system

Abstract

The system according to this embodiment aims to efficiently collect and provide information on other companies in the same industry based on information from companies with which business cards have been exchanged. [Solution] The system according to the embodiment comprises a collection unit, a search unit, and a provision unit. The collection unit collects information on companies with which business cards have been exchanged. The search unit searches for companies in the same industry based on the information collected by the collection unit. The provision unit provides the results found by the search unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently collect and provide information not only on the companies with which business cards have been exchanged but also on other companies in the same industry.

[0005] The system according to the embodiment aims to efficiently collect and provide information on other companies in the same industry based on the information of the companies with which business cards have been exchanged.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a search unit, and a provision unit. The collection unit collects information on the companies with which business cards have been exchanged. The search unit searches for companies in the same industry based on the information collected by the collection unit. The provision unit provides the results searched by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and provide information on other companies in the same industry based on information from companies with which business cards have been exchanged. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​application according to an embodiment of the present invention is a system that automatically searches for and provides companies in the same industry based on information from companies with which business cards have been exchanged. This AI application works in conjunction with a business card management tool to search for companies in the same industry based on information from companies with which business cards have been exchanged. For example, it searches for companies that manufacture parts similar to those of company A and provides information such as company B's address, capital, main clients, and business similarity. This allows companies to select companies they have never contacted and attempt to make contact with them at the same level as companies with which they have already exchanged business cards. This solves the problem faced by small business owners in rural areas who have technical capabilities but lack sales skills and name recognition. By using a generating AI to search for companies in the same industry according to their size based on information obtained from a business card exchange tool, it is possible to find business owners with whom they can effectively collaborate. This allows businesses to quickly find excellent business owners and conduct proactive sales activities. In terms of market size, partnering only with companies with which business cards have been exchanged will result in a bias, so it is necessary to constantly search for outstanding companies. There is demand for proactive action ahead of other companies, and now is the chance to enter the market. This tool aims to empower future leaders by supporting businesses in achieving success and creating mutually beneficial relationships. The AI ​​application will automatically search for and provide information on companies in the same industry based on the information exchanged with businesses using business cards.

[0029] The AI ​​application according to this embodiment comprises a collection unit, a search unit, and a provision unit. The collection unit collects information on companies from which business cards have been exchanged. The collection unit automatically collects information on companies from which business cards have been exchanged, for example, by linking with a business card management tool. The collection unit can collect information such as company name, address, contact information, and industry. The collection unit obtains information such as company name, address, contact information, and industry from a business card management tool, for example. The collection unit can also collect information manually entered at the time of business card exchange. The search unit searches for companies in the same industry based on the information collected by the collection unit. The search unit searches for companies in the same industry from a database, for example, based on the collected company information. The search unit can identify companies in the same industry using industry codes or industry classifications. The search unit searches for companies in the same industry based on industry codes, for example. The search unit can also search for companies in the same industry based on industry classifications. The search unit can improve the accuracy of search results using generative AI. The generative AI improves the accuracy of search results using text generation AI, for example. The generating AI can identify relevant companies based on the collected company information. The providing unit provides the search results generated by the searching unit. The providing unit, for example, displays the search results to the user. The providing unit can display the search results in a list format. For example, the providing unit can display the search results in a list format so that the user can view detailed information. The providing unit can also display the search results in a graph format. For example, the providing unit can display the search results in a graph format to make it easier to compare companies. This allows the AI ​​application to automatically search for and provide companies in the same industry based on the information of companies with which business cards have been exchanged.

[0030] The data collection unit collects information on companies with which business cards have been exchanged. For example, the unit can automatically collect information on companies with which business cards have been exchanged by linking with a business card management tool. Specifically, the business card management tool uses OCR (Optical Character Recognition) technology to digitize the information on business cards and extract information such as company name, address, contact information, and industry. This eliminates the need for manual data entry and allows for quick and accurate data collection. The data collection unit obtains information such as company name, address, contact information, and industry from the business card management tool. The data collection unit can also collect information manually entered during business card exchanges. For example, it can provide an interface for manually entering company information using a smartphone app during business card exchanges, making it easy for users to add information. Furthermore, the data collection unit can also collect additional information from publicly available information on the internet and the company's official website. For example, it can obtain the latest news and press releases from the company's official website to collect information on company trends and new services. This allows the data collection unit to collect not only basic information from business card exchanges but also the latest information and detailed data on companies. The collected information is stored in a central database and made accessible to the search and provision units. This allows the data collection unit to efficiently and comprehensively collect corporate information, thereby improving the overall performance of the system.

