System

Abstract

An object of a system according to an embodiment is to enable a small and medium-sized company to realize smooth M & A transactions.SOLUTION: A system includes a collection unit, an analysis unit, a matching unit, and a generation unit. The collection unit collects information on a buyer and a seller. The analysis unit analyzes the information collected by the collection unit. The matching unit adjusts the buyer and the seller based on the result analyzed by the analysis unit. The generation unit generates a proposal based on the result adjusted by the matching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process for small and medium-sized enterprises to carry out smooth M&A transactions was complex, time-consuming, and labor-intensive.

[0005] The system according to the embodiment aims to enable small and medium-sized enterprises to carry out smooth M&A transactions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a generation unit. The collection unit collects information on buyers and sellers. The analysis unit analyzes the information collected by the collection unit. The matching unit adjusts the buyer and seller based on the results of the analysis by the analysis unit. The generation unit generates a proposal based on the results of the adjustment by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment enables small and medium-sized enterprises to carry out smooth M&A transactions. [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) An M&A process automation system according to an embodiment of the present invention efficiently collects, analyzes, and adjusts information on buyers and sellers to generate proposals. The M&A process automation system utilizes AI technology to automate the entire M&A process and uses a generation AI to expedite the creation of proposals. For example, the M&A process automation system collects information on buyers and sellers and analyzes that information. Next, the AI ​​quickly and fairly coordinates the buyer and seller based on the analysis results. For example, it matches the buyer's desired terms with the seller's offered terms. In this process, the AI ​​considers related data to achieve optimal matching. Furthermore, the generation AI creates proposals. When a user inputs the necessary information, the generation AI automatically generates a proposal based on that information. For example, by inputting a company's financial information and business plan, the generation AI combines that information to create a proposal. This allows small and medium-sized enterprises to achieve smooth M&A transactions while saving time and effort. This allows small and medium-sized enterprises to achieve smooth M&A transactions while saving time and effort. For example, the process of gathering information and creating proposals, which was previously done manually, is now automated, reducing the burden on staff. Furthermore, AI analysis and matching improves the fairness and efficiency of transactions. This service is particularly beneficial for small and medium-sized enterprises, allowing them to efficiently advance M&A transactions with limited resources. For example, even small and medium-sized enterprises with limited capital and human resources can use this service to smoothly advance M&A transactions, even if the staff do not have specialized knowledge or skills.

[0029] An M&A process automation system according to an embodiment includes a collection unit, an analysis unit, a matching unit, and a generation unit. The collection unit collects information about buyers and sellers. The information about buyers and sellers includes, but is not limited to, contact information, transaction history, and desired terms. The collection unit may collect information through, for example, an online form. The collection unit may also obtain information from an external database using an API. The collection unit may also collect information manually entered by a user. For example, the collection unit may store the information entered by the user in a database in real time. The analysis unit analyzes the information collected by the collection unit. The analysis may be 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 may extract patterns from the collected information using data mining techniques. The analysis unit may also analyze trends in the information using statistical analysis. The analysis unit may also build a predictive model of the information using a machine learning algorithm. For example, the analysis unit may input the collected information into a machine learning model and output a prediction result. The matching unit adjusts the relationship between the buyer and the seller based on the results of the analysis by the analysis unit. The adjustment can be performed, for example, by determining the degree of match of conditions or by setting priorities, but is not limited to these examples. For example, the matching unit compares the buyer's desired conditions with the seller's offered conditions and selects a combination with a high degree of match. The matching unit can also set priorities and prioritize matching of important conditions. The matching unit can also perform optimal matching by taking related data into consideration. For example, the matching unit selects a match with a high success rate based on past transaction data. The generation unit generates a proposal based on the results of the adjustment by the matching unit. The proposal can be generated in the form of, for example, proposal items, format, and content, but is not limited to these examples. For example, the generation unit automatically generates each item of the proposal based on information entered by a user. The generation unit can also generate a proposal by combining a company's financial information, business plan, etc. The generation unit can also automatically generate the content of the proposal using a generation AI.For example, the generation unit inputs a company's financial information and business plan into the generation AI and generates a proposal. This allows the M&A process automation system according to the embodiment to efficiently collect, analyze, and adjust information on the buyer and seller and generate a proposal. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input information entered by a user into the generation AI and cause the generation AI to generate a proposal.

[0030] The collection unit can collect information and related data about buyers and sellers. Examples of related data include, but are not limited to, trading history, past feedback, and market data. The collection unit can collect information and related data about buyers and sellers through, for example, an online form. The collection unit can also obtain related data from an external database using an API. For example, the collection unit can obtain trading history from an external database and store it in the database. The collection unit can also collect information and related data manually entered by a user. For example, the collection unit can store trading history and market data entered by a user in a database in real time. This allows for efficient collection of information and related data about buyers and sellers. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input information collected through an online form into AI and have the AI ​​organize and classify the data.

[0031] The analysis unit can analyze the information and related data collected by the collection unit. Related data includes, but is not limited to, trading history, past feedback, and market data. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can extract patterns from the collected information using data mining techniques. The analysis unit can also analyze trends in the information using statistical analysis. The analysis unit can also build a predictive model of the information using a machine learning algorithm. For example, the analysis unit can input the collected information into a machine learning model and output the prediction results. This allows for efficient analysis of the collected information and related data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​analyze the data.

