collect relevant data. The AI ​​can analyze a company's social media activities in real time and collect relevant market trend data and customer feedback data. The AI ​​can also automatically collect competitor activity data, enabling efficient data collection.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. The analysis unit can also perform a concise analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis. AI can evaluate the importance of the data in real time and perform a detailed analysis on important data. AI can also perform a concise analysis on less important data, enabling efficient data analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial data. Furthermore, the analysis unit can apply a technical evaluation algorithm to technical data. In addition, the analysis unit can apply a market analysis algorithm to market trend data. By applying different analysis algorithms depending on the data category, highly accurate data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can classify data categories using AI and apply an appropriate analysis algorithm. AI can classify data categories in real time and apply an appropriate analysis algorithm. Furthermore, AI can automatically analyze data from different categories, such as financial data, technical data, and market trend data, to achieve highly accurate data analysis.

[0047] The analysis department can determine the priority of analysis based on the data collection timing. For example, the analysis department can prioritize the analysis of the most recent data. The analysis department can also prioritize the most recent data while referring to past data. Furthermore, the analysis department can adjust the priority of analysis in stages according to the data collection timing. This makes it possible to perform analysis that prioritizes the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can use AI to evaluate the data collection timing and determine the priority of analysis. AI can evaluate the data collection timing in real time and prioritize the analysis of the most recent data. AI can also perform analysis that prioritizes the most recent data while referring to past data.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the relevance of the data and adjust the order of analysis. AI can evaluate the relevance of the data in real time and prioritize the analysis of highly relevant data. AI can also postpone the analysis of less relevant data, thereby achieving efficient data analysis.

[0049] The proposal department can adjust the level of detail of a proposal based on the importance of the business partner. For example, the proposal department can provide detailed proposals to important business partners. The proposal department can also provide concise proposals to less important business partners. Furthermore, the proposal department can adjust the level of detail of a proposal in stages according to the importance of the business partner. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the business partner. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to evaluate the importance of business partners and adjust the level of detail of the proposal. The AI ​​can evaluate the importance of business partners in real time and provide detailed proposals to important partners. Furthermore, AI can provide concise proposals to less important partners, enabling more efficient proposal development.

[0050] The proposal department can apply different proposal algorithms depending on the business partner's category when making a proposal. For example, the proposal department can apply a technical proposal algorithm to a technical partner. The proposal department can also apply a sales proposal algorithm to a sales partner. Furthermore, the proposal department can apply a financial proposal algorithm to a financial partner. By applying different proposal algorithms depending on the business partner's category, highly accurate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can classify business partners using AI and apply an appropriate proposal algorithm. AI can classify business partners in real time and apply an appropriate proposal algorithm. AI can also automatically make proposals to partners in different categories such as technical partners, sales partners, and financial partners, achieving highly accurate proposals.

[0051] The proposal department can determine the priority of proposals based on the timing of business partner selection. For example, the proposal department can prioritize the most recent business partners. The proposal department can also prioritize the most recent partners while referring to past business partners. Furthermore, the proposal department can adjust the priority of proposals in stages according to the timing of business partner selection. This makes it possible to make proposals that prioritize the most recent business partners by determining the priority of proposals based on the timing of business partner selection. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to evaluate the timing of business partner selection and determine the priority of proposals. AI can evaluate the timing of business partner selection in real time and prioritize the most recent partners. AI can also make proposals that prioritize the most recent partners while referring to past partners.

[0052] The proposal department can adjust the order of proposals based on the relevance of business partners when making proposals. For example, the proposal department can prioritize proposing highly relevant business partners. The proposal department can also postpone proposing less relevant business partners. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of business partners. This allows for efficient proposals by adjusting the order of proposals based on the relevance of business partners. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to evaluate the relevance of business partners and adjust the order of proposals. The AI ​​can evaluate the relevance of business partners in real time and prioritize proposing highly relevant partners. The AI ​​can also postpone less relevant partners, enabling efficient proposals.

[0053] The generation unit can improve the accuracy of generation by considering the relationships between business partners during generation. For example, the generation unit can generate plans with a high success rate by considering the past collaboration history between business partners. The generation unit can generate plans with a high success rate by considering the past collaboration history between business partners. Furthermore, the generation unit can generate optimal collaboration plans by considering the technological complementarity between business partners. In addition, the generation unit can generate efficient new business proposals by considering the overlap in market share and customer base among business partners. The generation unit can generate efficient new business proposals by considering the overlap in market share and customer base among business partners. This allows for the generation of highly accurate new business proposals and collaboration plans by considering the relationships between business partners. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can analyze the relationships between business partners using generation AI to improve the accuracy of generation. The generation AI can automatically analyze past collaboration history and technological complementarity between business partners to generate optimal plans. Furthermore, the generation AI can consider overlapping market share and customer segments to generate efficient new business proposals.

[0054] The generation unit can generate plans while considering the attribute information of business partners. For example, the generation unit can generate collaboration plans of an appropriate size by considering the size of the business partner's company. The generation unit can also generate new business proposals specific to a particular industry by considering the industry of the business partner. Furthermore, the generation unit can generate collaboration plans suitable for a particular region by considering the regional characteristics of the business partner. By considering the attribute information of business partners, appropriate new business proposals and collaboration plans can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can analyze the attribute information of business partners using a generation AI and generate an appropriate plan. The generation AI can automatically analyze the size of the business partner's company, industry, and regional characteristics and generate the optimal plan. Furthermore, the generation AI can analyze a company's attribute information in real time and generate appropriate new business proposals and collaboration plans.

[0055] The generation unit can perform generation while considering the geographical distribution of business partners. For example, the generation unit can generate collaboration plans with geographically close partners by considering the location of business partners. The generation unit can also generate efficient collaboration plans by considering the logistics costs between geographically distant partners. Furthermore, the generation unit can generate new business proposals that leverage regional characteristics based on geographical distribution. This makes it possible to generate efficient new business proposals and collaboration plans by considering the geographical distribution of business partners. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can analyze the geographical distribution of business partners using a generation AI and generate an appropriate plan. The generation AI can automatically analyze the location and logistics costs of business partners and generate the optimal plan. Furthermore, the generation AI can analyze regional characteristics in real time and generate efficient new business proposals and collaboration plans.