[0031] The search unit searches for companies in the same industry based on the information collected by the data collection unit. For example, the search unit searches for companies in the same industry from a database based on the collected company information. Specifically, it can identify companies in the same industry using industry codes and industry classifications. Industry codes can use international classification standards such as the Standard Industrial Classification and NAICS codes, which makes it possible to accurately identify the industry of a company. For example, the search unit searches for companies in the same industry based on industry codes. The search unit can also search for companies in the same industry based on industry classifications. Industry classifications are based on the business content and types of products and services of companies, so more detailed searches are possible. The search unit can improve the accuracy of search results using generative AI. For example, generative AI improves the accuracy of search results using text generation AI. Specifically, generative AI can identify related companies based on the collected company information. Generative AI analyzes companies' business content and performance reports using natural language processing technology to identify companies in the same industry with high accuracy. Furthermore, the generating AI can learn from past search history and user feedback, continuously improving its search algorithm. This allows the search unit to provide optimal search results tailored to user needs, improving the overall accuracy and reliability of the system.

[0032] The service provider provides the results searched by the search provider. For example, the service provider displays the search results to the user. Specifically, it displays the search results in a list format, allowing the user to view detailed information. In the list format, basic information such as company name, address, contact information, and industry is displayed at a glance. The service provider can also display the search results in a graph format. In the graph format, data such as company size, performance, and growth rate can be visually compared, making it easy for users to understand the characteristics of each company. Furthermore, the service provider provides a function to customize the search results. For example, users can set specific conditions and filters to narrow down the search results. This allows users to quickly find company information that best suits their needs. The service provider displays the search results in a list format, allowing the user to view detailed information. It also displays the search results in a graph format to facilitate company comparison. Furthermore, the service provider also provides a function to export the search results. For example, search results can be downloaded in CSV or PDF format, allowing users to refer to them later or use them in other systems. This allows the service provider to provide users with flexible and diverse means of information delivery, improving the overall usability of the system.

[0033] The search unit can improve the accuracy of search results using generative AI. For example, the search unit can use generative AI to identify related companies based on collected company information. The search unit can also use generative AI to analyze a company's industry and business activities to identify companies in the same industry. For example, the search unit can use generative AI to analyze a company's industry code and industry classification to identify companies in the same industry. Furthermore, the search unit can also use generative AI to analyze a company's business activities to identify companies in the same industry. This improves the accuracy of search results by using generative AI. Generative AI can improve the accuracy of search results, for example, by using text generation AI. The generative AI can identify related companies based on collected company information. For example, the generative AI can analyze a company's industry code and industry classification to identify companies in the same industry. Furthermore, the generative AI can analyze a company's business activities to identify companies in the same industry. This improves the accuracy of search results by using generative AI.

[0034] The service provider can assist in selecting collaboration partners based on search results. For example, the service provider can assist in selecting collaboration partners based on search results. The service provider can evaluate the size and technological capabilities of companies and select collaboration partners based on search results. For example, the service provider can evaluate the size and technological capabilities of companies and select collaboration partners based on search results. The service provider can also evaluate the past collaboration performance of companies and select collaboration partners based on search results. For example, the service provider can evaluate the past collaboration performance of companies and select collaboration partners based on search results. This enables effective collaboration by assisting in the selection of collaboration partners based on search results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select collaboration partners using an AI model that assists in selecting collaboration partners based on search results.

[0035] The data collection unit can collect environmental information at the time of business card exchange and associate it with business card information. For example, the data collection unit can collect GPS information of the location where the business card exchange took place and add it to the business card information. The data collection unit can record the time the business card exchange took place and associate it with the business card information. The data collection unit can also collect the type of event (exhibition, conference, etc.) at which the business card exchange took place and add it to the business card information. This allows for more detailed information management by collecting environmental information at the time of business card exchange and associating it with the business card information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental information at the time of business card exchange into a generating AI and have the generating AI perform analysis of the environmental information.

[0036] The data collection unit can automatically acquire the latest news and press releases from companies and add them to the business card information. For example, the data collection unit can acquire the latest news from the company's official website and add it to the business card information. The data collection unit can automatically acquire the company's press releases and add them to the business card information. The data collection unit can also acquire the latest posts from the company's social media accounts and add them to the business card information. This ensures that the latest information is always maintained by automatically acquiring the latest news and press releases from companies and adding them to the business card information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the latest news and press releases from companies into a generating AI and have the generating AI acquire the latest information.

[0037] The data collection unit can analyze the user's social media activity and collect relevant company information when collecting business card information. This allows for broader information gathering by analyzing the user's social media activity and collecting relevant company information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant company information.

[0038] The data collection unit can automatically acquire and add company performance data and market valuation data when collecting business card information. For example, the data collection unit can automatically acquire and add company financial reports when collecting business card information. The data collection unit can also automatically acquire and add company market valuation data when collecting business card information. Furthermore, the data collection unit can automatically acquire and add company performance data when collecting business card information. This allows for more detailed information management by automatically acquiring and adding company performance data and market valuation data to business card information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input company performance data and market valuation data into a generating AI and have the generating AI perform the data acquisition.

