System

Abstract

An object of a system according to an embodiment is to efficiently manage business card information and utilize in-house connections.SOLUTION: A system according to an embodiment includes a collection unit, a storage unit, a matching unit, and an advice unit. The collection unit collects information of the business card. The storage unit stores the information collected by the collection unit in a cloud. The matching unit matches employees having in-company connections based on the information stored by the storage unit. The advice part provides advice for increasing the success rate of the business negotiation based on the employee specified by the matching 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies do not adequately manage business card information or utilize internal connections, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently manage business card information and utilize internal connections. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a storage unit, a matching unit, and an advice unit. The collection unit collects business card information. The storage unit stores the information collected by the collection unit in the cloud. The matching unit matches employees with internal connections based on the information stored by the storage unit. The advice unit provides advice to increase the success rate of business negotiations based on the employees identified by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage business card information and utilize internal connections. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business card management and business negotiation support system according to an embodiment of the present invention collects business card information, stores it in the cloud, matches employees with internal connections, and provides advice to increase the success rate of business negotiations. The business card management and business negotiation support system collects business card information, stores it in the cloud, and matches employees with internal connections to support smooth business negotiations. For example, when exchanging business cards, the business card management and business negotiation support system stores the business cards in a dedicated application. This application scans and digitizes the business card information. For example, information such as the name, company name, job title, and contact information on the business card is automatically extracted and stored in the cloud. Next, the business card management and business negotiation support system uses AI to match employees with internal connections based on past business negotiation history during business negotiations. For example, when conducting business negotiations with a specific customer, the AI ​​identifies employees who have previously conducted business negotiations with that customer and employees who have relationships with that customer. Furthermore, the business card management and business negotiation support system uses AI to analyze past business negotiation data and provide advice to increase the success rate of business negotiations. For example, it will make suggestions that can be applied to current negotiations based on strategies and approaches that were successful in past sales negotiations. This makes it easier for business card management and sales negotiation support systems to manage business cards and improve the efficiency of sales negotiations. This also reduces the risk of losing business cards by digitizing business card information and storing it in the cloud. In addition, AI can match employees with internal connections, which is expected to help sales negotiations proceed smoothly. Furthermore, AI can analyze past sales negotiation data and provide advice to increase the success rate of sales negotiations, thereby improving the efficiency of sales activities.

[0029] A business card management and business negotiation support system according to an embodiment includes a collection unit, a storage unit, a matching unit, and an advice unit. The collection unit collects business card information. The business card information includes, but is not limited to, a name, a company name, a job title, and contact information. For example, the collection unit digitizes the business card using scanning technology and extracts information from the business card. The collection unit can also convert the business card information into text data using OCR technology. For example, the collection unit scans the business card with a high-resolution scanner and converts it into text information using OCR technology. The storage unit stores the information collected by the collection unit in a cloud. Examples of cloud services include, but are not limited to, AWS (registered trademark), Google (registered trademark), and Microsoft Azure (registered trademark). For example, the storage unit can encrypt the collected business card information and store it in the cloud. For example, the storage unit protects the business card information using encryption technology such as AES or RSA. The matching unit matches employees with internal connections based on the information stored by the storage unit. Matching is performed based on, for example, past business negotiation history or common areas of interest, but is not limited to such examples. For example, the matching unit identifies employees who have previously conducted business negotiations with specific customers and matches those employees. The advice unit provides advice to increase the success rate of business negotiations based on the employees identified by the matching unit. The advice may, for example, analyze past business negotiation data and provide success stories and specific action suggestions, but is not limited to such examples. For example, the advice unit makes suggestions that can be applied to current business negotiations based on strategies and approaches that were successful in past business negotiations. This makes it easier for the business card management and business negotiation support system according to the embodiment to manage business cards and improve the efficiency of business negotiations. Some or all of the above-described processing by the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may provide advice using an AI model that inputs past business negotiation data and outputs advice to increase the success rate of business negotiations.

[0030] The storage unit can encrypt and store the business card information. For example, an algorithm such as AES, RSA, or SHA-256 is used for encryption, but is not limited to these examples. For example, the storage unit encrypts the business card information using AES-256 and stores it in the cloud. The storage unit can also encrypt the business card information using RSA. For example, the storage unit encrypts the business card information using RSA and stores it in the cloud. The storage unit can also hash the business card information using SHA-256 and store it in the cloud. For example, the storage unit hashes the business card information using SHA-256 and stores it in the cloud. This improves the security of the business card information. Some 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 perform encryption using an AI model that receives business card information as input and outputs encrypted data.

[0031] The collection unit can scan and digitize information on business cards. Scanning can be performed using, for example, OCR technology, barcode scanning, or other methods, but is not limited to these. For example, the collection unit scans the business cards with a scanner and saves the data as image data. The collection unit then converts the image data into text data using OCR technology. The collection unit can also take a photo of the business card using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The collection unit can also write on the business card with a dedicated digital pen, which can then convert the writing into digital data in real time. For example, a sensor can detect the pen's movement and store it as text data. This allows for efficient digitization of business card information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the scanned image data of the business card into a generation AI and have the generation AI convert it into text data.

[0032] The matching unit can identify employees with internal connections based on past business negotiation history. The past business negotiation history includes, for example, the date, time, content, and results of the business negotiation, but is not limited to such examples. The matching unit, for example, identifies employees who have previously conducted business negotiations with specific customers and matches those employees. The matching unit can also analyze the past business negotiation history to identify employees with common interests. For example, the matching unit can identify employees who are interested in a specific industry or topic based on the past business negotiation history. The matching unit can also identify employees with a high success rate in business negotiations based on the past business negotiation history. For example, the matching unit can identify employees with a high success rate in business negotiations based on the past business negotiation history and match those employees. This can be expected to ensure smooth progress in business negotiations. Some or all of the above-described processing by the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can identify employees using an AI model that inputs past business negotiation history and outputs the most suitable employee.

[0033] The advice unit can analyze past business negotiation data and provide advice to increase the success rate of business negotiations. Business negotiation data includes, but is not limited to, the date, time, content, and results of business negotiations. For example, the advice unit can analyze past business negotiation data and provide success stories and specific action suggestions. The advice unit can also propose strategies and approaches to increase the success rate of business negotiations based on the past business negotiation data. For example, the advice unit can make suggestions that can be applied to current business negotiations based on strategies and approaches that were successful in past business negotiations. The advice unit can also analyze past business negotiation data to identify factors that cause business negotiation failures and propose workarounds. For example, the advice unit can identify factors that cause business negotiation failures from past business negotiation data and propose workarounds. This improves the success rate of business negotiations. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can provide advice using an AI model that inputs past business negotiation data and outputs advice to increase the success rate of business negotiations.

[0034] The collection unit can guide the user through the operation procedure. The operation procedure includes, but is not limited to, step-by-step instructions, visual guides, etc. For example, when collecting business card information, the collection unit provides step-by-step instructions to the user. The collection unit can also use a visual guide to show the user the operation procedure. For example, the collection unit uses a visual guide to show how to scan a business card and how to input information. The collection unit can also use an audio guide to explain the operation procedure to the user. For example, when collecting business card information, the collection unit explains the operation procedure using an audio guide. This allows the user to collect business card information smoothly. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can guide the user through the operation procedure using an AI model that inputs the user's operation status and outputs optimal operation procedures.

[0035] The advice unit can provide specific content of advice to be provided during business negotiations. Specific content includes, but is not limited to, success stories and specific action suggestions. For example, the advice unit can provide advice based on past success stories during business negotiations. The advice unit can also provide specific action suggestions during business negotiations. For example, the advice unit can make specific action suggestions that can be applied to the current business negotiation based on strategies and approaches that were successful in past business negotiations. The advice unit can also provide advice based on the customer's needs and interests during business negotiations. For example, the advice unit can analyze the customer's needs and interests and make specific suggestions based on them. This further improves the success rate of business negotiations. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice unit can provide advice using an AI model that inputs the content of the business negotiation and outputs specific advice.

