System

Abstract

An object of the system according to the embodiment is to match advanced technologies with market needs and promote commercialization and monetization.SOLUTION: A system includes an acquisition unit, an element analysis unit, a collection unit, an organization unit, a matching unit, and a mediation unit. The acquisition unit acquires market needs. The element analysis part analyzes the technical element on the basis of the market needs acquired by the acquisition part. The collection part collects public technical information. The organization unit organizes and classifies the technical information collected by the collection unit. The matching unit matches technologies based on the technology information organized and classified by the organizing unit. The mediation unit proposes a combination of technologies matched by the matching unit to the company and mediates a contract.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 technologies, the commercialization and monetization of cutting-edge technologies has not been adequately matched with market needs, and there is room for improvement.

[0005] The system according to the embodiment aims to match cutting-edge technology with market needs and promote commercialization and profitability. [Means for solving the problem]

[0006] The system according to the embodiment comprises an acquisition unit, an element analysis unit, a collection unit, an organization unit, a matching unit, and an intermediation unit. The acquisition unit acquires market needs. The element analysis unit analyzes technical elements based on the market needs acquired by the acquisition unit. The collection unit collects public technical information. The organization unit organizes and classifies the technical information collected by the collection unit. The matching unit matches technologies based on the technical information organized and classified by the organization unit. The intermediation unit proposes combinations of technologies matched by the matching unit to companies and mediates contracts. [Effects of the Invention]

[0007] The system according to the embodiment can match cutting-edge technology with market needs and promote commercialization and profitability. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention accelerates the commercialization and monetization of Japan's cutting-edge technologies. This system acquires market needs, analyzes technological elements, collects publicly available technical information, organizes and categorizes the information, matches technologies, proposes them to companies, and brokers contracts. For example, the system manually analyzes the elements of technologies required for a service with growing market demand. Next, the system uses AI to organize and categorize technologies from the publicly available technical information that could potentially be used for the element analysis. Furthermore, the system uses generative AI to match the organized and classified technologies and create proposals for new applications. Finally, the system introduces utilization plans to domestic and overseas companies online and brokers contracts for corporate matching of combined technologies. This allows the system to coordinate and provide utilization consulting between technology-owning companies and organizations, thereby increasing intellectual property revenue and securing research and development funds. This also enables the system to strengthen and maintain technology development systems, accelerating the commercialization and monetization of Japan's cutting-edge technologies.

[0029] The technology commercialization system according to the embodiment includes an acquisition unit, an element analysis unit, a collection unit, a sorting unit, a matching unit, and an intermediation unit. The acquisition unit acquires market needs. Market needs include, but are not limited to, consumer demand, industry trends, and demand for specific products and services. The acquisition unit, for example, conducts a questionnaire survey to understand consumer demand. The acquisition unit can also analyze industry trends and predict future market needs. The acquisition unit can also collect demand for specific products and services and use the collected demand to analyze technological elements. The element analysis unit analyzes technological elements based on the market needs acquired by the acquisition unit. Technological elements include, but are not limited to, specific technical fields, technical functions, and technology application ranges. The element analysis unit extracts technological elements from market needs using, for example, text mining technology. The element analysis unit can also analyze market needs and identify technological elements using data analysis technology. The element analysis unit can also extract technological elements based on expert evaluations. The collection unit collects public technology information. Public technical information includes, but is not limited to, patent databases, academic papers, and technical reports. The collection unit, for example, searches patent databases to collect relevant technical information. The collection unit may also analyze academic papers to collect technical information. The collection unit may also collect technical reports and organize the technical information. The organization unit organizes and classifies the technical information collected by the collection unit. Organization and classification may include, but is not limited to, categorizing information, tagging, creating a hierarchical structure, and the like. For example, the organization unit may classify information by category and organize the technical information. The organization unit may also organize information by tagging. The organization unit may also organize information into a hierarchical structure and systematically manage the technical information. The matching unit matches technologies based on the technical information organized and classified by the organization unit. Matching may include, but is not limited to, technologies' mutual complementarity, technology compatibility, the use of algorithms, and the like. For example, the matching unit matches technologies based on technologies' mutual complementarity.The matching unit can also evaluate the compatibility of technologies and match optimal technologies together. The matching unit can also match technologies together using an algorithm. The intermediary unit proposes technology combinations matched by the matching unit to companies and brokers contracts. Brokering contracts includes, but is not limited to, adjusting contract terms, drafting contracts, and supporting negotiations. For example, the intermediary unit arranges meetings between companies and mediates opinions. The intermediary unit can also adjust contract terms and draft contracts. The intermediary unit can also support negotiations and help conclude contracts. As a result, the technology commercialization system according to the embodiment analyzes technology elements based on market needs, organizes and classifies technology information, matches optimal technologies together, proposes them to companies, and brokers contracts, thereby accelerating the commercialization and monetization of technology.

[0030] The element analysis unit can extract technological elements based on the market needs acquired by the acquisition unit. The element analysis unit extracts technological elements from market needs using, for example, text mining technology. For example, the element analysis unit analyzes text data of market needs using text mining technology and extracts technological elements. The element analysis unit can also analyze market needs using data analysis technology and identify technological elements. For example, the element analysis unit analyzes market needs data using data analysis technology and identify technological elements. The element analysis unit can also extract technological elements based on expert evaluations. For example, the element analysis unit extracts technological elements from market needs based on expert evaluations. In this way, extracting technological elements based on market needs enables more appropriate analysis of technological elements.

[0031] The organizing unit can analyze public technical information using AI and extract related technologies. The organizing unit, for example, uses AI to analyze public technical information and extract related technologies. For example, the organizing unit uses AI to analyze a patent database and extract related technologies. The organizing unit can also analyze academic papers and extract related technologies using AI. For example, the organizing unit uses AI to analyze text data of academic papers and extract related technologies. The organizing unit can also analyze technical reports and extract related technologies using AI. For example, the organizing unit uses AI to analyze text data of technical reports and extract related technologies. In this way, by using AI, related technologies can be efficiently extracted from public technical information.

[0032] The matching unit can simulate combinations of technologies using the generation AI and find the optimal combination. The matching unit, for example, uses the generation AI to simulate combinations of technologies and find the optimal combination. For example, the matching unit can simulate combinations of different technologies and find the optimal combination. The matching unit can also use the generation AI to evaluate the mutual complementarity of technologies and find the optimal combination. For example, the matching unit can use the generation AI to evaluate the mutual complementarity of technologies and find the optimal combination. The matching unit can also use the generation AI to evaluate the compatibility of technologies and find the optimal combination. For example, the matching unit can use the generation AI to evaluate the compatibility of technologies and find the optimal combination. In this way, the generation AI can be used to find the optimal combination of technologies. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0033] The intermediary department can support coordination and contracting between companies. For example, the intermediary department sets up meetings between companies and coordinates opinions. For example, the intermediary department sets up meetings between companies and coordinates opinions. The intermediary department can also adjust contract terms and prepare contracts. For example, the intermediary department can adjust contract terms and prepare contracts. The intermediary department can also support negotiations and assist in the conclusion of contracts. For example, the intermediary department can support negotiations and assist in the conclusion of contracts. In this way, by supporting coordination and contracting between companies, it is possible to smoothly proceed with the commercialization of technology.

[0034] The intermediary department can introduce the utilization plan online. The intermediary department, for example, introduces the utilization plan online. For example, the intermediary department introduces the utilization plan through an online platform. The intermediary department can also implement a marketing campaign to widely introduce the utilization plan. For example, the intermediary department can implement a marketing campaign to widely introduce the utilization plan. The intermediary department can also create digital content to introduce the utilization plan. For example, the intermediary department can create digital content to introduce the utilization plan. In this way, by introducing the utilization plan online, it is possible to widely introduce the use of technology to companies both domestic and overseas.

[0035] The acquisition unit can analyze past market needs data and select the optimal acquisition method. The acquisition unit, for example, analyzes past market needs data and identifies the most effective acquisition method. For example, the acquisition unit analyzes past market needs data and identifies the most effective acquisition method. The acquisition unit can also find a specific pattern from the past market needs data and select an acquisition method based on that pattern. For example, the acquisition unit finds a specific pattern from the past market needs data and selects an acquisition method based on that pattern. The acquisition unit can also evaluate the effectiveness of the acquisition method based on the past market needs data and select the optimal method. For example, the acquisition unit evaluates the effectiveness of the acquisition method based on the past market needs data and selects the optimal method. In this way, the optimal acquisition method can be selected by analyzing past market needs data.

[0036] When acquiring market needs, the acquisition unit can perform filtering by focusing on a specific industry or region. For example, the acquisition unit can focus on a specific industry and prioritize acquiring market needs related to that industry. For example, the acquisition unit can focus on a specific industry and prioritize acquiring market needs related to that industry. The acquisition unit can also focus on a specific region and prioritize acquiring market needs related to that region. For example, the acquisition unit can focus on a specific region and prioritize acquiring market needs related to that region. The acquisition unit can also set different filtering criteria for each industry or region to acquire optimal market needs. For example, the acquisition unit can set different filtering criteria for each industry or region to acquire optimal market needs. In this way, by acquiring market needs by focusing on a specific industry or region, more relevant information can be collected.

[0037] When acquiring market needs, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, when the user uses voice input, the acquisition unit acquires market needs using voice recognition technology. For example, when the user uses voice input, the acquisition unit acquires market needs using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire market needs using text analysis technology. For example, when the user uses text input, the acquisition unit acquires market needs using text analysis technology. Furthermore, when the user uses image input, the acquisition unit can also acquire market needs using image recognition technology. For example, when the user uses image input, the acquisition unit acquires market needs using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, market needs can be acquired efficiently.

