system

Abstract

The system according to the embodiment aims to identify and introduce compatible business partners based on the characteristics of a company. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and an introduction unit. The collection unit collects characteristic data for each company. The analysis unit analyzes the data collected by the collection unit to understand the characteristics of each company. The identification unit identifies compatible business partners based on the characteristics understood by the analysis unit. The introduction unit introduces business partners identified by the identification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to find the best business partner based on a company's characteristics.

[0005] The system according to the embodiment aims to identify and introduce compatible business partners based on the characteristics of a company. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and an introduction unit. The collection unit collects characteristic data for each company. The analysis unit analyzes the data collected by the collection unit to understand the characteristics of each company. The identification unit identifies compatible business partners based on the characteristics understood by the analysis unit. The introduction unit introduces the business partners identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify and introduce suitable business partners based on the characteristics of the company. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business partner introduction system according to an embodiment of the present invention uses AI to analyze the characteristics of companies and introduce business partners that can form cross-disciplinary partnerships, thereby boosting business. The business partner introduction system collects characteristic data for each company, and AI analyzes the collected data to understand each company's characteristics. The AI ​​then identifies and introduces compatible business partners from companies in different industries. For example, the business partner introduction system collects detailed data on each company, such as its industry, size, management policy, and past projects. Next, the AI ​​analyzes the collected data to understand each company's characteristics. For example, the AI ​​analyzes the technical capabilities and production capacity of a manufacturing company to identify its strengths and weaknesses. The business partner introduction system then identifies compatible business partners from companies in different industries. For example, if a manufacturing company and an IT company are determined to be compatible, the AI ​​introduces them to each other. At this time, the AI ​​also proposes collaborations that leverage the strengths of both companies. Finally, the business partner introduction system introduces the business partners identified by the AI. For example, AI can introduce a manufacturing company to an IT company and propose that the two companies jointly develop a new product. In this way, collaboration between different industries is promoted, creating new business opportunities. This allows a business partner introduction system to leverage its own characteristics while collaborating with companies in different fields to develop new businesses. For example, a manufacturing company can collaborate with an IT company to develop a new product that utilizes IoT technology. Collaborations proposed by AI can also provide companies with opportunities to enter new markets.

[0029] The business partner introduction system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and an introduction unit. The collection unit collects characteristic data of each company. The characteristic data includes, for example, industry, size, management policy, past projects, financial data, market share, etc., but is not limited to these examples. The collection unit collects, for example, industry data of each company. The collection unit can also collect size data of each company. The collection unit can also collect management policy data of each company. For example, the collection unit collects data on past projects of each company. The collection unit can also collect financial data of each company. The collection unit can also collect market share data of each company. The analysis unit analyzes the data collected by the collection unit to identify the characteristics of each company. The analysis is performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to these examples. For example, the analysis unit identifies the characteristics of each company using data mining. The analysis unit can also identify the characteristics of each company using statistical analysis. The analysis unit can also identify the characteristics of each company using machine learning algorithms. The identification unit identifies a compatible business partner based on the characteristics identified by the analysis unit. Evaluation criteria for compatibility include, but are not limited to, technical capabilities, management policies, and past project success rates. For example, the identification unit identifies a compatible business partner based on technical capabilities. The identification unit can also identify a compatible business partner based on management policies. The identification unit can also identify a compatible business partner based on past project success rates. The introduction unit introduces the business partner identified by the identification unit. The introduction is performed by, for example, but is not limited to, the format of the introduction, the content of the information to be introduced, and the timing of the introduction. For example, the introduction unit introduces the identified business partner by email. The introduction unit can also introduce the identified business partner on a website. The introduction unit can also introduce the identified business partner at a meeting.As a result, the business partner introduction system of the embodiment collects and analyzes characteristic data of each company, identifies and introduces compatible business partners, thereby promoting collaboration between different industries and creating new business opportunities.

[0030] The collection unit may collect multiple data including the company's industry, size, management policy, past projects, financial data, and market share. The collection unit, for example, collects company industry data. For example, the collection unit may collect industry data such as manufacturing, IT, or service industry. The collection unit may also collect company size data. For example, the collection unit may collect size data such as the number of employees, sales, and capital. The collection unit may also collect company management policy data. For example, the collection unit may collect management policy data such as the company's vision, mission, and management strategy. The collection unit may also collect company past project data. For example, the collection unit may collect data such as the content, results, and success rate of past projects. The collection unit may also collect company financial data. For example, the collection unit may collect financial data such as the company's financial statements, revenue, and profit margins. The collection unit may also collect company market share data. For example, the collection unit may collect the company's market share and comparison data with competitors. By collecting detailed data, more accurate analysis and identification are possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input industry data for each company into AI, which then automatically collects the data.

[0031] The analysis unit can identify the strengths and weaknesses of each company based on the collected data. The analysis unit, for example, identifies the strengths of each company based on the collected data. For example, the analysis unit analyzes data such as technical capabilities, financial status, and market share to identify the strengths of each company. The analysis unit can also identify the weaknesses of each company based on the collected data. For example, the analysis unit analyzes data such as technical challenges, financial risks, and low competitiveness to identify the weaknesses of each company. The analysis unit can also comprehensively evaluate the strengths and weaknesses of each company based on the collected data. For example, the analysis unit comprehensively evaluates the technical capabilities and financial status to identify the strengths and weaknesses of each company. By identifying the strengths and weaknesses of each company, more suitable business partners can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which can automatically identify the strengths and weaknesses of each company.

[0032] When identifying compatible business partners from among companies in different industries, the identification unit can evaluate compatibility based on technical capabilities, management policies, and past project success rates. The identification unit, for example, identifies compatible business partners based on technical capabilities. For example, the identification unit compares the technical capabilities of a manufacturing company and an IT company to identify compatible business partners. The identification unit can also identify compatible business partners based on management policies. For example, the identification unit compares the management policies of a manufacturing company and an IT company to identify compatible business partners. The identification unit can also identify compatible business partners based on past project success rates. For example, the identification unit compares the success rates of past projects of a manufacturing company and an IT company to identify compatible business partners. This allows for the identification of more suitable business partners by taking compatibility evaluation criteria into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can input technical capabilities, management policies, and past project success rates into AI, which can then automatically identify compatible business partners.

[0033] When introducing the identified business partner, the introduction unit may propose a collaboration that leverages the strengths of both parties. For example, when introducing the identified business partner, the introduction unit may propose a collaboration that leverages the strengths of both parties. For example, the introduction unit may propose the development of a new product that leverages the strengths of a manufacturing company and an IT company. Furthermore, when introducing the identified business partner, the introduction unit may propose a marketing strategy that leverages the strengths of both parties. For example, the introduction unit may propose a joint marketing campaign that leverages the strengths of a manufacturing company and an IT company. Furthermore, when introducing the identified business partner, the introduction unit may propose technology development that leverages the strengths of both parties. For example, the introduction unit may propose the joint development of a new technology that leverages the strengths of a manufacturing company and an IT company. This allows for a more effective business partnership by proposing a collaboration that leverages the strengths of both parties. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit may input the strengths of the identified business partner into AI, which may then automatically propose a collaboration.