[0036] The collection unit can select the optimal scanning method depending on the design and format of the business card. Examples of the design and format include, but are not limited to, the layout, font, and color of the business card. For example, if the business card is vertical, the collection unit selects a scanning method optimized for vertical format. Also, if the business card is horizontal, the collection unit can select a scanning method optimized for horizontal format. For example, if the business card is horizontal, the collection unit uses scanner settings optimized for horizontal format. Also, if the business card has a special design, the collection unit can select a scanning method that corresponds to the design. For example, if the business card is embossed, the collection unit selects a scanning method that corresponds to the embossing. This enables the optimal scan depending on the design and format of the business card. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input business card design data into a generation AI and have the generation AI select the optimal scanning method.

[0037] The collection unit can perform automatic translation depending on the language of the business card. For example, technologies such as machine translation and translation memory are used for the automatic translation, but are not limited to these examples. For example, if the business card is written in English, the collection unit automatically translates it into Japanese. Furthermore, if the business card is written in Chinese, the collection unit can also automatically translate it into Japanese. For example, if the business card is written in Chinese, the collection unit translates it from Chinese to Japanese. Furthermore, if the business card is written in French, the collection unit can also automatically translate it into Japanese. For example, if the business card is written in French, the collection unit translates it from French to Japanese. This allows efficient collection of business card information in different languages. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the language data of the business card into a generation AI and have the generation AI perform the translation.

[0038] The collection unit can automatically acquire social media information of the business card owner. Social media information includes, but is not limited to, information from LinkedIn (registered trademark), Twitter (registered trademark), Facebook (registered trademark), etc. For example, the collection unit automatically acquires the LinkedIn profile of the business card owner. The collection unit can also automatically acquire Twitter account information of the business card owner. For example, the collection unit acquires and stores Twitter account information of the business card owner. The collection unit can also automatically acquire Facebook profile of the business card owner. For example, the collection unit acquires and stores Facebook profile of the business card owner. This allows efficient collection of additional information of the business card owner. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media information of the business card owner to the generation AI and cause the generation AI to acquire the information.

[0039] The collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, etc. For example, when the user is in a specific area, the collection unit prioritizes collecting business card information related to that area. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting business card information related to the business trip destination. For example, when the user is on a business trip, the collection unit prioritizes collecting business card information related to the business trip destination. Furthermore, when the user is at an event venue, the collection unit can prioritize collecting business card information related to the event. For example, when the user is at an event venue, the collection unit prioritizes collecting business card information related to the event. This makes it possible to collect highly relevant business card information based on the user's location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant information.

[0040] The collection unit can customize the collection method by referring to the user's past business negotiation history. The past business negotiation history includes, for example, the date and time, content, and results of the business negotiation, but is not limited to such examples. For example, the collection unit prioritizes collecting business card information from business negotiations that the user has previously succeeded in. The collection unit can also reevaluate and collect business card information from business negotiations that the user has previously failed in. For example, the collection unit reevaluates and collects business card information from business negotiations that the user has previously failed in. The collection unit can also prioritize collecting business card information from a specific industry from the user's past business negotiation history. For example, the collection unit prioritizes collecting business card information from a specific industry from the user's past business negotiation history. This allows optimal business card information to be collected based on the past business negotiation history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past business negotiation history data into the generation AI and cause the generation AI to customize the collection method.

[0041] The collection unit can adjust the items of information to be collected depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the collection unit may prioritize collecting technical information. Furthermore, if the user is in the medical industry, the collection unit may prioritize collecting medical-related information. For example, if the user is in the medical industry, the collection unit may prioritize collecting medical-related information. Furthermore, if the user is in the financial industry, the collection unit may prioritize collecting financial-related information. For example, if the user is in the financial industry, the collection unit may prioritize collecting financial-related information. This allows optimal business card information to be collected according to the user's industry and occupation. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the items of information to be collected.

[0042] The storage unit can determine storage priorities based on the importance of the information. Examples of importance include, but are not limited to, business impact and urgency. For example, the storage unit prioritizes storage of business card information of important business partners. The storage unit can also prioritize storage of business card information of new customers. For example, the storage unit prioritizes storage of business card information of new customers. The storage unit can also prioritize storage of business card information with which a long-term relationship is expected. For example, the storage unit prioritizes storage of business card information with which a long-term relationship is expected. This allows important business card information to be stored preferentially. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input importance data of the business card information into the generation AI and have the generation AI determine the storage priority.

[0043] The storage unit can apply different storage formats depending on the category of information. Examples of categories include, but are not limited to, customer information, transaction information, and technical information. For example, the storage unit stores business card information of business partners in a specific format. The storage unit can also store business card information of new customers in a different format. For example, the storage unit stores business card information of new customers in a different format. The storage unit can also store business card information related to internal connections in a dedicated format. For example, the storage unit stores business card information related to internal connections in a dedicated format. This enables optimal storage according to the category of information. Some 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 category data of the business card information to the generation AI and cause the generation AI to apply the storage format.

[0044] The storage unit can optimize the storage method by referring to the user's past storage history. The past storage history includes, for example, the storage date and time, the storage content, and the storage format, but is not limited to these examples. The storage unit, for example, prioritizes suggesting a storage method that the user has used in the past. The storage unit can also suggest an optimal storage format based on the user's past storage history. For example, the storage unit can suggest an optimal storage format based on the user's past storage history. The storage unit can also analyze the user's past storage history and customize the storage method. For example, the storage unit analyzes the user's past storage history and customizes the storage method. This makes it possible to provide an optimal storage method based on the past storage history. Some or all of the above-described 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 user's past storage history data into a generation AI and cause the generation AI to optimize the storage method.

[0045] The storage unit can select a storage location taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the storage unit stores the data in a cloud server associated with that area. Furthermore, if the user is on a business trip, the storage unit can store the data in a cloud server associated with the business trip destination. For example, if the user is on a business trip, the storage unit stores the data in a cloud server associated with the business trip destination. Furthermore, if the user is at an event venue, the storage unit can store the data in a cloud server associated with the event. For example, if the user is at an event venue, the storage unit stores the data in a cloud server associated with the event. This allows the optimal storage location to be selected based on the user's location information. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input the user's geographical location data to a generation AI and cause the generation AI to select a storage location.

[0046] The storage unit can customize the storage method by analyzing the user's social media activity. Social media activity includes, but is not limited to, post content, number of followers, and engagement rate. For example, if the user is active on LinkedIn, the storage unit can suggest a storage method related to LinkedIn. Furthermore, if the user is active on Twitter, the storage unit can suggest a storage method related to Twitter. For example, if the user is active on Twitter, the storage unit can suggest a storage method related to Twitter. Furthermore, if the user is active on Facebook, the storage unit can suggest a storage method related to Facebook. For example, if the user is active on Facebook, the storage unit can suggest a storage method related to Facebook. This allows the optimal storage method to be provided based on the user's social media activity. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input the user's social media activity data into a generation AI and have the generation AI customize the storage method.

[0047] The storage unit can adjust the items of information to be stored depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the storage unit may prioritize storing technical information. Furthermore, if the user is in the medical industry, the storage unit may prioritize storing medical-related information. For example, if the user is in the medical industry, the storage unit may prioritize storing medical-related information. Furthermore, if the user is in the financial industry, the storage unit may prioritize storing financial-related information. For example, if the user is in the financial industry, the storage unit may prioritize storing financial-related information. This enables optimal storage according to the user's industry and occupation. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit may input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the items of information to be stored.

