System

Abstract

To provide a system for profiling a person of a client in detail by effectively managing information on a business card.SOLUTION: A system according to an embodiment, comprises an acquisition part, a text conversion part, a storing part, a coupling part, and a profiling part. The acquisition part acquires an image of a business card. The text conversion part converts an image acquired by the acquisition part into text data. The storing part stores the text data converted by the text conversion part into to a database. The coupling part couples data stored by the storing part to information on an open source. The profiling part profiles a person of a client on the basis of information coupled by the coupling part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult to efficiently manage business card information and to perform detailed profiling of business partners.

[0005] The system according to the embodiment aims to efficiently manage business card information and perform detailed profiling of business partners. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a text conversion unit, a storage unit, a combination unit, and a profiling unit. The acquisition unit acquires an image of a business card. The text conversion unit converts the image acquired by the acquisition unit into text data. The storage unit stores the text data converted by the text conversion unit in a database. The combination unit combines the data stored by the storage unit with information on an open source. The profiling unit profiles a business partner based on the information combined by the combination unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently manage business card information and perform detailed profiling of business partners. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The business card centralized management system according to the embodiment of the present invention is a system that imports business cards of business partners using a messenger app and stores them in a database. This system can profile business partners and utilize the information in future transactions by combining the information on the imported business cards with information on open sources. For example, the business cards of business partners are imported using a messenger app. For example, a photo of the business card is taken using a messenger app and the image is sent to the system. This image is converted into text data using OCR (optical character recognition) technology. Next, the converted text data is stored in a database. Next, the stored business card information is combined with information on the open source. For example, public information on the Internet and profile information on SNS are collected and compared with the information on the business cards. At this time, AI is used to determine the degree of agreement of the information and combine highly relevant information. Finally, the business partner is profiled based on the combined information. For example, the business partner's work history, field of expertise, interests, etc. are analyzed to provide information useful for future transactions. This profiling is performed using AI. This system allows you to efficiently manage your business partner's business card information and gain a deeper understanding of your business partner. This will allow you to develop more effective communication and strategies for future transactions. This allows the business card centralized management system to efficiently manage your business partner's business card information and gain a deeper understanding of your business partner.

[0029] The business card unified management system according to the embodiment includes an acquisition unit, a text conversion unit, a storage unit, a combination unit, and a profiling unit. The acquisition unit acquires an image of a business card. The image of the business card includes, for example, an image format such as JPEG or PNG, and an acquisition means such as a scanner or a camera, but is not limited to such examples. The acquisition unit acquires the image of the business card using, for example, a messenger app. The acquisition unit can also acquire the image of the business card using a dedicated scanner. For example, the acquisition unit takes a photo of the business card using a messenger app and transmits the image to the system. The acquisition unit can also scan the business card using a dedicated scanner and save it as image data. The text conversion unit converts the image acquired by the acquisition unit into text data. The text data includes, for example, a UTF-8 encoded text file, a database field format, and the like, but is not limited to such examples. The text conversion unit converts the image of the business card into text data using, for example, OCR technology. The OCR technology is realized by, for example, technology such as Tesseract (registered trademark) or Google (registered trademark) Cloud Vision API. The storage unit stores the text data converted by the text conversion unit in a database. The database includes, but is not limited to, an SQL database, a NoSQL database, etc., for example. The storage unit stores the converted text data in, for example, an SQL database. The storage unit can also store the data in a NoSQL database. The combination unit combines the data stored by the storage unit with information on an open source. The information on an open source includes, but is not limited to, public data on a website, information obtained through an API, etc., for example. The combination unit uses, for example, public information on the Internet and profile information on an SNS. The profiling unit profiles the person of the business partner based on the information combined by the combination unit. Profiling includes, but is not limited to, a method of analyzing work history, a method of identifying a field of expertise, a method of analyzing interests, etc., for example. The profiling unit analyzes, for example, the work history, field of expertise, and interests of the person of the business partner, and provides information useful for future transactions.As a result, the business card unified management system according to the embodiment can efficiently manage business card information of business partners and deeply understand business partners. Some or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI, or may be performed without using AI. For example, the profiling unit can perform profiling using an AI model that receives the information combined by the combining unit as input and outputs a profile of a business partner.

[0030] The acquisition unit acquires an image of the business card. The image of the business card includes, for example, image formats such as JPEG and PNG, and acquisition means such as a scanner and a camera, but is not limited to such examples. The acquisition unit acquires the image of the business card using, for example, a messenger app. The acquisition unit can also acquire the image of the business card using a dedicated scanner. For example, the acquisition unit takes a photo of the business card using a messenger app and transmits the image to the system. The acquisition unit can also scan the business card with a dedicated scanner and save it as image data. The acquisition unit can quickly and accurately acquire the image of the business card using these means. Furthermore, the acquisition unit can perform pre-processing to improve the quality of the image. For example, the image resolution can be adjusted, noise can be removed, and contrast can be optimized to improve the accuracy of the OCR process by the subsequent text conversion unit. Furthermore, the acquisition unit has a function of scanning multiple business cards at once, and can efficiently process a large number of business cards. As a result, the acquisition unit can acquire the image of the business card with high quality and improve the performance of the entire system.

[0031] The text conversion unit converts the image acquired by the acquisition unit into text data. The text data includes, for example, a UTF-8 encoded text file, a database field format, and the like, but is not limited to such examples. The text conversion unit converts the image of the business card into text data, for example, using OCR technology. The OCR technology is realized by, for example, technologies such as Tesseract and Google Cloud Vision API. The text conversion unit uses these OCR technologies to extract character information from the image of the business card and output it as text data. Furthermore, the text conversion unit can perform post-processing to improve the accuracy of the extracted text data. For example, the quality of the data can be improved by correcting misrecognized characters and adjusting the format. In addition, the text conversion unit supports multiple languages ​​and can process business cards in different languages. As a result, the text conversion unit can convert the acquired image of the business card into text data with high accuracy and improve the performance of the entire system.

[0032] The storage unit stores the text data converted by the text conversion unit in a database. The database includes, but is not limited to, an SQL database, a NoSQL database, and the like. For example, the storage unit stores the converted text data in an SQL database. The storage unit can also store the converted text data in a NoSQL database. The storage unit can use these databases to efficiently manage the text data and quickly access it. Furthermore, the storage unit has a function for ensuring the integrity and security of the data. For example, the data is backed up periodically to prevent data loss. Furthermore, the storage unit performs access control to prevent unauthorized access to the data. As a result, the storage unit can safely and efficiently store the text data and improve the performance of the entire system.

[0033] The combining unit combines the data stored by the storage unit with information on the open source. Information on the open source includes, but is not limited to, for example, public data on a website and information acquired through an API. The combining unit uses, for example, public information on the Internet and profile information on SNS. The combining unit can use these pieces of information to associate with the stored text data and provide more detailed information. Furthermore, the combining unit has an algorithm for evaluating the degree of agreement of the data and selecting highly reliable information. For example, highly relevant data is combined based on information such as a name, company name, and job title. In addition, the combining unit can acquire the latest information taking into account the frequency of data updates. As a result, the combining unit can effectively combine the stored data with information on the open source and improve the performance of the entire system.

[0034] The profiling unit profiles the business partner based on the information combined by the combining unit. Profiling includes, but is not limited to, methods for analyzing work history, identifying fields of expertise, and analyzing interests. The profiling unit analyzes, for example, the work history, fields of expertise, and interests of the business partner, and provides information useful for future transactions. Some or all of the above-mentioned processing in the profiling unit may be performed using, for example, AI, or may be performed without using AI. For example, the profiling unit can perform profiling using an AI model that inputs the information combined by the combining unit and outputs a profile of the business partner. Specifically, the AI ​​model uses natural language processing technology to extract and analyze important information from text data. For example, the analysis of work history identifies the career path of the business partner based on information such as past places of employment, positions, and achievements. The identification of a field of expertise evaluates the specialized knowledge and skills of the business partner based on information such as past projects, research content, and published papers. The interest analysis involves identifying the hobbies and interests of business partners based on information such as the content of their social media posts and the accounts they follow. This allows the profiling department to gain a deeper understanding of the business partner and provide information that will be useful for future transactions. Furthermore, the profiling department can continually update the profiling results and perform analysis based on the latest information. This allows the profiling department to always provide highly accurate profiling based on the latest information, strengthening relationships with business partners.