[0034] When introducing a specified business partner, the introduction unit may present specific effects of collaboration, such as increased sales or expanded market share. For example, when introducing a specified business partner, the introduction unit may present increased sales as a specific effect of collaboration. For example, the introduction unit may present that collaboration between a manufacturing company and an IT company will increase sales. Furthermore, when introducing a specified business partner, the introduction unit may present expanded market share as a specific effect of collaboration. For example, the introduction unit may present that collaboration between a manufacturing company and an IT company will increase market share. Furthermore, when introducing a specified business partner, the introduction unit may present improved technical capabilities as a specific effect of collaboration. For example, the introduction unit may present that collaboration between a manufacturing company and an IT company will improve technical capabilities. In this way, by presenting specific effects of collaboration, the value of the business partnership can be clearly communicated. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit may input the effects of collaboration between the specified business partners into AI, and the AI ​​may automatically present the effects.

[0035] During data collection, the collection unit can analyze the company's past collaboration history and select the optimal data collection method. For example, the collection unit can refer to data collection methods used in collaborations that were successful in the past and apply similar methods to the current data collection. The collection unit can also avoid data collection methods used in collaborations that failed in the past and select a different method. For example, the collection unit can analyze data collection methods used in collaborations that failed in the past and apply the different method to the current data collection. The collection unit can also analyze the past collaboration history and select the most effective data collection method. For example, the collection unit can analyze the past collaboration history and apply the most effective data collection method to the current data collection. By analyzing the past collaboration history, the optimal data collection method can be selected and the accuracy of data collection can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past collaboration history into AI, which can then automatically select the optimal data collection method.

[0036] When collecting data, the collection unit can perform filtering based on the company's current market situation and the trends of competitors. For example, the collection unit analyzes the current market situation and prioritizes collecting the most relevant data. For example, the collection unit analyzes the current market situation and prioritizes collecting the most relevant data. The collection unit can also collect data to enhance competitiveness by taking into account the trends of competitors. For example, the collection unit analyzes the trends of competitors and collects data to enhance competitiveness. The collection unit can also grasp market trends and collect data that will lead to future business opportunities. For example, the collection unit analyzes market trends and collects data that will lead to future business opportunities. In this way, by filtering based on the market situation and the trends of competitors, highly relevant data can be preferentially collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the current market situation and the trends of competitors into AI, and the AI ​​can automatically perform filtering.

[0037] At the time of collection, the collection unit can collect data based on the company's internal data. The collection unit, for example, analyzes employee skill sets and selects the optimal data collection method. For example, the collection unit analyzes employee skill sets and selects the optimal data collection method. The collection unit can also consider the company culture and select a data collection method that makes it easy for employees to cooperate. For example, the collection unit analyzes the company culture and selects a data collection method that makes it easy for employees to cooperate. The collection unit can also select a data collection method that makes use of the company's strengths based on the internal data. For example, the collection unit analyzes internal data and selects a data collection method that makes use of the company's strengths. This makes it possible to collect data that makes use of the company's strengths by considering the internal data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employee skill sets and the company culture into AI, and the AI ​​can automatically select the optimal data collection method.

[0038] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the company's geographical location information. The collection unit, for example, prioritizes collecting market data related to the company's location. For example, the collection unit prioritizes collecting market data related to the company's location. The collection unit can also prioritize collecting data on geographically nearby competitors. For example, the collection unit prioritizes collecting data on geographically nearby competitors. The collection unit can also prioritize collecting highly relevant data by taking into account the economic situation of the region. For example, the collection unit analyzes the economic situation of the region and prioritizes collecting highly relevant data. In this way, highly relevant data can be prioritized by taking geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the company's geographical location information into AI, and the AI ​​can automatically prioritize collecting highly relevant data.

[0039] During collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit analyzes the content of the company's social media posts and collects related data. For example, the collection unit analyzes the content of the company's social media posts and collects related data. The collection unit can also analyze the reactions of the company's followers on social media and collect related data. For example, the collection unit analyzes the reactions of the company's followers on social media and collects related data. The collection unit can also analyze the effectiveness of the company's social media campaigns and collect related data. For example, the collection unit analyzes the effectiveness of the company's social media campaigns and collects related data. In this way, by analyzing social media activities, related data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's social media activities into AI, and the AI ​​can automatically collect related data.

[0040] The collection unit can customize the collection method by reflecting the company's past feedback when collecting data. The collection unit, for example, selects the optimal data collection method based on past feedback. For example, the collection unit analyzes past feedback and selects the optimal data collection method. The collection unit can also improve the accuracy of data collection by reflecting past feedback. For example, the collection unit analyzes past feedback and improves the accuracy of data collection. The collection unit can also improve the efficiency of data collection by referring to past feedback. For example, the collection unit analyzes past feedback and improves the efficiency of data collection. In this way, by reflecting past feedback, the collection method can be optimized and the accuracy of data collection can be improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into AI, and the AI ​​can automatically customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the company's importance. For example, the analysis unit performs a detailed analysis on a company with a high importance. For example, the analysis unit evaluates the importance of a company and performs a detailed analysis on the company with a high importance. The analysis unit can also perform a simplified analysis on a company with a low importance. For example, the analysis unit evaluates the importance of a company and performs a simplified analysis on the company with a low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the company's importance. For example, the analysis unit evaluates the importance of a company and dynamically adjusts the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the company's importance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input company importance data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the company's industry and size. For example, the analysis unit applies an analysis algorithm specialized for the manufacturing process to a manufacturing company. For example, the analysis unit applies an analysis algorithm specialized for the manufacturing process to a manufacturing company. The analysis unit can also apply an analysis algorithm specialized for technical capabilities to an IT company. For example, the analysis unit applies an analysis algorithm specialized for technical capabilities to an IT company. The analysis unit can also select an appropriate analysis algorithm depending on the company's size. By applying an analysis algorithm tailored to the company's industry and size, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the company's industry and size into AI, and the AI ​​can automatically apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the past analysis results. For example, the analysis unit analyzes the past analysis results and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit analyzes the past analysis results and improves the accuracy of the analysis. The analysis unit can also analyze the past analysis results and identify areas for improvement in the analysis. For example, the analysis unit analyzes the past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results into AI, and the AI ​​can automatically improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also determine the analysis priority by referring to past data. For example, the analysis unit evaluates the time when the data was collected and determines the analysis priority by referring to the past data. The analysis unit can also adjust the order of analysis based on the time when the data was collected. For example, the analysis unit evaluates the time when the data was collected and adjusts the order of analysis. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can automatically determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates the relevance of the data and analyzes less relevant data later. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and dynamically adjusts the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, and the AI ​​can automatically adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the company's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to companies with high levels of expertise. For example, the analysis unit evaluates the company's level of expertise and provides analysis results that use a lot of technical terminology to companies with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to companies with low levels of expertise. For example, the analysis unit evaluates the company's level of expertise and provides concise and easy-to-understand analysis results to companies with low levels of expertise. The analysis unit can also dynamically adjust the use of technical terminology in the analysis according to the company's level of expertise. For example, the analysis unit evaluates the company's level of expertise and dynamically adjusts the use of technical terminology in the analysis. This makes it possible to provide analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the company's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's level of expertise into AI, and the AI ​​can automatically adjust the use of technical terminology in the analysis.

[0047] The identification unit can improve the accuracy of identification by taking into account the interrelationships of companies during identification. The identification unit, for example, analyzes the interrelationships of companies and improves the accuracy of identification. For example, the identification unit analyzes the interrelationships of companies and improves the accuracy of identification. The identification unit can also identify optimal business partners by taking into account the interrelationships of companies. For example, the identification unit analyzes the interrelationships of companies and identifies optimal business partners. The identification unit can also dynamically adjust the accuracy of identification based on the interrelationships of companies. For example, the identification unit analyzes the interrelationships of companies and dynamically adjusts the accuracy of identification. In this way, the accuracy of identification can be improved by taking the interrelationships of companies into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input company interrelationship data into AI, and the AI ​​can automatically improve the accuracy of identification.