[0039] The search unit can filter search results by considering factors such as a company's growth rate and market share. For example, the search unit can filter search results based on a company's growth rate over the past five years. The search unit can also filter search results based on a company's market share data. Furthermore, the search unit can filter search results based on a company's growth forecast data. This allows for the provision of more relevant information by filtering search results based on factors such as a company's growth rate and market share. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input company growth rate and market share data into a generating AI and have the generating AI perform the filtering of search results.

[0040] The search unit can refine search results by considering a company's technology patents and research and development activities during the search process. For example, the search unit can refine search results based on a company's patent data. The search unit can refine search results based on a company's research and development activity data. The search unit can also refine search results based on a company's history of technological innovation. By refining search results while considering a company's technology patents and research and development activities, more accurate information can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input a company's technology patents and research and development data into a generating AI and have the generating AI perform the refinement of the search results.

[0041] The search unit can filter search results by considering the geographical distribution of companies. For example, the search unit can filter search results based on company location data. The search unit can filter search results based on company geographical distribution data. The search unit can also filter search results based on company regional market share data. This allows for the provision of region-specific information by filtering search results while considering the geographical distribution of companies. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input company geographical distribution data into a generating AI and have the generating AI perform the filtering of search results.

[0042] The search unit can refine search results by considering a company's reputation within its industry and customer ratings during the search process. For example, the search unit can refine search results based on a company's customer rating data. The search unit can refine search results based on a company's reputation within its industry. The search unit can also refine search results based on a company's customer satisfaction data. By refining search results while considering a company's reputation within its industry and customer ratings, it can provide highly reliable information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input a company's customer rating data into a generating AI and have the generating AI perform the refinement of search results.

[0043] The information provision department can provide information including companies' past collaboration achievements and success stories at the time of provision. For example, the information provision department can provide information based on companies' past collaboration achievement data. The information provision department can provide information based on companies' success story data. The information provision department can also provide information based on evaluation data of companies' collaboration partners. By providing information including companies' past collaboration achievements and success stories, the possibility of collaboration is increased. Some or all of the above processing in the information provision department may be performed using AI, for example, or without using AI. For example, the information provision department can input companies' collaboration achievement data into a generating AI and have the generating AI perform the information provision.

[0044] The information provision unit can provide information including a company's financial status and investment information at the time of provision. For example, the information provision unit can provide information based on a company's financial report data. The information provision unit can provide information based on a company's investment information data. The information provision unit can also provide information based on a company's financial health data. This makes it possible to provide more detailed information by providing information including a company's financial status and investment information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input a company's financial status and investment information data into a generating AI and have the generating AI perform the information provision.

[0045] The information provider can provide information that includes the company's future plans and vision at the time of provision. For example, the information provider can provide information based on the company's future plan data. The information provider can provide information based on the company's vision data. The information provider can also provide information based on the company's long-term strategy data. By providing information that includes the company's future plans and vision, the possibility of future collaboration is increased. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the company's future plan and vision data into a generating AI and have the generating AI perform the information provision.

[0046] The information provider can provide information including a company's competitive analysis and market position at the time of provision. For example, the information provider can provide information based on a company's competitive analysis data. The information provider can provide information based on a company's market position data. The information provider can also provide information based on a company's competitive advantage data. By providing information including a company's competitive analysis and market position, it is possible to find competitive partners. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input a company's competitive analysis data into a generating AI and have the generating AI perform the information provision.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The data collection unit can collect the skill sets and expertise of company employees during business card exchanges and add them to the business card information. For example, the data collection unit can record the skill sets of company employees during business card exchanges and add them to the business card information. The data collection unit can collect employee expertise and qualification information and associate it with the business card information. The data collection unit can also collect employees' past project experience and add it to the business card information. This allows for more detailed information management by collecting the skill sets and expertise of company employees and adding them to the business card information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee skill set and expertise data into a generating AI and have the generating AI perform the data collection.

[0049] The search unit can refine search results by considering data related to companies' environmental initiatives and sustainability. For example, the search unit can refine search results based on data from companies' environmental reports. The search unit can refine search results based on data related to companies' sustainability initiatives. The search unit can also refine search results based on data related to companies' environmental certifications. This allows for the identification of environmentally conscious companies by refining search results while considering data related to companies' environmental initiatives and sustainability. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input company environmental data into a generating AI and have the generating AI perform the refinement of search results.