[0038] When acquiring market needs, the acquisition unit can prioritize acquiring highly relevant needs by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring market needs related to a region based on the user's current location. For example, the acquisition unit prioritizes acquiring market needs related to a region based on the user's current location. The acquisition unit can also acquire highly relevant market needs by taking into account the user's past movement history. For example, the acquisition unit acquires highly relevant market needs by taking into account the user's past movement history. The acquisition unit can also acquire market needs focused on a specific region based on the user's geographical location information. For example, the acquisition unit acquires market needs focused on a specific region based on the user's geographical location information. In this way, highly relevant market needs can be acquired by taking into account the user's geographical location information.

[0039] When acquiring market needs, the acquisition unit can analyze the user's social media activities and acquire related needs. The acquisition unit, for example, analyzes the content of the user's posts on social media to acquire related market needs. For example, the acquisition unit analyzes the content of the user's posts on social media to acquire related market needs. The acquisition unit can also acquire related market needs by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires related market needs by referring to the activities of the user's friends on social media. The acquisition unit can also acquire related market needs based on the user's check-in information on social media. For example, the acquisition unit acquires related market needs based on the user's check-in information on social media. In this way, related market needs can be efficiently acquired by analyzing the user's social media activities.

[0040] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring market needs. The acquisition unit, for example, selects an optimal market needs acquisition method based on the user's past feedback. For example, the acquisition unit selects an optimal market needs acquisition method based on the user's past feedback. The acquisition unit can also improve the acquisition method by reflecting the user's past feedback. For example, the acquisition unit improves the acquisition method by reflecting the user's past feedback. The acquisition unit can also customize the acquisition method by referring to the user's past feedback. For example, the acquisition unit customizes the acquisition method by referring to the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback, and more appropriate market needs can be acquired.

[0041] The element analysis unit can adjust the level of detail of the analysis based on specific market needs when analyzing technological elements. The element analysis unit, for example, performs a detailed analysis of technological elements based on specific market needs. For example, the element analysis unit performs a detailed analysis of technological elements based on specific market needs. The element analysis unit can also adjust the level of detail of the analysis according to the importance of the market needs. For example, the element analysis unit adjusts the level of detail of the analysis according to the importance of the market needs. The element analysis unit can also adjust the level of detail of the analysis according to the urgency of the market needs. For example, the element analysis unit adjusts the level of detail of the analysis according to the urgency of the market needs. In this way, by adjusting the level of detail of the analysis based on specific market needs, more appropriate analysis results can be provided.

[0042] The element analysis unit can apply different analysis algorithms according to the needs of different industries when analyzing technology elements. For example, the element analysis unit applies a specific analysis algorithm according to the needs of the medical industry. For example, the element analysis unit applies a specific analysis algorithm according to the needs of the medical industry. The element analysis unit can also apply a different analysis algorithm according to the needs of the environmental industry. For example, the element analysis unit applies a different analysis algorithm according to the needs of the environmental industry. The element analysis unit can also select an optimal analysis algorithm according to the characteristics of each industry. For example, the element analysis unit selects an optimal analysis algorithm according to the characteristics of each industry. In this way, by applying analysis algorithms according to the needs of different industries, more appropriate analysis results can be provided.

[0043] When analyzing technical elements, the element analysis unit can improve the accuracy of the analysis by referring to past analysis results. The element analysis unit, for example, improves the analysis accuracy of technical elements based on past analysis results. For example, the element analysis unit improves the analysis accuracy of technical elements based on past analysis results. The element analysis unit can also improve the analysis method by referring to past analysis results. For example, the element analysis unit improves the analysis method by referring to past analysis results. The element analysis unit can also improve the accuracy of the analysis by utilizing past analysis results. For example, the element analysis unit improves the accuracy of the analysis by utilizing past analysis results. In this way, the analysis accuracy of technical elements can be improved by referring to past analysis results.

[0044] When analyzing technical elements, the element analysis unit can determine the priority of analysis based on a specific time period or season. The element analysis unit, for example, prioritizes analysis of technical elements related to a specific time period. For example, the element analysis unit prioritizes analysis of technical elements related to a specific time period. The element analysis unit can also prioritize analysis of technical elements related to a season. For example, the element analysis unit prioritizes analysis of technical elements related to a season. The element analysis unit can also set different priorities for different time periods or seasons and analyze the optimal technical elements. For example, the element analysis unit sets different priorities for different time periods or seasons and analyzes the optimal technical elements. In this way, by determining the priority of analysis of technical elements based on a specific time period or season, analysis can be performed at a more appropriate time.

[0045] The element analysis unit can improve the accuracy of the analysis by referring to related market data when analyzing technological elements. For example, the element analysis unit improves the analysis accuracy of technological elements by referring to related market data. For example, the element analysis unit improves the analysis accuracy of technological elements by referring to related market data. The element analysis unit can also improve the analysis method of technological elements based on the market data. For example, the element analysis unit improves the analysis method of technological elements based on the market data. The element analysis unit can also improve the analysis accuracy of technological elements by utilizing the market data. For example, the element analysis unit improves the analysis accuracy of technological elements by utilizing the market data. In this way, the analysis accuracy of technological elements can be improved by referring to related market data.

[0046] The element analysis unit can adjust the analysis method based on the maturity of the technology. For example, the element analysis unit selects a detailed analysis method based on the maturity of the technology. For example, the element analysis unit selects a detailed analysis method based on the maturity of the technology. The element analysis unit can also adjust the analysis method taking into account the maturity of the technology. For example, the element analysis unit adjusts the analysis method taking into account the maturity of the technology. The element analysis unit can also select an optimal analysis method based on the maturity of the technology. For example, the element analysis unit selects an optimal analysis method based on the maturity of the technology. In this way, by adjusting the analysis method based on the maturity of the technology, more appropriate analysis results can be provided.

[0047] When collecting technical information, the collection unit can focus on a specific database or information source to perform collection. The collection unit, for example, focuses on a specific database and collects technical information from that database. For example, the collection unit focuses on a specific database and collects technical information from that database. The collection unit can also focus on a specific information source and collect technical information from that information source. For example, the collection unit focuses on a specific information source and collects technical information from that information source. The collection unit can also set a different collection method for each database or information source to collect optimal technical information. For example, the collection unit sets a different collection method for each database or information source to collect optimal technical information. In this way, by focusing on a specific database or information source to collect technical information, more relevant information can be collected.

[0048] When collecting technical information, the collection unit can improve the accuracy of the collection by referring to past collected data. The collection unit, for example, improves the accuracy of the collection of technical information based on past collected data. For example, the collection unit improves the accuracy of the collection of technical information based on past collected data. The collection unit can also improve the collection method by referring to past collected data. For example, the collection unit improves the collection method by referring to past collected data. The collection unit can also improve the accuracy of the collection by utilizing past collected data. For example, the collection unit improves the accuracy of the collection by utilizing past collected data. In this way, the accuracy of the collection of technical information can be improved by referring to past collected data.

[0049] When collecting technical information, the collection unit can perform collection based on specific keywords or topics. The collection unit, for example, collects technical information based on specific keywords. For example, the collection unit collects technical information based on specific keywords. The collection unit can also collect technical information based on specific topics. For example, the collection unit collects technical information based on specific topics. The collection unit can also set a different collection method for each keyword or topic and collect optimal technical information. For example, the collection unit sets a different collection method for each keyword or topic and collects optimal technical information. In this way, by collecting technical information based on specific keywords or topics, more relevant information can be collected.

[0050] When collecting technical information, the collection unit can focus on a specific region or country to collect the information. For example, the collection unit can focus on a specific region and prioritize collecting technical information related to that region. For example, the collection unit can focus on a specific region and prioritize collecting technical information related to that region. The collection unit can also focus on a specific country and prioritize collecting technical information related to that country. For example, the collection unit can focus on a specific country and prioritize collecting technical information related to that country. The collection unit can also set different collection criteria for each region or country to collect optimal technical information. For example, the collection unit sets different collection criteria for each region or country to collect optimal technical information. In this way, by collecting technical information with a focus on a specific region or country, more relevant information can be collected.

[0051] When collecting technical information, the collection unit can improve the accuracy of the collection by referring to related patent information and research papers. The collection unit, for example, refers to related patent information to improve the accuracy of the collection of technical information. For example, the collection unit refers to related patent information to improve the accuracy of the collection of technical information. The collection unit can also improve the accuracy of the collection of technical information by referring to related research papers. For example, the collection unit can improve the accuracy of the collection of technical information by referring to related research papers. The collection unit can also improve the collection method based on the patent information and research papers. For example, the collection unit improves the collection method based on the patent information and research papers. In this way, the accuracy of the collection of technical information can be improved by referring to related patent information and research papers.

[0052] When collecting technical information, the collection unit can perform the collection based on trends in a specific industry. The collection unit, for example, collects technical information based on trends in a specific industry. For example, the collection unit collects technical information based on trends in a specific industry. The collection unit can also analyze trends for each industry and collect optimal technical information. For example, the collection unit analyzes trends for each industry and collects optimal technical information. The collection unit can also adjust the collection method according to industry trends. For example, the collection unit adjusts the collection method according to industry trends. In this way, by collecting technical information based on trends in a specific industry, more relevant information can be collected.

[0053] When organizing the technical information, the organizing unit can organize the technical information based on specific categories or tags. For example, the organizing unit organizes the technical information based on specific categories. The organizing unit can also organize the technical information based on specific tags. For example, the organizing unit organizes the technical information based on specific tags. The organizing unit can also set a different organizing method for each category or tag and organize the optimal technical information. For example, the organizing unit sets a different organizing method for each category or tag and organizes the optimal technical information. In this way, by organizing the technical information based on specific categories or tags, more relevant information can be provided.

[0054] When organizing technical information, the organizing unit can improve the accuracy of the organizing by referring to past organizing results. The organizing unit, for example, improves the accuracy of organizing the technical information based on past organizing results. For example, the organizing unit improves the accuracy of organizing the technical information based on past organizing results. The organizing unit can also improve the organizing method by referring to past organizing results. For example, the organizing unit improves the organizing method by referring to past organizing results. The organizing unit can also improve the accuracy of organizing by utilizing past organizing results. For example, the organizing unit improves the accuracy of organizing by utilizing past organizing results. In this way, the accuracy of organizing the technical information can be improved by referring to past organizing results.