[0048] The matching unit can select the optimal matching method by referring to past business negotiation history. Past business negotiation history includes, for example, the date and time, content, and results of the business negotiation, but is not limited to such examples. The matching unit, for example, matches the optimal employee based on the history of past successful business negotiations. The matching unit can also match the appropriate employee by reevaluating the history of past failed business negotiations. For example, the matching unit can match the appropriate employee by reevaluating the history of past failed business negotiations. The matching unit can also match employees who are familiar with a specific industry based on the past business negotiation history. For example, the matching unit matches employees who are familiar with a specific industry based on the past business negotiation history. This makes it possible to match the optimal employee based on the past business negotiation history. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input past business negotiation history data into the generation AI and cause the generation AI to select the optimal matching method.

[0049] The matching unit can apply different matching algorithms depending on the content and purpose of the business negotiation. Examples of the content and purpose of the business negotiation include, but are not limited to, new transactions, contract renewals, and technical proposals. For example, in the case of a business negotiation with a new customer, the matching unit matches employees with extensive sales experience. In addition, in the case of a business negotiation with an existing customer, the matching unit can also match employees who will deepen the customer relationship. For example, in the case of a business negotiation with an existing customer, the matching unit can match employees who will deepen the customer relationship. In addition, in the case of a business negotiation related to a specific project, the matching unit can match employees who are knowledgeable about that project. For example, in the case of a business negotiation related to a specific project, the matching unit matches employees who are knowledgeable about that project. This enables optimal matching depending on the content and purpose of the business negotiation. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the content and purpose data of the business negotiation into the generation AI and cause the generation AI to apply a matching algorithm.

[0050] The matching unit can improve the accuracy of matching by referring to the user's past matching results. Past matching results include, for example, matching date and time, matching content, and results, but are not limited to these examples. For example, the matching unit matches the user with the most suitable employee based on past successful matching results. The matching unit can also reevaluate past unsuccessful matching results for the user to match an appropriate employee. For example, the matching unit can reevaluate past unsuccessful matching results for the user to match an appropriate employee. The matching unit can also analyze the user's past matching results and optimize the matching algorithm. For example, the matching unit can analyze the user's past matching results and optimize the matching algorithm. This allows the most suitable employee to be matched based on the past matching results. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's past matching result data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0051] The matching unit can prioritize matching highly relevant employees by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, etc. For example, if the user is in a specific area, the matching unit prioritizes matching with employees related to that area. Furthermore, if the user is on a business trip, the matching unit can also prioritize matching with employees related to the business trip destination. For example, if the user is on a business trip, the matching unit prioritizes matching with employees related to the business trip destination. Furthermore, if the user is at an event venue, the matching unit can also prioritize matching with employees related to the event. For example, if the user is at an event venue, the matching unit prioritizes matching with employees related to the event. This makes it possible to match optimal employees based on the user's location information. Some or all of the above-described processing by the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location data into the generation AI and cause the generation AI to match highly relevant employees.

[0052] The matching unit can analyze the user's social media activity to improve the accuracy of matching. Social media activity includes, but is not limited to, post content, number of followers, and engagement rate. For example, if the user is active on LinkedIn, the matching unit may prioritize matching with employees associated with LinkedIn. Furthermore, if the user is active on Twitter, the matching unit may prioritize matching with employees associated with Twitter. For example, if the user is active on Twitter, the matching unit may prioritize matching with employees associated with Twitter. Furthermore, if the user is active on Facebook, the matching unit may prioritize matching with employees associated with Facebook. For example, if the user is active on Facebook, the matching unit may prioritize matching with employees associated with Facebook. This allows matching with the optimal employee based on the user's social media activity. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit may input the user's social media activity data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0053] The matching unit can adjust the attributes of the employees to be matched depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the matching unit prioritizes matching with employees with technical knowledge. Furthermore, if the user is in the medical industry, the matching unit can also prioritize matching with employees with medical-related knowledge. For example, if the user is in the medical industry, the matching unit prioritizes matching with employees with medical-related knowledge. Furthermore, if the user is in the financial industry, the matching unit can also prioritize matching with employees with financial-related knowledge. For example, if the user is in the financial industry, the matching unit prioritizes matching with employees with financial-related knowledge. This allows the optimal employee to be matched depending on the user's industry and occupation. Some or all of the above-described processing by the matching unit may be performed using, or without, AI. For example, the matching unit can input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the attributes of the employees to be matched.

[0054] The advice unit can select the optimal advice method by referring to past business negotiation data. Past business negotiation data includes, for example, the date and time, content, and results of business negotiations, but is not limited to these examples. The advice unit provides optimal advice based on, for example, data from successful business negotiations in the past. The advice unit can also reevaluate data from failed business negotiations in the past to provide appropriate advice. For example, the advice unit can reevaluate data from failed business negotiations in the past to provide appropriate advice. The advice unit can also provide advice that is familiar with a specific industry from past business negotiation data. For example, the advice unit can provide advice that is familiar with a specific industry from past business negotiation data. This allows optimal advice to be provided based on the past business negotiation data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input past business negotiation data into the generation AI and have the generation AI select the optimal advice method.

[0055] The advice unit can apply different advice algorithms depending on the content and purpose of the business negotiation. Examples of the content and purpose of the business negotiation include, but are not limited to, new transactions, contract renewals, and technical proposals. For example, in a business negotiation with a new customer, the advice unit provides advice based on extensive sales experience. Furthermore, in a business negotiation with an existing customer, the advice unit can also provide advice for deepening the customer relationship. For example, in a business negotiation with an existing customer, the advice unit can provide advice for deepening the customer relationship. Furthermore, in a business negotiation related to a specific project, the advice unit can provide detailed advice for the project. For example, in a business negotiation related to a specific project, the advice unit provides detailed advice for the project. This enables optimal advice to be provided according to the content and purpose of the business negotiation. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the content and purpose data of the business negotiation into the generation AI and have the generation AI apply the advice algorithm.

[0056] The advice unit can improve the accuracy of advice by referring to the user's past advice results. Past advice results include, for example, advice date and time, advice content, and results, but are not limited to these examples. The advice unit provides optimal advice based on advice results that the user has previously succeeded in. The advice unit can also reevaluate advice results that the user has previously failed in and provide appropriate advice. For example, the advice unit can reevaluate advice results that the user has previously failed in and provide appropriate advice. The advice unit can also analyze the user's past advice results and optimize the advice algorithm. For example, the advice unit analyzes the user's past advice results and optimizes the advice algorithm. This makes it possible to provide optimal advice based on the past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0057] The advice unit can prioritize providing highly relevant advice by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the advice unit can prioritize providing advice related to that area. Furthermore, if the user is on a business trip, the advice unit can prioritize providing advice related to the business trip destination. For example, if the user is on a business trip, the advice unit can prioritize providing advice related to the business trip destination. Furthermore, if the user is at an event venue, the advice unit can prioritize providing advice related to the event. For example, if the user is at an event venue, the advice unit can prioritize providing advice related to the event. This makes it possible to provide optimal advice based on the user's location information. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the user's geographical location data to the generation AI and cause the generation AI to provide highly relevant advice.

[0058] The advice unit can analyze the user's social media activity to improve the accuracy of the advice. Social media activity includes, but is not limited to, post content, number of followers, and engagement rate. For example, if the user is active on LinkedIn, the advice unit can provide advice related to LinkedIn. Furthermore, if the user is active on Twitter, the advice unit can also provide advice related to Twitter. For example, if the user is active on Twitter, the advice unit can provide advice related to Twitter. Furthermore, if the user is active on Facebook, the advice unit can also provide advice related to Facebook. For example, if the user is active on Facebook, the advice unit can provide advice related to Facebook. This allows optimal advice to be provided based on the user's social media activity. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the user's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0059] The advice unit can adjust the content of the advice depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the advice unit can provide technical advice. Furthermore, if the user is in the medical industry, the advice unit can provide medical-related advice. For example, if the user is in the medical industry, the advice unit can provide medical-related advice. Furthermore, if the user is in the financial industry, the advice unit can provide financial-related advice. For example, if the user is in the financial industry, the advice unit can provide financial-related advice. This allows the provision of optimal advice depending on the user's industry and occupation. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the content of the advice.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When collecting business card information, the collection unit can suggest an optimal collection method by referring to the user's past business card collection history. For example, if the user previously collected business cards using a specific scanner, the collection unit can suggest using that scanner again. Also, if the user previously collected business cards using a specific application, the collection unit can suggest using that application again. Furthermore, if the user previously collected business cards during a specific time period, the collection unit can suggest collecting business cards during that time period. In this way, the collection unit can provide an optimal collection method based on the user's past collection history.