[0048] The identification unit may perform identification by taking into consideration the attribute information of the submitter of the company. For example, the identification unit may analyze the attribute information of the submitter to identify the most suitable business partner. For example, the identification unit may analyze the attribute information of the submitter to identify the most suitable business partner. The identification unit may also improve the accuracy of identification by taking into consideration the attribute information of the submitter. For example, the identification unit may analyze the attribute information of the submitter to improve the accuracy of identification. The identification unit may also dynamically adjust the identification criteria based on the attribute information of the submitter. For example, the identification unit may analyze the attribute information of the submitter and dynamically adjust the identification criteria. This allows the accuracy of identification to be improved by taking into consideration the attribute information of the submitter. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit may input the attribute information of the submitter into AI, and the AI ​​may automatically perform identification.

[0049] During identification, the identification unit can assign a specific weight based on the company's submission frequency. For example, the identification unit assigns a higher specific weight to a company with a high submission frequency. For example, the identification unit evaluates the submission frequency and assigns a higher specific weight to a company with a high submission frequency. The identification unit can also assign a lower specific weight to a company with a low submission frequency. For example, the identification unit evaluates the submission frequency and assigns a lower specific weight to a company with a low submission frequency. The identification unit can also dynamically adjust the specific weight based on the submission frequency. For example, the identification unit evaluates the submission frequency and dynamically adjusts the specific weight. In this way, the accuracy of identification can be improved by assigning a specific weight based on the submission frequency. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input submission frequency data into AI, and the AI ​​can automatically assign a specific weight.

[0050] The identification unit can perform identification taking into account the geographic distribution of companies. For example, the identification unit analyzes the geographic distribution of companies to identify optimal business partners. For example, the identification unit analyzes the geographic distribution of companies to identify optimal business partners. The identification unit can also prioritize identifying geographically close companies. For example, the identification unit prioritizes identifying geographically close companies. The identification unit can also improve the accuracy of identification based on the geographic distribution. For example, the identification unit analyzes the geographic distribution and improves the accuracy of identification. In this way, the accuracy of identification can be improved by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the geographic distribution of companies into AI, and the AI ​​can automatically perform identification.

[0051] The identification unit can improve the accuracy of identification by referring to the company's related literature during identification. For example, the identification unit analyzes the company's related literature to improve the accuracy of identification. For example, the identification unit analyzes the company's related literature to improve the accuracy of identification. The identification unit can also identify optimal business partners based on the related literature. For example, the identification unit analyzes the related literature to identify optimal business partners. The identification unit can also dynamically adjust the identification criteria by referring to the related literature. For example, the identification unit analyzes the related literature and dynamically adjusts the identification criteria. In this way, the accuracy of identification can be improved by referring to the related literature. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit inputs the company's related literature data into AI, and the AI ​​can automatically improve the accuracy of identification.

[0052] The identification unit can perform identification taking into account the market value of the company. For example, the identification unit prioritizes identifying companies with high market value. For example, the identification unit evaluates the market value of companies and prioritizes identifying companies with high market value. The identification unit can also improve the accuracy of identification based on market value. For example, the identification unit evaluates the market value of companies and improves the accuracy of identification. The identification unit can also dynamically adjust the identification criteria taking market value into account. For example, the identification unit evaluates the market value of companies and dynamically adjusts the identification criteria. In this way, the accuracy of identification can be improved by taking market value into account. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input company market value data into AI, and the AI ​​can automatically perform identification.

[0053] When making an introduction, the introduction department can analyze the company's past collaboration history to select the optimal introduction method. For example, the introduction department can refer to the introduction methods of successful collaborations in the past and apply a similar method. For example, the introduction department can analyze the introduction methods of successful collaborations in the past and apply the similar method to the current introduction. The introduction department can also avoid the introduction methods of collaborations that failed in the past and select a different method. For example, the introduction department can analyze the introduction methods of failed collaborations in the past and apply the different method to the current introduction. The introduction department can also analyze the past collaboration history to select the most effective introduction method. For example, the introduction department can analyze the past collaboration history and apply the most effective introduction method to the current introduction. In this way, by analyzing the past collaboration history, the optimal introduction method can be selected and the accuracy of the introduction can be improved. Some or all of the above-mentioned processing in the introduction department can be performed using, for example, AI, or can be performed without AI. For example, the introduction department can input the past collaboration history into AI, and the AI ​​can automatically select the optimal introduction method.

[0054] At the time of introduction, the introduction department can customize the introduction method based on the company's current market situation. For example, the introduction department analyzes the current market situation and selects the most relevant introduction method. The introduction department can also grasp market trends and select an introduction method that will lead to future business opportunities. For example, the introduction department analyzes market trends and selects an introduction method that will lead to future business opportunities. The introduction department can also dynamically customize the introduction method based on the market situation. For example, the introduction department analyzes the market situation and dynamically customizes the introduction method. In this way, by customizing the introduction method based on the market situation, it is possible to introduce highly relevant business partners. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department can input current market situation data into AI, and the AI ​​can automatically customize the introduction method.

[0055] The introduction department can improve the introduction method by reflecting the company's feedback when making an introduction. The introduction department, for example, selects the optimal introduction method based on past feedback. For example, the introduction department analyzes past feedback and selects the optimal introduction method. The introduction department can also reflect the feedback and improve the accuracy of introductions. For example, the introduction department analyzes past feedback and improves the accuracy of introductions. The introduction department can also improve the efficiency of introductions by referring to the feedback. For example, the introduction department analyzes past feedback and improves the efficiency of introductions. In this way, the accuracy of introductions can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department can input past feedback into AI, and the AI ​​can automatically improve the introduction method.

[0056] When making an introduction, the introduction department can select the optimal introduction method by taking into account the company's geographical location information. For example, the introduction department may prioritize introducing business partners related to the company's location. For example, the introduction department may evaluate the company's location and prioritize introducing business partners related to the location. The introduction department may also prioritize introducing geographically close business partners. For example, the introduction department may evaluate the company's location and prioritize introducing geographically close business partners. The introduction department may also select the optimal introduction method based on geographical distribution. For example, the introduction department may evaluate the company's geographical distribution and select the optimal introduction method. In this way, by taking geographical location information into consideration, it is possible to prioritize introducing highly relevant business partners. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department may input the company's geographical location information into AI, and the AI ​​may automatically select the optimal introduction method.

[0057] At the time of introduction, the introduction unit can analyze the company's social media activity and suggest a means for introduction. For example, the introduction unit analyzes the content of the company's social media posts and introduces relevant business partners. For example, the introduction unit analyzes the content of the company's social media posts and introduces relevant business partners. The introduction unit can also analyze the responses of the company's followers on social media and introduce relevant business partners. For example, the introduction unit analyzes the responses of the company's followers on social media and introduces relevant business partners. The introduction unit can also analyze the effectiveness of the company's social media campaigns and introduce relevant business partners. For example, the introduction unit analyzes the effectiveness of the company's social media campaigns and introduces relevant business partners. In this way, relevant business partners can be efficiently introduced by analyzing social media activity. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input the company's social media activity into AI, and the AI ​​can automatically introduce relevant business partners.