[0056] The generation unit can improve the accuracy of generation by referring to relevant literature of business partners during the generation process. For example, the generation unit can generate technically supported collaboration plans by referring to past research papers of business partners. The generation unit can also generate new business proposals that utilize intellectual property by referring to patent information of business partners. Furthermore, the generation unit can generate collaboration plans that are in line with market trends by referring to industry reports of business partners. This allows for the generation of highly accurate new business proposals and collaboration plans by referring to relevant literature of business partners. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can analyze relevant literature of business partners using generation AI and generate an appropriate plan. The generation AI can automatically analyze research papers, patent information, and industry reports from business partners to generate optimal plans. Furthermore, the generation AI can refer to relevant literature in real time to generate highly accurate new business proposals and collaboration plans.

[0057] The online negotiation function can select the optimal negotiation method by referring to past negotiation history during negotiations. For example, the online negotiation function can apply similar methods based on past successful negotiation methods. Furthermore, the online negotiation function can eliminate ineffective methods from past negotiation history and select efficient methods. In addition, the online negotiation function can analyze past negotiation history and select the optimal negotiation method under specific conditions. This allows for the selection of effective negotiation methods by referring to past negotiation history. Some or all of the above processing in the online negotiation function may be performed using AI, or not. For example, the online negotiation function can analyze past negotiation history using AI to select the optimal negotiation method. AI can automatically analyze past negotiation history and select effective negotiation methods. Furthermore, AI can select the optimal negotiation method in real time under specific conditions, enabling efficient negotiations.

[0058] The online negotiation function can select the optimal negotiation method during negotiations, taking into account the user's device information. For example, if the user is using a smartphone, the online negotiation function can provide a negotiation method adapted to the screen size. Furthermore, if the user is using a tablet, the online negotiation function can provide a negotiation method optimized for larger screens. Additionally, if the user is using a personal computer, the online negotiation function can provide a negotiation method that displays detailed information. This allows for the provision of the optimal negotiation method by considering the user's device information. Some or all of the above processing in the online negotiation function may be performed using AI, or not. For example, the online negotiation function can analyze the user's device information using AI to select the optimal negotiation method. AI can analyze the user's device information in real time and provide the optimal negotiation method. Furthermore, AI can automatically adjust screen size and display method according to the type of device, enabling more efficient business negotiations.

[0059] The virtual factory tour function can select the optimal tour method by referring to past tour history during the tour. For example, the virtual factory tour function can apply similar methods based on past successful tour methods. Furthermore, the virtual factory tour function can eliminate ineffective methods from past tour history and select efficient methods. In addition, the virtual factory tour function can analyze past tour history and select the optimal tour method under specific conditions. This allows for the selection of effective tour methods by referring to past tour history. Some or all of the above processing in the virtual factory tour function may be performed using AI, or not. For example, the virtual factory tour function can analyze past tour history using AI to select the optimal tour method. AI can automatically analyze past tour history and select effective tour methods. AI can also select the optimal tour method in real time under specific conditions, enabling efficient tours.

[0060] The virtual factory tour function can select the optimal tour method by considering the user's device information during the tour. For example, if the user is using a smartphone, the virtual factory tour function can provide a tour method that is adapted to the screen size. Furthermore, if the user is using a tablet, the virtual factory tour function can provide a tour method that is optimized for a larger screen. In addition, if the user is using a personal computer, the virtual factory tour function can provide a tour method that displays detailed information. This allows the system to provide the optimal tour method by considering the user's device information. Some or all of the above processing in the virtual factory tour function may be performed using AI, for example, or not. For example, the virtual factory tour function can analyze the user's device information using AI and select the optimal tour method. AI can analyze the user's device information in real time and provide the optimal tour method. Furthermore, the AI ​​can automatically adjust the screen size and display method according to the type of device, enabling a more efficient viewing experience.

[0061] The information delivery unit can select the optimal information delivery method by referring to past delivery history at the time of delivery. The information delivery unit can, for example, apply a similar method based on a previously successful information delivery method. The information delivery unit can, for example, apply a similar method based on a previously successful information delivery method. Furthermore, the information delivery unit can eliminate ineffective methods from past delivery history and select an efficient method. The information delivery unit can, for example, eliminate ineffective methods from past delivery history and select an efficient method. In addition, the information delivery unit can analyze past delivery history and select the optimal information delivery method under specific conditions. The information delivery unit can, for example, analyze past delivery history and select the optimal information delivery method under specific conditions. This allows for the selection of an effective information delivery method by referring to past delivery history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can analyze past delivery history using AI and select the optimal information delivery method. AI can automatically analyze past delivery history and select an effective information delivery method. AI can also select the optimal information delivery method in real time under specific conditions, thereby achieving efficient information delivery.

[0062] The information provider can select the optimal information delivery method by considering the user's geographical location information at the time of delivery. For example, the information provider can prioritize providing region-related information based on the user's location. The information provider can prioritize providing region-related information based on the user's location. Furthermore, the information provider can prioritize providing nearby market trend information by considering the user's geographical location information. In addition, the information provider can prioritize providing region-specific regulatory information based on the user's geographical location information. The information provider can prioritize providing region-specific regulatory information by considering the user's geographical location information. This makes it possible to provide optimal information by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can analyze the user's geographical location information using AI and select the optimal information delivery method. AI can analyze the user's geographical location information in real time and provide the optimal information delivery method. Furthermore, AI can automatically select region-specific information based on geographical location data, enabling efficient information provision.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can analyze a company's social media activities and collect relevant data. For example, it can analyze a company's social media activities and collect relevant market trend data. It can also collect customer feedback data based on a company's social media activities. Furthermore, it can collect competitor activity data, taking into account a company's social media activities. In this way, by analyzing a company's social media activities, it is possible to collect relevant market trend data and customer feedback data.

[0065] The generation unit can improve the accuracy of generation by considering the relationships between business partners. For example, it can generate plans with a high success rate by considering the past collaboration history between business partners. It can also generate optimal collaboration plans by considering the technological complementarity between business partners. Furthermore, it can generate efficient new business proposals by considering the overlap in market share and customer base among business partners. In this way, by considering the relationships between business partners, it is possible to generate highly accurate new business proposals and collaboration plans.

[0066] The data collection unit can prioritize the collection of highly relevant data, taking into account the geographical location of companies. For example, it can prioritize the collection of region-related data based on the company's location. It can also prioritize the collection of nearby market trend data, taking into account the company's geographical location. Furthermore, it can prioritize the collection of region-specific regulatory information based on the company's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data, taking into account the company's geographical location.