[0032] The matching unit can match the buyer's desired conditions with the seller's offered conditions based on the results of the analysis by the analysis unit. Desired conditions include, but are not limited to, price, delivery time, and quality. Offered conditions include, but are not limited to, price, delivery time, and quality. Matching can be performed, for example, by determining the degree of match or by setting priorities. For example, the matching unit compares the buyer's desired conditions with the seller's offered conditions and selects a combination with a high degree of match. The matching unit can also set priorities and prioritize matching for important conditions. The matching unit can also perform optimal matching by taking related data into consideration. For example, the matching unit selects a match with a high success rate based on past transaction data. This allows for efficient matching between the buyer's desired conditions and the seller's offered conditions. Some or all of the above-described processing by the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the analysis results into AI and have the AI ​​perform optimal matching.

[0033] The generation unit can generate a proposal based on information input by a user. Examples of the information input by a user include, but are not limited to, a company's financial information and a business plan. The proposal is generated in the form of, for example, proposal items, a format, and content, but are not limited to, examples. For example, the generation unit automatically generates each item of the proposal based on the information input by a user. The generation unit can also generate a proposal by combining a company's financial information and a business plan. The generation unit can also automatically generate the content of the proposal using a generation AI. For example, the generation unit inputs a company's financial information and a business plan into the generation AI and generates a proposal. This allows the proposal to be efficiently generated based on the information input by the user. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the information input by a user into the generation AI and cause the generation AI to generate a proposal.

[0034] The generation unit can generate a proposal by combining a company's financial information, business plan, etc. Financial information includes, but is not limited to, revenue, expenses, assets, and liabilities. The business plan includes, but is not limited to, short-term and long-term goals, strategies, and resource allocation. The proposal is generated in the form of, but is not limited to, proposal items, format, and content. For example, the generation unit automatically generates each item of the proposal based on the company's financial information and business plan. The generation unit can also generate a proposal by combining the company's financial information and business plan. The generation unit can also automatically generate the content of the proposal using a generation AI. For example, the generation unit inputs the company's financial information and business plan into the generation AI and generates a proposal. This allows for efficient generation of a proposal by combining the company's financial information and business plan. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the company's financial information and business plan into the generation AI and cause the generation AI to generate a proposal.

[0035] The collection unit can analyze past transaction data and select an optimal information collection method. Examples of optimal information collection methods include, but are not limited to, data reliability, collection cost, and collection speed. For example, the collection unit can analyze successful transaction patterns from past transaction data and select an information collection method based on those patterns. The collection unit can also analyze causes of failed transactions from past transaction data and select an information collection method to avoid those causes. The collection unit can also analyze successful patterns in a specific industry or region from past transaction data and select an information collection method based on those patterns. This improves the efficiency of information collection by selecting an optimal information collection method based on past transaction data. 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 past transaction data into AI and have the AI ​​select an optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current business situation and areas of interest. Examples of business situations include, but are not limited to, industry type, size, and growth stage. Examples of areas of interest include, but are not limited to, technology fields, market segments, and trends. For example, the collection unit analyzes the user's current business situation and collects only relevant information. The collection unit can also prioritize collecting relevant information based on the user's areas of interest. The collection unit can also filter unnecessary information and collect only necessary information based on the user's business situation and areas of interest. This allows for efficient collection of only necessary information by filtering information based on the user's business situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's business situation and areas of interest into AI and have the AI ​​perform information filtering.

[0037] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. When the user uses text input, the collection unit can also collect information using text analysis technology. When the user uses image input, the collection unit can also collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. 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 user's input method into AI and have the AI ​​select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location-based services. For example, the collection unit can prioritize collecting nearby business partner information based on the user's current location. The collection unit can also prioritize collecting region-specific business information based on the user's geographical location information. The collection unit can also prioritize collecting related market information taking the user's geographical location information into consideration. This improves the efficiency of information collection by prioritizing the collection of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0039] When collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit can analyze the content of the user's social media posts and collect related business information. The collection unit can also collect related business partner information by referring to the activities of the user's friends on social media. The collection unit can also collect related market information based on the user's social media check-in information. In this way, related information can be efficiently collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity into AI and have the AI ​​collect related information.

[0040] The collection unit can customize the information collection method by reflecting the user's past feedback when collecting information. Past feedback includes, but is not limited to, survey results, reviews, and comments. For example, the collection unit improves the information collection method based on feedback provided by the user in the past. The collection unit can also adjust the type of information to be collected by reflecting the user's past feedback. The collection unit can also adjust the timing of information collection by referring to the user's past feedback. In this way, the information collection method can be customized by reflecting the user's past feedback, allowing information to be collected efficiently. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback into AI and have the AI ​​customize the information collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, and relevance. For example, the analysis unit performs a detailed analysis of highly important information. The analysis unit can also perform a simplified analysis of less important information. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. 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 importance of the information into AI and have the AI ​​adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. Information categories include, but are not limited to, text data, numerical data, and image data. For example, the analysis unit applies a financial analysis algorithm to financial information. The analysis unit can also apply a market analysis algorithm to market information. The analysis unit can also apply a business plan analysis algorithm to a business plan. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. 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 category of information into AI and have the AI ​​apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, databases, log files, reports, etc. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. 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 user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The time of submission of information includes, but is not limited to, the submission date, deadline, urgency, etc. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. The analysis unit can also gradually adjust the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. 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 time of submission of information into AI and have the AI ​​determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, common keywords, related topics, and correlations. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also gradually adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. 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 relevance of the information into AI and have the AI ​​adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can provide analysis results that avoid technical terms. The analysis unit can also gradually adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user'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 AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0047] The matching unit can improve the accuracy of matching by taking into account the interrelationships between pieces of information during matching. Examples of interrelationships between pieces of information include, but are not limited to, common keywords, related topics, and correlations. For example, the matching unit analyzes the interrelationships between pieces of information and prioritizes matching of highly related information. The matching unit can also improve the accuracy of matching by taking into account the interrelationships between pieces of information. The matching unit can also perform optimal matching based on the interrelationships between pieces of information. In this way, the accuracy of matching is improved by taking the interrelationships between pieces of information into account. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the interrelationships between pieces of information into AI and have the AI ​​improve the accuracy of matching.