[0035] The acquisition unit can acquire an image of the business card using a messenger app or a dedicated scanner. The acquisition unit acquires an image of the business card using, for example, a messenger app. For example, the acquisition unit takes a photo of the business card using a messenger app and transmits the image to the system. The acquisition unit can also acquire an image of the business card using a dedicated scanner. For example, the acquisition unit scans the business card with a dedicated scanner and saves it as image data. This makes it possible to efficiently acquire an image of the business card by using a messenger app or a dedicated scanner. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input image data of the business card acquired using a messenger app to a generation AI and cause the generation AI to convert image data into text data.

[0036] The combining unit can use public information on the Internet or profile information on SNS. The combining unit uses, for example, public information on the Internet. For example, the combining unit collects public information such as official company websites and news articles, and compares it with the information on the business card. The combining unit can also use profile information on SNS. For example, the combining unit collects LinkedIn work history information, Twitter profile information, and the like, and compares it with the information on the business card. In this way, by using public information on the Internet and profile information on SNS, it is possible to combine information that is highly relevant to the information on the business card. Part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can combine information using an AI model that inputs public information on the Internet and profile information on SNS and determines the relevance to the information on the business card.

[0037] The profiling unit can analyze the work history, specialty, and interests of the business partner and provide information useful for future transactions. The profiling unit, for example, analyzes the work history of the business partner. For example, the profiling unit analyzes the work history based on resume information and LinkedIn work history data. The profiling unit can also analyze the specialty of the business partner. For example, the profiling unit identifies the specialty based on academic papers and job descriptions. The profiling unit can also analyze the interests of the business partner. For example, the profiling unit analyzes interests based on social media posts and survey results. This makes it possible to provide information useful for future transactions by analyzing the work history, specialty, and interests of the business partner. Some or all of the above-mentioned processing in the profiling unit may be performed using, for example, AI, or may be performed without using AI. For example, the profiling unit can input the work history data and specialty information of the business partner to the generation AI and cause the generation AI to perform profiling.

[0038] The text conversion unit can convert the image of the business card into text data using OCR technology. The text conversion unit converts the image of the business card into text data using, for example, OCR technology. The OCR technology includes, for example, technologies such as Tesseract and Google Cloud Vision API, but is not limited to such examples. For example, the text conversion unit converts the image of the business card into text data using Tesseract. The text conversion unit can also convert the image of the business card into text data using Google Cloud Vision API. As a result, the image of the business card can be accurately converted into text data by using OCR technology. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the image data of the business card into the generation AI and cause the generation AI to execute the conversion into text data.

[0039] The storage unit can store the converted text data in a database. The storage unit, for example, stores the converted text data in an SQL database. Examples of SQL databases include, but are not limited to, MySQL and PostgreSQL. The storage unit can also store the converted text data in a NoSQL database. Examples of NoSQL databases include, but are not limited to, MongoDB and Cassandra. For example, the storage unit stores the converted text data in a MySQL database. The storage unit can also store the converted text data in a MongoDB database. This allows the business card information to be efficiently managed by storing the converted text data in a database. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the converted text data to a generation AI and cause the generation AI to execute storage in the database.

[0040] The acquisition unit can analyze the user's past business card acquisition history and select an appropriate acquisition method when acquiring an image of a business card. For example, the acquisition unit analyzes the user's past business card acquisition history and selects the optimal acquisition method when acquiring an image of a business card. For example, the acquisition unit preferentially suggests an acquisition method (camera, scanner, etc.) that the user has used in the past. The acquisition unit can also select a method with the highest success rate from the user's past acquisition history. The acquisition unit can also suggest the optimal acquisition timing based on the user's past acquisition history. In this way, the optimal acquisition method can be selected by analyzing the user's past business card acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past business card acquisition history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0041] The acquisition unit can perform filtering based on the user's current project and field of interest when acquiring an image of a business card. For example, the acquisition unit performs filtering based on the user's current project and field of interest when acquiring an image of a business card. For example, the acquisition unit preferentially acquires business cards related to a project currently in progress by the user. The acquisition unit can also filter highly relevant business cards based on the user's field of interest. The acquisition unit can also adjust the priority of business cards to be acquired according to the progress of the user's project. This makes it possible to preferentially acquire highly relevant business cards by filtering based on the user's current project and field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's current project and field of interest to the generation AI and cause the generation AI to execute filtering.

[0042] The acquisition unit can preferentially acquire highly relevant business cards in consideration of the geographical location information of the user when acquiring an image of the business card. For example, the acquisition unit preferentially acquires highly relevant business cards in consideration of the geographical location information of the user when acquiring an image of the business card. For example, the acquisition unit preferentially acquires business cards related to the location where the user is currently located. The acquisition unit can also filter highly relevant business cards based on the geographical location information of the user. In addition, when the user is in a specific area, the acquisition unit can also preferentially acquire business cards related to the area. In this way, by considering the geographical location information of the user, it is possible to preferentially acquire highly relevant business cards. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select highly relevant business cards.

[0043] The acquisition unit can analyze the user's social media activity when acquiring an image of a business card, and acquire a related business card. For example, the acquisition unit can analyze the user's social media activity when acquiring an image of a business card, and acquire a related business card. For example, the acquisition unit filters highly relevant business cards from the user's social media activity. The acquisition unit can also preferentially acquire business cards of people with whom the user interacts on social media. The acquisition unit can also adjust the priority of business cards to be acquired based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially acquire highly relevant business cards. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to select a related business card.

[0044] The text conversion unit can adjust the level of detail of the conversion based on the importance of the business card during text conversion. For example, the text conversion unit adjusts the level of detail of the conversion based on the importance of the business card during text conversion. For example, the text conversion unit performs detailed text conversion on business cards with high importance. In addition, the text conversion unit can perform concise text conversion on business cards with low importance. In addition, the text conversion unit can automatically adjust the level of detail of the conversion according to the importance of the business card. As a result, important business card information can be converted in detail by adjusting the level of detail of the conversion based on the importance of the business card. Part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the importance data of the business card to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0045] The text conversion unit can apply different conversion algorithms depending on the category of the business card during text conversion. For example, the text conversion unit applies different conversion algorithms depending on the category of the business card during text conversion. For example, the text conversion unit applies a business conversion algorithm to a business business card. Also, the text conversion unit can apply a personal conversion algorithm to a personal business card. Also, the text conversion unit can automatically select an optimal conversion algorithm depending on the category of the business card. As a result, more accurate text conversion can be performed by applying an optimal conversion algorithm depending on the category of the business card. Part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input business card category data to the generation AI and cause the generation AI to select the conversion algorithm.

[0046] The text conversion unit can determine the priority of conversion based on the submission time of the business card during text conversion. The text conversion unit, for example, determines the priority of conversion based on the submission time of the business card during text conversion. For example, the text conversion unit preferentially converts the most recently submitted business card into text. In addition, the text conversion unit can convert the business card submitted earlier into text later. In addition, the text conversion unit can automatically adjust the priority of conversion according to the submission time of the business card. As a result, by determining the priority of conversion based on the submission time of the business card, more important business cards can be converted preferentially. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the submission time data of the business card to the generation AI and cause the generation AI to execute the determination of the priority of conversion.

[0047] The text conversion unit can adjust the order of conversion based on the relevance of the business cards during text conversion. The text conversion unit adjusts the order of conversion based on the relevance of the business cards during text conversion, for example. For example, the text conversion unit preferentially converts highly relevant business cards into text. In addition, the text conversion unit can convert less relevant business cards into text later. In addition, the text conversion unit can automatically adjust the order of conversion according to the relevance of the business cards. As a result, by adjusting the order of conversion based on the relevance of the business cards, more relevant business cards can be preferentially converted. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the relevance data of the business cards to the generation AI and cause the generation AI to adjust the order of conversion.

[0048] The storage unit can optimize the storage algorithm by referring to the past stored data at the time of storage. The storage unit optimizes the storage algorithm by referring to the past stored data, for example, at the time of storage. For example, the storage unit selects an optimal storage algorithm based on the past stored data. The storage unit can also apply an algorithm that removes duplicate data from the past stored data. The storage unit can also analyze the past stored data and apply an algorithm that improves storage efficiency. As a result, the storage algorithm can be optimized and the storage efficiency can be improved by referring to the past stored data. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI or may be performed without using AI. For example, the storage unit can input the past stored data to the generation AI and cause the generation AI to execute optimization of the storage algorithm.