[0062] When saving business card information, the saving unit can adjust the saving timing taking into account the user's work schedule. For example, if the user is in a meeting, it can suggest saving the business card information after the meeting ends. Also, if the user is on a business trip, it can suggest saving the business card information after work at the business trip destination is completed. Furthermore, if the user is on vacation, it can suggest saving the business card information after the vacation ends. This makes it possible to provide the optimal saving timing according to the user's work schedule.

[0063] When collecting business card information, the collection unit can propose an optimal collection method taking into account the user's geographical location information. For example, if the user is in a specific area, business card information related to that area can be collected preferentially. Also, if the user is on a business trip, business card information related to the business trip destination can be collected preferentially. Furthermore, if the user is at an event venue, business card information related to the event can be collected preferentially. This allows optimal business card information to be collected based on the user's location information.

[0064] The matching unit can select the optimal matching method by referring to the user's past business negotiation history. For example, it can match the optimal employee based on the history of past successful business negotiations. It can also reevaluate the history of past unsuccessful business negotiations to match the appropriate employee. It can also match employees who are familiar with a specific industry based on the past business negotiation history. This makes it possible to match the optimal employee based on the past business negotiation history.

[0065] The advice unit can improve the accuracy of advice by referring to past advice results for the user. For example, it can provide optimal advice based on advice results that were successful in the past. It can also reevaluate advice results that were unsuccessful in the past and provide appropriate advice. It can also analyze past advice results and optimize the advice algorithm. This makes it possible to provide optimal advice based on past advice results.

[0066] When collecting business card information, the collection unit can adjust the items of information to be collected depending on the user's industry and occupation. For example, if the user is in the IT industry, technical information can be collected with priority. Also, if the user is in the medical industry, medical-related information can be collected with priority. Furthermore, if the user is in the financial industry, financial-related information can be collected with priority. This makes it possible to collect optimal business card information depending on the user's industry and occupation.

[0067] When saving business card information, the saving unit can determine the priority of saving based on the importance of the information. For example, the saving unit can prioritize saving business card information of important business partners. It can also prioritize saving business card information of new customers. It can also prioritize saving business card information with which a long-term relationship is expected. This allows important business card information to be saved preferentially.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection department collects business card information. The business card information includes name, company name, job title, contact information, etc. The collection department digitizes the business card using scanning technology and extracts the information. The collection department also uses OCR technology to convert the business card information into text data. For example, the collection department scans the business card with a high-resolution scanner and converts it into text information using OCR technology. Step 2: The storage unit stores the information collected by the collection unit in the cloud. Cloud services include AWS, Google Cloud, and Microsoft Azure. The storage unit can also encrypt the collected business card information before storing it in the cloud. For example, encryption technologies such as AES and RSA are used to protect the business card information. Step 3: The matching unit matches employees with internal connections based on the information stored by the storage unit. Matching is performed based on past business negotiation history and common areas of interest. For example, an employee who has previously conducted business negotiations with a specific customer is identified and matched. Step 4: The Advice Department provides advice to increase the success rate of sales negotiations based on the employees identified by the Matching Department. The Advice Department analyzes past sales negotiation data and provides success stories and specific action suggestions. For example, it makes suggestions that can be applied to current sales negotiations based on strategies and approaches that have been successful in past sales negotiations. Some or all of the processing in the Advice Department may be performed using AI.

[0070] (Example 2) A business card management and business negotiation support system according to an embodiment of the present invention collects business card information, stores it in the cloud, matches employees with internal connections, and provides advice to increase the success rate of business negotiations. The business card management and business negotiation support system collects business card information, stores it in the cloud, and matches employees with internal connections to support smooth business negotiations. For example, when exchanging business cards, the business card management and business negotiation support system stores the business cards in a dedicated application. This application scans and digitizes the business card information. For example, information such as the name, company name, job title, and contact information on the business card is automatically extracted and stored in the cloud. Next, the business card management and business negotiation support system uses AI to match employees with internal connections based on past business negotiation history during business negotiations. For example, when conducting business negotiations with a specific customer, the AI ​​identifies employees who have previously conducted business negotiations with that customer and employees who have relationships with that customer. Furthermore, the business card management and business negotiation support system uses AI to analyze past business negotiation data and provide advice to increase the success rate of business negotiations. For example, it will make suggestions that can be applied to current negotiations based on strategies and approaches that were successful in past sales negotiations. This makes it easier for business card management and sales negotiation support systems to manage business cards and improve the efficiency of sales negotiations. This also reduces the risk of losing business cards by digitizing business card information and storing it in the cloud. In addition, AI can match employees with internal connections, which is expected to help sales negotiations proceed smoothly. Furthermore, AI can analyze past sales negotiation data and provide advice to increase the success rate of sales negotiations, thereby improving the efficiency of sales activities.

[0071] A business card management and business negotiation support system according to an embodiment includes a collection unit, a storage unit, a matching unit, and an advice unit. The collection unit collects business card information. The business card information includes, but is not limited to, a name, a company name, a job title, and contact information. For example, the collection unit digitizes the business card using scanning technology and extracts information from the business card. The collection unit can also convert the business card information into text data using OCR technology. For example, the collection unit scans the business card with a high-resolution scanner and converts it into text information using OCR technology. The storage unit stores the information collected by the collection unit in a cloud. Examples of cloud services include, but are not limited to, AWS, Google Cloud, and Microsoft Azure. For example, the storage unit can encrypt the collected business card information and store it in the cloud. For example, the storage unit protects the business card information using encryption technology such as AES or RSA. The matching unit matches employees with internal connections based on the information stored by the storage unit. Matching is performed based on, for example, past business negotiation history or common areas of interest, but is not limited to such examples. For example, the matching unit identifies employees who have previously conducted business negotiations with specific customers and matches those employees. The advice unit provides advice to increase the success rate of business negotiations based on the employees identified by the matching unit. The advice may, for example, analyze past business negotiation data and provide success stories and specific action suggestions, but is not limited to such examples. For example, the advice unit makes suggestions that can be applied to current business negotiations based on strategies and approaches that were successful in past business negotiations. This makes it easier for the business card management and business negotiation support system according to the embodiment to manage business cards and improve the efficiency of business negotiations. Some or all of the above-described processing by the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may provide advice using an AI model that inputs past business negotiation data and outputs advice to increase the success rate of business negotiations.

[0072] The storage unit can encrypt and store the business card information. For example, an algorithm such as AES, RSA, or SHA-256 is used for encryption, but is not limited to these examples. For example, the storage unit encrypts the business card information using AES-256 and stores it in the cloud. The storage unit can also encrypt the business card information using RSA. For example, the storage unit encrypts the business card information using RSA and stores it in the cloud. The storage unit can also hash the business card information using SHA-256 and store it in the cloud. For example, the storage unit hashes the business card information using SHA-256 and stores it in the cloud. This improves the security of the business card information. Some 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 perform encryption using an AI model that receives business card information as input and outputs encrypted data.