[0055] When organizing the technical information, the organizing unit can automatically organize the technical information using a specific algorithm. For example, the organizing unit automatically organizes the technical information using a specific algorithm. The organizing unit can also improve the method of organizing the technical information based on the algorithm. For example, the organizing unit improves the method of organizing the technical information based on the algorithm. The organizing unit can also improve the accuracy of the organizing by utilizing an automatic organizing algorithm. For example, the organizing unit improves the accuracy of the organizing by utilizing an automatic organizing algorithm. In this way, by using a specific algorithm, the technical information can be organized efficiently.

[0056] When organizing technical information, the organizing unit can focus on a specific industry or field to organize the information. For example, the organizing unit focuses on a specific industry and prioritizes organizing technical information related to that industry. For example, the organizing unit focuses on a specific industry and prioritizes organizing technical information related to that industry. The organizing unit can also focus on a specific field and prioritize organizing technical information related to that field. For example, the organizing unit focuses on a specific field and prioritizes organizing technical information related to that field. The organizing unit can also set different organizing criteria for each industry or field and organize optimal technical information. For example, the organizing unit sets different organizing criteria for each industry or field and organizes optimal technical information. In this way, by organizing technical information with a focus on a specific industry or field, more relevant information can be provided.

[0057] When organizing the technical information, the organizing unit can improve the accuracy of the organization by referring to related market data. For example, the organizing unit improves the accuracy of organizing the technical information by referring to related market data. For example, the organizing unit improves the accuracy of organizing the technical information by referring to related market data. The organizing unit can also improve the method of organizing the technical information based on the market data. For example, the organizing unit improves the method of organizing the technical information based on the market data. The organizing unit can also improve the accuracy of organizing the technical information by utilizing the market data. For example, the organizing unit improves the accuracy of organizing the technical information by utilizing the market data. In this way, the accuracy of organizing the technical information can be improved by referring to related market data.

[0058] When organizing technical information, the organizing unit can organize the information based on specific keywords or topics. The organizing unit organizes the technical information based on specific keywords, for example. For example, the organizing unit organizes the technical information based on specific keywords. The organizing unit can also organize the technical information based on specific topics. For example, the organizing unit organizes the technical information based on specific topics. The organizing unit can also set a different organizing method for each keyword or topic and organize the optimal technical information. For example, the organizing unit sets a different organizing method for each keyword or topic and organizes the optimal technical information. In this way, by organizing technical information based on specific keywords or topics, it is possible to provide more relevant information.

[0059] The matching unit can adjust the level of detail of matching based on specific market needs when matching technologies. The matching unit, for example, matches detailed technologies based on specific market needs. For example, the matching unit matches detailed technologies based on specific market needs. The matching unit can also adjust the level of detail of matching according to the importance of the market needs. For example, the matching unit adjusts the level of detail of matching according to the importance of the market needs. The matching unit can also adjust the level of detail of matching according to the urgency of the market needs. For example, the matching unit adjusts the level of detail of matching according to the urgency of the market needs. In this way, by adjusting the level of detail of matching based on specific market needs, it is possible to provide a more appropriate matching result.

[0060] When matching technologies, the matching unit can apply different matching algorithms according to technologies in different industries. For example, the matching unit applies a specific matching algorithm according to technologies in the medical industry. For example, the matching unit applies a specific matching algorithm according to technologies in the medical industry. The matching unit can also apply different matching algorithms according to technologies in the environmental industry. For example, the matching unit applies different matching algorithms according to technologies in the environmental industry. The matching unit can also select an optimal matching algorithm according to the characteristics of each industry. For example, the matching unit selects an optimal matching algorithm according to the characteristics of each industry. In this way, by applying matching algorithms according to technologies in different industries, more appropriate matching results can be provided.

[0061] The matching unit can improve the accuracy of matching by referring to past matching results when matching technologies. The matching unit, for example, improves the accuracy of matching between technologies based on past matching results. For example, the matching unit improves the accuracy of matching between technologies based on past matching results. The matching unit can also improve the matching method by referring to past matching results. For example, the matching unit improves the matching method by referring to past matching results. The matching unit can also improve the accuracy of matching by utilizing past matching results. For example, the matching unit improves the accuracy of matching by utilizing past matching results. In this way, the accuracy of matching between technologies can be improved by referring to past matching results.

[0062] When matching technologies, the matching unit can determine the priority of matching based on a specific time period or season. For example, the matching unit prioritizes matching between technologies related to a specific time period. For example, the matching unit prioritizes matching between technologies related to a specific time period. The matching unit can also prioritize matching between technologies related to a season. For example, the matching unit prioritizes matching between technologies related to a season. The matching unit can also set different priorities for different time periods or seasons and match optimal technologies. For example, the matching unit sets different priorities for different time periods or seasons and matches optimal technologies. In this way, by determining the priority of matching between technologies based on a specific time period or season, matching can be performed at a more appropriate time.

[0063] The matching unit can improve the accuracy of matching when matching technologies by referring to related market data. The matching unit, for example, refers to related market data to improve the accuracy of matching between technologies. For example, the matching unit refers to related market data to improve the accuracy of matching between technologies. The matching unit can also improve the method of matching technologies based on market data. For example, the matching unit improves the method of matching technologies based on market data. The matching unit can also utilize market data to improve the accuracy of matching technologies. For example, the matching unit utilizes market data to improve the accuracy of matching technologies. In this way, the accuracy of matching technologies can be improved by referring to related market data.

[0064] When matching technologies, the matching unit can adjust the matching method based on the maturity of the technologies. For example, the matching unit selects a detailed matching method based on the maturity of the technologies. For example, the matching unit selects a detailed matching method based on the maturity of the technologies. The matching unit can also adjust the matching method taking the maturity of the technologies into consideration. For example, the matching unit adjusts the matching method taking the maturity of the technologies into consideration. The matching unit can also select an optimal matching method based on the maturity of the technologies. For example, the matching unit selects an optimal matching method based on the maturity of the technologies. In this way, by adjusting the matching method based on the maturity of the technologies, it is possible to provide a more appropriate matching result.

[0065] When brokering a contract, the brokerage department can focus on a specific industry or region. For example, the brokerage department can focus on a specific industry and prioritize brokering contracts related to that industry. For example, the brokerage department can focus on a specific industry and prioritize brokering contracts related to that industry. The brokerage department can also focus on a specific region and prioritize brokering contracts related to that region. For example, the brokerage department can focus on a specific region and prioritize brokering contracts related to that region. The brokerage department can also set different brokerage criteria for each industry or region and broker optimal contracts. For example, the brokerage department can set different brokerage criteria for each industry or region and broker optimal contracts. In this way, by brokering contracts by focusing on a specific industry or region, it is possible to broker more relevant contracts.

[0066] When mediating a contract, the mediation unit can improve the accuracy of the mediation by referring to past mediation results. The mediation unit, for example, improves the accuracy of the mediation of the contract based on past mediation results. For example, the mediation unit improves the accuracy of the mediation of the contract based on past mediation results. The mediation unit can also improve the mediation method by referring to past mediation results. For example, the mediation unit improves the mediation method by referring to past mediation results. The mediation unit can also improve the accuracy of the mediation by utilizing past mediation results. For example, the mediation unit improves the accuracy of the mediation by utilizing past mediation results. In this way, the accuracy of the mediation of the contract can be improved by referring to past mediation results.

[0067] When mediating a contract, the mediation unit can mediate based on specific contract conditions. For example, the mediation unit mediates a contract based on specific contract conditions. The mediation unit can also set a different mediation method for each contract condition and mediate the optimal contract. For example, the mediation unit can set a different mediation method for each contract condition and mediate the optimal contract. The mediation unit can also improve the mediation method based on the contract conditions. For example, the mediation unit improves the mediation method based on the contract conditions. In this way, by mediating a contract based on specific contract conditions, it is possible to mediate a more appropriate contract.

[0068] When brokering a contract, the intermediation department can focus on a specific region or country. For example, the intermediation department can focus on a specific region and prioritize brokering contracts related to that region. For example, the intermediation department can focus on a specific region and prioritize brokering contracts related to that region. The intermediation department can also focus on a specific country and prioritize brokering contracts related to that country. For example, the intermediation department can focus on a specific country and prioritize brokering contracts related to that country. The intermediation department can also set different intermediation criteria for each region or country and broker optimal contracts. For example, the intermediation department can set different intermediation criteria for each region or country and broker optimal contracts. In this way, by brokering contracts with a focus on a specific region or country, it is possible to broker contracts that are more relevant.

[0069] The brokerage department can improve the accuracy of brokerage by referring to related market data when brokering a contract. The brokerage department, for example, refers to related market data to improve the accuracy of brokerage of the contract. For example, the brokerage department refers to related market data to improve the accuracy of brokerage of the contract. The brokerage department can also improve the method of brokering the contract based on the market data. For example, the brokerage department improves the method of brokering the contract based on the market data. The brokerage department can also utilize the market data to improve the accuracy of brokerage of the contract. For example, the brokerage department utilizes market data to improve the accuracy of brokerage of the contract. In this way, the accuracy of brokerage of the contract can be improved by referring to related market data.

[0070] When brokering a contract, the brokerage department can broker the contract based on trends in a specific industry. For example, the brokerage department brokers the contract based on trends in a specific industry. The brokerage department can also analyze trends for each industry and broker the optimal contract. For example, the brokerage department can analyze trends for each industry and broker the optimal contract. The brokerage department can also adjust the brokerage method according to industry trends. For example, the brokerage department adjusts the brokerage method according to industry trends. In this way, by brokering the contract based on trends in a specific industry, it is possible to broker a more relevant contract.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The acquisition unit can also analyze the user's purchase history and predict market needs based on past purchase patterns. For example, the acquisition unit can analyze the user's past purchase history and predict demand for a specific product or service. The acquisition unit can also find seasonal demand patterns from the user's purchase history and predict market needs according to the season. Furthermore, the acquisition unit can predict demand for specific events or campaigns based on the user's purchase history. This makes it possible to predict market needs with greater accuracy by utilizing the user's purchase history.