[0048] The matching unit can perform matching by taking into account attribute information of the information submitter. Attribute information includes, but is not limited to, age, gender, and occupation, for example. For example, the matching unit performs matching by taking into account the industry and region of the information submitter. The matching unit can also perform matching by taking into account the past transaction history of the information submitter. The matching unit can also perform optimal matching based on the attribute information of the information submitter. This enables more appropriate matching by taking into account the attribute information of the information submitter. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input attribute information of the information submitter into AI and have the AI ​​perform matching.

[0049] During matching, the matching unit can weight the matching based on the frequency of information submission. Examples of the frequency of submission include, but are not limited to, the number of submissions, the interval between submissions, and the consistency of submissions. For example, the matching unit prioritizes matching of information that is submitted frequently. The matching unit can also lower the matching weight for information that is submitted infrequently. The matching unit can also gradually adjust the matching weight based on the submission frequency. This enables efficient matching by weighting the matching based on the frequency of information submission. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the frequency of information submission into AI and have the AI ​​perform the matching weighting.

[0050] The matching unit can perform matching by taking into account the geographical distribution of information. Geographical distribution includes, but is not limited to, regions, cities, and countries. For example, the matching unit analyzes the geographical distribution of information and prioritizes matching with nearby business partners. The matching unit can also match business information specific to a region by taking the geographical distribution into account. The matching unit can also perform optimal matching based on the geographical distribution. This enables more appropriate matching by taking the geographical distribution of information into account. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the geographical distribution of information into AI and have the AI ​​perform matching.

[0051] The matching unit can improve the accuracy of matching by referring to related literature of the information during matching. Related literature includes, but is not limited to, academic papers, patent documents, and technical reports. For example, the matching unit can refer to related literature and prioritize matching of highly relevant information. The matching unit can also improve the accuracy of matching based on the related literature. The matching unit can also analyze related literature and perform optimal matching. As a result, the accuracy of matching is improved by referring to related literature of the information. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input related literature into AI and have the AI ​​improve the accuracy of matching.

[0052] The matching unit can perform matching by taking into account the market value of the information. Market value includes, but is not limited to, for example, price, demand, and competitive situation. For example, the matching unit prioritizes matching of information with high market value. The matching unit can also lower the matching priority for information with low market value. The matching unit can also gradually adjust the matching priority based on the market value. This enables more appropriate matching by taking the market value of the information into consideration. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input the market value of the information into AI and have the AI ​​perform matching.

[0053] When generating a proposal, the generation unit can adjust the level of detail of the proposal based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, and relevance. For example, the generation unit generates a detailed proposal for information with high importance. The generation unit can also generate a simplified proposal for information with low importance. The generation unit can also gradually adjust the level of detail of the proposal according to the importance of the information. This enables efficient proposal generation by adjusting the level of detail of the proposal based on the importance of the information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the information into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0054] When generating a proposal, the generation unit can apply different generation algorithms depending on the category of information. Information categories include, but are not limited to, text data, numerical data, and image data. For example, the generation unit can apply a financial analysis algorithm to financial information to generate a proposal. The generation unit can also apply a market analysis algorithm to market information to generate a proposal. The generation unit can also apply a business plan analysis algorithm to a business plan to generate a proposal. By applying an appropriate generation algorithm depending on the category of information, the accuracy of the proposal is improved. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the category of information into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0055] When generating a proposal, the generation unit can improve the accuracy of the generation by referring to the user's past proposal results. Past proposal results include, but are not limited to, databases, log files, reports, etc. For example, the generation unit adjusts the generation algorithm based on the user's past proposal results. The generation unit can also improve the accuracy of the generation by referring to the user's past proposal results. The generation unit can also analyze the user's past proposal results and identify areas for improvement in the generation. As a result, the accuracy of the proposal is improved by referring to the user's past proposal results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0056] When generating a proposal, the generation unit can determine the priority of the proposal based on the time of submission of the information. The time of submission of the information includes, but is not limited to, the submission date, deadline, urgency, etc. For example, the generation unit prioritizes the most recent information in the proposal. The generation unit can also lower the priority of information submitted earlier and reflect it in the proposal. The generation unit can also gradually adjust the priority of the proposal based on the time of submission. This enables efficient proposal generation by determining the priority of the proposal based on the time of submission of the information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the time of submission of the information into the generation AI and have the generation AI determine the priority of the proposal.

[0057] When generating a proposal, the generation unit can adjust the order of the proposal based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, common keywords, related topics, and correlations. For example, the generation unit prioritizes highly relevant information in the proposal. The generation unit can also postpone the order of the proposal for less relevant information. The generation unit can also gradually adjust the order of the proposal based on the relevance of the information. This enables efficient proposal generation by adjusting the order of the proposal based on the relevance of the information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of the information into the generation AI and have the generation AI adjust the order of the proposal.

[0058] When generating a proposal, the generation unit can adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of technical terms include, but are not limited to, beginner, intermediate, and expert. For example, if the user's level of expertise is high, the generation unit can generate a proposal that uses a lot of technical terms. Alternatively, if the user's level of expertise is low, the generation unit can generate a proposal that avoids technical terms. The generation unit can also gradually adjust the use of technical terms in the proposal according to the user's level of expertise. This allows for the provision of a more appropriate proposal by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[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] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit analyzes what kind of information the user has frequently collected in the past and selects the information collection method based on that pattern. The collection unit can also analyze what information collection method the user has preferred in the past and preferentially select that method. Furthermore, the collection unit can adjust the timing of information collection based on the user's past behavioral history. This improves the efficiency of information collection by selecting the optimal information collection method based on the user's past behavioral history.