[0067] The analysis department can apply different analysis algorithms depending on the data category. For example, financial analysis algorithms can be applied to financial data, technology evaluation algorithms to technical data, and market trend algorithms to market trend data. By applying different analysis algorithms according to the data category, highly accurate data analysis becomes possible.

[0068] The generation unit can generate proposals while considering the attribute information of business partners. For example, it can generate collaboration plans of an appropriate scale by considering the size of the business partner's company. It can also generate new business proposals specific to a particular industry by considering the industry of the business partner. Furthermore, it can generate collaboration plans suitable for a region by considering the regional characteristics of the business partner. In this way, by considering the attribute information of business partners, it is possible to generate appropriate new business proposals and collaboration plans.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects company data. The data collection unit collects information such as the company's financial data, business activities, technological capabilities, and market trends. This allows for an understanding of the company's management situation, business areas, technological strengths, and competitiveness. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department comprehensively analyzes the collected data to understand the company's strengths and weaknesses. This allows for an evaluation of the company's management situation, business areas, technological strengths, and competitiveness. Step 3: The Proposal Department proposes the most suitable business partners based on the analysis results obtained by the Analysis Department. For example, the Proposal Department selects the most suitable business partners by considering the strengths and weaknesses of each company. This allows them to propose companies with stable management, overlapping business areas, and technological strengths, based on the company's financial data, business content, and technological capabilities. Step 4: The generation unit generates new business proposals and collaboration plans by combining the strengths of the business partners proposed by the proposal unit. For example, the generation unit can combine the strengths of companies to generate new business proposals, new product development plans, and sales channel expansion plans.

[0071] (Example of form 2) The business matching platform according to an embodiment of the present invention is a system that performs business matching between companies using generative AI. This system comprehensively analyzes information such as companies' financial data, business content, technological capabilities, and market trends, and proposes the most suitable business partner. Furthermore, the system automatically generates new business proposals and collaboration plans that combine the strengths of both companies, and shows a concrete path for cooperation. The platform is also equipped with online business negotiation functions and virtual factory tour functions, enabling business exchange that transcends geographical constraints. In addition, the system provides information on regional subsidies and regulations to support smooth business development. This aims to simultaneously revitalize local economies and alleviate the overconcentration of power in urban areas. As a result, the business matching platform can efficiently perform business matching between companies and promote the creation of new value.

[0072] The business matching platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a generation unit. The data collection unit collects company data. The data collection unit collects information such as company financial data, business content, technological capabilities, and market trends. For example, the data collection unit can collect company financial data to understand the company's management situation. The data collection unit can also collect company business content to understand the company's business areas and strengths. Furthermore, the data collection unit can collect company technological capabilities to understand the company's technological strengths and competitiveness. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can comprehensively analyze the collected data to understand the company's strengths and weaknesses. For example, the analysis unit can analyze company financial data to evaluate the company's management situation. Furthermore, the analysis unit can analyze company business content to evaluate the company's business areas and strengths. Furthermore, the analysis unit can analyze company technological capabilities to evaluate the company's technological strengths and competitiveness. The proposal unit proposes the most suitable business partner based on the analysis results obtained by the analysis unit. For example, the proposal unit can select the most suitable business partner by considering the company's strengths and weaknesses. The proposal unit can, for example, propose companies with stable management conditions based on their financial data. It can also propose companies with overlapping business areas based on their business activities. Furthermore, it can propose companies with technological strengths based on their technological capabilities. The generation unit generates new business proposals and collaboration plans by combining the strengths of the business partners proposed by the proposal unit. For example, the generation unit can generate new business proposals by combining the strengths of companies. For example, the generation unit can generate new product development plans by combining the technological capabilities of companies. Furthermore, the generation unit can generate new sales channel expansion plans by combining the sales networks of companies. As a result, the business matching platform according to this embodiment can efficiently facilitate business matching between companies and promote the creation of new value. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and generation unit may be performed using AI, or not using AI.For example, the data collection unit can use AI to automate data collection when gathering company data. The analysis unit can use AI to analyze the collected data and evaluate the company's strengths and weaknesses. The proposal unit can use AI to select and propose the most suitable business partners. The generation unit can use AI to generate new business proposals and collaboration plans.

[0073] The data collection department collects corporate data. For example, it collects information such as corporate financial data, business activities, technological capabilities, and market trends. Specifically, it collects detailed financial data such as revenue, profit, liabilities, and assets to understand the company's management situation. Regarding corporate business activities, it collects information such as the types of products and services offered, the scale of the business, and target markets to understand the company's business domain and strengths. Furthermore, regarding corporate technological capabilities, it collects patent information, research and development results, and technical know-how to understand the company's technological strengths and competitiveness. Regarding market trends, it collects information such as industry trends, competitor activities, and consumer needs to understand the company's market environment. This data is collected from various sources, including publicly available information on the internet, official corporate websites, industry reports, news articles, and information provided directly by companies. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0074] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can comprehensively analyze the collected data to understand a company's strengths and weaknesses. Specifically, it analyzes a company's financial data to evaluate its management status from the perspectives of profitability, stability, and growth. It also analyzes a company's business activities to evaluate its business areas and strengths from the perspectives of business diversification, competitive advantage, and market share. Furthermore, it analyzes a company's technological capabilities to evaluate its technological strengths and competitiveness from the perspectives of technological originality, innovativeness, and applicability. It also analyzes market trends to evaluate the company's market environment, taking into account industry growth potential, competitor strategies, and consumer trends. These analyses are often automated using AI, which can process large amounts of data quickly and accurately and extract patterns and trends. For example, machine learning algorithms can be used to predict future earnings from a company's financial data, and natural language processing technology can be used to analyze text data related to a company's business activities. As a result, the analysis department can quickly and accurately analyze the collected data and comprehensively evaluate a company's strengths and weaknesses. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific industries or regions based on past performance data and formulate future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0075] The Proposal Department proposes the most suitable business partners based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can select the most suitable business partners by considering a company's strengths and weaknesses. Specifically, based on a company's financial data, it can propose companies with stable management conditions. For example, it can select and propose companies with high profitability and a solid financial foundation. It can also propose companies with overlapping business areas based on a company's business content. For example, it can select and propose companies operating in the same market segment or companies offering complementary products or services. Furthermore, based on a company's technological capabilities, it can propose companies with technological strengths. For example, it can select and propose companies with superior technology in a specific technological field or companies developing innovative technologies. The Proposal Department uses AI to analyze this data and select the most suitable business partners. The AI ​​comprehensively evaluates a company's strengths and weaknesses and compares multiple candidate companies to identify the most suitable business partner. For example, it can use machine learning algorithms to predict the optimal partnership under similar conditions based on past success stories. This allows the proposal department to provide companies with highly accurate business partner suggestions and promote collaboration between companies. Furthermore, the proposal department can collect user feedback and optimize its suggestion algorithm to continuously improve the accuracy and effectiveness of its suggestions. As a result, the proposal department can always provide highly accurate suggestions based on the latest information and efficiently facilitate business matching between companies.