[0058] When making an introduction, the introduction department can customize the introduction method by reflecting the company's past feedback. For example, the introduction department selects the optimal introduction method based on past feedback. For example, the introduction department analyzes past feedback and selects the optimal introduction method. The introduction department can also reflect the feedback to improve the accuracy of introductions. For example, the introduction department analyzes past feedback to improve the accuracy of introductions. The introduction department can also refer to the feedback to improve the efficiency of introductions. For example, the introduction department analyzes past feedback to improve the efficiency of introductions. In this way, by reflecting past feedback, the accuracy and efficiency of introductions can be improved. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department can input past feedback into AI, and the AI ​​can automatically customize the introduction method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting characteristic data for each company, the collection unit can also take into account the skill sets of the company's employees and its internal culture. For example, the collection unit can analyze employee skill sets and identify companies with many employees with specific skills. The collection unit can also analyze the internal culture and identify companies with an environment where employees can easily collaborate. Furthermore, the collection unit can comprehensively evaluate the employee skill sets and internal culture and select the optimal data collection method. This allows for more accurate data collection by taking into account the employee skill sets and internal culture. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI.

[0061] The identification unit can take into account the geographical distribution of companies when identifying compatible business partners from among companies in different genres. For example, the identification unit prioritizes identifying geographically close companies. The identification unit can also improve the accuracy of identification based on the geographical distribution. Furthermore, the identification unit can also identify optimal business partners by taking geographical distribution into consideration. In this way, the accuracy of identification can be improved by taking geographical distribution into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI.

[0062] When introducing identified business partners, the introduction unit can analyze the company's social media activities and suggest means for introduction. For example, the introduction unit can analyze the content of the company's social media posts and introduce relevant business partners. The introduction unit can also analyze the reactions of the company's social media followers and introduce relevant business partners. Furthermore, the introduction unit can analyze the effectiveness of the company's social media campaigns and introduce relevant business partners. In this way, relevant business partners can be introduced efficiently by analyzing social media activities. Some or all of the above-mentioned processing in the introduction unit may be performed using AI, or may be performed without using AI.

[0063] The collection unit can customize the collection method by reflecting the company's past feedback. For example, the optimal data collection method can be selected based on past feedback. The accuracy of data collection can also be improved by reflecting the feedback. Furthermore, the efficiency of data collection can be improved by referring to the feedback. In this way, by reflecting past feedback, the collection method can be optimized and the accuracy of data collection can be improved. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0064] The identification unit can perform identification taking into account the market value of the company. For example, it can prioritize identification of companies with high market value. It can also improve the accuracy of identification based on market value. Furthermore, it can dynamically adjust the identification criteria taking market value into account. In this way, it is possible to improve the accuracy of identification by taking market value into account. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI.

[0065] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the company's geographical location information. For example, market data related to the company's location can be collected preferentially. Data on geographically nearby competitors can also be collected preferentially. Furthermore, highly relevant data can be collected preferentially by taking into account the economic situation of the region. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection department collects characteristic data for each company. The characteristic data includes industry, size, management policy, past projects, financial data, market share, etc. For example, the collection department collects industry data, size data, management policy data, past project data, financial data, and market share data for each company. Step 2: The analysis department analyzes the data collected by the collection department and identifies the characteristics of each company. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis department uses data mining, statistical analysis, and machine learning algorithms to identify the characteristics of each company. Step 3: The Identification Department identifies business partners with good compatibility based on the characteristics identified by the Analysis Department. The compatibility evaluation criteria include technical capabilities, management policies, and the success rate of past projects. For example, the Identification Department identifies business partners with good compatibility based on technical capabilities, management policies, and the success rate of past projects. Step 4: The introduction department introduces the business partners identified by the identification department. The introduction is made by various methods, such as the format of the introduction, the content of the information to be introduced, and the timing of the introduction. For example, the introduction department introduces the identified business partners by email, website, or meeting.

[0068] (Example 2) A business partner introduction system according to an embodiment of the present invention uses AI to analyze the characteristics of companies and introduce business partners that can form cross-disciplinary partnerships, thereby boosting business. The business partner introduction system collects characteristic data for each company, and AI analyzes the collected data to understand each company's characteristics. The AI ​​then identifies and introduces compatible business partners from companies in different industries. For example, the business partner introduction system collects detailed data on each company, such as its industry, size, management policy, and past projects. Next, the AI ​​analyzes the collected data to understand each company's characteristics. For example, the AI ​​analyzes the technical capabilities and production capacity of a manufacturing company to identify its strengths and weaknesses. The business partner introduction system then identifies compatible business partners from companies in different industries. For example, if a manufacturing company and an IT company are determined to be compatible, the AI ​​introduces them to each other. At this time, the AI ​​also proposes collaborations that leverage the strengths of both companies. Finally, the business partner introduction system introduces the business partners identified by the AI. For example, AI can introduce a manufacturing company to an IT company and propose that the two companies jointly develop a new product. In this way, collaboration between different industries is promoted, creating new business opportunities. This allows a business partner introduction system to leverage its own characteristics while collaborating with companies in different fields to develop new businesses. For example, a manufacturing company can collaborate with an IT company to develop a new product that utilizes IoT technology. Collaborations proposed by AI can also provide companies with opportunities to enter new markets.

[0069] The business partner introduction system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and an introduction unit. The collection unit collects characteristic data of each company. The characteristic data includes, for example, industry, size, management policy, past projects, financial data, market share, etc., but is not limited to these examples. The collection unit collects, for example, industry data of each company. The collection unit can also collect size data of each company. The collection unit can also collect management policy data of each company. For example, the collection unit collects data on past projects of each company. The collection unit can also collect financial data of each company. The collection unit can also collect market share data of each company. The analysis unit analyzes the data collected by the collection unit to identify the characteristics of each company. The analysis is performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to these examples. For example, the analysis unit identifies the characteristics of each company using data mining. The analysis unit can also identify the characteristics of each company using statistical analysis. The analysis unit can also identify the characteristics of each company using machine learning algorithms. The identification unit identifies a compatible business partner based on the characteristics identified by the analysis unit. Evaluation criteria for compatibility include, but are not limited to, technical capabilities, management policies, and past project success rates. For example, the identification unit identifies a compatible business partner based on technical capabilities. The identification unit can also identify a compatible business partner based on management policies. The identification unit can also identify a compatible business partner based on past project success rates. The introduction unit introduces the business partner identified by the identification unit. The introduction is performed by, for example, but is not limited to, the format of the introduction, the content of the information to be introduced, and the timing of the introduction. For example, the introduction unit introduces the identified business partner by email. The introduction unit can also introduce the identified business partner on a website. The introduction unit can also introduce the identified business partner at a meeting.As a result, the business partner introduction system of the embodiment collects and analyzes characteristic data of each company, identifies and introduces compatible business partners, thereby promoting collaboration between different industries and creating new business opportunities.

[0070] The collection unit may collect multiple data including the company's industry, size, management policy, past projects, financial data, and market share. The collection unit, for example, collects company industry data. For example, the collection unit may collect industry data such as manufacturing, IT, or service industry. The collection unit may also collect company size data. For example, the collection unit may collect size data such as the number of employees, sales, and capital. The collection unit may also collect company management policy data. For example, the collection unit may collect management policy data such as the company's vision, mission, and management strategy. The collection unit may also collect company past project data. For example, the collection unit may collect data such as the content, results, and success rate of past projects. The collection unit may also collect company financial data. For example, the collection unit may collect financial data such as the company's financial statements, revenue, and profit margins. The collection unit may also collect company market share data. For example, the collection unit may collect the company's market share and comparison data with competitors. By collecting detailed data, more accurate analysis and identification are possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input industry data for each company into AI, which then automatically collects the data.