[0061] The collection unit can determine the priority of information collection based on the user's business goals. For example, if the user places importance on short-term profits, the collection unit can prioritize collecting information with high profitability. Also, if the user places importance on long-term growth, the collection unit can prioritize collecting information with high growth potential. Furthermore, if the user is focusing on a specific market segment, the collection unit can prioritize collecting information related to that market. Thus, by determining the priority of information collection based on the user's business goals, more effective information collection is possible.

[0062] The analysis unit can customize the analysis algorithm based on the industry characteristics of the user. For example, the analysis unit can apply an analysis algorithm specialized for manufacturing processes to a user in the manufacturing industry. The analysis unit can also apply an analysis algorithm specialized for service provision to a user in the service industry. Furthermore, the analysis unit can also apply an algorithm specialized for financial data analysis to a user in the financial industry. In this way, customizing the analysis algorithm based on the industry characteristics of the user improves the accuracy of the analysis.

[0063] The matching unit can adjust the matching criteria based on the user's business strategy. For example, if the user places importance on cost reduction, the matching unit can prioritize matching with cost-effective business partners. Also, if the user places importance on quality, the matching unit can prioritize matching with high-quality business partners. Furthermore, if the user aims to expand their market, the matching unit can prioritize matching with business partners related to new markets. This allows for more effective matching by adjusting the matching criteria based on the user's business strategy.

[0064] The generation unit can analyze the success rate of the user's past proposals and prioritize the use of formats with high success rates. For example, the generation unit can analyze the formats of proposals that have been successful in the past and generate a new proposal based on those formats. The generation unit can also adjust the content of the new proposal by referring to the content of proposals that have been successful in the past. Furthermore, the generation unit can adjust the importance of each item in the proposal based on the success rate of the past proposals. In this way, the success rate of the proposal is improved by generating a proposal based on the success rate of the user's past proposals.

[0065] The generation unit can reflect user feedback in real time and adjust the content of the proposal. For example, when a user provides feedback on the content of the proposal, the generation unit immediately modifies the content of the proposal based on the feedback. The generation unit can also adjust the format of the proposal based on the user feedback. Furthermore, the generation unit can also reevaluate the importance of each item in the proposal by referring to the user feedback. In this way, a more appropriate proposal can be provided by reflecting user feedback in real time.

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

[0067] Step 1: The collection unit collects information about buyers and sellers. This information includes contact information, transaction history, and desired conditions. The collection unit collects information through online forms and can also obtain information from external databases using APIs. It also saves information manually entered by users to the database in real time. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns, statistical analysis is used to analyze trends in the information, and machine learning algorithms are used to build predictive models. Step 3: The matching unit adjusts the buyer and seller based on the results of the analysis by the analysis unit. Adjustments are made based on the degree of match of conditions and by setting priorities. For example, it compares the buyer's desired conditions with the conditions offered by the seller and selects the combination with the highest degree of match. It is also possible to set priorities and prioritize matching of important conditions. Furthermore, it selects matches with a high success rate based on past transaction data. Step 4: The generation unit generates a proposal based on the results adjusted by the matching unit. The proposal is generated in the form of proposal items, format, and content. For example, each item in the proposal can be automatically generated based on information entered by the user, and the proposal can be generated by combining the company's financial information, business plan, etc. It is also possible to automatically generate the content of a proposal using generation AI.

[0068] (Example 2) An M&A process automation system according to an embodiment of the present invention efficiently collects, analyzes, and adjusts information on buyers and sellers to generate proposals. The M&A process automation system utilizes AI technology to automate the entire M&A process and uses a generation AI to expedite the creation of proposals. For example, the M&A process automation system collects information on buyers and sellers and analyzes that information. Next, the AI ​​quickly and fairly coordinates the buyer and seller based on the analysis results. For example, it matches the buyer's desired terms with the seller's offered terms. In this process, the AI ​​considers related data to achieve optimal matching. Furthermore, the generation AI creates proposals. When a user inputs the necessary information, the generation AI automatically generates a proposal based on that information. For example, by inputting a company's financial information and business plan, the generation AI combines that information to create a proposal. This allows small and medium-sized enterprises to achieve smooth M&A transactions while saving time and effort. This allows small and medium-sized enterprises to achieve smooth M&A transactions while saving time and effort. For example, the process of gathering information and creating proposals, which was previously done manually, is now automated, reducing the burden on staff. Furthermore, AI analysis and matching improves the fairness and efficiency of transactions. This service is particularly beneficial for small and medium-sized enterprises, allowing them to efficiently advance M&A transactions with limited resources. For example, even small and medium-sized enterprises with limited capital and human resources can use this service to smoothly advance M&A transactions, even if the staff do not have specialized knowledge or skills.