[0050] The data collection unit can collect information about a company's culture and values ​​during business card exchanges and add it to the business card information. For example, the data collection unit can collect a company's mission statement during business card exchanges and add it to the business card information. The data collection unit can collect information about a company's values ​​and culture and associate it with the business card information. The data collection unit can also collect information about a company's internal events and activities and add it to the business card information. This allows for more detailed information management by collecting information about a company's culture and values ​​and adding it to the business card information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input company culture and values ​​data into a generating AI and have the generating AI perform the data collection.

[0051] The search unit can refine search results by considering data related to corporate social responsibility (CSR) activities. For example, the search unit can refine search results based on corporate CSR report data. The search unit can refine search results based on corporate social contribution activity data. The search unit can also refine search results based on corporate ethical initiatives data. By refining search results while considering data related to corporate social responsibility (CSR) activities, it is possible to identify socially responsible companies. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input corporate CSR data into a generating AI and have the generating AI perform the refinement of search results.

[0052] The data collection unit can collect information about a company's history and background when exchanging business cards and add it to the business card information. For example, the data collection unit can collect information about the company's founding year and founders when exchanging business cards and add it to the business card information. The data collection unit can collect information about important milestones and historical events of a company and associate it with the business card information. The data collection unit can also collect information about a company's growth process and evolution and add it to the business card information. This allows for more detailed information management by collecting information about a company's history and background and adding it to the business card information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input company history and background data into a generating AI and have the generating AI perform the data collection.

[0053] The search unit can refine search results by considering data related to employee satisfaction and ease of work at a company. For example, the search unit can refine search results based on employee satisfaction survey data. The search unit can refine search results based on data related to ease of work at a company. The search unit can also refine search results based on employee welfare data. By refining search results while considering data related to employee satisfaction and ease of work at a company, it is possible to identify companies that are easy for employees to work at. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input employee satisfaction data at a company into a generating AI and have the generating AI perform the refinement of search results.

[0054] The search unit can refine search results by considering data related to a company's international expansion and global activities. For example, the search unit can refine search results based on a company's international expansion data. The search unit can refine search results based on data related to a company's global activities. The search unit can also refine search results based on data related to a company's overseas bases and international partnerships. This allows for the identification of companies with an international perspective by refining search results while considering data related to a company's international expansion and global activities. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input a company's international expansion data into a generating AI and have the generating AI perform the refinement of search results.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection unit collects information about companies from which business cards have been exchanged. The collection unit can automatically collect information such as company name, address, contact information, and industry by linking with a business card management tool. It can also collect information that was manually entered during the business card exchange. Step 2: The search unit searches for companies in the same industry based on the information collected by the data collection unit. The search unit can search for companies in the same industry from the database using industry codes and industry classifications. It can also improve the accuracy of search results using generation AI. Step 3: The delivery unit provides the search results generated by the search unit. The delivery unit displays the search results to the user in list or graph format, making it easy to review detailed information and compare companies.

[0057] (Example of form 2) The AI ​​application according to an embodiment of the present invention is a system that automatically searches for and provides companies in the same industry based on information from companies with which business cards have been exchanged. This AI application works in conjunction with a business card management tool to search for companies in the same industry based on information from companies with which business cards have been exchanged. For example, it searches for companies that manufacture parts similar to those of company A and provides information such as company B's address, capital, main clients, and business similarity. This allows companies to select companies they have never contacted and attempt to make contact with them at the same level as companies with which they have already exchanged business cards. This solves the problem faced by small business owners in rural areas who have technical capabilities but lack sales skills and name recognition. By using a generating AI to search for companies in the same industry according to their size based on information obtained from a business card exchange tool, it is possible to find business owners with whom they can effectively collaborate. This allows businesses to quickly find excellent business owners and conduct proactive sales activities. In terms of market size, partnering only with companies with which business cards have been exchanged will result in a bias, so it is necessary to constantly search for outstanding companies. There is demand for proactive action ahead of other companies, and now is the chance to enter the market. This tool aims to empower future leaders by supporting businesses in achieving success and creating mutually beneficial relationships. The AI ​​application will automatically search for and provide information on companies in the same industry based on the information exchanged with businesses using business cards.

[0058] The AI ​​application according to this embodiment comprises a collection unit, a search unit, and a provision unit. The collection unit collects information on companies from which business cards have been exchanged. The collection unit automatically collects information on companies from which business cards have been exchanged, for example, by linking with a business card management tool. The collection unit can collect information such as company name, address, contact information, and industry. The collection unit obtains information such as company name, address, contact information, and industry from a business card management tool, for example. The collection unit can also collect information manually entered at the time of business card exchange. The search unit searches for companies in the same industry based on the information collected by the collection unit. The search unit searches for companies in the same industry from a database, for example, based on the collected company information. The search unit can identify companies in the same industry using industry codes or industry classifications. The search unit searches for companies in the same industry based on industry codes, for example. The search unit can also search for companies in the same industry based on industry classifications. The search unit can improve the accuracy of search results using generative AI. The generative AI improves the accuracy of search results using text generation AI, for example. The generating AI can identify relevant companies based on the collected company information. The providing unit provides the search results generated by the searching unit. The providing unit, for example, displays the search results to the user. The providing unit can display the search results in a list format. For example, the providing unit can display the search results in a list format so that the user can view detailed information. The providing unit can also display the search results in a graph format. For example, the providing unit can display the search results in a graph format to make it easier to compare companies. This allows the AI ​​application to automatically search for and provide companies in the same industry based on the information of companies with which business cards have been exchanged.