[0049] The storage unit can weight the stored data based on the submission time of the business card when storing. The storage unit, for example, weights the stored data based on the submission time of the business card when storing. For example, the storage unit prioritizes storing data of the most recently submitted business card. Also, the storage unit can postpone storing data of business cards submitted earlier. Also, the storage unit can automatically adjust the weighting of the stored data according to the submission time of the business card. In this way, by weighting the stored data based on the submission time of the business card, more important data can be prioritized for storage. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the submission time data of the business card to the generation AI and cause the generation AI to execute the weighting of the stored data.

[0050] The combining unit can improve the accuracy of the combining by considering the interrelationships of the business cards when combining. For example, the combining unit improves the accuracy of the combining by considering the interrelationships of the business cards when combining. For example, the combining unit preferentially combines highly related information based on the interrelationships of the business cards. The combining unit can also apply an algorithm that analyzes the interrelationships of the business cards and improves the accuracy of the combining. The combining unit can also automatically adjust the priority of the combining according to the interrelationships of the business cards. In this way, the accuracy of the combining can be improved by considering the interrelationships of the business cards. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input the interrelationship data of the business cards to the generating AI and cause the generating AI to execute the improvement of the accuracy of the combining.

[0051] The combining unit can combine the data while considering the attribute information of the person who submitted the business card. For example, the combining unit combines the data while considering the attribute information of the person who submitted the business card. For example, the combining unit combines highly relevant information preferentially based on the work history of the person who submitted the business card. The combining unit can also improve the accuracy of the combining by considering the specialty field of the person who submitted the business card. The combining unit can also automatically adjust the priority of the combining according to the attribute information of the person who submitted the business card. In this way, more relevant information can be combined by considering the attribute information of the person who submitted the business card. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input attribute information data of the person who submitted the business card to the generating AI, and cause the generating AI to improve the accuracy of the combining.

[0052] The combining unit can combine the data while taking into consideration the geographical distribution of the business cards. For example, the combining unit combines the data while taking into consideration the geographical distribution of the business cards. For example, the combining unit preferentially combines highly relevant information based on the geographical distribution of the business cards. The combining unit can also apply an algorithm that analyzes the geographical distribution of the business cards and improves the accuracy of the combining. The combining unit can also automatically adjust the priority of the combining according to the geographical distribution of the business cards. In this way, more relevant information can be combined by taking into consideration the geographical distribution of the business cards. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input the geographical distribution data of the business cards to the generating AI, and cause the generating AI to improve the accuracy of the combining.

[0053] The combining unit can improve the accuracy of the combination by referring to the related literature of the business card at the time of combination. The combining unit can improve the accuracy of the combination by referring to the related literature of the business card at the time of combination, for example. For example, the combining unit preferentially combines highly relevant information based on the related literature of the business card. The combining unit can also apply an algorithm that analyzes the related literature of the business card and improves the accuracy of the combination. The combining unit can also automatically adjust the priority of the combination according to the related literature of the business card. Thereby, the accuracy of the combination can be improved by referring to the related literature of the business card. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input the related literature data of the business card to the generating AI and cause the generating AI to execute the improvement of the accuracy of the combination.

[0054] The profiling unit can select the optimal profiling method by analyzing the user's past transaction history during profiling. For example, the profiling unit selects the optimal profiling method by analyzing the user's past transaction history during profiling. For example, the profiling unit selects the optimal profiling method based on the user's past transaction history. In addition, the profiling unit can preferentially profile highly relevant information from the user's past transaction history. In addition, the profiling unit can also analyze the user's past transaction history to improve the accuracy of profiling. As a result, the optimal profiling method can be selected by analyzing the user's past transaction history. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI or may be performed without using AI. For example, the profiling unit can input the user's past transaction history data to the generation AI and cause the generation AI to select the profiling method.

[0055] The profiling unit can customize the profiling means based on the user's current trading situation during profiling. The profiling unit customizes the profiling means based on the user's current trading situation during profiling, for example. For example, the profiling unit selects an optimal profiling means based on the user's current trading situation. The profiling unit can also preferentially profile highly relevant information from the user's current trading situation. The profiling unit can also analyze the user's current trading situation and improve the accuracy of profiling. As a result, more relevant information can be provided by customizing the profiling means based on the user's current trading situation. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI, or may be performed without using AI. For example, the profiling unit can input the user's current trading situation data to the generation AI and cause the generation AI to execute customization of the profiling means.

[0056] The profiling unit can select an optimal profiling method in consideration of the geographical location information of the user during profiling. The profiling unit, for example, selects an optimal profiling method in consideration of the geographical location information of the user during profiling. For example, the profiling unit selects an optimal profiling method based on the geographical location information of the user. The profiling unit can also preferentially profile highly relevant information from the geographical location information of the user. The profiling unit can also analyze the geographical location information of the user and improve the accuracy of profiling. As a result, more relevant information can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI or may be performed without using AI. For example, the profiling unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select a profiling method.

[0057] The profiling unit can analyze the user's social media activity and propose profiling means at the time of profiling. For example, the profiling unit can analyze the user's social media activity and propose profiling means at the time of profiling. For example, the profiling unit proposes optimal profiling means based on the user's social media activity. In addition, the profiling unit can preferentially profile highly relevant information from the user's social media activity. In addition, the profiling unit can also analyze the user's social media activity and improve the accuracy of profiling. As a result, more relevant information can be provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI or may be performed without using AI. For example, the profiling unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the proposal of profiling means.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When acquiring an image of a business card, the acquisition unit can analyze the user's past business card acquisition history and suggest the optimal acquisition method. For example, the acquisition unit preferentially suggests an acquisition method (camera, scanner, etc.) that the user has used in the past. The acquisition unit can also select the method with the highest success rate from the user's past acquisition history. Furthermore, the acquisition unit can also suggest the optimal acquisition timing based on the user's past acquisition history. In this way, the optimal acquisition method can be selected by analyzing the user's past business card acquisition history.

[0060] The acquisition unit can perform filtering based on the user's current project or field of interest when acquiring an image of the business card. For example, the acquisition unit preferentially acquires business cards related to a project currently in progress by the user. The acquisition unit can also filter highly relevant business cards based on the user's field of interest. Furthermore, the acquisition unit can adjust the priority of business cards to be acquired according to the progress of the user's project. This makes it possible to preferentially acquire highly relevant business cards by filtering based on the user's current project or field of interest.

[0061] When acquiring an image of a business card, the acquisition unit can preferentially acquire highly relevant business cards by taking into consideration the geographical location information of the user. For example, the acquisition unit preferentially acquires business cards related to the location where the user is currently located. The acquisition unit can also filter highly relevant business cards based on the geographical location information of the user. Furthermore, when the user is in a specific area, the acquisition unit can also preferentially acquire business cards related to that area. In this way, by taking into consideration the geographical location information of the user, highly relevant business cards can be preferentially acquired.

[0062] The acquisition unit can analyze the user's social media activity and acquire relevant business cards when acquiring an image of the business card. For example, the acquisition unit filters highly relevant business cards from the user's social media activity. The acquisition unit can also preferentially acquire business cards of people with whom the user interacts on social media. Furthermore, the acquisition unit can adjust the priority of business cards to be acquired based on the user's social media activity. In this way, highly relevant business cards can be preferentially acquired by analyzing the user's social media activity.

[0063] During profiling, the profiling unit can select the optimal profiling method by analyzing the user's past transaction history. For example, the profiling unit selects the optimal profiling method based on the user's past transaction history. Also, the profiling unit can preferentially profile highly relevant information from the user's past transaction history. Furthermore, the profiling unit can also analyze the user's past transaction history to improve the accuracy of profiling. In this way, the optimal profiling method can be selected by analyzing the user's past transaction history.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The acquisition unit acquires an image of a business card. The image of the business card may be in an image format such as JPEG or PNG, or may be acquired by a scanner or camera. The acquisition unit acquires the image of the business card using, for example, a messenger app. The image of the business card may also be acquired using a dedicated scanner. For example, a photo of the business card may be taken using a messenger app, and the image may be sent to the system. The business card may also be scanned using a dedicated scanner and saved as image data. Step 2: The text conversion unit converts the image acquired by the acquisition unit into text data. The text data includes, for example, a UTF-8 encoded text file, a database field format, and the like. The text conversion unit converts the image of the business card into text data using, for example, OCR technology. The OCR technology is realized by, for example, technology such as Tesseract or Google Cloud Vision API. Step 3: The storage unit stores the text data converted by the text conversion unit in a database. The database includes, for example, an SQL database, a NoSQL database, etc. The storage unit stores, for example, the converted text data in a SQL database. It can also store the converted text data in a NoSQL database. Step 4: The combining unit combines the data stored by the storage unit with information on open sources. Information on open sources includes, for example, public data on websites, information obtained through APIs, etc. The combining unit uses, for example, public information on the Internet and profile information on SNS. Step 5: The profiling unit profiles the business partner based on the information combined by the combining unit. Profiling includes, for example, a method for analyzing work history, a method for identifying a field of expertise, and a method for analyzing interests. For example, the profiling unit analyzes the work history, field of expertise, and interests of the business partner to provide information useful for future transactions. Some or all of the processing in the profiling unit may be performed using, for example, AI, or may be performed without using AI. For example, the profiling unit may perform profiling using an AI model that receives the information combined by the combining unit as input and outputs a profile of the business partner.