[0069] An M&A process automation system according to an embodiment includes a collection unit, an analysis unit, a matching unit, and a generation unit. The collection unit collects information about buyers and sellers. The information about buyers and sellers includes, but is not limited to, contact information, transaction history, and desired terms. The collection unit may collect information through, for example, an online form. The collection unit may also obtain information from an external database using an API. The collection unit may also collect information manually entered by a user. For example, the collection unit may store the information entered by the user in a database in real time. The analysis unit analyzes the information collected by the collection unit. The analysis may be 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 may extract patterns from the collected information using data mining techniques. The analysis unit may also analyze trends in the information using statistical analysis. The analysis unit may also build a predictive model of the information using a machine learning algorithm. For example, the analysis unit may input the collected information into a machine learning model and output a prediction result. The matching unit adjusts the relationship between the buyer and the seller based on the results of the analysis by the analysis unit. The adjustment can be performed, for example, by determining the degree of match of conditions or by setting priorities, but is not limited to these examples. For example, the matching unit compares the buyer's desired conditions with the seller's offered conditions and selects a combination with a high degree of match. The matching unit can also set priorities and prioritize matching of important conditions. The matching unit can also perform optimal matching by taking related data into consideration. For example, the matching unit selects a match with a high success rate based on past transaction data. The generation unit generates a proposal based on the results of the adjustment by the matching unit. The proposal can be generated in the form of, for example, proposal items, format, and content, but is not limited to these examples. For example, the generation unit automatically generates each item of the proposal based on information entered by a user. The generation unit can also generate a proposal by combining a company's financial information, business plan, etc. The generation unit can also automatically generate the content of the proposal using a generation AI.For example, the generation unit inputs a company's financial information and business plan into the generation AI and generates a proposal. This allows the M&A process automation system according to the embodiment to efficiently collect, analyze, and adjust information on the buyer and seller and generate a proposal. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input information entered by a user into the generation AI and cause the generation AI to generate a proposal.

[0070] The collection unit can collect information and related data about buyers and sellers. Examples of related data include, but are not limited to, trading history, past feedback, and market data. The collection unit can collect information and related data about buyers and sellers through, for example, an online form. The collection unit can also obtain related data from an external database using an API. For example, the collection unit can obtain trading history from an external database and store it in the database. The collection unit can also collect information and related data manually entered by a user. For example, the collection unit can store trading history and market data entered by a user in a database in real time. This allows for efficient collection of information and related data about buyers and sellers. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input information collected through an online form into AI and have the AI ​​organize and classify the data.

[0071] The analysis unit can analyze the information and related data collected by the collection unit. Related data includes, but is not limited to, trading history, past feedback, and market data. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can extract patterns from the collected information using data mining techniques. The analysis unit can also analyze trends in the information using statistical analysis. The analysis unit can also build a predictive model of the information using a machine learning algorithm. For example, the analysis unit can input the collected information into a machine learning model and output the prediction results. This allows for efficient analysis of the collected information and related data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​analyze the data.

[0072] The matching unit can match the buyer's desired conditions with the seller's offered conditions based on the results of the analysis by the analysis unit. Desired conditions include, but are not limited to, price, delivery time, and quality. Offered conditions include, but are not limited to, price, delivery time, and quality. Matching can be performed, for example, by determining the degree of match or by setting priorities. For example, the matching unit compares the buyer's desired conditions with the seller's offered conditions and selects a combination with a high degree of match. The matching unit can also set priorities and prioritize matching for important conditions. The matching unit can also perform optimal matching by taking related data into consideration. For example, the matching unit selects a match with a high success rate based on past transaction data. This allows for efficient matching between the buyer's desired conditions and the seller's offered conditions. Some or all of the above-described processing by the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the analysis results into AI and have the AI ​​perform optimal matching.

[0073] The generation unit can generate a proposal based on information input by a user. Examples of the information input by a user include, but are not limited to, a company's financial information and a business plan. The proposal is generated in the form of, for example, proposal items, a format, and content, but are not limited to, examples. For example, the generation unit automatically generates each item of the proposal based on the information input by a user. The generation unit can also generate a proposal by combining a company's financial information and a business plan. The generation unit can also automatically generate the content of the proposal using a generation AI. For example, the generation unit inputs a company's financial information and a business plan into the generation AI and generates a proposal. This allows the proposal to be efficiently generated based on the information input by the user. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the information input by a user into the generation AI and cause the generation AI to generate a proposal.

[0074] The generation unit can generate a proposal by combining a company's financial information, business plan, etc. Financial information includes, but is not limited to, revenue, expenses, assets, and liabilities. The business plan includes, but is not limited to, short-term and long-term goals, strategies, and resource allocation. The proposal is generated in the form of, but is not limited to, proposal items, format, and content. For example, the generation unit automatically generates each item of the proposal based on the company's financial information and business plan. The generation unit can also generate a proposal by combining the company's financial information and business plan. The generation unit can also automatically generate the content of the proposal using a generation AI. For example, the generation unit inputs the company's financial information and business plan into the generation AI and generates a proposal. This allows for efficient generation of a proposal by combining the company's financial information and business plan. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the company's financial information and business plan into the generation AI and cause the generation AI to generate a proposal.

[0075] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the collection unit can reduce the frequency of information collection when the user is stressed and collect information when the user is relaxed. Furthermore, the collection unit can increase the frequency of information collection and collect detailed information when the user is relaxed. Furthermore, the collection unit can quickly collect information when the user is in a hurry, prioritizing the collection of the minimum necessary information. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] The collection unit can analyze past transaction data and select an optimal information collection method. Examples of optimal information collection methods include, but are not limited to, data reliability, collection cost, and collection speed. For example, the collection unit can analyze successful transaction patterns from past transaction data and select an information collection method based on those patterns. The collection unit can also analyze causes of failed transactions from past transaction data and select an information collection method to avoid those causes. The collection unit can also analyze successful patterns in a specific industry or region from past transaction data and select an information collection method based on those patterns. This improves the efficiency of information collection by selecting an optimal information collection method based on past transaction data. 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 past transaction data into AI and have the AI ​​select an optimal information collection method.