[0076] The generation unit generates new business proposals and collaboration plans by combining the strengths of business partners proposed by the proposal unit. For example, the generation unit can combine the strengths of companies to generate new business proposals. Specifically, it can combine the technological capabilities of companies to generate new product development plans. For example, it can generate plans for companies with superior technologies in different technological fields to collaborate and develop new products or services. It can also combine the sales networks of companies to generate plans for expanding sales channels. For example, it can generate plans for companies with strong sales networks in different regions or markets to collaborate and enter new markets. Furthermore, it can combine the financial strengths of companies to generate funding plans for realizing large-scale projects. The generation unit generates these plans using AI. The AI ​​can comprehensively evaluate the strengths and weaknesses of companies and find the optimal combination. For example, it can use machine learning algorithms to predict the optimal collaboration plan under similar conditions based on past success stories. As a result, the generation unit can provide companies with highly accurate new business proposals and collaboration plans, promoting collaboration between companies. Furthermore, the generation unit can evaluate the feasibility and effectiveness of the generated plans and modify them as needed. For example, it evaluates the resources and risks required to execute a plan and develops an optimal execution plan. Furthermore, the generation unit collects user feedback and optimizes its generation algorithm, enabling it to consistently provide highly accurate plans based on the latest information. This allows the generation unit to efficiently facilitate collaboration between companies and realize the creation of new value.

[0077] The business matching platform includes an online business negotiation function. This function allows companies to conduct business negotiations online, for example, using video conferencing tools. It enables real-time business negotiations between companies. Furthermore, it includes a chat function, allowing text messages to be sent and received during negotiations. Additionally, it includes a file sharing function, enabling the sharing of documents and data during negotiations. This online business negotiation function makes business exchange possible, transcending geographical limitations. Some or all of the processes described above in the online business negotiation function may be performed using AI, or not. For example, the online business negotiation function can use AI to support the progress of negotiations. The AI ​​can analyze conversations during negotiations in real time and provide appropriate advice and suggestions. The AI ​​can also automatically organize documents and data during negotiations, ensuring a smoother process.

[0078] The business matching platform includes a virtual factory tour function. This function allows users to virtually tour a company's factory, for example, using VR technology. It also allows real-time tours of the factory. Furthermore, the virtual factory tour function includes a guide that provides detailed explanations of the factory's manufacturing process. Additionally, it can display factory equipment and products in 3D models, providing detailed information. This enables business exchange that transcends geographical limitations. Some or all of the above-described processes in the virtual factory tour function may be performed using AI, for example, or without AI. For example, the virtual factory tour function can use AI to support the tour's progress. The AI ​​can answer questions during the tour in real time, deepening the visitor's understanding. The AI ​​can also automatically organize data and information during the tour, ensuring a smooth tour.

[0079] The business matching platform includes a provision unit that provides regional subsidy and regulatory information. This unit can, for example, collect subsidy information provided by local governments and provide it to companies. It can also automatically collect and provide subsidy information provided by local governments. Furthermore, it can collect and provide regional regulatory information to companies. In addition, the unit can provide companies with the information they need in real time, supporting smooth business development. This supports smooth business development by providing regional subsidy and regulatory information. Some or all of the above processing in the provision unit may be performed using AI, for example, or without AI. For example, the unit can use AI to automatically collect subsidy and regulatory information and provide it to companies. AI can collect the latest information in real time and provide it to companies. Furthermore, AI can select and provide the most suitable information according to the needs of the companies.

[0080] The data collection unit can collect information such as a company's financial data, business activities, technological capabilities, and market trends. For example, the data collection unit can collect a company's financial data to understand its management situation. For example, the data collection unit can collect a company's financial data to evaluate its profitability and financial soundness. The data collection unit can also collect a company's business activities to understand its business domain and strengths. For example, the data collection unit can collect a company's business activities to evaluate the characteristics of a company's products and services. Furthermore, the data collection unit can collect a company's technological capabilities to understand its technological strengths and competitiveness. For example, the data collection unit can collect a company's technological capabilities to evaluate its technological superiority. This enables comprehensive data analysis by collecting information such as a company's financial data, business activities, technological capabilities, and market trends. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, when collecting company data, the data collection unit can use AI to automate data collection. AI can automatically collect a company's financial data and business activities and store them in a database. Furthermore, AI can collect information on companies' technological capabilities and market trends in real time, and provide the latest information.

[0081] The analysis department can comprehensively analyze the collected data. For example, the analysis department can comprehensively analyze the collected data to understand a company's strengths and weaknesses. For example, the analysis department can analyze a company's financial data to evaluate its management status. For example, the analysis department can analyze a company's financial data to evaluate its profitability and financial soundness. The analysis department can also analyze a company's business operations to evaluate its business areas and strengths. For example, the analysis department can analyze a company's business operations to evaluate the characteristics of its products and services. Furthermore, the analysis department can analyze a company's technological capabilities to evaluate its technological strengths and competitiveness. For example, the analysis department can analyze a company's technological capabilities to evaluate its technological advantages. By comprehensively analyzing the collected data, the analysis department can obtain basic data to propose the most suitable business partners. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can use AI to analyze the collected data and evaluate a company's strengths and weaknesses. AI can automatically analyze a company's financial data and business operations to evaluate its management status and business areas. Furthermore, AI can analyze a company's technological capabilities and market trends in real time, providing the latest information.

[0082] The proposal department can propose the most suitable business partners. For example, the proposal department can select the most suitable business partners by considering the strengths and weaknesses of a company. For example, the proposal department can propose companies with stable management based on a company's financial data. For example, the proposal department can propose companies with high profitability based on a company's financial data. Furthermore, the proposal department can propose companies with overlapping business areas based on a company's business content. For example, the proposal department can propose companies with complementary products and services based on a company's business content. In addition, the proposal department can propose companies with technological strengths based on a company's technological capabilities. For example, the proposal department can propose companies with technological advantages based on a company's technological capabilities. In this way, by proposing the most suitable business partners, collaboration between companies can be promoted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to select and propose the most suitable business partners. AI can automatically analyze a company's financial data and business content to select the most suitable business partners. Furthermore, AI can analyze a company's technological capabilities and market trends in real time, and propose the most suitable business partners based on the latest information.