[0073] When analyzing technical elements, the element analysis unit can also refer to the latest research results in a specific technical field. For example, the element analysis unit can refer to the latest academic papers and reflect this in the analysis of technical elements. The element analysis unit can also refer to the latest patent information and use it in the analysis of technical elements. Furthermore, the element analysis unit can refer to the latest technical reports in the technical field and incorporate them into the analysis of technical elements. This allows for more accurate analysis of technical elements by referring to the latest research results.

[0074] When matching technologies, the matching unit can take into account competing technologies in a specific technical field. For example, the matching unit analyzes the characteristics of competing technologies and finds the optimal combination of technologies. The matching unit can also consider the market share of competing technologies to match competitive technologies. Furthermore, the matching unit can refer to patent information on competing technologies and use it when matching technologies. By taking competing technologies into consideration, this makes it possible to match more competitive technologies.

[0075] The acquisition unit can analyze the user's social media activities and acquire related market needs. For example, the acquisition unit can analyze the content of the user's posts on social media to acquire related market needs. The acquisition unit can also acquire related market needs by referring to the activities of the user's friends on social media. Furthermore, the acquisition unit can acquire related market needs based on the user's check-in information on social media. In this way, related market needs can be efficiently acquired by analyzing the user's social media activities.

[0076] When collecting technical information, the collection unit can focus on a specific database or information source. For example, the collection unit can focus on a specific database and collect technical information from that database. The collection unit can also focus on a specific information source and collect technical information from that information source. Furthermore, the collection unit can set a different collection method for each database or information source and collect optimal technical information. In this way, by focusing on a specific database or information source to collect technical information, more relevant information can be collected.

[0077] When organizing technical information, the organizing unit can organize the information based on specific keywords or topics. For example, the organizing unit organizes the technical information based on specific keywords. The organizing unit can also organize the technical information based on specific topics. Furthermore, the organizing unit can set different organizing methods for each keyword or topic and organize the optimal technical information. In this way, by organizing technical information based on specific keywords or topics, more relevant information can be provided.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The Acquisition Department acquires market needs. Market needs include consumer demand, industry trends, and requirements for specific products and services. The Acquisition Department conducts surveys to understand consumer demand. It can also analyze industry trends and predict future market needs. It can also collect requirements for specific products and services and use them to analyze technical elements. Step 2: The element analysis unit analyzes technological elements based on the market needs acquired by the acquisition unit. Technological elements include specific technical fields, technological functions, and the scope of technological application. The element analysis unit uses text mining technology to extract technological elements from market needs. It can also analyze market needs and identify technological elements using data analysis technology. It can also extract technological elements based on expert evaluations. Step 3: The collection department collects publicly available technical information. Publicly available technical information includes patent databases, academic papers, technical reports, etc. The collection department searches patent databases to collect relevant technical information. It can also analyze academic papers to collect technical information. It can also collect technical reports and organize the technical information. Step 4: The organizing unit organizes and classifies the technical information collected by the collecting unit. Organizing and classifying includes categorizing information, tagging, and creating a hierarchical structure. The organizing unit categorizes information and organizes the technical information. It can also organize information by tagging it. It can also organize information into a hierarchical structure to systematically manage technical information. Step 5: The matching department matches technologies based on the technical information organized and classified by the organization department. Matching includes technologies' complementarity, compatibility, and the use of algorithms. The matching department matches technologies based on their complementarity. It can also evaluate compatibility and match optimal technologies. It can also match technologies using algorithms. Step 6: The mediation department proposes the technology combinations matched by the matching department to the companies and mediates the contracts. Mediation of contracts includes adjusting contract terms, drafting contracts, and supporting negotiations. The mediation department sets up meetings between companies and mediates opinions. It can also adjust contract terms and draft contracts. It can also support negotiations and help conclude contracts.

[0080] (Example 2) A system according to an embodiment of the present invention accelerates the commercialization and monetization of Japan's cutting-edge technologies. This system acquires market needs, analyzes technological elements, collects publicly available technical information, organizes and categorizes the information, matches technologies, proposes them to companies, and brokers contracts. For example, the system manually analyzes the elements of technologies required for a service with growing market demand. Next, the system uses AI to organize and categorize technologies from the publicly available technical information that could potentially be used for the element analysis. Furthermore, the system uses generative AI to match the organized and classified technologies and create proposals for new applications. Finally, the system introduces utilization plans to domestic and overseas companies online and brokers contracts for corporate matching of combined technologies. This allows the system to coordinate and provide utilization consulting between technology-owning companies and organizations, thereby increasing intellectual property revenue and securing research and development funds. This also enables the system to strengthen and maintain technology development systems, accelerating the commercialization and monetization of Japan's cutting-edge technologies.

[0081] The technology commercialization system according to the embodiment includes an acquisition unit, an element analysis unit, a collection unit, a sorting unit, a matching unit, and an intermediation unit. The acquisition unit acquires market needs. Market needs include, but are not limited to, consumer demand, industry trends, and demand for specific products and services. The acquisition unit, for example, conducts a questionnaire survey to understand consumer demand. The acquisition unit can also analyze industry trends and predict future market needs. The acquisition unit can also collect demand for specific products and services and use the collected demand to analyze technological elements. The element analysis unit analyzes technological elements based on the market needs acquired by the acquisition unit. Technological elements include, but are not limited to, specific technical fields, technical functions, and technology application ranges. The element analysis unit extracts technological elements from market needs using, for example, text mining technology. The element analysis unit can also analyze market needs and identify technological elements using data analysis technology. The element analysis unit can also extract technological elements based on expert evaluations. The collection unit collects public technology information. Public technical information includes, but is not limited to, patent databases, academic papers, and technical reports. The collection unit, for example, searches patent databases to collect relevant technical information. The collection unit may also analyze academic papers to collect technical information. The collection unit may also collect technical reports and organize the technical information. The organization unit organizes and classifies the technical information collected by the collection unit. Organization and classification may include, but is not limited to, categorizing information, tagging, creating a hierarchical structure, and the like. For example, the organization unit may classify information by category and organize the technical information. The organization unit may also organize information by tagging. The organization unit may also organize information into a hierarchical structure and systematically manage the technical information. The matching unit matches technologies based on the technical information organized and classified by the organization unit. Matching may include, but is not limited to, technologies' mutual complementarity, technology compatibility, the use of algorithms, and the like. For example, the matching unit matches technologies based on technologies' mutual complementarity.The matching unit can also evaluate the compatibility of technologies and match optimal technologies together. The matching unit can also match technologies together using an algorithm. The intermediary unit proposes technology combinations matched by the matching unit to companies and brokers contracts. Brokering contracts includes, but is not limited to, adjusting contract terms, drafting contracts, and supporting negotiations. For example, the intermediary unit arranges meetings between companies and mediates opinions. The intermediary unit can also adjust contract terms and draft contracts. The intermediary unit can also support negotiations and help conclude contracts. As a result, the technology commercialization system according to the embodiment analyzes technology elements based on market needs, organizes and classifies technology information, matches optimal technologies together, proposes them to companies, and brokers contracts, thereby accelerating the commercialization and monetization of technology.

[0082] The element analysis unit can extract technological elements based on the market needs acquired by the acquisition unit. The element analysis unit extracts technological elements from market needs using, for example, text mining technology. For example, the element analysis unit analyzes text data of market needs using text mining technology and extracts technological elements. The element analysis unit can also analyze market needs using data analysis technology and identify technological elements. For example, the element analysis unit analyzes market needs data using data analysis technology and identify technological elements. The element analysis unit can also extract technological elements based on expert evaluations. For example, the element analysis unit extracts technological elements from market needs based on expert evaluations. In this way, extracting technological elements based on market needs enables more appropriate analysis of technological elements.

[0083] The organizing unit can analyze public technical information using AI and extract related technologies. The organizing unit, for example, uses AI to analyze public technical information and extract related technologies. For example, the organizing unit uses AI to analyze a patent database and extract related technologies. The organizing unit can also analyze academic papers and extract related technologies using AI. For example, the organizing unit uses AI to analyze text data of academic papers and extract related technologies. The organizing unit can also analyze technical reports and extract related technologies using AI. For example, the organizing unit uses AI to analyze text data of technical reports and extract related technologies. In this way, by using AI, related technologies can be efficiently extracted from public technical information.

[0084] The matching unit can simulate combinations of technologies using the generation AI and find the optimal combination. The matching unit, for example, uses the generation AI to simulate combinations of technologies and find the optimal combination. For example, the matching unit can simulate combinations of different technologies and find the optimal combination. The matching unit can also use the generation AI to evaluate the mutual complementarity of technologies and find the optimal combination. For example, the matching unit can use the generation AI to evaluate the mutual complementarity of technologies and find the optimal combination. The matching unit can also use the generation AI to evaluate the compatibility of technologies and find the optimal combination. For example, the matching unit can use the generation AI to evaluate the compatibility of technologies and find the optimal combination. In this way, the generation AI can be used to find the optimal combination of technologies. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The intermediary department can support coordination and contracting between companies. For example, the intermediary department sets up meetings between companies and coordinates opinions. For example, the intermediary department sets up meetings between companies and coordinates opinions. The intermediary department can also adjust contract terms and prepare contracts. For example, the intermediary department can adjust contract terms and prepare contracts. The intermediary department can also support negotiations and assist in the conclusion of contracts. For example, the intermediary department can support negotiations and assist in the conclusion of contracts. In this way, by supporting coordination and contracting between companies, it is possible to smoothly proceed with the commercialization of technology.