[0077] When collecting information, the collection unit can filter the information based on the user's current business situation and areas of interest. Examples of business situations include, but are not limited to, industry type, size, and growth stage. Examples of areas of interest include, but are not limited to, technology fields, market segments, and trends. For example, the collection unit analyzes the user's current business situation and collects only relevant information. The collection unit can also prioritize collecting relevant information based on the user's areas of interest. The collection unit can also filter unnecessary information and collect only necessary information based on the user's business situation and areas of interest. This allows for efficient collection of only necessary information by filtering information based on the user's business situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's business situation and areas of interest into AI and have the AI ​​perform information filtering.

[0078] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. When the user uses text input, the collection unit can also collect information using text analysis technology. When the user uses image input, the collection unit can also collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. 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 user's input method into AI and have the AI ​​select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, when the user is stressed, the collection unit can prioritize collecting information of high importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This enables more appropriate information collection by determining the priority of information according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0080] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location-based services. For example, the collection unit can prioritize collecting nearby business partner information based on the user's current location. The collection unit can also prioritize collecting region-specific business information based on the user's geographical location information. The collection unit can also prioritize collecting related market information taking the user's geographical location information into consideration. This improves the efficiency of information collection by prioritizing the collection of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0081] When collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit can analyze the content of the user's social media posts and collect related business information. The collection unit can also collect related business partner information by referring to the activities of the user's friends on social media. The collection unit can also collect related market information based on the user's social media check-in information. In this way, related information can be efficiently collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity into AI and have the AI ​​collect related information.

[0082] The collection unit can customize the information collection method by reflecting the user's past feedback when collecting information. Past feedback includes, but is not limited to, survey results, reviews, and comments. For example, the collection unit improves the information collection method based on feedback provided by the user in the past. The collection unit can also adjust the type of information to be collected by reflecting the user's past feedback. The collection unit can also adjust the timing of information collection by referring to the user's past feedback. In this way, the information collection method can be customized by reflecting the user's past feedback, allowing information to be collected efficiently. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback into AI and have the AI ​​customize the information 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. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, and relevance. For example, the analysis unit performs a detailed analysis of highly important information. The analysis unit can also perform a simplified analysis of less important information. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. 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 importance of the information into AI and have the AI ​​adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. Information categories include, but are not limited to, text data, numerical data, and image data. For example, the analysis unit applies a financial analysis algorithm to financial information. The analysis unit can also apply a market analysis algorithm to market information. The analysis unit can also apply a business plan analysis algorithm to a business plan. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. 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 category of information into AI and have the AI ​​apply an appropriate analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, databases, log files, reports, etc. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. 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 user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. This allows for adjusting the length of the analysis according to the user's emotion to provide a more appropriate analysis result. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The time of submission of information includes, but is not limited to, the submission date, deadline, urgency, etc. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. The analysis unit can also gradually adjust the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. 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 time of submission of information into AI and have the AI ​​determine the priority of analysis.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, common keywords, related topics, and correlations. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also gradually adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. 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 relevance of the information into AI and have the AI ​​adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can provide analysis results that avoid technical terms. The analysis unit can also gradually adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user'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 AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0091] The matching unit can estimate the user's emotions and adjust matching criteria based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to these examples. For example, if the user is nervous, the matching unit can provide simple, highly visible matching criteria. Furthermore, if the user is relaxed, the matching unit can provide detailed matching criteria. Furthermore, if the user is in a hurry, the matching unit can provide matching criteria that focus on the key points. This enables more appropriate matching by adjusting the matching criteria according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The matching unit can improve the accuracy of matching by taking into account the interrelationships between pieces of information during matching. Examples of interrelationships between pieces of information include, but are not limited to, common keywords, related topics, and correlations. For example, the matching unit analyzes the interrelationships between pieces of information and prioritizes matching of highly related information. The matching unit can also improve the accuracy of matching by taking into account the interrelationships between pieces of information. The matching unit can also perform optimal matching based on the interrelationships between pieces of information. In this way, the accuracy of matching is improved by taking the interrelationships between pieces of information into account. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the interrelationships between pieces of information into AI and have the AI ​​improve the accuracy of matching.

[0093] The matching unit can perform matching by taking into account attribute information of the information submitter. Attribute information includes, but is not limited to, age, gender, and occupation, for example. For example, the matching unit performs matching by taking into account the industry and region of the information submitter. The matching unit can also perform matching by taking into account the past transaction history of the information submitter. The matching unit can also perform optimal matching based on the attribute information of the information submitter. This enables more appropriate matching by taking into account the attribute information of the information submitter. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input attribute information of the information submitter into AI and have the AI ​​perform matching.

[0094] During matching, the matching unit can weight the matching based on the frequency of information submission. Examples of the frequency of submission include, but are not limited to, the number of submissions, the interval between submissions, and the consistency of submissions. For example, the matching unit prioritizes matching of information that is submitted frequently. The matching unit can also lower the matching weight for information that is submitted infrequently. The matching unit can also gradually adjust the matching weight based on the submission frequency. This enables efficient matching by weighting the matching based on the frequency of information submission. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the frequency of information submission into AI and have the AI ​​perform the matching weighting.

[0095] The matching unit can estimate the user's emotion and adjust the order in which matching results are displayed based on the estimated user emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is nervous, the matching unit can prioritize and display matching results with high importance. Furthermore, if the user is relaxed, the matching unit can also display detailed matching results in an orderly manner. Furthermore, if the user is in a hurry, the matching unit can quickly display matching results that focus on the main points. This allows for more appropriate matching results to be provided by adjusting the order in which matching results are displayed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit can be performed using, for example, an AI. For example, the matching unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The matching unit can perform matching by taking into account the geographical distribution of information. Geographical distribution includes, but is not limited to, regions, cities, and countries. For example, the matching unit analyzes the geographical distribution of information and prioritizes matching with nearby business partners. The matching unit can also match business information specific to a region by taking the geographical distribution into account. The matching unit can also perform optimal matching based on the geographical distribution. This enables more appropriate matching by taking the geographical distribution of information into account. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the geographical distribution of information into AI and have the AI ​​perform matching.