[0071] The analysis unit can identify the strengths and weaknesses of each company based on the collected data. The analysis unit, for example, identifies the strengths of each company based on the collected data. For example, the analysis unit analyzes data such as technical capabilities, financial status, and market share to identify the strengths of each company. The analysis unit can also identify the weaknesses of each company based on the collected data. For example, the analysis unit analyzes data such as technical challenges, financial risks, and low competitiveness to identify the weaknesses of each company. The analysis unit can also comprehensively evaluate the strengths and weaknesses of each company based on the collected data. For example, the analysis unit comprehensively evaluates the technical capabilities and financial status to identify the strengths and weaknesses of each company. By identifying the strengths and weaknesses of each company, more suitable business partners can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which can automatically identify the strengths and weaknesses of each company.

[0072] When identifying compatible business partners from among companies in different industries, the identification unit can evaluate compatibility based on technical capabilities, management policies, and past project success rates. The identification unit, for example, identifies compatible business partners based on technical capabilities. For example, the identification unit compares the technical capabilities of a manufacturing company and an IT company to identify compatible business partners. The identification unit can also identify compatible business partners based on management policies. For example, the identification unit compares the management policies of a manufacturing company and an IT company to identify compatible business partners. The identification unit can also identify compatible business partners based on past project success rates. For example, the identification unit compares the success rates of past projects of a manufacturing company and an IT company to identify compatible business partners. This allows for the identification of more suitable business partners by taking compatibility evaluation criteria into consideration. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can input technical capabilities, management policies, and past project success rates into AI, which can then automatically identify compatible business partners.

[0073] When introducing the identified business partner, the introduction unit may propose a collaboration that leverages the strengths of both parties. For example, when introducing the identified business partner, the introduction unit may propose a collaboration that leverages the strengths of both parties. For example, the introduction unit may propose the development of a new product that leverages the strengths of a manufacturing company and an IT company. Furthermore, when introducing the identified business partner, the introduction unit may propose a marketing strategy that leverages the strengths of both parties. For example, the introduction unit may propose a joint marketing campaign that leverages the strengths of a manufacturing company and an IT company. Furthermore, when introducing the identified business partner, the introduction unit may propose technology development that leverages the strengths of both parties. For example, the introduction unit may propose the joint development of a new technology that leverages the strengths of a manufacturing company and an IT company. This allows for a more effective business partnership by proposing a collaboration that leverages the strengths of both parties. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit may input the strengths of the identified business partner into AI, which may then automatically propose a collaboration.

[0074] When introducing a specified business partner, the introduction unit may present specific effects of collaboration, such as increased sales or expanded market share. For example, when introducing a specified business partner, the introduction unit may present increased sales as a specific effect of collaboration. For example, the introduction unit may present that collaboration between a manufacturing company and an IT company will increase sales. Furthermore, when introducing a specified business partner, the introduction unit may present expanded market share as a specific effect of collaboration. For example, the introduction unit may present that collaboration between a manufacturing company and an IT company will increase market share. Furthermore, when introducing a specified business partner, the introduction unit may present improved technical capabilities as a specific effect of collaboration. For example, the introduction unit may present that collaboration between a manufacturing company and an IT company will improve technical capabilities. In this way, by presenting specific effects of collaboration, the value of the business partnership can be clearly communicated. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit may input the effects of collaboration between the specified business partners into AI, and the AI ​​may automatically present the effects.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. For example, if the collection unit estimates the user's emotions and determines that the user is feeling stressed, it reduces the frequency of data collection from once a week to once a month. The collection unit can also increase the frequency of data collection to collect more detailed data if the user is relaxed. For example, if the collection unit estimates the user's emotions and determines that the user is relaxed, it increases the frequency of data collection from once a month to once a week. The collection unit can also shorten the timing of data collection to collect data more quickly if the user is in a hurry. For example, if the collection unit estimates the user's emotions and determines that the user is in a hurry, it shortens the timing of data collection from one hour to 30 minutes. This adjusts the timing of data collection according to the user's emotions, reducing the burden on the user and enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI, and the AI ​​may automatically adjust the timing of data collection.

[0076] During data collection, the collection unit can analyze the company's past collaboration history and select the optimal data collection method. For example, the collection unit can refer to data collection methods used in collaborations that were successful in the past and apply similar methods to the current data collection. The collection unit can also avoid data collection methods used in collaborations that failed in the past and select a different method. For example, the collection unit can analyze data collection methods used in collaborations that failed in the past and apply the different method to the current data collection. The collection unit can also analyze the past collaboration history and select the most effective data collection method. For example, the collection unit can analyze the past collaboration history and apply the most effective data collection method to the current data collection. By analyzing the past collaboration history, the optimal data collection method can be selected and the accuracy of data collection can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past collaboration history into AI, which can then automatically select the optimal data collection method.

[0077] When collecting data, the collection unit can perform filtering based on the company's current market situation and the trends of competitors. For example, the collection unit analyzes the current market situation and prioritizes collecting the most relevant data. For example, the collection unit analyzes the current market situation and prioritizes collecting the most relevant data. The collection unit can also collect data to enhance competitiveness by taking into account the trends of competitors. For example, the collection unit analyzes the trends of competitors and collects data to enhance competitiveness. The collection unit can also grasp market trends and collect data that will lead to future business opportunities. For example, the collection unit analyzes market trends and collects data that will lead to future business opportunities. In this way, by filtering based on the market situation and the trends of competitors, highly relevant data can be preferentially collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the current market situation and the trends of competitors into AI, and the AI ​​can automatically perform filtering.

[0078] At the time of collection, the collection unit can collect data based on the company's internal data. The collection unit, for example, analyzes employee skill sets and selects the optimal data collection method. For example, the collection unit analyzes employee skill sets and selects the optimal data collection method. The collection unit can also consider the company culture and select a data collection method that makes it easy for employees to cooperate. For example, the collection unit analyzes the company culture and selects a data collection method that makes it easy for employees to cooperate. The collection unit can also select a data collection method that makes use of the company's strengths based on the internal data. For example, the collection unit analyzes internal data and selects a data collection method that makes use of the company's strengths. This makes it possible to collect data that makes use of the company's strengths by considering the internal data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employee skill sets and the company culture into AI, and the AI ​​can automatically select the optimal data collection method.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting data of high importance. For example, if the collection unit estimates the user's emotions and determines that the user is feeling stressed, it prioritizes collecting data of high importance. The collection unit can also prioritize collecting detailed data if the user is relaxed. For example, if the collection unit estimates the user's emotions and determines that the user is relaxed, it prioritizes collecting detailed data. The collection unit can also prioritize collecting data that can be collected quickly if the user is in a hurry. For example, if the collection unit estimates the user's emotions and determines that the user is in a hurry, it prioritizes collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and determine the priority of data to be automatically collected by the AI.

[0080] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the company's geographical location information. The collection unit, for example, prioritizes collecting market data related to the company's location. For example, the collection unit prioritizes collecting market data related to the company's location. The collection unit can also prioritize collecting data on geographically nearby competitors. For example, the collection unit prioritizes collecting data on geographically nearby competitors. The collection unit can also prioritize collecting highly relevant data by taking into account the economic situation of the region. For example, the collection unit analyzes the economic situation of the region and prioritizes collecting highly relevant data. In this way, highly relevant data can be prioritized by taking geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the company's geographical location information into AI, and the AI ​​can automatically prioritize collecting highly relevant data.

[0081] During collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit analyzes the content of the company's social media posts and collects related data. For example, the collection unit analyzes the content of the company's social media posts and collects related data. The collection unit can also analyze the reactions of the company's followers on social media and collect related data. For example, the collection unit analyzes the reactions of the company's followers on social media and collects related data. The collection unit can also analyze the effectiveness of the company's social media campaigns and collect related data. For example, the collection unit analyzes the effectiveness of the company's social media campaigns and collects related data. In this way, by analyzing social media activities, related data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's social media activities into AI, and the AI ​​can automatically collect related data.