[0083] The generation unit can generate new business proposals and collaboration plans by combining the strengths of business partners. For example, the generation unit can generate new business proposals by combining the strengths of companies. For example, the generation unit can generate new product development plans by combining the technological capabilities of companies. For example, the generation unit can generate new technology development plans by combining the technological capabilities of companies. Furthermore, the generation unit can generate new sales channel expansion plans by combining the sales networks of companies. For example, the generation unit can generate new market development plans by combining the sales networks of companies. In addition, the generation unit can generate new business development plans by combining the resources of companies. For example, the generation unit can generate new business expansion plans by combining the resources of companies. This allows for the generation of concrete collaboration paths by generating new business proposals and collaboration plans that combine the strengths of business partners. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate new business proposals and collaboration plans using a generation AI. The generation AI can automatically analyze the strengths and resources of companies and generate optimal business proposals and plans. Furthermore, the generation AI can analyze a company's technological capabilities and market trends in real time, and generate optimal business proposals and plans based on the latest information.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Furthermore, if the user is relaxed, the data collection unit can collect more detailed data and obtain more information. In addition, if the user is in a hurry, the data collection unit can prioritize collecting only the important data and process it quickly. This allows for efficient data collection by adjusting the timing of data collection based on the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The data collection unit can analyze a company's past data collection history and select the optimal collection method. For example, the data collection unit can apply similar methods based on past successful data collection methods. Furthermore, the data collection unit can eliminate ineffective methods from past data collection history and select efficient methods. In addition, the data collection unit can analyze past data collection history and select the optimal collection method under specific conditions. This allows for the selection of effective data collection methods by analyzing a company's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can analyze past data collection history using AI to select the optimal collection method. AI can automatically analyze past data collection history and select effective collection methods. Furthermore, AI can select the optimal collection method in real time under specific conditions, enabling efficient data collection.

[0086] The data collection unit can filter data based on the company's current business status and areas of interest during data collection. For example, the data collection unit can collect only highly relevant data based on the company's current business status. The data collection unit can also prioritize the collection of data related to specific areas based on the company's areas of interest. Furthermore, the data collection unit can efficiently collect data by eliminating unnecessary data, taking into account the company's business status and areas of interest. This allows for the efficient collection of highly relevant data by filtering based on the company's current business status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the company's business status and areas of interest using AI and perform filtering. AI can analyze the company's business status and areas of interest in real time and select highly relevant data. Furthermore, AI can automatically eliminate unnecessary data, enabling efficient data collection.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can prioritize the collection of region-related data based on the company's location. The data collection unit can prioritize the collection of region-related data based on the company's location. Furthermore, the data collection unit can prioritize the collection of nearby market trend data by considering the geographical location information of companies. In addition, the data collection unit can prioritize the collection of region-specific regulatory information based on the company's geographical location information. The data collection unit can prioritize the collection of region-specific regulatory information based on the company's geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the company's geographical location information using AI and select highly relevant data. AI can analyze the company's geographical location information in real time and select highly relevant data. Furthermore, AI can automatically collect region-specific regulatory information, enabling efficient data collection.

[0089] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media activities and collect relevant market trend data. The data collection unit can analyze a company's social media activities and collect relevant market trend data. The data collection unit can also collect customer feedback data based on a company's social media activities. The data collection unit can also collect competitor activity data, taking into account a company's social media activities. In addition, the data collection unit can collect competitor activity data, taking into account a company's social media activities. This allows for the collection of relevant market trend data and customer feedback data by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze a company's social media activities using AI and collect relevant data. The AI ​​can analyze a company's social media activities in real time and collect relevant market trend data and customer feedback data. The AI ​​can also automatically collect competitor activity data, enabling efficient data collection.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. The analysis unit can also perform a concise analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis. AI can evaluate the importance of the data in real time and perform a detailed analysis on important data. AI can also perform a concise analysis on less important data, enabling efficient data analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial data. Furthermore, the analysis unit can apply a technical evaluation algorithm to technical data. In addition, the analysis unit can apply a market analysis algorithm to market trend data. By applying different analysis algorithms depending on the data category, highly accurate data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can classify data categories using AI and apply an appropriate analysis algorithm. AI can classify data categories in real time and apply an appropriate analysis algorithm. Furthermore, AI can automatically analyze data from different categories, such as financial data, technical data, and market trend data, to achieve highly accurate data analysis.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. The analysis unit can also provide a detailed analysis if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis department can determine the priority of analysis based on the data collection timing. For example, the analysis department can prioritize the analysis of the most recent data. The analysis department can also prioritize the most recent data while referring to past data. Furthermore, the analysis department can adjust the priority of analysis in stages according to the data collection timing. This makes it possible to perform analysis that prioritizes the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can use AI to evaluate the data collection timing and determine the priority of analysis. AI can evaluate the data collection timing in real time and prioritize the analysis of the most recent data. AI can also perform analysis that prioritizes the most recent data while referring to past data.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the relevance of the data and adjust the order of analysis. AI can evaluate the relevance of the data in real time and prioritize the analysis of highly relevant data. AI can also postpone the analysis of less relevant data, thereby achieving efficient data analysis.

[0096] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion section can provide simple and easy-to-understand suggestions. The suggestion section can also provide detailed suggestions if the user is relaxed. Furthermore, if the user is in a hurry, the suggestion section can provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0097] The proposal department can adjust the level of detail of a proposal based on the importance of the business partner. For example, the proposal department can provide detailed proposals to important business partners. The proposal department can also provide concise proposals to less important business partners. Furthermore, the proposal department can adjust the level of detail of a proposal in stages according to the importance of the business partner. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the business partner. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to evaluate the importance of business partners and adjust the level of detail of the proposal. The AI ​​can evaluate the importance of business partners in real time and provide detailed proposals to important partners. Furthermore, AI can provide concise proposals to less important partners, enabling more efficient proposal development.