[0097] The matching unit can improve the accuracy of matching by referring to related literature of the information during matching. Related literature includes, but is not limited to, academic papers, patent documents, and technical reports. For example, the matching unit can refer to related literature and prioritize matching of highly relevant information. The matching unit can also improve the accuracy of matching based on the related literature. The matching unit can also analyze related literature and perform optimal matching. As a result, the accuracy of matching is improved by referring to related literature of the information. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input related literature into AI and have the AI ​​improve the accuracy of matching.

[0098] The matching unit can perform matching by taking into account the market value of the information. Market value includes, but is not limited to, for example, price, demand, and competitive situation. For example, the matching unit prioritizes matching of information with high market value. The matching unit can also lower the matching priority for information with low market value. The matching unit can also gradually adjust the matching priority based on the market value. This enables more appropriate matching by taking the market value of the information into consideration. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input the market value of the information into AI and have the AI ​​perform matching.

[0099] The generation unit can estimate the user's emotions and adjust the presentation style of the proposal based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to these examples. For example, if the user is nervous, the generation unit can generate a simple, highly readable proposal. Furthermore, if the user is relaxed, the generation unit can generate a detailed proposal. Furthermore, if the user is in a hurry, the generation unit can generate a proposal that focuses on the main points. This allows the proposal presentation style to be adjusted according to the user's emotions, thereby providing a more appropriate proposal. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0100] When generating a proposal, the generation unit can adjust the level of detail of the proposal based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, and relevance. For example, the generation unit generates a detailed proposal for information with high importance. The generation unit can also generate a simplified proposal for information with low importance. The generation unit can also gradually adjust the level of detail of the proposal according to the importance of the information. This enables efficient proposal generation by adjusting the level of detail of the proposal based on the importance of the information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the information into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0101] When generating a proposal, the generation unit can apply different generation algorithms depending on the category of information. Information categories include, but are not limited to, text data, numerical data, and image data. For example, the generation unit can apply a financial analysis algorithm to financial information to generate a proposal. The generation unit can also apply a market analysis algorithm to market information to generate a proposal. The generation unit can also apply a business plan analysis algorithm to a business plan to generate a proposal. By applying an appropriate generation algorithm depending on the category of information, the accuracy of the proposal is improved. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the category of information into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0102] When generating a proposal, the generation unit can improve the accuracy of the generation by referring to the user's past proposal results. Past proposal results include, but are not limited to, databases, log files, reports, etc. For example, the generation unit adjusts the generation algorithm based on the user's past proposal results. The generation unit can also improve the accuracy of the generation by referring to the user's past proposal results. The generation unit can also analyze the user's past proposal results and identify areas for improvement in the generation. As a result, the accuracy of the proposal is improved by referring to the user's past proposal results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0103] The generation unit can estimate the user's emotions and adjust the length of the proposal based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, the generation unit can generate a short and concise proposal when the user is in a hurry. The generation unit can also generate a detailed proposal when the user is relaxed. The generation unit can also generate a visually stimulating proposal when the user is excited. This allows the length of the proposal to be adjusted according to the user's emotions, thereby providing a more appropriate proposal. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0104] When generating a proposal, the generation unit can determine the priority of the proposal based on the time of submission of the information. The time of submission of the information includes, but is not limited to, the submission date, deadline, urgency, etc. For example, the generation unit prioritizes the most recent information in the proposal. The generation unit can also lower the priority of information submitted earlier and reflect it in the proposal. The generation unit can also gradually adjust the priority of the proposal based on the time of submission. This enables efficient proposal generation by determining the priority of the proposal based on the time of submission of the information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the time of submission of the information into the generation AI and have the generation AI determine the priority of the proposal.

[0105] When generating a proposal, the generation unit can adjust the order of the proposal based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, common keywords, related topics, and correlations. For example, the generation unit prioritizes highly relevant information in the proposal. The generation unit can also postpone the order of the proposal for less relevant information. The generation unit can also gradually adjust the order of the proposal based on the relevance of the information. This enables efficient proposal generation by adjusting the order of the proposal based on the relevance of the information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of the information into the generation AI and have the generation AI adjust the order of the proposal.

[0106] When generating a proposal, the generation unit can adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of technical terms include, but are not limited to, beginner, intermediate, and expert. For example, if the user's level of expertise is high, the generation unit can generate a proposal that uses a lot of technical terms. Alternatively, if the user's level of expertise is low, the generation unit can generate a proposal that avoids technical terms. The generation unit can also gradually adjust the use of technical terms in the proposal according to the user's level of expertise. This allows for the provision of a more appropriate proposal by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, matching unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, matching unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, matching unit, and generation unit 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 is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, matching unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the matching unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[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] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit analyzes what kind of information the user has frequently collected in the past and selects the information collection method based on that pattern. The collection unit can also analyze what information collection method the user has preferred in the past and preferentially select that method. Furthermore, the collection unit can adjust the timing of information collection based on the user's past behavioral history. This improves the efficiency of information collection by selecting the optimal information collection method based on the user's past behavioral history.

[0109] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing information of high importance. Also, if the user is relaxed, the analysis unit can prioritize detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing information that can be analyzed quickly. In this way, by determining the priority of analysis according to the user's emotions, more appropriate analysis is possible.