[0086] The intermediary department can introduce the utilization plan online. The intermediary department, for example, introduces the utilization plan online. For example, the intermediary department introduces the utilization plan through an online platform. The intermediary department can also implement a marketing campaign to widely introduce the utilization plan. For example, the intermediary department can implement a marketing campaign to widely introduce the utilization plan. The intermediary department can also create digital content to introduce the utilization plan. For example, the intermediary department can create digital content to introduce the utilization plan. In this way, by introducing the utilization plan online, it is possible to widely introduce the use of technology to companies both domestic and overseas.

[0087] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring market needs based on the estimated user emotions. For example, when the user is excited, the acquisition unit immediately acquires market needs and responds promptly. For example, when the user is excited, the acquisition unit immediately acquires market needs and responds promptly. Furthermore, when the user is relaxed, the acquisition unit can acquire market needs at regular intervals to ensure stable information collection. For example, when the user is relaxed, the acquisition unit can acquire market needs at regular intervals to ensure stable information collection. Furthermore, when the user is stressed, the acquisition unit can reduce the frequency of acquiring market needs to reduce the burden on the user. For example, when the user is stressed, the acquisition unit reduces the frequency of acquiring market needs to reduce the burden on the user. In this way, by adjusting the timing of acquiring market needs according to the user's emotions, market needs can be acquired at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The acquisition unit can analyze past market needs data and select the optimal acquisition method. The acquisition unit, for example, analyzes past market needs data and identifies the most effective acquisition method. For example, the acquisition unit analyzes past market needs data and identifies the most effective acquisition method. The acquisition unit can also find a specific pattern from the past market needs data and select an acquisition method based on that pattern. For example, the acquisition unit finds a specific pattern from the past market needs data and selects an acquisition method based on that pattern. The acquisition unit can also evaluate the effectiveness of the acquisition method based on the past market needs data and select the optimal method. For example, the acquisition unit evaluates the effectiveness of the acquisition method based on the past market needs data and selects the optimal method. In this way, the optimal acquisition method can be selected by analyzing past market needs data.

[0089] When acquiring market needs, the acquisition unit can perform filtering by focusing on a specific industry or region. For example, the acquisition unit can focus on a specific industry and prioritize acquiring market needs related to that industry. For example, the acquisition unit can focus on a specific industry and prioritize acquiring market needs related to that industry. The acquisition unit can also focus on a specific region and prioritize acquiring market needs related to that region. For example, the acquisition unit can focus on a specific region and prioritize acquiring market needs related to that region. The acquisition unit can also set different filtering criteria for each industry or region to acquire optimal market needs. For example, the acquisition unit can set different filtering criteria for each industry or region to acquire optimal market needs. In this way, by acquiring market needs by focusing on a specific industry or region, more relevant information can be collected.

[0090] When acquiring market needs, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, when the user uses voice input, the acquisition unit acquires market needs using voice recognition technology. For example, when the user uses voice input, the acquisition unit acquires market needs using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire market needs using text analysis technology. For example, when the user uses text input, the acquisition unit acquires market needs using text analysis technology. Furthermore, when the user uses image input, the acquisition unit can also acquire market needs using image recognition technology. For example, when the user uses image input, the acquisition unit acquires market needs using image recognition technology. In this way, by selecting the optimal acquisition means depending on the user's input method, market needs can be acquired efficiently.

[0091] The acquisition unit can estimate the user's emotions and determine the priority of market needs to be acquired based on the estimated user emotions. For example, when the user is excited, the acquisition unit prioritizes acquiring market needs with high urgency. For example, when the user is excited, the acquisition unit prioritizes acquiring market needs with high urgency. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring long-term market needs. For example, when the user is relaxed, the acquisition unit prioritizes acquiring long-term market needs. Furthermore, when the user is stressed, the acquisition unit can postpone market needs with low importance to reduce the user's burden. For example, when the user is stressed, the acquisition unit postpones market needs with low importance to reduce the user's burden. In this way, by determining the priority of market needs according to the user's emotions, more important market needs can be preferentially acquired. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] When acquiring market needs, the acquisition unit can prioritize acquiring highly relevant needs by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring market needs related to a region based on the user's current location. For example, the acquisition unit prioritizes acquiring market needs related to a region based on the user's current location. The acquisition unit can also acquire highly relevant market needs by taking into account the user's past movement history. For example, the acquisition unit acquires highly relevant market needs by taking into account the user's past movement history. The acquisition unit can also acquire market needs focused on a specific region based on the user's geographical location information. For example, the acquisition unit acquires market needs focused on a specific region based on the user's geographical location information. In this way, highly relevant market needs can be acquired by taking into account the user's geographical location information.

[0093] When acquiring market needs, the acquisition unit can analyze the user's social media activities and acquire related needs. The acquisition unit, for example, analyzes the content of the user's posts on social media to acquire related market needs. For example, the acquisition unit analyzes the content of the user's posts on social media to acquire related market needs. The acquisition unit can also acquire related market needs by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires related market needs by referring to the activities of the user's friends on social media. The acquisition unit can also acquire related market needs based on the user's check-in information on social media. For example, the acquisition unit acquires related market needs based on the user's check-in information on social media. In this way, related market needs can be efficiently acquired by analyzing the user's social media activities.

[0094] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring market needs. The acquisition unit, for example, selects an optimal market needs acquisition method based on the user's past feedback. For example, the acquisition unit selects an optimal market needs acquisition method based on the user's past feedback. The acquisition unit can also improve the acquisition method by reflecting the user's past feedback. For example, the acquisition unit improves the acquisition method by reflecting the user's past feedback. The acquisition unit can also customize the acquisition method by referring to the user's past feedback. For example, the acquisition unit customizes the acquisition method by referring to the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback, and more appropriate market needs can be acquired.

[0095] The element analysis unit can estimate the user's emotions and adjust the analysis method of technical elements based on the estimated user emotions. For example, when the user is excited, the element analysis unit can quickly analyze technical elements and provide results immediately. For example, when the user is excited, the element analysis unit can quickly analyze technical elements and provide results immediately. Furthermore, when the user is relaxed, the element analysis unit can perform a detailed analysis of technical elements and provide comprehensive results. For example, when the user is relaxed, the element analysis unit can perform a detailed analysis of technical elements and provide comprehensive results. Furthermore, when the user is stressed, the element analysis unit can perform a concise analysis of technical elements to reduce the user's burden. For example, when the user is stressed, the element analysis unit can perform a concise analysis of technical elements to reduce the user's burden. This allows the analysis method of technical elements to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The element analysis unit can adjust the level of detail of the analysis based on specific market needs when analyzing technological elements. The element analysis unit, for example, performs a detailed analysis of technological elements based on specific market needs. For example, the element analysis unit performs a detailed analysis of technological elements based on specific market needs. The element analysis unit can also adjust the level of detail of the analysis according to the importance of the market needs. For example, the element analysis unit adjusts the level of detail of the analysis according to the importance of the market needs. The element analysis unit can also adjust the level of detail of the analysis according to the urgency of the market needs. For example, the element analysis unit adjusts the level of detail of the analysis according to the urgency of the market needs. In this way, by adjusting the level of detail of the analysis based on specific market needs, more appropriate analysis results can be provided.

[0097] The element analysis unit can apply different analysis algorithms according to the needs of different industries when analyzing technology elements. For example, the element analysis unit applies a specific analysis algorithm according to the needs of the medical industry. For example, the element analysis unit applies a specific analysis algorithm according to the needs of the medical industry. The element analysis unit can also apply a different analysis algorithm according to the needs of the environmental industry. For example, the element analysis unit applies a different analysis algorithm according to the needs of the environmental industry. The element analysis unit can also select an optimal analysis algorithm according to the characteristics of each industry. For example, the element analysis unit selects an optimal analysis algorithm according to the characteristics of each industry. In this way, by applying analysis algorithms according to the needs of different industries, more appropriate analysis results can be provided.

[0098] When analyzing technical elements, the element analysis unit can improve the accuracy of the analysis by referring to past analysis results. The element analysis unit, for example, improves the analysis accuracy of technical elements based on past analysis results. For example, the element analysis unit improves the analysis accuracy of technical elements based on past analysis results. The element analysis unit can also improve the analysis method by referring to past analysis results. For example, the element analysis unit improves the analysis method by referring to past analysis results. The element analysis unit can also improve the accuracy of the analysis by utilizing past analysis results. For example, the element analysis unit improves the accuracy of the analysis by utilizing past analysis results. In this way, the analysis accuracy of technical elements can be improved by referring to past analysis results.

[0099] The element analysis unit can estimate the user's emotions and determine the priority of analysis of technical elements based on the estimated user emotions. For example, when the user is excited, the element analysis unit prioritizes analysis of technical elements with high urgency. For example, when the user is excited, the element analysis unit prioritizes analysis of technical elements with high urgency. Furthermore, when the user is relaxed, the element analysis unit can also prioritize analysis of long-term technical elements. For example, when the user is relaxed, the element analysis unit prioritizes analysis of long-term technical elements. Furthermore, when the user is stressed, the element analysis unit can postpone analysis of less important technical elements to reduce the user's burden. For example, when the user is stressed, the element analysis unit postpones analysis of less important technical elements to reduce the user's burden. In this way, by determining the priority of analysis of technical elements according to the user's emotions, more important technical elements can be prioritized for analysis. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When analyzing technical elements, the element analysis unit can determine the priority of analysis based on a specific time period or season. The element analysis unit, for example, prioritizes analysis of technical elements related to a specific time period. For example, the element analysis unit prioritizes analysis of technical elements related to a specific time period. The element analysis unit can also prioritize analysis of technical elements related to a season. For example, the element analysis unit prioritizes analysis of technical elements related to a season. The element analysis unit can also set different priorities for different time periods or seasons and analyze the optimal technical elements. For example, the element analysis unit sets different priorities for different time periods or seasons and analyzes the optimal technical elements. In this way, by determining the priority of analysis of technical elements based on a specific time period or season, analysis can be performed at a more appropriate time.