[0073] The collection unit can scan and digitize information on business cards. Scanning can be performed using, for example, OCR technology, barcode scanning, or other methods, but is not limited to these. For example, the collection unit scans the business cards with a scanner and saves the data as image data. The collection unit then converts the image data into text data using OCR technology. The collection unit can also take a photo of the business card using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The collection unit can also write on the business card with a dedicated digital pen, which can then convert the writing into digital data in real time. For example, a sensor can detect the pen's movement and store it as text data. This allows for efficient digitization of business card information. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the scanned image data of the business card into a generation AI and have the generation AI convert it into text data.

[0074] The matching unit can identify employees with internal connections based on past business negotiation history. The past business negotiation history includes, for example, the date, time, content, and results of the business negotiation, but is not limited to such examples. The matching unit, for example, identifies employees who have previously conducted business negotiations with specific customers and matches those employees. The matching unit can also analyze the past business negotiation history to identify employees with common interests. For example, the matching unit can identify employees who are interested in a specific industry or topic based on the past business negotiation history. The matching unit can also identify employees with a high success rate in business negotiations based on the past business negotiation history. For example, the matching unit can identify employees with a high success rate in business negotiations based on the past business negotiation history and match those employees. This can be expected to ensure smooth progress in business negotiations. Some or all of the above-described processing by the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can identify employees using an AI model that inputs past business negotiation history and outputs the most suitable employee.

[0075] The advice unit can analyze past business negotiation data and provide advice to increase the success rate of business negotiations. Business negotiation data includes, but is not limited to, the date, time, content, and results of business negotiations. For example, the advice unit can analyze past business negotiation data and provide success stories and specific action suggestions. The advice unit can also propose strategies and approaches to increase the success rate of business negotiations based on the past business negotiation data. For example, the advice unit can make suggestions that can be applied to current business negotiations based on strategies and approaches that were successful in past business negotiations. The advice unit can also analyze past business negotiation data to identify factors that cause business negotiation failures and propose workarounds. For example, the advice unit can identify factors that cause business negotiation failures from past business negotiation data and propose workarounds. This improves the success rate of business negotiations. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can provide advice using an AI model that inputs past business negotiation data and outputs advice to increase the success rate of business negotiations.

[0076] The collection unit can guide the user through the operation procedure. The operation procedure includes, but is not limited to, step-by-step instructions, visual guides, etc. For example, when collecting business card information, the collection unit provides step-by-step instructions to the user. The collection unit can also use a visual guide to show the user the operation procedure. For example, the collection unit uses a visual guide to show how to scan a business card and how to input information. The collection unit can also use an audio guide to explain the operation procedure to the user. For example, when collecting business card information, the collection unit explains the operation procedure using an audio guide. This allows the user to collect business card information smoothly. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can guide the user through the operation procedure using an AI model that inputs the user's operation status and outputs optimal operation procedures.

[0077] The advice unit can provide specific content of advice to be provided during business negotiations. Specific content includes, but is not limited to, success stories and specific action suggestions. For example, the advice unit can provide advice based on past success stories during business negotiations. The advice unit can also provide specific action suggestions during business negotiations. For example, the advice unit can make specific action suggestions that can be applied to the current business negotiation based on strategies and approaches that were successful in past business negotiations. The advice unit can also provide advice based on the customer's needs and interests during business negotiations. For example, the advice unit can analyze the customer's needs and interests and make specific suggestions based on them. This further improves the success rate of business negotiations. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice unit can provide advice using an AI model that inputs the content of the business negotiation and outputs specific advice.

[0078] The collection unit can estimate the user's emotions and adjust the timing of collecting business card information based on the estimated user emotions. Emotion estimation can be achieved using, for example, facial expression recognition, voice analysis, or other technologies, but is not limited to these examples. For example, if the user is nervous, the collection unit can automatically collect business card information immediately after exchanging business cards. Furthermore, if the user is relaxed, the collection unit can collect business card information after a certain time has passed since exchanging business cards. For example, if the user is relaxed, the collection unit can collect business card information several minutes after exchanging business cards. Furthermore, if the user is in a hurry, the collection unit can collect business card information immediately at the same time as exchanging business cards. For example, if the user is in a hurry, the collection unit can scan and digitize the business card information at the same time as exchanging business cards. This allows business card information to be collected at the optimal timing depending on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The collection unit can select the optimal scanning method depending on the design and format of the business card. Examples of the design and format include, but are not limited to, the layout, font, and color of the business card. For example, if the business card is vertical, the collection unit selects a scanning method optimized for vertical format. Also, if the business card is horizontal, the collection unit can select a scanning method optimized for horizontal format. For example, if the business card is horizontal, the collection unit uses scanner settings optimized for horizontal format. Also, if the business card has a special design, the collection unit can select a scanning method that corresponds to the design. For example, if the business card is embossed, the collection unit selects a scanning method that corresponds to the embossing. This enables the optimal scan depending on the design and format of the business card. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input business card design data into a generation AI and have the generation AI select the optimal scanning method.

[0080] The collection unit can perform automatic translation depending on the language of the business card. For example, technologies such as machine translation and translation memory are used for the automatic translation, but are not limited to these examples. For example, if the business card is written in English, the collection unit automatically translates it into Japanese. Furthermore, if the business card is written in Chinese, the collection unit can also automatically translate it into Japanese. For example, if the business card is written in Chinese, the collection unit translates it from Chinese to Japanese. Furthermore, if the business card is written in French, the collection unit can also automatically translate it into Japanese. For example, if the business card is written in French, the collection unit translates it from French to Japanese. This allows efficient collection of business card information in different languages. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the language data of the business card into a generation AI and have the generation AI perform the translation.

[0081] The collection unit can automatically acquire social media information of the business card owner. Social media information includes, but is not limited to, information on LinkedIn, Twitter, Facebook, etc. For example, the collection unit automatically acquires the LinkedIn profile of the business card owner. The collection unit can also automatically acquire Twitter account information of the business card owner. For example, the collection unit acquires and stores Twitter account information of the business card owner. The collection unit can also automatically acquire Facebook profile of the business card owner. For example, the collection unit acquires and stores Facebook profile of the business card owner. This allows for efficient collection of additional information about the business card owner. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media information of the business card owner to the generation AI and cause the generation AI to acquire the information.

[0082] The collection unit can estimate the user's emotions and determine the priority of the business card information to be collected based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, or other technologies, but is not limited to these examples. For example, when the user is nervous, the collection unit prioritizes collecting business card information of important business partners. Furthermore, when the user is relaxed, the collection unit can collect all business card information evenly. For example, when the user is relaxed, the collection unit collects all business card information evenly. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the most recently exchanged business card information. For example, when the user is in a hurry, the collection unit prioritizes collecting the most recently exchanged business card information. This allows important business card information to be collected preferentially according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, etc. For example, when the user is in a specific area, the collection unit prioritizes collecting business card information related to that area. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting business card information related to the business trip destination. For example, when the user is on a business trip, the collection unit prioritizes collecting business card information related to the business trip destination. Furthermore, when the user is at an event venue, the collection unit can prioritize collecting business card information related to the event. For example, when the user is at an event venue, the collection unit prioritizes collecting business card information related to the event. This makes it possible to collect highly relevant business card information based on the user's location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant information.

[0084] The collection unit can customize the collection method by referring to the user's past business negotiation history. The past business negotiation history includes, for example, the date and time, content, and results of the business negotiation, but is not limited to such examples. For example, the collection unit prioritizes collecting business card information from business negotiations that the user has previously succeeded in. The collection unit can also reevaluate and collect business card information from business negotiations that the user has previously failed in. For example, the collection unit reevaluates and collects business card information from business negotiations that the user has previously failed in. The collection unit can also prioritize collecting business card information from a specific industry from the user's past business negotiation history. For example, the collection unit prioritizes collecting business card information from a specific industry from the user's past business negotiation history. This allows optimal business card information to be collected based on the past business negotiation history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past business negotiation history data into the generation AI and cause the generation AI to customize the collection method.