[0059] The data collection unit collects information on companies with which business cards have been exchanged. For example, the unit can automatically collect information on companies with which business cards have been exchanged by linking with a business card management tool. Specifically, the business card management tool uses OCR (Optical Character Recognition) technology to digitize the information on business cards and extract information such as company name, address, contact information, and industry. This eliminates the need for manual data entry and allows for quick and accurate data collection. The data collection unit obtains information such as company name, address, contact information, and industry from the business card management tool. The data collection unit can also collect information manually entered during business card exchanges. For example, it can provide an interface for manually entering company information using a smartphone app during business card exchanges, making it easy for users to add information. Furthermore, the data collection unit can also collect additional information from publicly available information on the internet and the company's official website. For example, it can obtain the latest news and press releases from the company's official website to collect information on company trends and new services. This allows the data collection unit to collect not only basic information from business card exchanges but also the latest information and detailed data on companies. The collected information is stored in a central database and made accessible to the search and provision units. This allows the data collection unit to efficiently and comprehensively collect corporate information, thereby improving the overall performance of the system.

[0060] The search unit searches for companies in the same industry based on the information collected by the data collection unit. For example, the search unit searches for companies in the same industry from a database based on the collected company information. Specifically, it can identify companies in the same industry using industry codes and industry classifications. Industry codes can use international classification standards such as the Standard Industrial Classification and NAICS codes, which makes it possible to accurately identify the industry of a company. For example, the search unit searches for companies in the same industry based on industry codes. The search unit can also search for companies in the same industry based on industry classifications. Industry classifications are based on the business content and types of products and services of companies, so more detailed searches are possible. The search unit can improve the accuracy of search results using generative AI. For example, generative AI improves the accuracy of search results using text generation AI. Specifically, generative AI can identify related companies based on the collected company information. Generative AI analyzes companies' business content and performance reports using natural language processing technology to identify companies in the same industry with high accuracy. Furthermore, the generating AI can learn from past search history and user feedback, continuously improving its search algorithm. This allows the search unit to provide optimal search results tailored to user needs, improving the overall accuracy and reliability of the system.

[0061] The service provider provides the results searched by the search provider. For example, the service provider displays the search results to the user. Specifically, it displays the search results in a list format, allowing the user to view detailed information. In the list format, basic information such as company name, address, contact information, and industry is displayed at a glance. The service provider can also display the search results in a graph format. In the graph format, data such as company size, performance, and growth rate can be visually compared, making it easy for users to understand the characteristics of each company. Furthermore, the service provider provides a function to customize the search results. For example, users can set specific conditions and filters to narrow down the search results. This allows users to quickly find company information that best suits their needs. The service provider displays the search results in a list format, allowing the user to view detailed information. It also displays the search results in a graph format to facilitate company comparison. Furthermore, the service provider also provides a function to export the search results. For example, search results can be downloaded in CSV or PDF format, allowing users to refer to them later or use them in other systems. This allows the service provider to provide users with flexible and diverse means of information delivery, improving the overall usability of the system.

[0062] The search unit can improve the accuracy of search results using generative AI. For example, the search unit can use generative AI to identify related companies based on collected company information. The search unit can also use generative AI to analyze a company's industry and business activities to identify companies in the same industry. For example, the search unit can use generative AI to analyze a company's industry code and industry classification to identify companies in the same industry. Furthermore, the search unit can also use generative AI to analyze a company's business activities to identify companies in the same industry. This improves the accuracy of search results by using generative AI. Generative AI can improve the accuracy of search results, for example, by using text generation AI. The generative AI can identify related companies based on collected company information. For example, the generative AI can analyze a company's industry code and industry classification to identify companies in the same industry. Furthermore, the generative AI can analyze a company's business activities to identify companies in the same industry. This improves the accuracy of search results by using generative AI.