[0098] The proposal department can apply different proposal algorithms depending on the business partner's category when making a proposal. For example, the proposal department can apply a technical proposal algorithm to a technical partner. The proposal department can also apply a sales proposal algorithm to a sales partner. Furthermore, the proposal department can apply a financial proposal algorithm to a financial partner. By applying different proposal algorithms depending on the business partner's category, highly accurate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can classify business partners using AI and apply an appropriate proposal algorithm. AI can classify business partners in real time and apply an appropriate proposal algorithm. AI can also automatically make proposals to partners in different categories such as technical partners, sales partners, and financial partners, achieving highly accurate proposals.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the proposal unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0100] The proposal department can determine the priority of proposals based on the timing of business partner selection. For example, the proposal department can prioritize the most recent business partners. The proposal department can also prioritize the most recent partners while referring to past business partners. Furthermore, the proposal department can adjust the priority of proposals in stages according to the timing of business partner selection. This makes it possible to make proposals that prioritize the most recent business partners by determining the priority of proposals based on the timing of business partner selection. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to evaluate the timing of business partner selection and determine the priority of proposals. AI can evaluate the timing of business partner selection in real time and prioritize the most recent partners. AI can also make proposals that prioritize the most recent partners while referring to past partners.

[0101] The proposal department can adjust the order of proposals based on the relevance of business partners when making proposals. For example, the proposal department can prioritize proposing highly relevant business partners. The proposal department can also postpone proposing less relevant business partners. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of business partners. This allows for efficient proposals by adjusting the order of proposals based on the relevance of business partners. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to evaluate the relevance of business partners and adjust the order of proposals. The AI ​​can evaluate the relevance of business partners in real time and prioritize proposing highly relevant partners. The AI ​​can also postpone less relevant partners, enabling efficient proposals.

[0102] The generation unit can estimate the user's emotions and determine the priority of new business proposals and collaboration plans based on the estimated emotions. For example, if the user is stressed, the generation unit can prioritize generating simple and actionable plans. If the user is relaxed, the generation unit can prioritize generating detailed and complex plans. Furthermore, if the user is in a hurry, the generation unit can prioritize generating plans that can be quickly implemented. This enables efficient plan generation by prioritizing new business proposals and collaboration plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0103] The generation unit can improve the accuracy of generation by considering the relationships between business partners during generation. For example, the generation unit can generate plans with a high success rate by considering the past collaboration history between business partners. The generation unit can generate plans with a high success rate by considering the past collaboration history between business partners. Furthermore, the generation unit can generate optimal collaboration plans by considering the technological complementarity between business partners. In addition, the generation unit can generate efficient new business proposals by considering the overlap in market share and customer base among business partners. The generation unit can generate efficient new business proposals by considering the overlap in market share and customer base among business partners. This allows for the generation of highly accurate new business proposals and collaboration plans by considering the relationships between business partners. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can analyze the relationships between business partners using generation AI to improve the accuracy of generation. The generation AI can automatically analyze past collaboration history and technological complementarity between business partners to generate optimal plans. Furthermore, the generation AI can consider overlapping market share and customer segments to generate efficient new business proposals.

[0104] The generation unit can generate plans while considering the attribute information of business partners. For example, the generation unit can generate collaboration plans of an appropriate size by considering the size of the business partner's company. The generation unit can also generate new business proposals specific to a particular industry by considering the industry of the business partner. Furthermore, the generation unit can generate collaboration plans suitable for a particular region by considering the regional characteristics of the business partner. By considering the attribute information of business partners, appropriate new business proposals and collaboration plans can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can analyze the attribute information of business partners using a generation AI and generate an appropriate plan. The generation AI can automatically analyze the size of the business partner's company, industry, and regional characteristics and generate the optimal plan. Furthermore, the generation AI can analyze a company's attribute information in real time and generate appropriate new business proposals and collaboration plans.

[0105] The generation unit can estimate the user's emotions and adjust the display method of new business proposals and collaboration plans generated based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method that includes detailed information. In addition, if the user is in a hurry, the generation unit can provide a concise display method that gets straight to the point. This makes it possible to provide a display that is easy for the user to understand by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0106] The generation unit can perform generation while considering the geographical distribution of business partners. For example, the generation unit can generate collaboration plans with geographically close partners by considering the location of business partners. The generation unit can also generate efficient collaboration plans by considering the logistics costs between geographically distant partners. Furthermore, the generation unit can generate new business proposals that leverage regional characteristics based on geographical distribution. This makes it possible to generate efficient new business proposals and collaboration plans by considering the geographical distribution of business partners. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can analyze the geographical distribution of business partners using a generation AI and generate an appropriate plan. The generation AI can automatically analyze the location and logistics costs of business partners and generate the optimal plan. Furthermore, the generation AI can analyze regional characteristics in real time and generate efficient new business proposals and collaboration plans.

[0107] The generation unit can improve the accuracy of generation by referring to relevant literature of business partners during the generation process. For example, the generation unit can generate technically supported collaboration plans by referring to past research papers of business partners. The generation unit can also generate new business proposals that utilize intellectual property by referring to patent information of business partners. Furthermore, the generation unit can generate collaboration plans that are in line with market trends by referring to industry reports of business partners. This allows for the generation of highly accurate new business proposals and collaboration plans by referring to relevant literature of business partners. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can analyze relevant literature of business partners using generation AI and generate an appropriate plan. The generation AI can automatically analyze research papers, patent information, and industry reports from business partners to generate optimal plans. Furthermore, the generation AI can refer to relevant literature in real time to generate highly accurate new business proposals and collaboration plans.

[0108] The online negotiation function can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is nervous, the online negotiation function can slow down the negotiation to help them relax. Furthermore, if the user is relaxed, the online negotiation function can provide detailed information while conducting the negotiation. Additionally, if the user is in a hurry, the online negotiation function can conduct a concise and efficient negotiation. This allows for an optimal negotiation experience for the user by adjusting the negotiation process based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the online business negotiation function may be performed using AI, for example, or without AI. For example, the online business negotiation function may input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0109] The online negotiation function can select the optimal negotiation method by referring to past negotiation history during negotiations. For example, the online negotiation function can apply similar methods based on past successful negotiation methods. Furthermore, the online negotiation function can eliminate ineffective methods from past negotiation history and select efficient methods. In addition, the online negotiation function can analyze past negotiation history and select the optimal negotiation method under specific conditions. This allows for the selection of effective negotiation methods by referring to past negotiation history. Some or all of the above processing in the online negotiation function may be performed using AI, or not. For example, the online negotiation function can analyze past negotiation history using AI to select the optimal negotiation method. AI can automatically analyze past negotiation history and select effective negotiation methods. Furthermore, AI can select the optimal negotiation method in real time under specific conditions, enabling efficient negotiations.