[0082] The collection unit can customize the collection method by reflecting the company's past feedback when collecting data. The collection unit, for example, selects the optimal data collection method based on past feedback. For example, the collection unit analyzes past feedback and selects the optimal data collection method. The collection unit can also improve the accuracy of data collection by reflecting past feedback. For example, the collection unit analyzes past feedback and improves the accuracy of data collection. The collection unit can also improve the efficiency of data collection by referring to past feedback. For example, the collection unit analyzes past feedback and improves the efficiency of data collection. In this way, by reflecting past feedback, the collection method can be optimized and the accuracy of data collection can be improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into AI, and the AI ​​can automatically customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the analysis unit estimates the user's emotions and determines that the user is nervous, it provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it provides a detailed analysis result. The analysis unit can also provide a summary analysis result if the user is in a hurry. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it provides a summary analysis result. This allows the analysis result to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI, which may then automatically adjust the way the analysis is presented.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the company's importance. For example, the analysis unit performs a detailed analysis on a company with a high importance. For example, the analysis unit evaluates the importance of a company and performs a detailed analysis on the company with a high importance. The analysis unit can also perform a simplified analysis on a company with a low importance. For example, the analysis unit evaluates the importance of a company and performs a simplified analysis on the company with a low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the company's importance. For example, the analysis unit evaluates the importance of a company and dynamically adjusts the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the company's importance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input company importance data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the company's industry and size. For example, the analysis unit applies an analysis algorithm specialized for the manufacturing process to a manufacturing company. For example, the analysis unit applies an analysis algorithm specialized for the manufacturing process to a manufacturing company. The analysis unit can also apply an analysis algorithm specialized for technical capabilities to an IT company. For example, the analysis unit applies an analysis algorithm specialized for technical capabilities to an IT company. The analysis unit can also select an appropriate analysis algorithm depending on the company's size. By applying an analysis algorithm tailored to the company's industry and size, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the company's industry and size into AI, and the AI ​​can automatically apply an appropriate analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the past analysis results. For example, the analysis unit analyzes the past analysis results and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit analyzes the past analysis results and improves the accuracy of the analysis. The analysis unit can also analyze the past analysis results and identify areas for improvement in the analysis. For example, the analysis unit analyzes the past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results into AI, and the AI ​​can automatically improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the analysis unit estimates the user's emotions and determines that the user is excited, it provides a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI, which may then automatically adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also determine the analysis priority by referring to past data. For example, the analysis unit evaluates the time when the data was collected and determines the analysis priority by referring to the past data. The analysis unit can also adjust the order of analysis based on the time when the data was collected. For example, the analysis unit evaluates the time when the data was collected and adjusts the order of analysis. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can automatically determine the analysis priority.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates the relevance of the data and analyzes less relevant data later. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and dynamically adjusts the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, and the AI ​​can automatically adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the company's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to companies with high levels of expertise. For example, the analysis unit evaluates the company's level of expertise and provides analysis results that use a lot of technical terminology to companies with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to companies with low levels of expertise. For example, the analysis unit evaluates the company's level of expertise and provides concise and easy-to-understand analysis results to companies with low levels of expertise. The analysis unit can also dynamically adjust the use of technical terminology in the analysis according to the company's level of expertise. For example, the analysis unit evaluates the company's level of expertise and dynamically adjusts the use of technical terminology in the analysis. This makes it possible to provide analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the company's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's level of expertise into AI, and the AI ​​can automatically adjust the use of technical terminology in the analysis.

[0091] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user's emotions. For example, if the user is nervous, the identification unit provides simple, highly visible identification criteria. For example, if the identification unit estimates the user's emotions and determines that the user is nervous, it provides simple, highly visible identification criteria. The identification unit can also provide detailed identification criteria if the user is relaxed. For example, if the identification unit estimates the user's emotions and determines that the user is relaxed, it provides detailed identification criteria. The identification unit can also provide specific identification criteria if the user is in a hurry. For example, if the identification unit estimates the user's emotions and determines that the user is in a hurry, it provides specific identification criteria. This allows the identification criteria to be adjusted according to the user's emotions, thereby providing an optimal identification result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input user emotion data into AI, which may then automatically adjust the determination criteria.

[0092] The identification unit can improve the accuracy of identification by taking into account the interrelationships of companies during identification. The identification unit, for example, analyzes the interrelationships of companies and improves the accuracy of identification. For example, the identification unit analyzes the interrelationships of companies and improves the accuracy of identification. The identification unit can also identify optimal business partners by taking into account the interrelationships of companies. For example, the identification unit analyzes the interrelationships of companies and identifies optimal business partners. The identification unit can also dynamically adjust the accuracy of identification based on the interrelationships of companies. For example, the identification unit analyzes the interrelationships of companies and dynamically adjusts the accuracy of identification. In this way, the accuracy of identification can be improved by taking the interrelationships of companies into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input company interrelationship data into AI, and the AI ​​can automatically improve the accuracy of identification.

[0093] The identification unit may perform identification by taking into consideration the attribute information of the submitter of the company. For example, the identification unit may analyze the attribute information of the submitter to identify the most suitable business partner. For example, the identification unit may analyze the attribute information of the submitter to identify the most suitable business partner. The identification unit may also improve the accuracy of identification by taking into consideration the attribute information of the submitter. For example, the identification unit may analyze the attribute information of the submitter to improve the accuracy of identification. The identification unit may also dynamically adjust the identification criteria based on the attribute information of the submitter. For example, the identification unit may analyze the attribute information of the submitter and dynamically adjust the identification criteria. This allows the accuracy of identification to be improved by taking into consideration the attribute information of the submitter. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit may input the attribute information of the submitter into AI, and the AI ​​may automatically perform identification.

[0094] During identification, the identification unit can assign a specific weight based on the company's submission frequency. For example, the identification unit assigns a higher specific weight to a company with a high submission frequency. For example, the identification unit evaluates the submission frequency and assigns a higher specific weight to a company with a high submission frequency. The identification unit can also assign a lower specific weight to a company with a low submission frequency. For example, the identification unit evaluates the submission frequency and assigns a lower specific weight to a company with a low submission frequency. The identification unit can also dynamically adjust the specific weight based on the submission frequency. For example, the identification unit evaluates the submission frequency and dynamically adjusts the specific weight. In this way, the accuracy of identification can be improved by assigning a specific weight based on the submission frequency. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input submission frequency data into AI, and the AI ​​can automatically assign a specific weight.

[0095] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user emotions. For example, if the user is nervous, the identification unit displays the specific results in a simple, highly visible order. For example, if the identification unit estimates the user's emotions and determines that the user is nervous, it displays the specific results in a simple, highly visible order. Furthermore, if the user is relaxed, the identification unit can display the specific results in an order that includes detailed information. For example, if the identification unit estimates the user's emotions and determines that the user is relaxed, it displays the specific results in an order that includes detailed information. Furthermore, if the user is in a hurry, the identification unit can display the specific results in an order that emphasizes the main points. For example, if the identification unit estimates the user's emotions and determines that the user is in a hurry, it displays the specific results in an order that emphasizes the main points. In this way, by adjusting the order in which the specific results are displayed according to the user's emotions, it is possible to provide the user with optimal specific results. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input user emotion data into AI and automatically adjust the order in which the AI ​​displays the specified results.