[0066] (Example 2) The business card centralized management system according to the embodiment of the present invention is a system that imports business cards of business partners using a messenger app and stores them in a database. This system can profile business partners and utilize the information in future transactions by combining the information on the imported business cards with information on open sources. For example, the business cards of business partners are imported using a messenger app. For example, a photo of the business card is taken using a messenger app and the image is sent to the system. This image is converted into text data using OCR (optical character recognition) technology. Next, the converted text data is stored in a database. Next, the stored business card information is combined with information on the open source. For example, public information on the Internet and profile information on SNS are collected and compared with the information on the business cards. At this time, AI is used to determine the degree of agreement of the information and combine highly relevant information. Finally, the business partner is profiled based on the combined information. For example, the business partner's work history, field of expertise, interests, etc. are analyzed to provide information useful for future transactions. This profiling is performed using AI. This system allows you to efficiently manage your business partner's business card information and gain a deeper understanding of your business partner. This will allow you to develop more effective communication and strategies for future transactions. This allows the business card centralized management system to efficiently manage your business partner's business card information and gain a deeper understanding of your business partner.

[0067] The business card unified management system according to the embodiment includes an acquisition unit, a text conversion unit, a storage unit, a combination unit, and a profiling unit. The acquisition unit acquires an image of a business card. The image of the business card includes, for example, an image format such as JPEG or PNG, and an acquisition means such as a scanner or a camera, but is not limited to such examples. The acquisition unit acquires the image of the business card using, for example, a messenger app. The acquisition unit can also acquire the image of the business card using a dedicated scanner. For example, the acquisition unit takes a photo of the business card using a messenger app and transmits the image to the system. The acquisition unit can also scan the business card using a dedicated scanner and save it as image data. The text conversion unit converts the image acquired by the acquisition unit into text data. The text data includes, for example, a UTF-8 encoded text file, a database field format, and the like, but is not limited to such examples. The text conversion unit converts the image of the business card into text data using, for example, OCR technology. The OCR technology is realized by, for example, technology such as Tesseract or Google Cloud Vision API. The storage unit stores the text data converted by the text conversion unit in a database. The database includes, but is not limited to, an SQL database, a NoSQL database, etc., for example. The storage unit stores the converted text data in, for example, an SQL database. The storage unit can also store the data in a NoSQL database. The combination unit combines the data stored by the storage unit with information on an open source. The information on an open source includes, but is not limited to, public data on a website, information obtained through an API, etc., for example. The combination unit uses, for example, public information on the Internet and profile information on an SNS. The profiling unit profiles the person of the business partner based on the information combined by the combination unit. Profiling includes, but is not limited to, a method of analyzing work history, a method of identifying a field of expertise, a method of analyzing interests, etc., for example. The profiling unit analyzes, for example, the work history, field of expertise, and interests of the person of the business partner, and provides information useful for future transactions.As a result, the business card unified management system according to the embodiment can efficiently manage business card information of business partners and deeply understand business partners. Some or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI, or may be performed without using AI. For example, the profiling unit can perform profiling using an AI model that receives the information combined by the combining unit as input and outputs a profile of a business partner.

[0068] The acquisition unit acquires an image of the business card. The image of the business card includes, for example, image formats such as JPEG and PNG, and acquisition means such as a scanner and a camera, but is not limited to such examples. The acquisition unit acquires the image of the business card using, for example, a messenger app. The acquisition unit can also acquire the image of the business card using a dedicated scanner. For example, the acquisition unit takes a photo of the business card using a messenger app and transmits the image to the system. The acquisition unit can also scan the business card with a dedicated scanner and save it as image data. The acquisition unit can quickly and accurately acquire the image of the business card using these means. Furthermore, the acquisition unit can perform pre-processing to improve the quality of the image. For example, the image resolution can be adjusted, noise can be removed, and contrast can be optimized to improve the accuracy of the OCR process by the subsequent text conversion unit. Furthermore, the acquisition unit has a function of scanning multiple business cards at once, and can efficiently process a large number of business cards. As a result, the acquisition unit can acquire the image of the business card with high quality and improve the performance of the entire system.

[0069] The text conversion unit converts the image acquired by the acquisition unit into text data. The text data includes, for example, a UTF-8 encoded text file, a database field format, and the like, but is not limited to such examples. The text conversion unit converts the image of the business card into text data, for example, using OCR technology. The OCR technology is realized by, for example, technologies such as Tesseract and Google Cloud Vision API. The text conversion unit uses these OCR technologies to extract character information from the image of the business card and output it as text data. Furthermore, the text conversion unit can perform post-processing to improve the accuracy of the extracted text data. For example, the quality of the data can be improved by correcting misrecognized characters and adjusting the format. In addition, the text conversion unit supports multiple languages ​​and can process business cards in different languages. As a result, the text conversion unit can convert the acquired image of the business card into text data with high accuracy and improve the performance of the entire system.

[0070] The storage unit stores the text data converted by the text conversion unit in a database. The database includes, but is not limited to, an SQL database, a NoSQL database, and the like. For example, the storage unit stores the converted text data in an SQL database. The storage unit can also store the converted text data in a NoSQL database. The storage unit can use these databases to efficiently manage the text data and quickly access it. Furthermore, the storage unit has a function for ensuring the integrity and security of the data. For example, the data is backed up periodically to prevent data loss. Furthermore, the storage unit performs access control to prevent unauthorized access to the data. As a result, the storage unit can safely and efficiently store the text data and improve the performance of the entire system.

[0071] The combining unit combines the data stored by the storage unit with information on the open source. Information on the open source includes, but is not limited to, for example, public data on a website and information acquired through an API. The combining unit uses, for example, public information on the Internet and profile information on SNS. The combining unit can use these pieces of information to associate with the stored text data and provide more detailed information. Furthermore, the combining unit has an algorithm for evaluating the degree of agreement of the data and selecting highly reliable information. For example, highly relevant data is combined based on information such as a name, company name, and job title. In addition, the combining unit can acquire the latest information taking into account the frequency of data updates. As a result, the combining unit can effectively combine the stored data with information on the open source and improve the performance of the entire system.

[0072] The profiling unit profiles the business partner based on the information combined by the combining unit. Profiling includes, but is not limited to, methods for analyzing work history, identifying fields of expertise, and analyzing interests. The profiling unit analyzes, for example, the work history, fields of expertise, and interests of the business partner, and provides information useful for future transactions. Some or all of the above-mentioned processing in the profiling unit may be performed using, for example, AI, or may be performed without using AI. For example, the profiling unit can perform profiling using an AI model that inputs the information combined by the combining unit and outputs a profile of the business partner. Specifically, the AI ​​model uses natural language processing technology to extract and analyze important information from text data. For example, the analysis of work history identifies the career path of the business partner based on information such as past places of employment, positions, and achievements. The identification of a field of expertise evaluates the specialized knowledge and skills of the business partner based on information such as past projects, research content, and published papers. The interest analysis involves identifying the hobbies and interests of business partners based on information such as the content of their social media posts and the accounts they follow. This allows the profiling department to gain a deeper understanding of the business partner and provide information that will be useful for future transactions. Furthermore, the profiling department can continually update the profiling results and perform analysis based on the latest information. This allows the profiling department to always provide highly accurate profiling based on the latest information, strengthening relationships with business partners.