[0110] The online negotiation function can estimate the user's emotions and prioritize negotiations based on those emotions. For example, if the user is feeling stressed, the online negotiation function can prioritize only important negotiations. Furthermore, if the user is relaxed, the online negotiation function can prioritize detailed negotiations. Additionally, if the user is in a hurry, the online negotiation function can prioritize negotiations that can be completed quickly. This enables efficient negotiations by prioritizing negotiations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the online business negotiation function may be performed using AI, for example, or without AI. For example, the online business negotiation function may input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0111] The online negotiation function can select the optimal negotiation method during negotiations, taking into account the user's device information. For example, if the user is using a smartphone, the online negotiation function can provide a negotiation method adapted to the screen size. Furthermore, if the user is using a tablet, the online negotiation function can provide a negotiation method optimized for larger screens. Additionally, if the user is using a personal computer, the online negotiation function can provide a negotiation method that displays detailed information. This allows for the provision of the optimal negotiation method by considering the user's device information. Some or all of the above processing in the online negotiation function may be performed using AI, or not. For example, the online negotiation function can analyze the user's device information using AI to select the optimal negotiation method. AI can analyze the user's device information in real time and provide the optimal negotiation method. Furthermore, AI can automatically adjust screen size and display method according to the type of device, enabling more efficient business negotiations.

[0112] The virtual factory tour function can estimate the user's emotions and adjust the tour's progression based on those emotions. For example, if the user is nervous, the virtual factory tour can proceed slowly to help them relax. Furthermore, if the user is relaxed, the virtual factory tour can provide detailed information as it progresses. Additionally, if the user is in a hurry, the virtual factory tour can provide a concise and rapid tour focusing on the key points. This allows for an optimal tour experience for the user by adjusting the tour's progression based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the virtual factory tour function may be performed using AI, for example, or without AI. For example, the virtual factory tour function can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0113] The virtual factory tour function can select the optimal tour method by referring to past tour history during the tour. For example, the virtual factory tour function can apply similar methods based on past successful tour methods. Furthermore, the virtual factory tour function can eliminate ineffective methods from past tour history and select efficient methods. In addition, the virtual factory tour function can analyze past tour history and select the optimal tour method under specific conditions. This allows for the selection of effective tour methods by referring to past tour history. Some or all of the above processing in the virtual factory tour function may be performed using AI, or not. For example, the virtual factory tour function can analyze past tour history using AI to select the optimal tour method. AI can automatically analyze past tour history and select effective tour methods. AI can also select the optimal tour method in real time under specific conditions, enabling efficient tours.

[0114] The virtual factory tour function can estimate the user's emotions and determine the priority of the tour based on those emotions. For example, if the user is feeling stressed, the virtual factory tour function can prioritize only the most important parts of the tour. If the user is relaxed, the virtual factory tour function can prioritize detailed tours. Furthermore, if the user is in a hurry, the virtual factory tour function can prioritize tours that can be completed quickly. This allows for an efficient tour by prioritizing the tour based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the virtual factory tour function may be performed using AI, for example, or without AI. For example, the virtual factory tour function can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0115] The virtual factory tour function can select the optimal tour method by considering the user's device information during the tour. For example, if the user is using a smartphone, the virtual factory tour function can provide a tour method that is adapted to the screen size. Furthermore, if the user is using a tablet, the virtual factory tour function can provide a tour method that is optimized for a larger screen. In addition, if the user is using a personal computer, the virtual factory tour function can provide a tour method that displays detailed information. This allows the system to provide the optimal tour method by considering the user's device information. Some or all of the above processing in the virtual factory tour function may be performed using AI, for example, or not. For example, the virtual factory tour function can analyze the user's device information using AI and select the optimal tour method. AI can analyze the user's device information in real time and provide the optimal tour method. Furthermore, the AI ​​can automatically adjust the screen size and display method according to the type of device, enabling a more efficient viewing experience.

[0116] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider can prioritize providing only important information. If the user is relaxed, the information provider can prioritize providing detailed information. Furthermore, if the user is in a hurry, the information provider can prioritize providing information that can be delivered quickly. This enables efficient information delivery by prioritizing the information to be delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0117] The information delivery unit can select the optimal information delivery method by referring to past delivery history at the time of delivery. The information delivery unit can, for example, apply a similar method based on a previously successful information delivery method. The information delivery unit can, for example, apply a similar method based on a previously successful information delivery method. Furthermore, the information delivery unit can eliminate ineffective methods from past delivery history and select an efficient method. The information delivery unit can, for example, eliminate ineffective methods from past delivery history and select an efficient method. In addition, the information delivery unit can analyze past delivery history and select the optimal information delivery method under specific conditions. The information delivery unit can, for example, analyze past delivery history and select the optimal information delivery method under specific conditions. This allows for the selection of an effective information delivery method by referring to past delivery history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can analyze past delivery history using AI and select the optimal information delivery method. AI can automatically analyze past delivery history and select an effective information delivery method. AI can also select the optimal information delivery method in real time under specific conditions, thereby achieving efficient information delivery.

[0118] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method that gets straight to the point. For example, if the user is in a hurry, the service provider can provide a concise display method that gets straight to the point. By adjusting the display method based on the user's emotions, it becomes possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0119] The information provider can select the optimal information delivery method by considering the user's geographical location information at the time of delivery. For example, the information provider can prioritize providing region-related information based on the user's location. The information provider can prioritize providing region-related information based on the user's location. Furthermore, the information provider can prioritize providing nearby market trend information by considering the user's geographical location information. In addition, the information provider can prioritize providing region-specific regulatory information based on the user's geographical location information. The information provider can prioritize providing region-specific regulatory information by considering the user's geographical location information. This makes it possible to provide optimal information by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can analyze the user's geographical location information using AI and select the optimal information delivery method. AI can analyze the user's geographical location information in real time and provide the optimal information delivery method. Furthermore, AI can automatically select region-specific information based on geographical location data, enabling efficient information provision.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion function can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion function can provide concise suggestions that get straight to the point. In this way, by adjusting the content of suggestions based on the user's emotions, it is possible to provide suggestions that are easy for the user to understand.

[0122] The data collection unit can analyze a company's social media activities and collect relevant data. For example, it can analyze a company's social media activities and collect relevant market trend data. It can also collect customer feedback data based on a company's social media activities. Furthermore, it can collect competitor activity data, taking into account a company's social media activities. In this way, by analyzing a company's social media activities, it is possible to collect relevant market trend data and customer feedback data.