[0096] The identification unit can perform identification taking into account the geographic distribution of companies. For example, the identification unit analyzes the geographic distribution of companies to identify optimal business partners. For example, the identification unit analyzes the geographic distribution of companies to identify optimal business partners. The identification unit can also prioritize identifying geographically close companies. For example, the identification unit prioritizes identifying geographically close companies. The identification unit can also improve the accuracy of identification based on the geographic distribution. For example, the identification unit analyzes the geographic distribution and improves the accuracy of identification. In this way, the accuracy of identification can be improved by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the geographic distribution of companies into AI, and the AI ​​can automatically perform identification.

[0097] The identification unit can improve the accuracy of identification by referring to the company's related literature during identification. For example, the identification unit analyzes the company's related literature to improve the accuracy of identification. For example, the identification unit analyzes the company's related literature to improve the accuracy of identification. The identification unit can also identify optimal business partners based on the related literature. For example, the identification unit analyzes the related literature to identify optimal business partners. The identification unit can also dynamically adjust the identification criteria by referring to the related literature. For example, the identification unit analyzes the related literature and dynamically adjusts the identification criteria. In this way, the accuracy of identification can be improved by referring to the related literature. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit inputs the company's related literature data into AI, and the AI ​​can automatically improve the accuracy of identification.

[0098] The identification unit can perform identification taking into account the market value of the company. For example, the identification unit prioritizes identifying companies with high market value. For example, the identification unit evaluates the market value of companies and prioritizes identifying companies with high market value. The identification unit can also improve the accuracy of identification based on market value. For example, the identification unit evaluates the market value of companies and improves the accuracy of identification. The identification unit can also dynamically adjust the identification criteria taking market value into account. For example, the identification unit evaluates the market value of companies and dynamically adjusts the identification criteria. In this way, the accuracy of identification can be improved by taking market value into account. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input company market value data into AI, and the AI ​​can automatically perform identification.

[0099] The introduction unit can estimate the user's emotions and adjust the introduction method based on the estimated user's emotions. For example, if the user is nervous, the introduction unit provides a simple and highly visible introduction method. For example, if the introduction unit estimates the user's emotions and determines that the user is nervous, it provides a simple and highly visible introduction method. The introduction unit can also provide an introduction method that includes detailed information if the user is relaxed. For example, if the introduction unit estimates the user's emotions and determines that the user is relaxed, it provides an introduction method that includes detailed information. The introduction unit can also provide an introduction method that focuses on the main points if the user is in a hurry. For example, if the introduction unit estimates the user's emotions and determines that the user is in a hurry, it provides an introduction method that focuses on the main points. This allows the introduction method to be adjusted according to the user's emotions, thereby providing an optimal introduction method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit may input the user's emotional data into AI, and the AI ​​may automatically adjust the introduction method.

[0100] When making an introduction, the introduction department can analyze the company's past collaboration history to select the optimal introduction method. For example, the introduction department can refer to the introduction methods of successful collaborations in the past and apply a similar method. For example, the introduction department can analyze the introduction methods of successful collaborations in the past and apply the similar method to the current introduction. The introduction department can also avoid the introduction methods of collaborations that failed in the past and select a different method. For example, the introduction department can analyze the introduction methods of failed collaborations in the past and apply the different method to the current introduction. The introduction department can also analyze the past collaboration history to select the most effective introduction method. For example, the introduction department can analyze the past collaboration history and apply the most effective introduction method to the current introduction. In this way, by analyzing the past collaboration history, the optimal introduction method can be selected and the accuracy of the introduction can be improved. Some or all of the above-mentioned processing in the introduction department can be performed using, for example, AI, or can be performed without AI. For example, the introduction department can input the past collaboration history into AI, and the AI ​​can automatically select the optimal introduction method.

[0101] At the time of introduction, the introduction department can customize the introduction method based on the company's current market situation. For example, the introduction department analyzes the current market situation and selects the most relevant introduction method. The introduction department can also grasp market trends and select an introduction method that will lead to future business opportunities. For example, the introduction department analyzes market trends and selects an introduction method that will lead to future business opportunities. The introduction department can also dynamically customize the introduction method based on the market situation. For example, the introduction department analyzes the market situation and dynamically customizes the introduction method. In this way, by customizing the introduction method based on the market situation, it is possible to introduce highly relevant business partners. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department can input current market situation data into AI, and the AI ​​can automatically customize the introduction method.

[0102] The introduction department can improve the introduction method by reflecting the company's feedback when making an introduction. The introduction department, for example, selects the optimal introduction method based on past feedback. For example, the introduction department analyzes past feedback and selects the optimal introduction method. The introduction department can also reflect the feedback and improve the accuracy of introductions. For example, the introduction department analyzes past feedback and improves the accuracy of introductions. The introduction department can also improve the efficiency of introductions by referring to the feedback. For example, the introduction department analyzes past feedback and improves the efficiency of introductions. In this way, the accuracy of introductions can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department can input past feedback into AI, and the AI ​​can automatically improve the introduction method.

[0103] The introduction unit can estimate the user's emotions and determine the priority of introductions based on the estimated user emotions. For example, if the user is nervous, the introduction unit prioritizes introducing business partners of high importance. For example, if the introduction unit estimates the user's emotions and determines that the user is nervous, it prioritizes introducing business partners of high importance. Furthermore, if the user is relaxed, the introduction unit can prioritize introducing business partners with detailed information. For example, if the introduction unit estimates the user's emotions and determines that the user is relaxed, it prioritizes introducing business partners with detailed information. Furthermore, if the user is in a hurry, the introduction unit can prioritize introducing business partners that can be introduced quickly. For example, if the introduction unit estimates the user's emotions and determines that the user is in a hurry, it prioritizes introducing business partners that can be introduced quickly. In this way, by determining the priority of introductions according to the user's emotions, it is possible to prioritize introducing the most suitable business partners for the user. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit may input user emotion data into AI, and the AI ​​may automatically determine the priority of introductions.

[0104] When making an introduction, the introduction department can select the optimal introduction method by taking into account the company's geographical location information. For example, the introduction department may prioritize introducing business partners related to the company's location. For example, the introduction department may evaluate the company's location and prioritize introducing business partners related to the location. The introduction department may also prioritize introducing geographically close business partners. For example, the introduction department may evaluate the company's location and prioritize introducing geographically close business partners. The introduction department may also select the optimal introduction method based on geographical distribution. For example, the introduction department may evaluate the company's geographical distribution and select the optimal introduction method. In this way, by taking geographical location information into consideration, it is possible to prioritize introducing highly relevant business partners. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department may input the company's geographical location information into AI, and the AI ​​may automatically select the optimal introduction method.

[0105] At the time of introduction, the introduction unit can analyze the company's social media activity and suggest a means for introduction. For example, the introduction unit analyzes the content of the company's social media posts and introduces relevant business partners. For example, the introduction unit analyzes the content of the company's social media posts and introduces relevant business partners. The introduction unit can also analyze the responses of the company's followers on social media and introduce relevant business partners. For example, the introduction unit analyzes the responses of the company's followers on social media and introduces relevant business partners. The introduction unit can also analyze the effectiveness of the company's social media campaigns and introduce relevant business partners. For example, the introduction unit analyzes the effectiveness of the company's social media campaigns and introduces relevant business partners. In this way, relevant business partners can be efficiently introduced by analyzing social media activity. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input the company's social media activity into AI, and the AI ​​can automatically introduce relevant business partners.