[0073] The acquisition unit can acquire an image of the business card using a messenger app or a dedicated scanner. The acquisition unit acquires an image of the business card using, for example, a messenger app. For example, the acquisition unit takes a photo of the business card using a messenger app and transmits the image to the system. The acquisition unit can also acquire an image of the business card using a dedicated scanner. For example, the acquisition unit scans the business card with a dedicated scanner and saves it as image data. This makes it possible to efficiently acquire an image of the business card by using a messenger app or a dedicated scanner. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input image data of the business card acquired using a messenger app to a generation AI and cause the generation AI to convert image data into text data.

[0074] The combining unit can use public information on the Internet or profile information on SNS. The combining unit uses, for example, public information on the Internet. For example, the combining unit collects public information such as official company websites and news articles, and compares it with the information on the business card. The combining unit can also use profile information on SNS. For example, the combining unit collects LinkedIn work history information, Twitter profile information, and the like, and compares it with the information on the business card. In this way, by using public information on the Internet and profile information on SNS, it is possible to combine information that is highly relevant to the information on the business card. Part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can combine information using an AI model that inputs public information on the Internet and profile information on SNS and determines the relevance to the information on the business card.

[0075] The profiling unit can analyze the work history, specialty, and interests of the business partner and provide information useful for future transactions. The profiling unit, for example, analyzes the work history of the business partner. For example, the profiling unit analyzes the work history based on resume information and LinkedIn work history data. The profiling unit can also analyze the specialty of the business partner. For example, the profiling unit identifies the specialty based on academic papers and job descriptions. The profiling unit can also analyze the interests of the business partner. For example, the profiling unit analyzes interests based on social media posts and survey results. This makes it possible to provide information useful for future transactions by analyzing the work history, specialty, and interests of the business partner. Some or all of the above-mentioned processing in the profiling unit may be performed using, for example, AI, or may be performed without using AI. For example, the profiling unit can input the work history data and specialty information of the business partner to the generation AI and cause the generation AI to perform profiling.

[0076] The text conversion unit can convert the image of the business card into text data using OCR technology. The text conversion unit converts the image of the business card into text data using, for example, OCR technology. The OCR technology includes, for example, technologies such as Tesseract and Google Cloud Vision API, but is not limited to such examples. For example, the text conversion unit converts the image of the business card into text data using Tesseract. The text conversion unit can also convert the image of the business card into text data using Google Cloud Vision API. As a result, the image of the business card can be accurately converted into text data by using OCR technology. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the image data of the business card into the generation AI and cause the generation AI to execute the conversion into text data.

[0077] The storage unit can store the converted text data in a database. The storage unit, for example, stores the converted text data in an SQL database. Examples of SQL databases include, but are not limited to, MySQL and PostgreSQL. The storage unit can also store the converted text data in a NoSQL database. Examples of NoSQL databases include, but are not limited to, MongoDB and Cassandra. For example, the storage unit stores the converted text data in a MySQL database. The storage unit can also store the converted text data in a MongoDB database. This allows the business card information to be efficiently managed by storing the converted text data in a database. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the converted text data to a generation AI and cause the generation AI to execute storage in the database.

[0078] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the image of the business card based on the estimated emotion of the user. The acquisition unit, for example, estimates the user's emotion and adjusts the timing of acquiring the image of the business card based on the estimated emotion of the user. For example, if the user is relaxed, the acquisition unit immediately acquires the image of the business card. Also, if the user is busy, the acquisition unit can set a reminder to acquire the image of the business card later. Also, if the user is stressed, the acquisition unit can display a guide to simplify the image acquisition of the business card. In this way, the image of the business card can be acquired at a more appropriate timing by adjusting the image acquisition timing of the business card according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the timing of acquiring an image of the business card.

[0079] The acquisition unit can analyze the user's past business card acquisition history and select an appropriate acquisition method when acquiring an image of a business card. For example, the acquisition unit analyzes the user's past business card acquisition history and selects the optimal acquisition method when acquiring an image of a business card. For example, the acquisition unit preferentially suggests an acquisition method (camera, scanner, etc.) that the user has used in the past. The acquisition unit can also select a method with the highest success rate from the user's past acquisition history. The acquisition unit can also suggest the optimal acquisition timing based on the user's past acquisition history. In this way, the optimal acquisition method can be selected by analyzing the user's past business card acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past business card acquisition history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0080] The acquisition unit can perform filtering based on the user's current project and field of interest when acquiring an image of a business card. For example, the acquisition unit performs filtering based on the user's current project and field of interest when acquiring an image of a business card. For example, the acquisition unit preferentially acquires business cards related to a project currently in progress by the user. The acquisition unit can also filter highly relevant business cards based on the user's field of interest. The acquisition unit can also adjust the priority of business cards to be acquired according to the progress of the user's project. This makes it possible to preferentially acquire highly relevant business cards by filtering based on the user's current project and field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's current project and field of interest to the generation AI and cause the generation AI to execute filtering.

[0081] The acquisition unit can estimate the user's emotion and determine the priority order of business cards to be acquired based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and determines the priority order of business cards to be acquired based on the estimated user's emotion. For example, when the user is relaxed, the acquisition unit preferentially acquires business cards with high importance. In addition, when the user is busy, the acquisition unit can create a list of business cards to be acquired later. In addition, when the user is stressed, the acquisition unit can also preferentially acquire business cards that can be easily acquired. In this way, by determining the priority order of business cards to be acquired according to the user's emotion, more important business cards can be preferentially acquired. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input a user's emotion data into the generation AI and have the generation AI determine the priorities of the business cards.

[0082] The acquisition unit can preferentially acquire highly relevant business cards in consideration of the geographical location information of the user when acquiring an image of the business card. For example, the acquisition unit preferentially acquires highly relevant business cards in consideration of the geographical location information of the user when acquiring an image of the business card. For example, the acquisition unit preferentially acquires business cards related to the location where the user is currently located. The acquisition unit can also filter highly relevant business cards based on the geographical location information of the user. In addition, when the user is in a specific area, the acquisition unit can also preferentially acquire business cards related to the area. In this way, by considering the geographical location information of the user, it is possible to preferentially acquire highly relevant business cards. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select highly relevant business cards.

[0083] The acquisition unit can analyze the user's social media activity when acquiring an image of a business card, and acquire a related business card. For example, the acquisition unit can analyze the user's social media activity when acquiring an image of a business card, and acquire a related business card. For example, the acquisition unit filters highly relevant business cards from the user's social media activity. The acquisition unit can also preferentially acquire business cards of people with whom the user interacts on social media. The acquisition unit can also adjust the priority of business cards to be acquired based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially acquire highly relevant business cards. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to select a related business card.

[0084] The text conversion unit can estimate the user's emotion and adjust the expression method of the text conversion based on the estimated user's emotion. The text conversion unit, for example, estimates the user's emotion and adjusts the expression method of the text conversion based on the estimated user's emotion. For example, the text conversion unit performs detailed text conversion when the user is relaxed. Also, the text conversion unit can perform concise text conversion when the user is in a hurry. Also, the text conversion unit can perform text conversion in an easy-to-read format when the user is stressed. This allows more appropriate text conversion to be performed by adjusting the expression method of the text conversion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the text conversion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the text conversion.

[0085] The text conversion unit can adjust the level of detail of the conversion based on the importance of the business card during text conversion. For example, the text conversion unit adjusts the level of detail of the conversion based on the importance of the business card during text conversion. For example, the text conversion unit performs detailed text conversion on business cards with high importance. In addition, the text conversion unit can perform concise text conversion on business cards with low importance. In addition, the text conversion unit can automatically adjust the level of detail of the conversion according to the importance of the business card. As a result, important business card information can be converted in detail by adjusting the level of detail of the conversion based on the importance of the business card. Part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the importance data of the business card to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0086] The text conversion unit can apply different conversion algorithms depending on the category of the business card during text conversion. For example, the text conversion unit applies different conversion algorithms depending on the category of the business card during text conversion. For example, the text conversion unit applies a business conversion algorithm to a business business card. Also, the text conversion unit can apply a personal conversion algorithm to a personal business card. Also, the text conversion unit can automatically select an optimal conversion algorithm depending on the category of the business card. As a result, more accurate text conversion can be performed by applying an optimal conversion algorithm depending on the category of the business card. Part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input business card category data to the generation AI and cause the generation AI to select the conversion algorithm.