[0063] The service provider can assist in selecting collaboration partners based on search results. For example, the service provider can assist in selecting collaboration partners based on search results. The service provider can evaluate the size and technological capabilities of companies and select collaboration partners based on search results. For example, the service provider can evaluate the size and technological capabilities of companies and select collaboration partners based on search results. The service provider can also evaluate the past collaboration performance of companies and select collaboration partners based on search results. For example, the service provider can evaluate the past collaboration performance of companies and select collaboration partners based on search results. This enables effective collaboration by assisting in the selection of collaboration partners based on search results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select collaboration partners using an AI model that assists in selecting collaboration partners based on search results.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of business card information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone collecting business card information and collect it when the user is relaxed. If the user is relaxed, the data collection unit can immediately collect business card information and efficiently accumulate data. If the user is in a hurry, the data collection unit can quickly collect business card information and add detailed information later. This allows for efficient data collection by adjusting the timing of business card information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can collect environmental information at the time of business card exchange and associate it with business card information. For example, the data collection unit can collect GPS information of the location where the business card exchange took place and add it to the business card information. The data collection unit can record the time the business card exchange took place and associate it with the business card information. The data collection unit can also collect the type of event (exhibition, conference, etc.) at which the business card exchange took place and add it to the business card information. This allows for more detailed information management by collecting environmental information at the time of business card exchange and associating it with the business card information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental information at the time of business card exchange into a generating AI and have the generating AI perform analysis of the environmental information.

[0066] The data collection unit can automatically acquire the latest news and press releases from companies and add them to the business card information. For example, the data collection unit can acquire the latest news from the company's official website and add it to the business card information. The data collection unit can automatically acquire the company's press releases and add them to the business card information. The data collection unit can also acquire the latest posts from the company's social media accounts and add them to the business card information. This ensures that the latest information is always maintained by automatically acquiring the latest news and press releases from companies and adding them to the business card information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the latest news and press releases from companies into a generating AI and have the generating AI acquire the latest information.

[0067] The data collection unit can estimate the user's emotions and determine the priority of business card information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting important business card information. If the user is relaxed, the data collection unit can collect all business card information equally. If the user is tired, the data collection unit can also collect only the most important business card information. In this way, by prioritizing business card information according to the user's emotions, important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can analyze the user's social media activity and collect relevant company information when collecting business card information. This allows for broader information gathering by analyzing the user's social media activity and collecting relevant company information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant company information.

[0069] The data collection unit can automatically acquire and add company performance data and market valuation data when collecting business card information. For example, the data collection unit can automatically acquire and add company financial reports when collecting business card information. The data collection unit can also automatically acquire and add company market valuation data when collecting business card information. Furthermore, the data collection unit can automatically acquire and add company performance data when collecting business card information. This allows for more detailed information management by automatically acquiring and adding company performance data and market valuation data to business card information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input company performance data and market valuation data into a generating AI and have the generating AI perform the data acquisition.

[0070] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is nervous, the search unit can provide a simple and highly visible display. If the user is relaxed, the search unit can provide a display that includes detailed information. If the user is in a hurry, the search unit can also provide a display that gets straight to the point. By adjusting how search results are displayed according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The search unit can filter search results by considering factors such as a company's growth rate and market share. For example, the search unit can filter search results based on a company's growth rate over the past five years. The search unit can also filter search results based on a company's market share data. Furthermore, the search unit can filter search results based on a company's growth forecast data. This allows for the provision of more relevant information by filtering search results based on factors such as a company's growth rate and market share. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input company growth rate and market share data into a generating AI and have the generating AI perform the filtering of search results.

[0072] The search unit can refine search results by considering a company's technology patents and research and development activities during the search process. For example, the search unit can refine search results based on a company's patent data. The search unit can refine search results based on a company's research and development activity data. The search unit can also refine search results based on a company's history of technological innovation. By refining search results while considering a company's technology patents and research and development activities, more accurate information can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input a company's technology patents and research and development data into a generating AI and have the generating AI perform the refinement of the search results.

[0073] The search unit can estimate the user's emotions and prioritize search results based on the estimated emotions. For example, if the user is excited, the search unit will prioritize displaying the most relevant search results. If the user is relaxed, the search unit can display all search results equally. If the user is tired, the search unit can display only the most important search results. This allows for the priority of important information to be provided by prioritizing search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The search unit can filter search results by considering the geographical distribution of companies. For example, the search unit can filter search results based on company location data. The search unit can filter search results based on company geographical distribution data. The search unit can also filter search results based on company regional market share data. This allows for the provision of region-specific information by filtering search results while considering the geographical distribution of companies. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input company geographical distribution data into a generating AI and have the generating AI perform the filtering of search results.

[0075] The search unit can refine search results by considering a company's reputation within its industry and customer ratings during the search process. For example, the search unit can refine search results based on a company's customer rating data. The search unit can refine search results based on a company's reputation within its industry. The search unit can also refine search results based on a company's customer satisfaction data. By refining search results while considering a company's reputation within its industry and customer ratings, it can provide highly reliable information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input a company's customer rating data into a generating AI and have the generating AI perform the refinement of search results.