[0101] The element analysis unit can improve the accuracy of the analysis by referring to related market data when analyzing technological elements. For example, the element analysis unit improves the analysis accuracy of technological elements by referring to related market data. For example, the element analysis unit improves the analysis accuracy of technological elements by referring to related market data. The element analysis unit can also improve the analysis method of technological elements based on the market data. For example, the element analysis unit improves the analysis method of technological elements based on the market data. The element analysis unit can also improve the analysis accuracy of technological elements by utilizing the market data. For example, the element analysis unit improves the analysis accuracy of technological elements by utilizing the market data. In this way, the analysis accuracy of technological elements can be improved by referring to related market data.

[0102] The element analysis unit can adjust the analysis method based on the maturity of the technology. For example, the element analysis unit selects a detailed analysis method based on the maturity of the technology. For example, the element analysis unit selects a detailed analysis method based on the maturity of the technology. The element analysis unit can also adjust the analysis method taking into account the maturity of the technology. For example, the element analysis unit adjusts the analysis method taking into account the maturity of the technology. The element analysis unit can also select an optimal analysis method based on the maturity of the technology. For example, the element analysis unit selects an optimal analysis method based on the maturity of the technology. In this way, by adjusting the analysis method based on the maturity of the technology, more appropriate analysis results can be provided.

[0103] The collection unit can estimate the user's emotions and adjust the method of collecting technical information based on the estimated user emotions. For example, when the user is excited, the collection unit quickly collects technical information and immediately provides it. For example, when the user is excited, the collection unit quickly collects technical information and immediately provides it. The collection unit can also collect detailed technical information and provide comprehensive information when the user is relaxed. For example, when the user is relaxed, the collection unit collects detailed technical information and provides comprehensive information. The collection unit can also collect concise technical information to reduce the user's burden when the user is stressed. For example, when the user is stressed, the collection unit collects concise technical information to reduce the user's burden. This allows more appropriate information to be collected by adjusting the method of collecting technical information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When collecting technical information, the collection unit can focus on a specific database or information source to perform collection. The collection unit, for example, focuses on a specific database and collects technical information from that database. For example, the collection unit focuses on a specific database and collects technical information from that database. The collection unit can also focus on a specific information source and collect technical information from that information source. For example, the collection unit focuses on a specific information source and collects technical information from that information source. The collection unit can also set a different collection method for each database or information source to collect optimal technical information. For example, the collection unit sets a different collection method for each database or information source to collect optimal technical information. In this way, by focusing on a specific database or information source to collect technical information, more relevant information can be collected.

[0105] When collecting technical information, the collection unit can improve the accuracy of the collection by referring to past collected data. The collection unit, for example, improves the accuracy of the collection of technical information based on past collected data. For example, the collection unit improves the accuracy of the collection of technical information based on past collected data. The collection unit can also improve the collection method by referring to past collected data. For example, the collection unit improves the collection method by referring to past collected data. The collection unit can also improve the accuracy of the collection by utilizing past collected data. For example, the collection unit improves the accuracy of the collection by utilizing past collected data. In this way, the accuracy of the collection of technical information can be improved by referring to past collected data.

[0106] When collecting technical information, the collection unit can perform collection based on specific keywords or topics. The collection unit, for example, collects technical information based on specific keywords. For example, the collection unit collects technical information based on specific keywords. The collection unit can also collect technical information based on specific topics. For example, the collection unit collects technical information based on specific topics. The collection unit can also set a different collection method for each keyword or topic and collect optimal technical information. For example, the collection unit sets a different collection method for each keyword or topic and collects optimal technical information. In this way, by collecting technical information based on specific keywords or topics, more relevant information can be collected.

[0107] The collection unit can estimate the user's emotions and determine the priority of technical information to be collected based on the estimated user's emotions. For example, when the user is excited, the collection unit prioritizes collecting technical information with high urgency. For example, when the user is excited, the collection unit prioritizes collecting technical information with high urgency. The collection unit can also prioritize collecting long-term technical information when the user is relaxed. For example, when the user is relaxed, the collection unit prioritizes collecting long-term technical information. The collection unit can also postpone collecting less important technical information to reduce the burden on the user when the user is stressed. For example, when the user is stressed, the collection unit postpones collecting less important technical information to reduce the burden on the user. In this way, by determining the priority of technical information according to the user's emotions, more important technical information can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] When collecting technical information, the collection unit can focus on a specific region or country to collect the information. For example, the collection unit can focus on a specific region and prioritize collecting technical information related to that region. For example, the collection unit can focus on a specific region and prioritize collecting technical information related to that region. The collection unit can also focus on a specific country and prioritize collecting technical information related to that country. For example, the collection unit can focus on a specific country and prioritize collecting technical information related to that country. The collection unit can also set different collection criteria for each region or country to collect optimal technical information. For example, the collection unit sets different collection criteria for each region or country to collect optimal technical information. In this way, by collecting technical information with a focus on a specific region or country, more relevant information can be collected.

[0109] When collecting technical information, the collection unit can improve the accuracy of the collection by referring to related patent information and research papers. The collection unit, for example, refers to related patent information to improve the accuracy of the collection of technical information. For example, the collection unit refers to related patent information to improve the accuracy of the collection of technical information. The collection unit can also improve the accuracy of the collection of technical information by referring to related research papers. For example, the collection unit can improve the accuracy of the collection of technical information by referring to related research papers. The collection unit can also improve the collection method based on the patent information and research papers. For example, the collection unit improves the collection method based on the patent information and research papers. In this way, the accuracy of the collection of technical information can be improved by referring to related patent information and research papers.

[0110] When collecting technical information, the collection unit can perform the collection based on trends in a specific industry. The collection unit, for example, collects technical information based on trends in a specific industry. For example, the collection unit collects technical information based on trends in a specific industry. The collection unit can also analyze trends for each industry and collect optimal technical information. For example, the collection unit analyzes trends for each industry and collects optimal technical information. The collection unit can also adjust the collection method according to industry trends. For example, the collection unit adjusts the collection method according to industry trends. In this way, by collecting technical information based on trends in a specific industry, more relevant information can be collected.

[0111] The organizing unit can estimate the user's emotions and adjust the way in which technical information is organized based on the estimated user's emotions. For example, when the user is excited, the organizing unit quickly organizes the technical information and provides it immediately. For example, when the user is excited, the organizing unit quickly organizes the technical information and provides it immediately. Furthermore, when the user is relaxed, the organizing unit can organize detailed technical information and provide comprehensive information. For example, when the user is relaxed, the organizing unit organizes detailed technical information and provides comprehensive information. Furthermore, when the user is stressed, the organizing unit can organize concise technical information to reduce the user's burden. For example, when the user is stressed, the organizing unit organizes concise technical information to reduce the user's burden. In this way, by adjusting the way in which technical information is organized according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] When organizing the technical information, the organizing unit can organize the technical information based on specific categories or tags. For example, the organizing unit organizes the technical information based on specific categories. The organizing unit can also organize the technical information based on specific tags. For example, the organizing unit organizes the technical information based on specific tags. The organizing unit can also set a different organizing method for each category or tag and organize the optimal technical information. For example, the organizing unit sets a different organizing method for each category or tag and organizes the optimal technical information. In this way, by organizing the technical information based on specific categories or tags, more relevant information can be provided.

[0113] When organizing technical information, the organizing unit can improve the accuracy of the organizing by referring to past organizing results. The organizing unit, for example, improves the accuracy of organizing the technical information based on past organizing results. For example, the organizing unit improves the accuracy of organizing the technical information based on past organizing results. The organizing unit can also improve the organizing method by referring to past organizing results. For example, the organizing unit improves the organizing method by referring to past organizing results. The organizing unit can also improve the accuracy of organizing by utilizing past organizing results. For example, the organizing unit improves the accuracy of organizing by utilizing past organizing results. In this way, the accuracy of organizing the technical information can be improved by referring to past organizing results.

[0114] When organizing the technical information, the organizing unit can automatically organize the technical information using a specific algorithm. For example, the organizing unit automatically organizes the technical information using a specific algorithm. The organizing unit can also improve the method of organizing the technical information based on the algorithm. For example, the organizing unit improves the method of organizing the technical information based on the algorithm. The organizing unit can also improve the accuracy of the organizing by utilizing an automatic organizing algorithm. For example, the organizing unit improves the accuracy of the organizing by utilizing an automatic organizing algorithm. In this way, by using a specific algorithm, the technical information can be organized efficiently.

[0115] The organizing unit can estimate the user's emotions and determine the priority of the technical information to be organized based on the estimated user's emotions. For example, when the user is excited, the organizing unit prioritizes organizing technical information with high urgency. For example, when the user is excited, the organizing unit prioritizes organizing technical information with high urgency. Furthermore, when the user is relaxed, the organizing unit can prioritize organizing long-term technical information. For example, when the user is relaxed, the organizing unit prioritizes organizing long-term technical information. Furthermore, when the user is stressed, the organizing unit can postpone technical information with low importance to reduce the user's burden. For example, when the user is stressed, the organizing unit postpones technical information with low importance to reduce the user's burden. In this way, by determining the priority of technical information according to the user's emotions, more important information can be prioritized and organized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] When organizing technical information, the organizing unit can focus on a specific industry or field to organize the information. For example, the organizing unit focuses on a specific industry and prioritizes organizing technical information related to that industry. For example, the organizing unit focuses on a specific industry and prioritizes organizing technical information related to that industry. The organizing unit can also focus on a specific field and prioritize organizing technical information related to that field. For example, the organizing unit focuses on a specific field and prioritizes organizing technical information related to that field. The organizing unit can also set different organizing criteria for each industry or field and organize optimal technical information. For example, the organizing unit sets different organizing criteria for each industry or field and organizes optimal technical information. In this way, by organizing technical information with a focus on a specific industry or field, more relevant information can be provided.