[0087] The text conversion unit can estimate the user's emotion and adjust the length of the text conversion based on the estimated user's emotion. The text conversion unit, for example, estimates the user's emotion and adjusts the length of the text conversion based on the estimated user's emotion. For example, the text conversion unit performs detailed text conversion when the user is relaxed. Also, the text conversion unit can perform concise text conversion when the user is in a hurry. Also, the text conversion unit can perform text conversion in an easy-to-read format when the user is stressed. This allows more appropriate text conversion to be performed by adjusting the length of the text conversion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the text conversion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the text conversion.

[0088] The text conversion unit can determine the priority of conversion based on the submission time of the business card during text conversion. The text conversion unit, for example, determines the priority of conversion based on the submission time of the business card during text conversion. For example, the text conversion unit preferentially converts the most recently submitted business card into text. In addition, the text conversion unit can convert the business card submitted earlier into text later. In addition, the text conversion unit can automatically adjust the priority of conversion according to the submission time of the business card. As a result, by determining the priority of conversion based on the submission time of the business card, more important business cards can be converted preferentially. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the submission time data of the business card to the generation AI and cause the generation AI to execute the determination of the priority of conversion.

[0089] The text conversion unit can adjust the order of conversion based on the relevance of the business cards during text conversion. The text conversion unit adjusts the order of conversion based on the relevance of the business cards during text conversion, for example. For example, the text conversion unit preferentially converts highly relevant business cards into text. In addition, the text conversion unit can convert less relevant business cards into text later. In addition, the text conversion unit can automatically adjust the order of conversion according to the relevance of the business cards. As a result, by adjusting the order of conversion based on the relevance of the business cards, more relevant business cards can be preferentially converted. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the text conversion unit can input the relevance data of the business cards to the generation AI and cause the generation AI to adjust the order of conversion.

[0090] The storage unit can estimate the user's emotion and select the stored data based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion and selects the stored data based on the estimated user's emotion. For example, when the user is relaxed, the storage unit stores detailed data. Also, when the user is in a hurry, the storage unit can store only important data. Also, when the user is stressed, the storage unit can store concise data. In this way, more appropriate data can be stored by selecting the stored data according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and cause the generation AI to select the stored data.

[0091] The storage unit can optimize the storage algorithm by referring to the past stored data at the time of storage. The storage unit optimizes the storage algorithm by referring to the past stored data, for example, at the time of storage. For example, the storage unit selects an optimal storage algorithm based on the past stored data. The storage unit can also apply an algorithm that removes duplicate data from the past stored data. The storage unit can also analyze the past stored data and apply an algorithm that improves storage efficiency. As a result, the storage algorithm can be optimized and the storage efficiency can be improved by referring to the past stored data. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI or may be performed without using AI. For example, the storage unit can input the past stored data to the generation AI and cause the generation AI to execute optimization of the storage algorithm.

[0092] The storage unit can estimate the user's emotion and adjust the frequency of saving based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion and adjusts the frequency of saving based on the estimated user's emotion. For example, the storage unit frequently saves data when the user is relaxed. Also, the storage unit can save only important data when the user is in a hurry. Also, the storage unit can save concise data when the user is stressed. In this way, by adjusting the frequency of saving according to the user's emotion, data can be saved at a more appropriate frequency. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the frequency of saving.

[0093] The storage unit can weight the stored data based on the submission time of the business card when storing. The storage unit, for example, weights the stored data based on the submission time of the business card when storing. For example, the storage unit prioritizes storing data of the most recently submitted business card. Also, the storage unit can postpone storing data of business cards submitted earlier. Also, the storage unit can automatically adjust the weighting of the stored data according to the submission time of the business card. In this way, by weighting the stored data based on the submission time of the business card, more important data can be prioritized for storage. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the submission time data of the business card to the generation AI and cause the generation AI to execute the weighting of the stored data.

[0094] The combining unit can estimate the user's emotion and adjust the combining criteria based on the estimated user's emotion. The combining unit, for example, estimates the user's emotion and adjusts the combining criteria based on the estimated user's emotion. For example, the combining unit applies detailed combining criteria when the user is relaxed. Also, the combining unit can apply concise combining criteria when the user is in a hurry. Also, the combining unit can apply combining criteria in an easy-to-read format when the user is stressed. In this way, by adjusting the combining criteria according to the user's emotion, information can be combined with more appropriate criteria. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the combining unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the combining unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the combining criteria.

[0095] The combining unit can improve the accuracy of the combining by considering the interrelationships of the business cards when combining. For example, the combining unit improves the accuracy of the combining by considering the interrelationships of the business cards when combining. For example, the combining unit preferentially combines highly related information based on the interrelationships of the business cards. The combining unit can also apply an algorithm that analyzes the interrelationships of the business cards and improves the accuracy of the combining. The combining unit can also automatically adjust the priority of the combining according to the interrelationships of the business cards. In this way, the accuracy of the combining can be improved by considering the interrelationships of the business cards. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input the interrelationship data of the business cards to the generating AI and cause the generating AI to execute the improvement of the accuracy of the combining.

[0096] The combining unit can combine the data while considering the attribute information of the person who submitted the business card. For example, the combining unit combines the data while considering the attribute information of the person who submitted the business card. For example, the combining unit combines highly relevant information preferentially based on the work history of the person who submitted the business card. The combining unit can also improve the accuracy of the combining by considering the specialty field of the person who submitted the business card. The combining unit can also automatically adjust the priority of the combining according to the attribute information of the person who submitted the business card. In this way, more relevant information can be combined by considering the attribute information of the person who submitted the business card. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input attribute information data of the person who submitted the business card to the generating AI, and cause the generating AI to improve the accuracy of the combining.

[0097] The combining unit can estimate the user's emotion and adjust the order of displaying the combined results based on the estimated user's emotion. The combining unit, for example, estimates the user's emotion and adjusts the order of displaying the combined results based on the estimated user's emotion. For example, the combining unit preferentially displays detailed combined results when the user is relaxed. Also, the combining unit can preferentially display concise combined results when the user is in a hurry. Also, the combining unit can display the combined results in an easy-to-read format when the user is stressed. In this way, by adjusting the order of displaying the combined results according to the user's emotion, information can be displayed in a more appropriate order. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the combining unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the combining unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the combined results.

[0098] The combining unit can combine the data while taking into consideration the geographical distribution of the business cards. For example, the combining unit combines the data while taking into consideration the geographical distribution of the business cards. For example, the combining unit preferentially combines highly relevant information based on the geographical distribution of the business cards. The combining unit can also apply an algorithm that analyzes the geographical distribution of the business cards and improves the accuracy of the combining. The combining unit can also automatically adjust the priority of the combining according to the geographical distribution of the business cards. In this way, more relevant information can be combined by taking into consideration the geographical distribution of the business cards. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input the geographical distribution data of the business cards to the generating AI, and cause the generating AI to improve the accuracy of the combining.

[0099] The combining unit can improve the accuracy of the combination by referring to the related literature of the business card at the time of combination. The combining unit can improve the accuracy of the combination by referring to the related literature of the business card at the time of combination, for example. For example, the combining unit preferentially combines highly relevant information based on the related literature of the business card. The combining unit can also apply an algorithm that analyzes the related literature of the business card and improves the accuracy of the combination. The combining unit can also automatically adjust the priority of the combination according to the related literature of the business card. Thereby, the accuracy of the combination can be improved by referring to the related literature of the business card. A part or all of the above-mentioned processing in the combining unit may be performed, for example, using AI, or may be performed without using AI. For example, the combining unit can input the related literature data of the business card to the generating AI and cause the generating AI to execute the improvement of the accuracy of the combination.

[0100] The profiling unit can estimate the user's emotion and adjust the profiling method based on the estimated user's emotion. The profiling unit, for example, estimates the user's emotion and adjusts the profiling method based on the estimated user's emotion. For example, the profiling unit performs detailed profiling when the user is relaxed. Also, the profiling unit can perform concise profiling when the user is in a hurry. Also, the profiling unit can perform profiling in an easy-to-read format when the user is stressed. This allows more appropriate profiling to be performed by adjusting the profiling method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the profiling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the profiling unit can input a user's emotional data into the generation AI and cause the generation AI to adjust the profiling method.

[0101] The profiling unit can select the optimal profiling method by analyzing the user's past transaction history during profiling. For example, the profiling unit selects the optimal profiling method by analyzing the user's past transaction history during profiling. For example, the profiling unit selects the optimal profiling method based on the user's past transaction history. In addition, the profiling unit can preferentially profile highly relevant information from the user's past transaction history. In addition, the profiling unit can also analyze the user's past transaction history to improve the accuracy of profiling. As a result, the optimal profiling method can be selected by analyzing the user's past transaction history. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI or may be performed without using AI. For example, the profiling unit can input the user's past transaction history data to the generation AI and cause the generation AI to select the profiling method.