[0123] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0124] The generation unit can improve the accuracy of generation by considering the relationships between business partners. For example, it can generate plans with a high success rate by considering the past collaboration history between business partners. It can also generate optimal collaboration plans by considering the technological complementarity between business partners. Furthermore, it can generate efficient new business proposals by considering the overlap in market share and customer base among business partners. In this way, by considering the relationships between business partners, it is possible to generate highly accurate new business proposals and collaboration plans.

[0125] The online sales meeting function can estimate the user's emotions and adjust the sales meeting process based on those emotions. For example, if the user is nervous, the meeting can be conducted slowly to help them relax. If the user is relaxed, the meeting can proceed while providing detailed information. Furthermore, if the user is in a hurry, the meeting can be conducted quickly and to the point. In this way, by adjusting the sales meeting process based on the user's emotions, it becomes possible to conduct the optimal sales meeting for the user.

[0126] The virtual factory tour feature can estimate the user's emotions and adjust the tour's pace based on those emotions. For example, if the user is nervous, the tour can be slowed down to help them relax. If the user is relaxed, the tour can proceed with detailed information. Furthermore, if the user is in a hurry, a concise and efficient tour can be conducted. By adjusting the tour's pace based on the user's emotions, the system can provide the optimal tour experience for each user.

[0127] The information delivery system can estimate the user's emotions and prioritize the information to be delivered based on those emotions. For example, if the user is stressed, only important information can be prioritized. If the user is relaxed, detailed information can be prioritized. Furthermore, if the user is in a hurry, information that can be delivered quickly can be prioritized. This enables efficient information delivery by prioritizing information based on the user's emotions.

[0128] The data collection unit can prioritize the collection of highly relevant data, taking into account the geographical location of companies. For example, it can prioritize the collection of region-related data based on the company's location. It can also prioritize the collection of nearby market trend data, taking into account the company's geographical location. Furthermore, it can prioritize the collection of region-specific regulatory information based on the company's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data, taking into account the company's geographical location.

[0129] The analysis department can apply different analysis algorithms depending on the data category. For example, financial analysis algorithms can be applied to financial data, technology evaluation algorithms to technical data, and market trend algorithms to market trend data. By applying different analysis algorithms according to the data category, highly accurate data analysis becomes possible.

[0130] The generation unit can generate proposals while considering the attribute information of business partners. For example, it can generate collaboration plans of an appropriate scale by considering the size of the business partner's company. It can also generate new business proposals specific to a particular industry by considering the industry of the business partner. Furthermore, it can generate collaboration plans suitable for a region by considering the regional characteristics of the business partner. In this way, by considering the attribute information of business partners, it is possible to generate appropriate new business proposals and collaboration plans.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects company data. The data collection unit collects information such as the company's financial data, business activities, technological capabilities, and market trends. This allows for an understanding of the company's management situation, business areas, technological strengths, and competitiveness. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department comprehensively analyzes the collected data to understand the company's strengths and weaknesses. This allows for an evaluation of the company's management situation, business areas, technological strengths, and competitiveness. Step 3: The Proposal Department proposes the most suitable business partners based on the analysis results obtained by the Analysis Department. For example, the Proposal Department selects the most suitable business partners by considering the strengths and weaknesses of each company. This allows them to propose companies with stable management, overlapping business areas, and technological strengths, based on the company's financial data, business content, and technological capabilities. Step 4: The generation unit generates new business proposals and collaboration plans by combining the strengths of the business partners proposed by the proposal unit. For example, the generation unit can combine the strengths of companies to generate new business proposals, new product development plans, and sales channel expansion plans.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects company data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the most suitable business partner. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates new business proposals and collaboration plans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects company data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable business partner. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates new business proposals and collaboration plans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects company data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the most suitable business partner. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates new business proposals and collaboration plans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects corporate data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable business partner. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates new business proposals and collaboration plans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) The data collection department collects corporate data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable business partner, The system includes a generation unit that generates new business proposals and collaboration plans by combining the strengths of business partners proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) Equipped with online business negotiation functionality The system described in Appendix 1, characterized by the features described herein. (Note 3) Features a virtual factory tour function. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a department that provides information on local subsidies and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect information such as a company's financial data, business operations, technological capabilities, and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyze the collected data comprehensively. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose the most suitable business partner. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Generate new business proposals and collaboration plans by combining the strengths of business partners. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze a company's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on the company's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, we analyze the company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the business partner. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the business partner. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when you selected your business partner. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the business partners. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates user emotions and determines the priority of new business proposals and collaboration plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, we improve the accuracy of the generation by considering the relationships between business partners. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the attribute information of business partners is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is We estimate user emotions and adjust how new business proposals and collaboration plans are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is During generation, the geographical distribution of business partners is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is During generation, we refer to relevant literature from our business partners to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned online business negotiation function is It estimates the user's emotions and adjusts the sales process based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned online business negotiation function is During business negotiations, refer to past negotiation history to select the most suitable negotiation method. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned online business negotiation function is It estimates user emotions and prioritizes business opportunities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned online business negotiation function is During business negotiations, the optimal negotiation method is selected by considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned virtual factory tour function is The system estimates the user's emotions and adjusts the tour's progression based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned virtual factory tour function is During the tour, we will refer to past tour records to select the most suitable tour method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned virtual factory tour function is It estimates the user's emotions and determines the priority of the tour based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned virtual factory tour function is During the tour, the optimal tour method will be selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When providing information, the most suitable method of information provision is selected by referring to past provision history. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects corporate data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable business partner, The system includes a generation unit that generates new business proposals and collaboration plans by combining the strengths of business partners proposed by the aforementioned proposal unit. A system characterized by the following features.

2. Equipped with online business meeting functionality The system according to feature 1.

3. Features a virtual factory tour function. The system according to feature 1.

4. It has a department that provides information on local subsidies and regulations. The system according to feature 1.

5. The aforementioned collection unit is Collect information such as a company's financial data, business operations, technological capabilities, and market trends. The system according to feature 1.

6. The aforementioned analysis unit is Analyze the collected data comprehensively. The system according to feature 1.

7. The aforementioned proposal section is, We propose the most suitable business partner. The system according to feature 1.

8. The generating unit is Generate new business proposals and collaboration plans by combining the strengths of business partners. The system according to feature 1.

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