[0085] The collection unit can adjust the items of information to be collected depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the collection unit may prioritize collecting technical information. Furthermore, if the user is in the medical industry, the collection unit may prioritize collecting medical-related information. For example, if the user is in the medical industry, the collection unit may prioritize collecting medical-related information. Furthermore, if the user is in the financial industry, the collection unit may prioritize collecting financial-related information. For example, if the user is in the financial industry, the collection unit may prioritize collecting financial-related information. This allows optimal business card information to be collected according to the user's industry and occupation. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the items of information to be collected.

[0086] The storage unit can estimate the user's emotions and adjust the method for saving the business card information based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, or other technologies, but is not limited to these examples. For example, if the user is nervous, the storage unit can enable the user to save the business card information with a simple operation. Furthermore, the storage unit can provide more detailed saving options if the user is relaxed. For example, the storage unit can provide more detailed saving options if the user is relaxed. Furthermore, the storage unit can enable the user to save the business card information with one click if the user is in a hurry. For example, the storage unit can enable the user to save the business card information with one click if the user is in a hurry. This allows the optimal saving method to be provided depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0087] The storage unit can determine storage priorities based on the importance of the information. Examples of importance include, but are not limited to, business impact and urgency. For example, the storage unit prioritizes storage of business card information of important business partners. The storage unit can also prioritize storage of business card information of new customers. For example, the storage unit prioritizes storage of business card information of new customers. The storage unit can also prioritize storage of business card information with which a long-term relationship is expected. For example, the storage unit prioritizes storage of business card information with which a long-term relationship is expected. This allows important business card information to be stored preferentially. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input importance data of the business card information into the generation AI and have the generation AI determine the storage priority.

[0088] The storage unit can apply different storage formats depending on the category of information. Examples of categories include, but are not limited to, customer information, transaction information, and technical information. For example, the storage unit stores business card information of business partners in a specific format. The storage unit can also store business card information of new customers in a different format. For example, the storage unit stores business card information of new customers in a different format. The storage unit can also store business card information related to internal connections in a dedicated format. For example, the storage unit stores business card information related to internal connections in a dedicated format. This enables optimal storage according to the category of information. Some 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 category data of the business card information to the generation AI and cause the generation AI to apply the storage format.

[0089] The storage unit can optimize the storage method by referring to the user's past storage history. The past storage history includes, for example, the storage date and time, the storage content, and the storage format, but is not limited to these examples. The storage unit, for example, prioritizes suggesting a storage method that the user has used in the past. The storage unit can also suggest an optimal storage format based on the user's past storage history. For example, the storage unit can suggest an optimal storage format based on the user's past storage history. The storage unit can also analyze the user's past storage history and customize the storage method. For example, the storage unit analyzes the user's past storage history and customizes the storage method. This makes it possible to provide an optimal storage method based on the past storage history. Some or all of the above-described 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 user's past storage history data into a generation AI and cause the generation AI to optimize the storage method.

[0090] The storage unit can estimate the user's emotions and adjust the encryption level of the business card information to be stored based on the estimated user's emotions. Examples of encryption levels include, but are not limited to, standards such as AES-128 and AES-256. For example, if the user is nervous, the storage unit can store the business card information at a high encryption level. Alternatively, if the user is relaxed, the storage unit can store the business card information at a standard encryption level. For example, if the user is relaxed, the storage unit can store the business card information at a standard encryption level. Alternatively, if the user is in a hurry, the storage unit can select an encryption level that allows for quick storage. This allows the business card information to be stored at an optimal encryption level depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0091] The storage unit can select a storage location taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the storage unit stores the data in a cloud server associated with that area. Furthermore, if the user is on a business trip, the storage unit can store the data in a cloud server associated with the business trip destination. For example, if the user is on a business trip, the storage unit stores the data in a cloud server associated with the business trip destination. Furthermore, if the user is at an event venue, the storage unit can store the data in a cloud server associated with the event. For example, if the user is at an event venue, the storage unit stores the data in a cloud server associated with the event. This allows the optimal storage location to be selected based on the user's location information. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input the user's geographical location data to a generation AI and cause the generation AI to select a storage location.

[0092] The storage unit can customize the storage method by analyzing the user's social media activity. Social media activity includes, but is not limited to, post content, number of followers, and engagement rate. For example, if the user is active on LinkedIn, the storage unit can suggest a storage method related to LinkedIn. Furthermore, if the user is active on Twitter, the storage unit can suggest a storage method related to Twitter. For example, if the user is active on Twitter, the storage unit can suggest a storage method related to Twitter. Furthermore, if the user is active on Facebook, the storage unit can suggest a storage method related to Facebook. For example, if the user is active on Facebook, the storage unit can suggest a storage method related to Facebook. This allows the optimal storage method to be provided based on the user's social media activity. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit can input the user's social media activity data into a generation AI and have the generation AI customize the storage method.

[0093] The storage unit can adjust the items of information to be stored depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the storage unit may prioritize storing technical information. Furthermore, if the user is in the medical industry, the storage unit may prioritize storing medical-related information. For example, if the user is in the medical industry, the storage unit may prioritize storing medical-related information. Furthermore, if the user is in the financial industry, the storage unit may prioritize storing financial-related information. For example, if the user is in the financial industry, the storage unit may prioritize storing financial-related information. This enables optimal storage according to the user's industry and occupation. Some or all of the above-described processing in the storage unit may be performed using, or without, AI. For example, the storage unit may input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the items of information to be stored.

[0094] The matching unit can estimate the user's emotions and adjust matching criteria based on the estimated user emotions. Emotion estimation can use, for example, facial expression recognition, voice analysis, or other technologies, but is not limited to these examples. For example, if the user is nervous, the matching unit prioritizes matching with highly reliable employees. Furthermore, if the user is relaxed, the matching unit can also perform matching from a wide range of options. For example, if the user is relaxed, the matching unit can perform matching from a wide range of options. Furthermore, if the user is in a hurry, the matching unit can also prioritize matching with employees who can respond quickly. For example, if the user is in a hurry, the matching unit prioritizes matching with employees who can respond quickly. This makes it possible to provide optimal matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0095] The matching unit can select the optimal matching method by referring to past business negotiation history. Past business negotiation history includes, for example, the date and time, content, and results of the business negotiation, but is not limited to such examples. The matching unit, for example, matches the optimal employee based on the history of past successful business negotiations. The matching unit can also match the appropriate employee by reevaluating the history of past failed business negotiations. For example, the matching unit can match the appropriate employee by reevaluating the history of past failed business negotiations. The matching unit can also match employees who are familiar with a specific industry based on the past business negotiation history. For example, the matching unit matches employees who are familiar with a specific industry based on the past business negotiation history. This makes it possible to match the optimal employee based on the past business negotiation history. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input past business negotiation history data into the generation AI and cause the generation AI to select the optimal matching method.

[0096] The matching unit can apply different matching algorithms depending on the content and purpose of the business negotiation. Examples of the content and purpose of the business negotiation include, but are not limited to, new transactions, contract renewals, and technical proposals. For example, in the case of a business negotiation with a new customer, the matching unit matches employees with extensive sales experience. In addition, in the case of a business negotiation with an existing customer, the matching unit can also match employees who will deepen the customer relationship. For example, in the case of a business negotiation with an existing customer, the matching unit can match employees who will deepen the customer relationship. In addition, in the case of a business negotiation related to a specific project, the matching unit can match employees who are knowledgeable about that project. For example, in the case of a business negotiation related to a specific project, the matching unit matches employees who are knowledgeable about that project. This enables optimal matching depending on the content and purpose of the business negotiation. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the content and purpose data of the business negotiation into the generation AI and cause the generation AI to apply a matching algorithm.