[0110] The matching unit can estimate the user's emotions and adjust the timing of matching based on the estimated user's emotions. For example, if the user is feeling stressed, the matching unit can reduce the frequency of matching and perform matching when the user is relaxed. Also, if the user is relaxed, the matching unit can increase the frequency of matching and perform detailed matching. Furthermore, if the user is in a hurry, the matching unit can perform matching quickly and prioritize matching with the minimum necessary information. This allows for more appropriate matching by adjusting the timing of matching according to the user's emotions.

[0111] The generation unit can estimate the user's emotions and adjust the content of the proposal based on the estimated user's emotions. For example, if the user is nervous, the generation unit can generate a simple, highly visible proposal. If the user is relaxed, the generation unit can also generate a detailed proposal. Furthermore, if the user is in a hurry, the generation unit can also generate a proposal that focuses on the main points. In this way, by adjusting the content of the proposal according to the user's emotions, a more appropriate proposal can be provided.

[0112] The collection unit can estimate the user's emotions and determine the type of information to collect based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information of high importance. Also, if the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. In this way, by determining the type of information to collect according to the user's emotions, more appropriate information collection is possible.

[0113] The collection unit can determine the priority of information collection based on the user's business goals. For example, if the user places importance on short-term profits, the collection unit can prioritize collecting information with high profitability. Also, if the user places importance on long-term growth, the collection unit can prioritize collecting information with high growth potential. Furthermore, if the user is focusing on a specific market segment, the collection unit can prioritize collecting information related to that market. Thus, by determining the priority of information collection based on the user's business goals, more effective information collection is possible.

[0114] The analysis unit can customize the analysis algorithm based on the industry characteristics of the user. For example, the analysis unit can apply an analysis algorithm specialized for manufacturing processes to a user in the manufacturing industry. The analysis unit can also apply an analysis algorithm specialized for service provision to a user in the service industry. Furthermore, the analysis unit can also apply an algorithm specialized for financial data analysis to a user in the financial industry. In this way, customizing the analysis algorithm based on the industry characteristics of the user improves the accuracy of the analysis.

[0115] The matching unit can adjust the matching criteria based on the user's business strategy. For example, if the user places importance on cost reduction, the matching unit can prioritize matching with cost-effective business partners. Also, if the user places importance on quality, the matching unit can prioritize matching with high-quality business partners. Furthermore, if the user aims to expand their market, the matching unit can prioritize matching with business partners related to new markets. This allows for more effective matching by adjusting the matching criteria based on the user's business strategy.

[0116] The generation unit can analyze the success rate of the user's past proposals and prioritize the use of formats with high success rates. For example, the generation unit can analyze the formats of proposals that have been successful in the past and generate a new proposal based on those formats. The generation unit can also adjust the content of the new proposal by referring to the content of proposals that have been successful in the past. Furthermore, the generation unit can adjust the importance of each item in the proposal based on the success rate of the past proposals. In this way, the success rate of the proposal is improved by generating a proposal based on the success rate of the user's past proposals.

[0117] The generation unit can reflect user feedback in real time and adjust the content of the proposal. For example, when a user provides feedback on the content of the proposal, the generation unit immediately modifies the content of the proposal based on the feedback. The generation unit can also adjust the format of the proposal based on the user feedback. Furthermore, the generation unit can also reevaluate the importance of each item in the proposal by referring to the user feedback. In this way, a more appropriate proposal can be provided by reflecting user feedback in real time.

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

[0119] Step 1: The collection unit collects information about buyers and sellers. This information includes contact information, transaction history, and desired conditions. The collection unit collects information through online forms and can also obtain information from external databases using APIs. It also saves information manually entered by users to the database in real time. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns, statistical analysis is used to analyze trends in the information, and machine learning algorithms are used to build predictive models. Step 3: The matching unit adjusts the buyer and seller based on the results of the analysis by the analysis unit. Adjustments are made based on the degree of match of conditions and by setting priorities. For example, it compares the buyer's desired conditions with the conditions offered by the seller and selects the combination with the highest degree of match. It is also possible to set priorities and prioritize matching of important conditions. Furthermore, it selects matches with a high success rate based on past transaction data. Step 4: The generation unit generates a proposal based on the results adjusted by the matching unit. The proposal is generated in the form of proposal items, format, and content. For example, each item in the proposal can be automatically generated based on information entered by the user, and the proposal can be generated by combining the company's financial information, business plan, etc. It is also possible to automatically generate the content of a proposal using generation AI.

[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 the 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 AI 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 AI 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 AI 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, 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 for collecting information on buyers and sellers; an analysis unit that analyzes the information collected by the collection unit; a matching unit that adjusts between buyers and sellers based on the results of the analysis by the analysis unit; a generation unit that generates a proposal based on the result of adjustment by the matching unit. A system characterized by:

2. The collecting unit Collecting buyer and seller information and related data The system of claim 1 .

3. The analysis unit Analyzing the information and related data collected by the collection unit The system of claim 1 .

4. The matching unit Based on the results of the analysis by the analysis unit, the desired conditions of the buyer are matched with the conditions offered by the seller. The system of claim 1 .

5. The generation unit Generate a proposal based on user-entered information The system of claim 1 .

6. The generation unit Generate proposals by combining company financial information, business plans, etc. The system of claim 1 .

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Analyze past transaction data and select the most appropriate information gathering method The system of claim 1 .

9. The collecting unit When collecting information, filter it based on the user's current business situation or areas of interest. The system of claim 1 .

10. The collecting unit When collecting information, select the optimal collection method according to the user's input method. The system of claim 1 .

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