[0102] The profiling unit can customize the profiling means based on the user's current trading situation during profiling. The profiling unit customizes the profiling means based on the user's current trading situation during profiling, for example. For example, the profiling unit selects an optimal profiling means based on the user's current trading situation. The profiling unit can also preferentially profile highly relevant information from the user's current trading situation. The profiling unit can also analyze the user's current trading situation and improve the accuracy of profiling. As a result, more relevant information can be provided by customizing the profiling means based on the user's current trading situation. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI, or may be performed without using AI. For example, the profiling unit can input the user's current trading situation data to the generation AI and cause the generation AI to execute customization of the profiling means.

[0103] The profiling unit can estimate the user's emotion and determine the priority of profiling based on the estimated user's emotion. The profiling unit, for example, estimates the user's emotion and determines the priority of profiling based on the estimated user's emotion. For example, the profiling unit prioritizes detailed profiling when the user is relaxed. Also, the profiling unit can prioritize concise profiling when the user is in a hurry. Also, the profiling unit can prioritize profiling in an easy-to-read format when the user is stressed. In this way, by determining the priority of profiling according to the user's emotion, more important information can be profiled preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the profiling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the profiling unit can input a user's emotional data into the generation AI and have the generation AI determine profiling priorities.

[0104] The profiling unit can select an optimal profiling method in consideration of the geographical location information of the user during profiling. The profiling unit, for example, selects an optimal profiling method in consideration of the geographical location information of the user during profiling. For example, the profiling unit selects an optimal profiling method based on the geographical location information of the user. The profiling unit can also preferentially profile highly relevant information from the geographical location information of the user. The profiling unit can also analyze the geographical location information of the user and improve the accuracy of profiling. As a result, more relevant information can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI or may be performed without using AI. For example, the profiling unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select a profiling method.

[0105] The profiling unit can analyze the user's social media activity and propose profiling means at the time of profiling. For example, the profiling unit can analyze the user's social media activity and propose profiling means at the time of profiling. For example, the profiling unit proposes optimal profiling means based on the user's social media activity. In addition, the profiling unit can preferentially profile highly relevant information from the user's social media activity. In addition, the profiling unit can also analyze the user's social media activity and improve the accuracy of profiling. As a result, more relevant information can be provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the profiling unit may be performed, for example, using AI or may be performed without using AI. For example, the profiling unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the proposal of profiling means.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When acquiring an image of a business card, the acquisition unit can analyze the user's past business card acquisition history and suggest the optimal acquisition method. For example, the acquisition unit preferentially suggests an acquisition method (camera, scanner, etc.) that the user has used in the past. The acquisition unit can also select the method with the highest success rate from the user's past acquisition history. Furthermore, the acquisition unit can also suggest the optimal acquisition timing based on the user's past acquisition history. In this way, the optimal acquisition method can be selected by analyzing the user's past business card acquisition history.

[0108] The acquisition unit can perform filtering based on the user's current project or field of interest when acquiring an image of the business card. For example, the acquisition unit preferentially acquires business cards related to a project currently in progress by the user. The acquisition unit can also filter highly relevant business cards based on the user's field of interest. Furthermore, the acquisition unit can adjust the priority of business cards to be acquired according to the progress of the user's project. This makes it possible to preferentially acquire highly relevant business cards by filtering based on the user's current project or field of interest.

[0109] When acquiring an image of a business card, the acquisition unit can preferentially acquire highly relevant business cards by taking into consideration the geographical location information of the user. For example, the acquisition unit preferentially acquires business cards related to the location where the user is currently located. The acquisition unit can also filter highly relevant business cards based on the geographical location information of the user. Furthermore, when the user is in a specific area, the acquisition unit can also preferentially acquire business cards related to that area. In this way, by taking into consideration the geographical location information of the user, highly relevant business cards can be preferentially acquired.

[0110] The acquisition unit can analyze the user's social media activity and acquire relevant business cards when acquiring an image of the business card. For example, the acquisition unit filters highly relevant business cards from the user's social media activity. The acquisition unit can also preferentially acquire business cards of people with whom the user interacts on social media. Furthermore, the acquisition unit can adjust the priority of business cards to be acquired based on the user's social media activity. In this way, highly relevant business cards can be preferentially acquired by analyzing the user's social media activity.

[0111] The acquisition unit can estimate the user's emotion when acquiring an image of the business card, and adjust the timing of acquiring the image of the business card based on the estimated emotion of the user. For example, if the user is relaxed, the acquisition unit can immediately acquire the image of the business card. Also, if the user is busy, the acquisition unit can set a reminder to acquire the image of the business card later. Furthermore, if the user is feeling stressed, the acquisition unit can display a guide to simplify the acquisition of the image of the business card. In this way, the timing of acquiring the image of the business card can be adjusted according to the emotion of the user, and the image of the business card can be acquired at a more appropriate timing.

[0112] The text conversion unit can estimate the user's emotion and adjust the expression method of the text conversion based on the estimated user's emotion. For example, the text conversion unit can perform detailed text conversion when the user is relaxed. Also, the text conversion unit can perform concise text conversion when the user is in a hurry. Furthermore, the text conversion unit can perform text conversion in an easy-to-read format when the user is stressed. In this way, more appropriate text conversion can be performed by adjusting the expression method of the text conversion according to the user's emotion.

[0113] The storage unit can estimate the user's emotion and select data to be stored based on the estimated user's emotion. For example, the storage unit can store detailed data when the user is relaxed. Also, the storage unit can store only important data when the user is in a hurry. Furthermore, the storage unit can store concise data when the user is stressed. In this way, more appropriate data can be stored by selecting data to be stored according to the user's emotion.

[0114] The combining unit can estimate the user's emotion and adjust the combining criteria based on the estimated user's emotion. For example, the combining unit can apply detailed combining criteria when the user is relaxed. Also, the combining unit can apply concise combining criteria when the user is in a hurry. Furthermore, the combining unit can apply combining criteria in an easy-to-read format when the user is stressed. In this way, by adjusting the combining criteria according to the user's emotion, information can be combined with more appropriate criteria.

[0115] The profiling unit can estimate the user's emotions and adjust the profiling method based on the estimated user's emotions. For example, the profiling unit can perform detailed profiling when the user is relaxed. Also, the profiling unit can perform concise profiling when the user is in a hurry. Furthermore, the profiling unit can perform profiling in an easy-to-read format when the user is stressed. This allows more appropriate profiling to be performed by adjusting the profiling method according to the user's emotions.