[0097] The matching unit can improve the accuracy of matching by referring to the user's past matching results. Past matching results include, for example, matching date and time, matching content, and results, but are not limited to these examples. For example, the matching unit matches the user with the most suitable employee based on past successful matching results. The matching unit can also reevaluate past unsuccessful matching results for the user to match an appropriate employee. For example, the matching unit can reevaluate past unsuccessful matching results for the user to match an appropriate employee. The matching unit can also analyze the user's past matching results and optimize the matching algorithm. For example, the matching unit can analyze the user's past matching results and optimize the matching algorithm. This allows the most suitable employee to be matched based on the past matching results. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's past matching result data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0098] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, or other technologies, but is not limited to these examples. For example, if the user is nervous, the matching unit prioritizes matching with highly reliable employees. Furthermore, if the user is relaxed, the matching unit can also perform matching from a wide range of options. For example, if the user is relaxed, the matching unit can perform matching from a wide range of options. Furthermore, if the user is in a hurry, the matching unit can also prioritize matching with employees who can respond quickly. For example, if the user is in a hurry, the matching unit prioritizes matching with employees who can respond quickly. This allows for optimal matching priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0099] The matching unit can prioritize matching highly relevant employees by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, etc. For example, if the user is in a specific area, the matching unit prioritizes matching with employees related to that area. Furthermore, if the user is on a business trip, the matching unit can also prioritize matching with employees related to the business trip destination. For example, if the user is on a business trip, the matching unit prioritizes matching with employees related to the business trip destination. Furthermore, if the user is at an event venue, the matching unit can also prioritize matching with employees related to the event. For example, if the user is at an event venue, the matching unit prioritizes matching with employees related to the event. This makes it possible to match optimal employees based on the user's location information. Some or all of the above-described processing by the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location data into the generation AI and cause the generation AI to match highly relevant employees.

[0100] The matching unit can analyze the user's social media activity to improve the accuracy of matching. Social media activity includes, but is not limited to, post content, number of followers, and engagement rate. For example, if the user is active on LinkedIn, the matching unit may prioritize matching with employees associated with LinkedIn. Furthermore, if the user is active on Twitter, the matching unit may prioritize matching with employees associated with Twitter. For example, if the user is active on Twitter, the matching unit may prioritize matching with employees associated with Twitter. Furthermore, if the user is active on Facebook, the matching unit may prioritize matching with employees associated with Facebook. For example, if the user is active on Facebook, the matching unit may prioritize matching with employees associated with Facebook. This allows matching with the optimal employee based on the user's social media activity. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit may input the user's social media activity data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0101] The matching unit can adjust the attributes of the employees to be matched depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the matching unit prioritizes matching with employees with technical knowledge. Furthermore, if the user is in the medical industry, the matching unit can also prioritize matching with employees with medical-related knowledge. For example, if the user is in the medical industry, the matching unit prioritizes matching with employees with medical-related knowledge. Furthermore, if the user is in the financial industry, the matching unit can also prioritize matching with employees with financial-related knowledge. For example, if the user is in the financial industry, the matching unit prioritizes matching with employees with financial-related knowledge. This allows the optimal employee to be matched depending on the user's industry and occupation. Some or all of the above-described processing by the matching unit may be performed using, or without, AI. For example, the matching unit can input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the attributes of the employees to be matched.

[0102] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, or other technologies, but is not limited to these examples. For example, if the user is nervous, the advice unit can provide advice to help the user relax. Furthermore, the advice unit can also provide detailed advice if the user is relaxed. For example, the advice unit can provide detailed advice if the user is relaxed. Furthermore, the advice unit can also provide advice that can be implemented quickly if the user is in a hurry. For example, the advice unit can provide advice that can be implemented quickly if the user is in a hurry. This allows optimal advice to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the advice unit can be performed using, for example, an AI. For example, the advice unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0103] The advice unit can select the optimal advice method by referring to past business negotiation data. Past business negotiation data includes, for example, the date and time, content, and results of business negotiations, but is not limited to these examples. The advice unit provides optimal advice based on, for example, data from successful business negotiations in the past. The advice unit can also reevaluate data from failed business negotiations in the past to provide appropriate advice. For example, the advice unit can reevaluate data from failed business negotiations in the past to provide appropriate advice. The advice unit can also provide advice that is familiar with a specific industry from past business negotiation data. For example, the advice unit can provide advice that is familiar with a specific industry from past business negotiation data. This allows optimal advice to be provided based on the past business negotiation data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input past business negotiation data into the generation AI and have the generation AI select the optimal advice method.

[0104] The advice unit can apply different advice algorithms depending on the content and purpose of the business negotiation. Examples of the content and purpose of the business negotiation include, but are not limited to, new transactions, contract renewals, and technical proposals. For example, in a business negotiation with a new customer, the advice unit provides advice based on extensive sales experience. Furthermore, in a business negotiation with an existing customer, the advice unit can also provide advice for deepening the customer relationship. For example, in a business negotiation with an existing customer, the advice unit can provide advice for deepening the customer relationship. Furthermore, in a business negotiation related to a specific project, the advice unit can provide detailed advice for the project. For example, in a business negotiation related to a specific project, the advice unit provides detailed advice for the project. This enables optimal advice to be provided according to the content and purpose of the business negotiation. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the content and purpose data of the business negotiation into the generation AI and have the generation AI apply the advice algorithm.

[0105] The advice unit can improve the accuracy of advice by referring to the user's past advice results. Past advice results include, for example, advice date and time, advice content, and results, but are not limited to these examples. The advice unit provides optimal advice based on advice results that the user has previously succeeded in. The advice unit can also reevaluate advice results that the user has previously failed in and provide appropriate advice. For example, the advice unit can reevaluate advice results that the user has previously failed in and provide appropriate advice. The advice unit can also analyze the user's past advice results and optimize the advice algorithm. For example, the advice unit analyzes the user's past advice results and optimizes the advice algorithm. This makes it possible to provide optimal advice based on the past advice results. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0106] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. Emotion estimation can use, for example, facial expression recognition, voice analysis, or other technologies, but is not limited to, these examples. For example, if the user is nervous, the advice unit can prioritize providing advice that helps the user relax. Furthermore, the advice unit can also prioritize providing detailed advice if the user is relaxed. For example, if the user is relaxed, the advice unit can prioritize providing detailed advice. Furthermore, if the user is in a hurry, the advice unit can prioritize providing advice that can be implemented quickly. For example, if the user is in a hurry, the advice unit prioritizes advice that can be implemented quickly. This allows optimal advice prioritization to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the advice unit can be performed, for example, using AI, or without AI. For example, the advice unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0107] The advice unit can prioritize providing highly relevant advice by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the advice unit can prioritize providing advice related to that area. Furthermore, if the user is on a business trip, the advice unit can prioritize providing advice related to the business trip destination. For example, if the user is on a business trip, the advice unit can prioritize providing advice related to the business trip destination. Furthermore, if the user is at an event venue, the advice unit can prioritize providing advice related to the event. For example, if the user is at an event venue, the advice unit can prioritize providing advice related to the event. This makes it possible to provide optimal advice based on the user's location information. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the user's geographical location data to the generation AI and cause the generation AI to provide highly relevant advice.