[0117] When organizing the technical information, the organizing unit can improve the accuracy of the organization by referring to related market data. For example, the organizing unit improves the accuracy of organizing the technical information by referring to related market data. For example, the organizing unit improves the accuracy of organizing the technical information by referring to related market data. The organizing unit can also improve the method of organizing the technical information based on the market data. For example, the organizing unit improves the method of organizing the technical information based on the market data. The organizing unit can also improve the accuracy of organizing the technical information by utilizing the market data. For example, the organizing unit improves the accuracy of organizing the technical information by utilizing the market data. In this way, the accuracy of organizing the technical information can be improved by referring to related market data.

[0118] When organizing technical information, the organizing unit can organize the information based on specific keywords or topics. The organizing unit organizes the technical information based on specific keywords, for example. For example, the organizing unit organizes the technical information based on specific keywords. The organizing unit can also organize the technical information based on specific topics. For example, the organizing unit organizes the technical information based on specific topics. The organizing unit can also set a different organizing method for each keyword or topic and organize the optimal technical information. For example, the organizing unit sets a different organizing method for each keyword or topic and organizes the optimal technical information. In this way, by organizing technical information based on specific keywords or topics, it is possible to provide more relevant information.

[0119] The matching unit can estimate the user's emotions and adjust the matching method between technologies based on the estimated user emotions. For example, when the user is excited, the matching unit quickly matches technologies and immediately provides results. For example, when the user is excited, the matching unit quickly matches technologies and immediately provides results. Furthermore, when the user is relaxed, the matching unit can match detailed technologies and provide comprehensive results. For example, when the user is relaxed, the matching unit can match detailed technologies and provide comprehensive results. Furthermore, when the user is stressed, the matching unit can match concise technologies to reduce the user's burden. For example, when the user is stressed, the matching unit matches concise technologies to reduce the user's burden. This allows the matching method between technologies to be adjusted according to the user's emotions, thereby providing more appropriate matching results. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] The matching unit can adjust the level of detail of matching based on specific market needs when matching technologies. The matching unit, for example, matches detailed technologies based on specific market needs. For example, the matching unit matches detailed technologies based on specific market needs. The matching unit can also adjust the level of detail of matching according to the importance of the market needs. For example, the matching unit adjusts the level of detail of matching according to the importance of the market needs. The matching unit can also adjust the level of detail of matching according to the urgency of the market needs. For example, the matching unit adjusts the level of detail of matching according to the urgency of the market needs. In this way, by adjusting the level of detail of matching based on specific market needs, it is possible to provide a more appropriate matching result.

[0121] When matching technologies, the matching unit can apply different matching algorithms according to technologies in different industries. For example, the matching unit applies a specific matching algorithm according to technologies in the medical industry. For example, the matching unit applies a specific matching algorithm according to technologies in the medical industry. The matching unit can also apply different matching algorithms according to technologies in the environmental industry. For example, the matching unit applies different matching algorithms according to technologies in the environmental industry. The matching unit can also select an optimal matching algorithm according to the characteristics of each industry. For example, the matching unit selects an optimal matching algorithm according to the characteristics of each industry. In this way, by applying matching algorithms according to technologies in different industries, more appropriate matching results can be provided.

[0122] The matching unit can improve the accuracy of matching by referring to past matching results when matching technologies. The matching unit, for example, improves the accuracy of matching between technologies based on past matching results. For example, the matching unit improves the accuracy of matching between technologies based on past matching results. The matching unit can also improve the matching method by referring to past matching results. For example, the matching unit improves the matching method by referring to past matching results. The matching unit can also improve the accuracy of matching by utilizing past matching results. For example, the matching unit improves the accuracy of matching by utilizing past matching results. In this way, the accuracy of matching between technologies can be improved by referring to past matching results.

[0123] The matching unit can estimate the user's emotions and determine the priority of matching technologies based on the estimated user's emotions. For example, when the user is excited, the matching unit prioritizes matching technologies with high urgency. For example, when the user is excited, the matching unit prioritizes matching technologies with high urgency. Furthermore, when the user is relaxed, the matching unit can prioritize matching technologies with long-term needs. For example, when the user is relaxed, the matching unit prioritizes matching technologies with long-term needs. Furthermore, when the user is stressed, the matching unit can postpone matching technologies with less important needs to reduce the user's burden. For example, when the user is stressed, the matching unit postpones matching technologies with less important needs to reduce the user's burden. In this way, by determining the priority of matching technologies according to the user's emotions, it is possible to prioritize matching technologies with more important needs. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0124] When matching technologies, the matching unit can determine the priority of matching based on a specific time period or season. For example, the matching unit prioritizes matching between technologies related to a specific time period. For example, the matching unit prioritizes matching between technologies related to a specific time period. The matching unit can also prioritize matching between technologies related to a season. For example, the matching unit prioritizes matching between technologies related to a season. The matching unit can also set different priorities for different time periods or seasons and match optimal technologies. For example, the matching unit sets different priorities for different time periods or seasons and matches optimal technologies. In this way, by determining the priority of matching between technologies based on a specific time period or season, matching can be performed at a more appropriate time.

[0125] The matching unit can improve the accuracy of matching when matching technologies by referring to related market data. The matching unit, for example, refers to related market data to improve the accuracy of matching between technologies. For example, the matching unit refers to related market data to improve the accuracy of matching between technologies. The matching unit can also improve the method of matching technologies based on market data. For example, the matching unit improves the method of matching technologies based on market data. The matching unit can also utilize market data to improve the accuracy of matching technologies. For example, the matching unit utilizes market data to improve the accuracy of matching technologies. In this way, the accuracy of matching technologies can be improved by referring to related market data.

[0126] When matching technologies, the matching unit can adjust the matching method based on the maturity of the technologies. For example, the matching unit selects a detailed matching method based on the maturity of the technologies. For example, the matching unit selects a detailed matching method based on the maturity of the technologies. The matching unit can also adjust the matching method taking the maturity of the technologies into consideration. For example, the matching unit adjusts the matching method taking the maturity of the technologies into consideration. The matching unit can also select an optimal matching method based on the maturity of the technologies. For example, the matching unit selects an optimal matching method based on the maturity of the technologies. In this way, by adjusting the matching method based on the maturity of the technologies, it is possible to provide a more appropriate matching result.

[0127] The intermediation unit can estimate the user's emotions and adjust the contract mediation method based on the estimated user emotions. For example, if the user is excited, the intermediation unit can quickly mediate the contract and provide immediate results. For example, if the user is excited, the intermediation unit can quickly mediate the contract and provide immediate results. Furthermore, if the user is relaxed, the intermediation unit can mediate a detailed contract and provide comprehensive information. For example, if the user is relaxed, the intermediation unit can mediate a detailed contract and provide comprehensive information. Furthermore, if the user is stressed, the intermediation unit can mediate a concise contract to reduce the user's burden. For example, if the user is stressed, the intermediation unit mediates a concise contract to reduce the user's burden. This allows for more appropriate contract mediation by adjusting the contract mediation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] When brokering a contract, the brokerage department can focus on a specific industry or region. For example, the brokerage department can focus on a specific industry and prioritize brokering contracts related to that industry. For example, the brokerage department can focus on a specific industry and prioritize brokering contracts related to that industry. The brokerage department can also focus on a specific region and prioritize brokering contracts related to that region. For example, the brokerage department can focus on a specific region and prioritize brokering contracts related to that region. The brokerage department can also set different brokerage criteria for each industry or region and broker optimal contracts. For example, the brokerage department can set different brokerage criteria for each industry or region and broker optimal contracts. In this way, by brokering contracts by focusing on a specific industry or region, it is possible to broker more relevant contracts.

[0129] When mediating a contract, the mediation unit can improve the accuracy of the mediation by referring to past mediation results. The mediation unit, for example, improves the accuracy of the mediation of the contract based on past mediation results. For example, the mediation unit improves the accuracy of the mediation of the contract based on past mediation results. The mediation unit can also improve the mediation method by referring to past mediation results. For example, the mediation unit improves the mediation method by referring to past mediation results. The mediation unit can also improve the accuracy of the mediation by utilizing past mediation results. For example, the mediation unit improves the accuracy of the mediation by utilizing past mediation results. In this way, the accuracy of the mediation of the contract can be improved by referring to past mediation results.

[0130] When mediating a contract, the mediation unit can mediate based on specific contract conditions. For example, the mediation unit mediates a contract based on specific contract conditions. The mediation unit can also set a different mediation method for each contract condition and mediate the optimal contract. For example, the mediation unit can set a different mediation method for each contract condition and mediate the optimal contract. The mediation unit can also improve the mediation method based on the contract conditions. For example, the mediation unit improves the mediation method based on the contract conditions. In this way, by mediating a contract based on specific contract conditions, it is possible to mediate a more appropriate contract.

[0131] The intermediary unit can estimate the user's emotions and prioritize contracts based on the estimated user emotions. For example, when the user is excited, the intermediary unit prioritizes urgent contracts. For example, when the user is excited, the intermediary unit prioritizes urgent contracts. The intermediary unit can also prioritize long-term contracts when the user is relaxed. For example, when the user is relaxed, the intermediary unit prioritizes long-term contracts. The intermediary unit can also postpone less important contracts to reduce the user's burden when the user is stressed. For example, when the user is stressed, the intermediary unit postpones less important contracts to reduce the user's burden. In this way, by determining the priority of contracts according to the user's emotions, more important contracts can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0132] When brokering a contract, the intermediation department can focus on a specific region or country. For example, the intermediation department can focus on a specific region and prioritize brokering contracts related to that region. For example, the intermediation department can focus on a specific region and prioritize brokering contracts related to that region. The intermediation department can also focus on a specific country and prioritize brokering contracts related to that country. For example, the intermediation department can focus on a specific country and prioritize brokering contracts related to that country. The intermediation department can also set different intermediation criteria for each region or country and broker optimal contracts. For example, the intermediation department can set different intermediation criteria for each region or country and broker optimal contracts. In this way, by brokering contracts with a focus on a specific region or country, it is possible to broker contracts that are more relevant.