[0106] When making an introduction, the introduction department can customize the introduction method by reflecting the company's past feedback. For example, the introduction department selects the optimal introduction method based on past feedback. For example, the introduction department analyzes past feedback and selects the optimal introduction method. The introduction department can also reflect the feedback to improve the accuracy of introductions. For example, the introduction department analyzes past feedback to improve the accuracy of introductions. The introduction department can also refer to the feedback to improve the efficiency of introductions. For example, the introduction department analyzes past feedback to improve the efficiency of introductions. In this way, by reflecting past feedback, the accuracy and efficiency of introductions can be improved. Some or all of the above-mentioned processing in the introduction department may be performed using, for example, AI, or may be performed without using AI. For example, the introduction department can input past feedback into AI, and the AI ​​can automatically customize the introduction method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and introduction unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects characteristic data of each company using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to understand the characteristics of each company. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies compatible business partners. The introduction unit is realized, for example, by the control unit 46A of the smart device 14, and introduces the identified business partners. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, and introduction unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects characteristic data of each company using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to understand the characteristics of each company. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies compatible business partners. The introduction unit is realized, for example, by the control unit 46A of the smart glasses 214, and introduces the identified business partners. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and introduction unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects characteristic data of each company using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to understand the characteristics of each company. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies compatible business partners. The introduction unit is realized, for example, by the control unit 46A of the headset type terminal 314, and introduces the identified business partners. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and introduction unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects characteristic data of each company using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to understand the characteristics of each company. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies compatible business partners. The introduction unit is realized, for example, by the control unit 46A of the robot 414, and introduces the identified business partners.

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

[0108] When collecting characteristic data for each company, the collection unit can also take into account the skill sets of the company's employees and its internal culture. For example, the collection unit can analyze employee skill sets and identify companies with many employees with specific skills. The collection unit can also analyze the internal culture and identify companies with an environment where employees can easily collaborate. Furthermore, the collection unit can comprehensively evaluate the employee skill sets and internal culture and select the optimal data collection method. This allows for more accurate data collection by taking into account the employee skill sets and internal culture. Some or all of the above-mentioned processing by the collection unit may be performed using AI, or may be performed without using AI.

[0109] The analysis unit can take into account the emotional data of the company's employees when identifying the strengths and weaknesses of each company based on the collected data. For example, the analysis unit can analyze the emotional data of employees and identify a company's weakness if the employees are feeling stressed. Alternatively, the analysis unit can identify a company's strengths if the employees are relaxed. Furthermore, the analysis unit can comprehensively evaluate the emotional data of employees and identify the company's strengths and weaknesses. This makes it possible to identify more suitable business partners by taking the emotional data of employees into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0110] The identification unit can take into account the geographical distribution of companies when identifying compatible business partners from among companies in different genres. For example, the identification unit prioritizes identifying geographically close companies. The identification unit can also improve the accuracy of identification based on the geographical distribution. Furthermore, the identification unit can also identify optimal business partners by taking geographical distribution into consideration. In this way, the accuracy of identification can be improved by taking geographical distribution into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI.

[0111] When introducing identified business partners, the introduction unit can analyze the company's social media activities and suggest means for introduction. For example, the introduction unit can analyze the content of the company's social media posts and introduce relevant business partners. The introduction unit can also analyze the reactions of the company's social media followers and introduce relevant business partners. Furthermore, the introduction unit can analyze the effectiveness of the company's social media campaigns and introduce relevant business partners. In this way, relevant business partners can be introduced efficiently by analyzing social media activities. Some or all of the above-mentioned processing in the introduction unit may be performed using AI, or may be performed without using AI.

[0112] When introducing a identified business partner, the introduction unit can estimate the user's emotions and adjust the introduction method based on the estimated user emotions. For example, if the user is nervous, the introduction unit can provide a simple, highly visible introduction method. If the user is relaxed, the introduction unit can provide an introduction method that includes detailed information. Furthermore, if the user is in a hurry, the introduction unit can provide an introduction method that focuses on the main points. In this way, by adjusting the introduction method according to the user's emotions, it is possible to provide the optimal introduction method for the user. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generation AI. Some or all of the above-mentioned processing in the introduction unit may be performed using AI, or may be performed without using AI.

[0113] The collection unit can customize the collection method by reflecting the company's past feedback. For example, the optimal data collection method can be selected based on past feedback. The accuracy of data collection can also be improved by reflecting the feedback. Furthermore, the efficiency of data collection can be improved by referring to the feedback. In this way, by reflecting past feedback, the collection method can be optimized and the accuracy of data collection can be improved. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0114] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. By adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0115] The identification unit can perform identification taking into account the market value of the company. For example, it can prioritize identification of companies with high market value. It can also improve the accuracy of identification based on market value. Furthermore, it can dynamically adjust the identification criteria taking market value into account. In this way, it is possible to improve the accuracy of identification by taking market value into account. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI.

[0116] The introduction unit can estimate the user's emotions and determine the priority of introductions based on the estimated user emotions. For example, if the user is nervous, it can prioritize introductions of business partners with high importance. Also, if the user is relaxed, it can prioritize introductions of business partners with detailed information. Furthermore, if the user is in a hurry, it can prioritize introductions of business partners that can be introduced quickly. In this way, by determining the priority of introductions according to the user's emotions, it is possible to prioritize introductions of business partners that are most suitable for the user. Emotion estimation is realized using an emotion estimation function using an emotion engine or generation AI. Some or all of the above-mentioned processing in the introduction unit may be performed using AI, or may be performed without using AI.

[0117] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the company's geographical location information. For example, market data related to the company's location can be collected preferentially. Data on geographically nearby competitors can also be collected preferentially. Furthermore, highly relevant data can be collected preferentially by taking into account the economic situation of the region. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection department collects characteristic data for each company. The characteristic data includes industry, size, management policy, past projects, financial data, market share, etc. For example, the collection department collects industry data, size data, management policy data, past project data, financial data, and market share data for each company. Step 2: The analysis department analyzes the data collected by the collection department and identifies the characteristics of each company. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis department uses data mining, statistical analysis, and machine learning algorithms to identify the characteristics of each company. Step 3: The Identification Department identifies business partners with good compatibility based on the characteristics identified by the Analysis Department. The compatibility evaluation criteria include technical capabilities, management policies, and the success rate of past projects. For example, the Identification Department identifies business partners with good compatibility based on technical capabilities, management policies, and the success rate of past projects. Step 4: The introduction department introduces the business partners identified by the identification department. The introduction is made by various methods, such as the format of the introduction, the content of the information to be introduced, and the timing of the introduction. For example, the introduction department introduces the identified business partners by email, website, or meeting.

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

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

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

Claims

1. a collection department that collects characteristic data of each company; an analysis unit that analyzes the data collected by the collection unit and grasps the characteristics of each company; an identification unit that identifies a compatible business partner based on the characteristics identified by the analysis unit; an introduction unit that introduces business partners identified by the identification unit; A system characterized by:

2. The collecting unit Collect multiple data including the company's industry, size, management policy, past projects, financial data, and market share 2. The system of claim 1.

3. The analysis unit Identify the strengths and weaknesses of each company based on the data collected 2. The system of claim 1.

4. The identification unit When identifying suitable business partners from among companies in different fields, we evaluate compatibility based on technical capabilities, management policies, and the success rate of past projects.

2. The system of claim 1.

5. The introduction unit When introducing identified business partners, we propose collaborations that leverage the strengths of both parties.

2. The system of claim 1.

6. The introduction unit When introducing identified business partners, present concrete collaboration benefits such as increased sales and market share.

2. The system of claim 1.

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

8. The collecting unit At the time of collection, analyze the company's past collaboration history and select the most appropriate data collection method 2. The system of claim 1.

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