[0116] During profiling, the profiling unit can select the optimal profiling method by analyzing the user's past transaction history. For example, the profiling unit selects the optimal profiling method based on the user's past transaction history. Also, the profiling unit can preferentially profile highly relevant information from the user's past transaction history. Furthermore, the profiling unit can also analyze the user's past transaction history to improve the accuracy of profiling. In this way, the optimal profiling method can be selected by analyzing the user's past transaction history.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The acquisition unit acquires an image of a business card. The image of the business card may be in an image format such as JPEG or PNG, or may be acquired by a scanner or camera. The acquisition unit acquires the image of the business card using, for example, a messenger app. The image of the business card may also be acquired using a dedicated scanner. For example, a photo of the business card may be taken using a messenger app, and the image may be sent to the system. The business card may also be scanned using a dedicated scanner and saved as image data. Step 2: The text conversion unit converts the image acquired by the acquisition unit into text data. The text data includes, for example, a UTF-8 encoded text file, a database field format, and the like. The text conversion unit converts the image of the business card into text data using, for example, OCR technology. The OCR technology is realized by, for example, technology such as Tesseract or Google Cloud Vision API. Step 3: The storage unit stores the text data converted by the text conversion unit in a database. The database includes, for example, an SQL database, a NoSQL database, etc. The storage unit stores, for example, the converted text data in a SQL database. It can also store the converted text data in a NoSQL database. Step 4: The combining unit combines the data stored by the storage unit with information on open sources. Information on open sources includes, for example, public data on websites, information obtained through APIs, etc. The combining unit uses, for example, public information on the Internet and profile information on SNS. Step 5: The profiling unit profiles the business partner based on the information combined by the combining unit. Profiling includes, for example, a method for analyzing work history, a method for identifying a field of expertise, and a method for analyzing interests. For example, the profiling unit analyzes the work history, field of expertise, and interests of the business partner to provide information useful for future transactions. Some or all of the processing in the profiling unit may be performed using, for example, AI, or may be performed without using AI. For example, the profiling unit may perform profiling using an AI model that receives the information combined by the combining unit as input and outputs a profile of the business partner.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0121] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements including the above-mentioned acquisition unit, text conversion unit, storage unit, combination unit, and profiling unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires an image of a business card using the camera 42 of the smart device 14, and transmits it to the system by the control unit 46A. The text conversion unit converts the image of the business card into text data using OCR technology by the specific processing unit 290 of the data processing device 12, for example. The storage unit stores the text data in the database 24 of the data processing device 12, for example. The combination unit combines information on the open source with business card information by the specific processing unit 290 of the data processing device 12, for example. The profiling unit profiles a person who is a business partner based on the information combined by the specific processing unit 290 of the data processing device 12, for example. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements including the above-mentioned acquisition unit, text conversion unit, storage unit, combination unit, and profiling unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires an image of a business card using the camera 42 of the smart glasses 214, and transmits it to the system by the control unit 46A. The text conversion unit converts the image of the business card into text data using OCR technology by the specific processing unit 290 of the data processing device 12, for example. The storage unit stores the text data in the database 24 of the data processing device 12, for example. The combination unit combines information on the open source with business card information by the specific processing unit 290 of the data processing device 12, for example. The profiling unit profiles a person who is a business partner based on the information combined by the specific processing unit 290 of the data processing device 12, for example. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements including the above-mentioned acquisition unit, text conversion unit, storage unit, combination unit, and profiling unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires an image of a business card using the camera 42 of the headset type terminal 314, and transmits it to the system by the control unit 46A. The text conversion unit converts the image of the business card into text data using OCR technology by the specific processing unit 290 of the data processing device 12, for example. The storage unit stores the text data in the database 24 of the data processing device 12, for example. The combination unit combines information on the open source with business card information by the specific processing unit 290 of the data processing device 12, for example. The profiling unit profiles a person who is a business partner based on the information combined by the specific processing unit 290 of the data processing device 12, for example. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] Each of the multiple elements including the above-mentioned acquisition unit, text conversion unit, storage unit, combination unit, and profiling unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires an image of a business card using the camera 42 of the robot 414, and transmits it to the system by the control unit 46A. The text conversion unit converts the image of the business card into text data using OCR technology by the specific processing unit 290 of the data processing device 12, for example. The storage unit stores the text data in the database 24 of the data processing device 12, for example. The combination unit combines information on the open source with business card information by the specific processing unit 290 of the data processing device 12, for example. The profiling unit profiles a person who is a business partner based on the information combined by the specific processing unit 290 of the data processing device 12, for example. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0173] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0176] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0177] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] In addition, 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 upon request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0184] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0188] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0190] (Appendix 1) An acquisition unit for acquiring an image of a business card; a text conversion unit that converts the image acquired by the acquisition unit into text data; a storage unit that stores the text data converted by the text conversion unit in a database; a combining unit that combines the data stored by the storage unit with information on an open source; A profiling unit that profiles a person who is a business partner based on the information combined by the combining unit. A system characterized by: (Appendix 2) The acquisition unit is Take a picture of the business card using a messenger app or a dedicated scanner 2. The system of claim 1, (Appendix 3) The coupling portion is Using publicly available information from the internet or social media profiles 2. The system of claim 1, (Appendix 4) The profiling unit is Analyzing the work history, specialty, and interests of business partners to provide information that will be useful for future business transactions 2. The system of claim 1, (Appendix 5) The text conversion unit is Convert business card images into text data using OCR technology 2. The system of claim 1, (Appendix 6) The storage unit includes: Save the converted text data in the database 2. The system of claim 1, (Appendix 7) The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring an image of a business card based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The acquisition unit is When acquiring an image of a business card, the system analyzes the user's past business card acquisition history and selects the appropriate acquisition method. 2. The system of claim 1, (Appendix 9) The acquisition unit is Filter business card images based on the user's current project or area of ​​interest 2. The system of claim 1, (Appendix 10) The acquisition unit is A user's emotion is estimated, and a priority order for business cards to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The acquisition unit is When acquiring business card images, the system prioritizes acquiring business cards that are highly relevant to the user based on the user's geographic location information. 2. The system of claim 1, (Appendix 12) The acquisition unit is When acquiring a business card image, the system analyzes the user's social media activity and acquires related business cards. 2. The system of claim 1, (Appendix 13) The text conversion unit is Estimate user sentiment and adjust the way text is converted based on the estimated user sentiment. 2. The system of claim 1, (Appendix 14) The text conversion unit is When converting text, adjust the conversion detail level based on the importance of the business card. 2. The system of claim 1, (Appendix 15) The text conversion unit is When converting text, apply different conversion algorithms depending on the business card category. 2. The system of claim 1, (Appendix 16) The text conversion unit is Estimating a user's emotion and adjusting the length of the text conversion based on the estimated user's emotion 2. The system of claim 1, (Appendix 17) The text conversion unit is When converting text, prioritize conversion based on when the business card was submitted 2. The system of claim 1, (Appendix 18) The text conversion unit is When converting text, adjust the conversion order based on the relevance of business cards 2. The system of claim 1, (Appendix 19) The storage unit includes: Inferring user emotions and selecting data to store based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The storage unit includes: When saving, optimize the saving algorithm by referencing past saved data. 2. The system of claim 1, (Appendix 21) The storage unit includes: Inferring a user's emotions and adjusting the frequency of saving based on the estimated user's emotions 2. The system of claim 1, (Appendix 22) The storage unit includes: When saving, the saved data is weighted based on when the business card was submitted 2. The system of claim 1, (Appendix 23) The coupling portion is Inferring a user's sentiment and adjusting the combining criteria based on the estimated user's sentiment 2. The system of claim 1, (Appendix 24) The coupling portion is When merging, the accuracy of merging is improved by considering the interrelationships between business cards. 2. The system of claim 1, (Appendix 25) The coupling portion is When combining, the attribute information of the business card submitter is taken into consideration. 2. The system of claim 1, (Appendix 26) The coupling portion is Infer user sentiment and adjust the order in which join results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 27) The coupling portion is When merging, the geographical distribution of business cards is taken into account. 2. The system of claim 1, (Appendix 28) The coupling portion is When merging, improve the accuracy of merging by referring to related documents of business cards 2. The system of claim 1, (Appendix 29) The profiling unit is Inferring user emotions and adjusting a profiling method based on the inferred user emotions 2. The system of claim 1, (Appendix 30) The profiling unit is During profiling, the system analyzes the user's past transaction history to select the most appropriate profiling method. 2. The system of claim 1, (Appendix 31) The profiling unit is During profiling, customize the profiling experience based on the user's current transactional context 2. The system of claim 1, (Appendix 32) The profiling unit is To estimate a user's emotion and determine a profiling priority based on the estimated user's emotion. 2. The system of claim 1, (Appendix 33) The profiling unit is During profiling, consider the user's geographic location to select the most appropriate profiling method 2. The system of claim 1, (Appendix 34) The profiling unit is When profiling, analyze your social media activity to suggest profiling measures 2. The system of claim 1, [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit for acquiring an image of a business card; a text conversion unit that converts the image acquired by the acquisition unit into text data; a storage unit that stores the text data converted by the text conversion unit in a database; a combining unit for combining the data stored by the storing unit with information on an open source; A profiling unit that profiles a person who is a business partner based on the information combined by the combining unit. A system characterized by:

2. The acquisition unit is Take a picture of the business card using a messenger app or a dedicated scanner 2. The system of claim 1 .

3. The coupling portion is Using publicly available information from the internet or social media profiles 2. The system of claim 1 .

4. The profiling unit is Analyzing the work history, specialty, and interests of business partners to provide information that will be useful for future business transactions 2. The system of claim 1 .

5. The text conversion unit is Convert business card images into text data using OCR technology 2. The system of claim 1 .

6. The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring an image of a business card based on the estimated emotion of the user.

2. The system of claim 1 .

7. The acquisition unit is When acquiring an image of a business card, the system analyzes the user's past business card acquisition history and selects the appropriate acquisition method.

2. The system of claim 1 .

8. The acquisition unit is Filter business card images based on the user's current project or area of ​​interest 2. The system of claim 1 .

Images