[0108] The advice unit can analyze the user's social media activity to improve the accuracy of the advice. Social media activity includes, but is not limited to, post content, number of followers, and engagement rate. For example, if the user is active on LinkedIn, the advice unit can provide advice related to LinkedIn. Furthermore, if the user is active on Twitter, the advice unit can also provide advice related to Twitter. For example, if the user is active on Twitter, the advice unit can provide advice related to Twitter. Furthermore, if the user is active on Facebook, the advice unit can also provide advice related to Facebook. For example, if the user is active on Facebook, the advice unit can provide advice related to Facebook. This allows optimal advice to be provided based on the user's social media activity. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the user's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0109] The advice unit can adjust the content of the advice depending on the user's industry and occupation. Examples of industries and occupations include, but are not limited to, the IT industry, manufacturing, sales, and technical occupations. For example, if the user is in the IT industry, the advice unit can provide technical advice. Furthermore, if the user is in the medical industry, the advice unit can provide medical-related advice. For example, if the user is in the medical industry, the advice unit can provide medical-related advice. Furthermore, if the user is in the financial industry, the advice unit can provide financial-related advice. For example, if the user is in the financial industry, the advice unit can provide financial-related advice. This allows the provision of optimal advice depending on the user's industry and occupation. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the user's industry and occupation data into the generation AI and cause the generation AI to adjust the content of the advice. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, storage unit, matching unit, and advice unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects business card information using the camera 42 and microphone 38B of the smart device 14 and digitizes the information using the control unit 46A. The storage unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, stores the collected business card information in the cloud. The matching unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, matches employees with internal connections based on the stored information. The advice unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes past business negotiation data and provides advice to increase the success rate of business negotiations. The collection unit, for example, can estimate a user's emotions and adjust the timing of collecting business card information based on the estimated user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, storage unit, matching unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects business card information using the camera 42 and microphone 238 of the smart glasses 214 and digitizes the information using the control unit 46A. The storage unit, realized, for example, by the specific processing unit 290 of the data processing device 12, stores the collected business card information in the cloud. The matching unit, realized, for example, by the specific processing unit 290 of the data processing device 12, matches employees with internal connections based on the stored information. The advice unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes past business negotiation data and provides advice to increase the success rate of business negotiations. The collection unit, for example, can estimate a user's emotions and adjust the timing of collecting business card information based on the estimated user's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, storage unit, matching unit, and advice unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects business card information using the camera 42 and microphone 238 of the headset-type terminal 314 and digitizes the information using the control unit 46A. The storage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and stores the collected business card information in the cloud. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and matches employees with internal connections based on the stored information. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past business negotiation data and provides advice to increase the success rate of business negotiations. The collection unit can, for example, estimate a user's emotions and adjust the timing of collecting business card information based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, storage unit, matching unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects business card information using the camera 42 and microphone 238 of the robot 414 and digitizes the information using the control unit 46A. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores the collected business card information in the cloud. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches employees with internal connections based on the stored information. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes past business negotiation data and provides advice to increase the success rate of business negotiations. The collection unit can, for example, estimate a user's emotions and adjust the timing of collecting business card information based on the estimated user's emotions.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] When collecting business card information, the collection unit can suggest an optimal collection method by referring to the user's past business card collection history. For example, if the user previously collected business cards using a specific scanner, the collection unit can suggest using that scanner again. Also, if the user previously collected business cards using a specific application, the collection unit can suggest using that application again. Furthermore, if the user previously collected business cards during a specific time period, the collection unit can suggest collecting business cards during that time period. In this way, the collection unit can provide an optimal collection method based on the user's past collection history.

[0112] When saving business card information, the saving unit can adjust the saving timing taking into account the user's work schedule. For example, if the user is in a meeting, it can suggest saving the business card information after the meeting ends. Also, if the user is on a business trip, it can suggest saving the business card information after work at the business trip destination is completed. Furthermore, if the user is on vacation, it can suggest saving the business card information after the vacation ends. This makes it possible to provide the optimal saving timing according to the user's work schedule.

[0113] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, if the user is nervous, it can prioritize matching with highly reliable employees. Also, if the user is relaxed, it can perform matching from a wide range of options. Furthermore, if the user is in a hurry, it can prioritize matching with employees who can respond quickly. This makes it possible to provide optimal matching criteria according to the user's emotions.

[0114] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is nervous, the advice unit can provide advice to help the user relax. If the user is relaxed, the advice unit can also provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can also provide advice that can be implemented quickly. In this way, the advice unit can provide optimal advice according to the user's emotions.

[0115] When collecting business card information, the collection unit can propose an optimal collection method taking into account the user's geographical location information. For example, if the user is in a specific area, business card information related to that area can be collected preferentially. Also, if the user is on a business trip, business card information related to the business trip destination can be collected preferentially. Furthermore, if the user is at an event venue, business card information related to the event can be collected preferentially. This allows optimal business card information to be collected based on the user's location information.

[0116] The storage unit can estimate the user's emotions when saving business card information and adjust the saving method based on the estimated user emotions. For example, if the user is nervous, the storage unit can allow the user to save the business card information with a simple operation. If the user is relaxed, the storage unit can provide detailed saving options. Furthermore, if the user is in a hurry, the storage unit can allow the user to save the business card information with one click. This allows the storage unit to provide the optimal saving method according to the user's emotions.

[0117] The matching unit can select the optimal matching method by referring to the user's past business negotiation history. For example, it can match the optimal employee based on the history of past successful business negotiations. It can also reevaluate the history of past unsuccessful business negotiations to match the appropriate employee. It can also match employees who are familiar with a specific industry based on the past business negotiation history. This makes it possible to match the optimal employee based on the past business negotiation history.

[0118] The advice unit can improve the accuracy of advice by referring to past advice results for the user. For example, it can provide optimal advice based on advice results that were successful in the past. It can also reevaluate advice results that were unsuccessful in the past and provide appropriate advice. It can also analyze past advice results and optimize the advice algorithm. This makes it possible to provide optimal advice based on past advice results.

[0119] When collecting business card information, the collection unit can adjust the items of information to be collected depending on the user's industry and occupation. For example, if the user is in the IT industry, technical information can be collected with priority. Also, if the user is in the medical industry, medical-related information can be collected with priority. Furthermore, if the user is in the financial industry, financial-related information can be collected with priority. This makes it possible to collect optimal business card information depending on the user's industry and occupation.

[0120] When saving business card information, the saving unit can determine the priority of saving based on the importance of the information. For example, the saving unit can prioritize saving business card information of important business partners. It can also prioritize saving business card information of new customers. It can also prioritize saving business card information with which a long-term relationship is expected. This allows important business card information to be saved preferentially.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection department collects business card information. The business card information includes name, company name, job title, contact information, etc. The collection department digitizes the business card using scanning technology and extracts the information. The collection department also uses OCR technology to convert the business card information into text data. For example, the collection department scans the business card with a high-resolution scanner and converts it into text information using OCR technology. Step 2: The storage unit stores the information collected by the collection unit in the cloud. Cloud services include AWS, Google Cloud, and Microsoft Azure. The storage unit can also encrypt the collected business card information before storing it in the cloud. For example, encryption technologies such as AES and RSA are used to protect the business card information. Step 3: The matching unit matches employees with internal connections based on the information stored by the storage unit. Matching is performed based on past business negotiation history and common areas of interest. For example, an employee who has previously conducted business negotiations with a specific customer is identified and matched. Step 4: The Advice Department provides advice to increase the success rate of sales negotiations based on the employees identified by the Matching Department. The Advice Department analyzes past sales negotiation data and provides success stories and specific action suggestions. For example, it makes suggestions that can be applied to current sales negotiations based on strategies and approaches that have been successful in past sales negotiations. Some or all of the processing in the Advice Department may be performed using AI.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] 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.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] 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.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] 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.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects business card information; a storage unit that stores the information collected by the collection unit in a cloud; a matching unit that matches employees who have internal connections based on the information stored by the storage unit; an advice unit that provides advice to increase the success rate of business negotiations based on the employee identified by the matching unit; A system characterized by:

2. The storage unit Encrypt and store business card information 2. The system of claim 1.

3. The collecting unit Scan and digitize business card information 2. The system of claim 1.

4. The matching unit Identify employees with internal connections based on past business dealings 2. The system of claim 1.

5. The advice unit Analyze past sales data and provide advice to increase the success rate of sales.

2. The system of claim 1.

6. The collecting unit Guide the user through the operation procedure 2. The system of claim 1.

7. The advice unit Provide specific advice to be given during business negotiations 2. The system of claim 1.

8. The collecting unit To estimate a user's emotion and adjust the timing of collecting business card information based on the estimated user's emotion.

2. The system of claim 1.

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