[0133] The brokerage department can improve the accuracy of brokerage by referring to related market data when brokering a contract. The brokerage department, for example, refers to related market data to improve the accuracy of brokerage of the contract. For example, the brokerage department refers to related market data to improve the accuracy of brokerage of the contract. The brokerage department can also improve the method of brokering the contract based on the market data. For example, the brokerage department improves the method of brokering the contract based on the market data. The brokerage department can also utilize the market data to improve the accuracy of brokerage of the contract. For example, the brokerage department utilizes market data to improve the accuracy of brokerage of the contract. In this way, the accuracy of brokerage of the contract can be improved by referring to related market data.

[0134] When brokering a contract, the brokerage department can broker the contract based on trends in a specific industry. For example, the brokerage department brokers the contract based on trends in a specific industry. The brokerage department can also analyze trends for each industry and broker the optimal contract. For example, the brokerage department can analyze trends for each industry and broker the optimal contract. The brokerage department can also adjust the brokerage method according to industry trends. For example, the brokerage department adjusts the brokerage method according to industry trends. In this way, by brokering the contract based on trends in a specific industry, it is possible to broker a more relevant contract. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, element analysis unit, collection unit, organization unit, matching unit, and intermediation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires market needs using the camera 42 and microphone 38B of the smart device 14 and transmits the acquired market needs to the data processing device 12 via the control unit 46A. The element analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes technology elements based on the acquired market needs. The collection unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, collects public technology information. The organization unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, organizes and classifies the collected technology information. The matching unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, matches technologies based on the organized and classified technology information. The intermediation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes combinations of matched technologies to companies and brokers contracts. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, element analysis unit, collection unit, organization unit, matching unit, and intermediation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires market needs using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing device 12 via the control unit 46A. The element analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes technology elements based on the acquired market needs. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects public technology information. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and organizes and classifies the collected technology information. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches technologies based on the organized and classified technology information. The intermediation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes combinations of matched technologies to companies and brokers contracts. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, element analysis unit, collection unit, organization unit, matching unit, and intermediation unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires market needs using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the market needs to the data processing device 12 via the control unit 46A. The element analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes technology elements based on the acquired market needs. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects public technology information. The organization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and organizes and classifies the collected technology information. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and matches technologies based on the organized and classified technology information. The intermediation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes combinations of matched technologies to companies and brokers contracts. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, element analysis unit, collection unit, organization unit, matching unit, and intermediation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires market needs using the camera 42 and microphone 238 of the robot 414 and transmits the market needs to the data processing device 12 via the control unit 46A. The element analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes technology elements based on the acquired market needs. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects public technology information. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and organizes and classifies the collected technology information. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches technologies based on the organized and classified technology information. The intermediation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes combinations of matched technologies to companies and brokers contracts.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The acquisition unit can also analyze the user's purchase history and predict market needs based on past purchase patterns. For example, the acquisition unit can analyze the user's past purchase history and predict demand for a specific product or service. The acquisition unit can also find seasonal demand patterns from the user's purchase history and predict market needs according to the season. Furthermore, the acquisition unit can predict demand for specific events or campaigns based on the user's purchase history. This makes it possible to predict market needs with greater accuracy by utilizing the user's purchase history.

[0137] When analyzing technical elements, the element analysis unit can also refer to the latest research results in a specific technical field. For example, the element analysis unit can refer to the latest academic papers and reflect this in the analysis of technical elements. The element analysis unit can also refer to the latest patent information and use it in the analysis of technical elements. Furthermore, the element analysis unit can refer to the latest technical reports in the technical field and incorporate them into the analysis of technical elements. This allows for more accurate analysis of technical elements by referring to the latest research results.

[0138] When organizing technical information, the organizing unit can estimate the user's emotions and adjust the information display method based on the estimated user emotions. For example, if the user is excited, the organizing unit can highlight important technical information and provide it quickly. Also, if the user is relaxed, the organizing unit can gradually display detailed technical information to provide comprehensive information. Furthermore, if the user is feeling stressed, the organizing unit can simplify the information display to reduce the user's burden. This allows for more appropriate information to be provided by adjusting the information display method according to the user's emotions.

[0139] When matching technologies, the matching unit can take into account competing technologies in a specific technical field. For example, the matching unit analyzes the characteristics of competing technologies and finds the optimal combination of technologies. The matching unit can also consider the market share of competing technologies to match competitive technologies. Furthermore, the matching unit can refer to patent information on competing technologies and use it when matching technologies. By taking competing technologies into consideration, this makes it possible to match more competitive technologies.

[0140] The mediation unit can estimate the user's emotions when mediating a contract and adjust the negotiation process based on the estimated user emotions. For example, if the user is excited, the mediation unit can quickly proceed with the negotiation and provide immediate results. If the user is relaxed, the mediation unit can also conduct detailed negotiations and provide comprehensive information. Furthermore, if the user is feeling stressed, the mediation unit can simplify the negotiation process and reduce the user's burden. This allows for more appropriate contract mediation by adjusting the negotiation process according to the user's emotions.

[0141] The acquisition unit can analyze the user's social media activities and acquire related market needs. For example, the acquisition unit can analyze the content of the user's posts on social media to acquire related market needs. The acquisition unit can also acquire related market needs by referring to the activities of the user's friends on social media. Furthermore, the acquisition unit can acquire related market needs based on the user's check-in information on social media. In this way, related market needs can be efficiently acquired by analyzing the user's social media activities.

[0142] The element analysis unit can estimate the user's emotions when analyzing technical elements and adjust the level of detail of the analysis based on the estimated user emotions. For example, if the user is excited, the element analysis unit can quickly analyze technical elements and provide results immediately. Alternatively, if the user is relaxed, the element analysis unit can perform a detailed analysis of technical elements and provide comprehensive results. Furthermore, if the user is feeling stressed, the element analysis unit can perform a concise analysis of technical elements to reduce the user's burden. In this way, by adjusting the level of detail of the analysis of technical elements according to the user's emotions, more appropriate analysis results can be provided.

[0143] When collecting technical information, the collection unit can focus on a specific database or information source. For example, the collection unit can focus on a specific database and collect technical information from that database. The collection unit can also focus on a specific information source and collect technical information from that information source. Furthermore, the collection unit can set a different collection method for each database or information source and collect optimal technical information. In this way, by focusing on a specific database or information source to collect technical information, more relevant information can be collected.

[0144] When organizing technical information, the organizing unit can organize the information based on specific keywords or topics. For example, the organizing unit organizes the technical information based on specific keywords. The organizing unit can also organize the technical information based on specific topics. Furthermore, the organizing unit can set different organizing methods for each keyword or topic and organize the optimal technical information. In this way, by organizing technical information based on specific keywords or topics, more relevant information can be provided.

[0145] The matching unit can estimate the user's emotions when matching technologies and determine the priority of matching based on the estimated user's emotions. For example, if the user is excited, the matching unit can prioritize matching technologies with high urgency. Also, if the user is relaxed, the matching unit can prioritize matching technologies with long-term needs. Furthermore, if the user is feeling stressed, the matching unit can postpone matching technologies with less important needs in order to reduce the burden on the user. In this way, by determining the priority of matching technologies according to the user's emotions, it is possible to prioritize matching technologies with more important needs.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The Acquisition Department acquires market needs. Market needs include consumer demand, industry trends, and requirements for specific products and services. The Acquisition Department conducts surveys to understand consumer demand. It can also analyze industry trends and predict future market needs. It can also collect requirements for specific products and services and use them to analyze technical elements. Step 2: The element analysis unit analyzes technological elements based on the market needs acquired by the acquisition unit. Technological elements include specific technical fields, technological functions, and the scope of technological application. The element analysis unit uses text mining technology to extract technological elements from market needs. It can also analyze market needs and identify technological elements using data analysis technology. It can also extract technological elements based on expert evaluations. Step 3: The collection department collects publicly available technical information. Publicly available technical information includes patent databases, academic papers, technical reports, etc. The collection department searches patent databases to collect relevant technical information. It can also analyze academic papers to collect technical information. It can also collect technical reports and organize the technical information. Step 4: The organizing unit organizes and classifies the technical information collected by the collecting unit. Organizing and classifying includes categorizing information, tagging, and creating a hierarchical structure. The organizing unit categorizes information and organizes the technical information. It can also organize information by tagging it. It can also organize information into a hierarchical structure to systematically manage technical information. Step 5: The matching department matches technologies based on the technical information organized and classified by the organization department. Matching includes technologies' complementarity, compatibility, and the use of algorithms. The matching department matches technologies based on their complementarity. It can also evaluate compatibility and match optimal technologies. It can also match technologies using algorithms. Step 6: The mediation department proposes the technology combinations matched by the matching department to the companies and mediates the contracts. Mediation of contracts includes adjusting contract terms, drafting contracts, and supporting negotiations. The mediation department sets up meetings between companies and mediates opinions. It can also adjust contract terms and draft contracts. It can also support negotiations and help conclude contracts.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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. An acquisition department that acquires market needs; an element analysis unit that analyzes technical elements based on the market needs acquired by the acquisition unit; a collection department that collects publicly available technical information; an organizing unit that organizes and classifies the technical information collected by the collecting unit; a matching unit that matches technologies based on the technical information organized and classified by the organizing unit; an intermediation unit that proposes the combination of technologies matched by the matching unit to a company and mediates a contract. A system characterized by:

2. The element analysis unit Extracting technical elements based on the market needs acquired by the acquisition unit 2. The system of claim 1.

3. The organizing unit Analyze publicly available technical information using AI and extract related technologies 2. The system of claim 1.

4. The matching unit Generative AI simulates combinations of technologies to find the optimal combination 2. The system of claim 1.

5. The intermediate portion is Supporting intercompany coordination and contracting 2. The system of claim 1.

6. The intermediate portion is Introducing utilization plans on the web 2. The system of claim 1.

7. The acquisition unit To estimate user emotions and adjust timing for acquiring market needs based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze past market needs data and select the optimal acquisition method 2. The system of claim 1.

Images