[0076] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. By adjusting how information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The information provision department can provide information including companies' past collaboration achievements and success stories at the time of provision. For example, the information provision department can provide information based on companies' past collaboration achievement data. The information provision department can provide information based on companies' success story data. The information provision department can also provide information based on evaluation data of companies' collaboration partners. By providing information including companies' past collaboration achievements and success stories, the possibility of collaboration is increased. Some or all of the above processing in the information provision department may be performed using AI, for example, or without using AI. For example, the information provision department can input companies' collaboration achievement data into a generating AI and have the generating AI perform the information provision.

[0078] The information provision unit can provide information including a company's financial status and investment information at the time of provision. For example, the information provision unit can provide information based on a company's financial report data. The information provision unit can provide information based on a company's investment information data. The information provision unit can also provide information based on a company's financial health data. This makes it possible to provide more detailed information by providing information including a company's financial status and investment information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input a company's financial status and investment information data into a generating AI and have the generating AI perform the information provision.

[0079] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is excited, the service provider will prioritize providing the most relevant information. If the user is relaxed, the service provider can provide all information equally. If the user is tired, the service provider can provide only the most important information. In this way, by prioritizing information according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The information provider can provide information that includes the company's future plans and vision at the time of provision. For example, the information provider can provide information based on the company's future plan data. The information provider can provide information based on the company's vision data. The information provider can also provide information based on the company's long-term strategy data. By providing information that includes the company's future plans and vision, the possibility of future collaboration is increased. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the company's future plan and vision data into a generating AI and have the generating AI perform the information provision.

[0081] The information provider can provide information including a company's competitive analysis and market position at the time of provision. For example, the information provider can provide information based on a company's competitive analysis data. The information provider can provide information based on a company's market position data. The information provider can also provide information based on a company's competitive advantage data. By providing information including a company's competitive analysis and market position, it is possible to find competitive partners. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input a company's competitive analysis data into a generating AI and have the generating AI perform the information provision.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The data collection unit can collect the skill sets and expertise of company employees during business card exchanges and add them to the business card information. For example, the data collection unit can record the skill sets of company employees during business card exchanges and add them to the business card information. The data collection unit can collect employee expertise and qualification information and associate it with the business card information. The data collection unit can also collect employees' past project experience and add it to the business card information. This allows for more detailed information management by collecting the skill sets and expertise of company employees and adding them to the business card information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee skill set and expertise data into a generating AI and have the generating AI perform the data collection.

[0084] The search unit can refine search results by considering data related to companies' environmental initiatives and sustainability. For example, the search unit can refine search results based on data from companies' environmental reports. The search unit can refine search results based on data related to companies' sustainability initiatives. The search unit can also refine search results based on data related to companies' environmental certifications. This allows for the identification of environmentally conscious companies by refining search results while considering data related to companies' environmental initiatives and sustainability. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input company environmental data into a generating AI and have the generating AI perform the refinement of search results.

[0085] The service provider can estimate the user's emotions and adjust the content of the information it provides based on the estimated emotions. For example, if the user is excited, the service provider can prioritize providing positive information. If the user is relaxed, the service provider can provide detailed information. If the user is tired, the service provider can also provide concise and to-the-point information. By adjusting the content of the information provided according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The data collection unit can collect information about a company's culture and values ​​during business card exchanges and add it to the business card information. For example, the data collection unit can collect a company's mission statement during business card exchanges and add it to the business card information. The data collection unit can collect information about a company's values ​​and culture and associate it with the business card information. The data collection unit can also collect information about a company's internal events and activities and add it to the business card information. This allows for more detailed information management by collecting information about a company's culture and values ​​and adding it to the business card information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input company culture and values ​​data into a generating AI and have the generating AI perform the data collection.

[0087] The search unit can refine search results by considering data related to corporate social responsibility (CSR) activities. For example, the search unit can refine search results based on corporate CSR report data. The search unit can refine search results based on corporate social contribution activity data. The search unit can also refine search results based on corporate ethical initiatives data. By refining search results while considering data related to corporate social responsibility (CSR) activities, it is possible to identify socially responsible companies. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input corporate CSR data into a generating AI and have the generating AI perform the refinement of search results.

[0088] The service provider can estimate the user's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible format. If the user is relaxed, the service provider can provide a format that includes detailed information. If the user is in a hurry, the service provider can also provide a format that gets straight to the point. By adjusting the format of the information provided according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit can collect information about a company's history and background when exchanging business cards and add it to the business card information. For example, the data collection unit can collect information about the company's founding year and founders when exchanging business cards and add it to the business card information. The data collection unit can collect information about important milestones and historical events of a company and associate it with the business card information. The data collection unit can also collect information about a company's growth process and evolution and add it to the business card information. This allows for more detailed information management by collecting information about a company's history and background and adding it to the business card information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input company history and background data into a generating AI and have the generating AI perform the data collection.

[0090] The search unit can refine search results by considering data related to employee satisfaction and ease of work at a company. For example, the search unit can refine search results based on employee satisfaction survey data. The search unit can refine search results based on data related to ease of work at a company. The search unit can also refine search results based on employee welfare data. By refining search results while considering data related to employee satisfaction and ease of work at a company, it is possible to identify companies that are easy for employees to work at. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input employee satisfaction data at a company into a generating AI and have the generating AI perform the refinement of search results.

[0091] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is excited, the service provider will prioritize providing the most relevant information. If the user is relaxed, the service provider can provide all information equally. If the user is tired, the service provider can provide only the most important information. In this way, by prioritizing information according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The search unit can refine search results by considering data related to a company's international expansion and global activities. For example, the search unit can refine search results based on a company's international expansion data. The search unit can refine search results based on data related to a company's global activities. The search unit can also refine search results based on data related to a company's overseas bases and international partnerships. This allows for the identification of companies with an international perspective by refining search results while considering data related to a company's international expansion and global activities. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input a company's international expansion data into a generating AI and have the generating AI perform the refinement of search results.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The collection unit collects information about companies from which business cards have been exchanged. The collection unit can automatically collect information such as company name, address, contact information, and industry by linking with a business card management tool. It can also collect information that was manually entered during the business card exchange. Step 2: The search unit searches for companies in the same industry based on the information collected by the data collection unit. The search unit can search for companies in the same industry from the database using industry codes and industry classifications. It can also improve the accuracy of search results using generation AI. Step 3: The delivery unit provides the search results generated by the search unit. The delivery unit displays the search results to the user in list or graph format, making it easy to review detailed information and compare companies.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] Each of the multiple elements described above, including the collection unit, search unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects company information in cooperation with a business card management tool via the control unit 46A of the smart device 14. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and searches for companies in the same industry based on the collected company information. The provision unit displays the search results to the user via the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, search unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects company information in cooperation with a business card management tool via the control unit 46A of the smart glasses 214. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and searches for companies in the same industry based on the collected company information. The provision unit provides the search results to the user via the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, search unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects company information in cooperation with a business card management tool via the control unit 46A of the headset terminal 314. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and searches for companies in the same industry based on the collected company information. The provision unit displays the search results to the user via the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, search unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects company information in cooperation with a business card management tool via the control unit 46A of the robot 414. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and searches for companies in the same industry based on the collected company information. The provision unit provides the search results to the user via, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] 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.

[0158] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] (Note 1) The collection department collects information on companies with which business cards have been exchanged, A search unit searches for companies in the same industry based on the information collected by the aforementioned collection unit, The system comprises a providing unit that provides the results retrieved by the search unit. A system characterized by the following features. (Note 2) The aforementioned search unit, Using generative AI to improve the accuracy of search results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Supporting the selection of collaboration partners based on search results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of business card information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect environmental information during business card exchange and associate it with the business card information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting business card information, the system automatically retrieves the latest news and press releases from companies and adds them to the business card information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the business card information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting business card information, we analyze the user's social media activity and collect relevant company information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting business card information, the system automatically retrieves company performance data and market valuation and adds them to the business card information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned search unit, When searching, filter search results considering factors such as company growth rate and market share. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, When searching, the search results are carefully reviewed, taking into account the company's technology patents and research and development activities. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, filter search results considering the geographical distribution of companies. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, the search results are carefully reviewed, taking into account the company's reputation within the industry and customer ratings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing information, we will include the companies' track record of collaborations and success stories. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, we will include the company's financial status and investment information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, we will also provide information including the company's future plans and vision. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, we will include a competitive analysis of the company and its market position. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information on companies with which business cards have been exchanged, A search unit searches for companies in the same industry based on the information collected by the aforementioned collection unit, The system comprises a providing unit that provides the results retrieved by the search unit. A system characterized by the following features.

2. The aforementioned search unit, Using generative AI to improve the accuracy of search results. The system according to feature 1.

3. The aforementioned supply unit is, Supporting the selection of collaboration partners based on search results. The system according to feature 1.

4. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of business card information collection based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Collect environmental information during business card exchange and associate it with the business card information. The system according to feature 1.

6. The aforementioned collection unit is When collecting business card information, the system automatically retrieves the latest news and press releases from companies and adds them to the business card information. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and prioritizes the business card information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting business card information, we analyze the user's social media activity and collect relevant company information. The system according to feature 1.

9. The aforementioned collection unit is When collecting business card information, the system automatically retrieves company performance data and market valuation and adds them to the business card information. The system according to feature 1.

10. The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system according to feature 1.

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