system

Abstract

The system according to this embodiment aims to automatically classify received emails, score their priority, and display them. [Solution] The system according to the embodiment comprises a classification unit, a scoring unit, and a display unit. The classification unit automatically classifies received emails. The scoring unit scores priority based on the emails classified by the classification unit. The display unit displays the priority scored by the scoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the classification and priority determination of received mails are performed manually, the efficiency is low and there is a risk of overlooking important mails.

[0005] The system according to the embodiment aims to automatically classify received mails, score the priorities, and display them.

Means for Solving the Problems

[0006] The system according to the embodiment includes a classification unit, a scoring unit, and a display unit. The classification unit automatically classifies received mails. The scoring unit scores the priorities based on the mails classified by the classification unit. The display unit displays the priorities scored by the scoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically classify received emails, score them by priority, and display them. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The corporate order receiving efficiency system according to an embodiment of the present invention is a system that streamlines corporate order receiving operations by utilizing natural language processing (NLP) and AI. This system consists of the following steps. First, a mail classification system utilizing natural language processing (NLP) is constructed. AI is used to automatically classify received mail and sort it by service and priority. By detecting specific keywords and phrases and dividing mail into categories, priorities are clarified. For example, mail containing keywords such as "urgent" or "important" is classified as having a high priority. Next, priority recommendations are made using a priority algorithm. An algorithm is designed to score priority for each mail and notify the person in charge of processing of the priority. For example, it is set to give a high score to cases with approaching deadlines or requests from specific important customers. This allows the person in charge of processing to quickly determine which mail should be prioritized. Furthermore, a dashboard is created that allows the priority and status to be checked. Management performs task management based on the priorities suggested by the AI. The dashboard displays the priority and processing status of each mail at a glance. This makes it easier for management to grasp the overall progress. In addition, a reminder notification system is introduced. Using a tool with a calendar function linked to the customer database, the system automatically tracks each customer's contract renewal date. A few weeks before the renewal month, AI generates and sends personalized renewal reminder emails. This ensures that customers do not forget their contract renewal and can complete the process smoothly. Finally, a customer portal is implemented. A system is built that allows customers to complete orders themselves, with chatbots answering any questions. This allows customers to complete the process themselves without the need for a representative. This system dramatically streamlines corporate order processing, reduces email response delays, and improves customer satisfaction. Thus, the corporate order processing efficiency system can streamline corporate order processing, reduce email response delays, and improve customer satisfaction.

[0029] The corporate order receiving efficiency system according to this embodiment comprises a classification unit, a scoring unit, and a display unit. The classification unit automatically classifies received emails. The classification unit, for example, detects specific keywords or phrases and divides emails into categories. The classification unit determines categories based on the content of emails, for example, using keyword matching technology. The classification unit can also analyze the content of emails using machine learning algorithms and classify them into appropriate categories. For example, the classification unit analyzes the content of emails using natural language processing technology and detects specific keywords or phrases. The classification unit classifies emails containing keywords such as "urgent" or "important" as having a high priority. The scoring unit scores the priority of emails based on the classification of the classification unit. The scoring unit, for example, assigns high scores to projects with approaching deadlines or requests from specific important customers. The scoring unit calculates scores based on factors such as proximity of deadlines and customer importance. The scoring unit can also score the priority of emails using machine learning algorithms. For example, the scoring unit learns from past data and builds a model to predict email priority. The scoring unit assigns high scores to cases with approaching deadlines and low scores to cases of low importance. The display unit displays the priority assigned by the scoring unit. The display unit provides a dashboard where priority and status can be checked. The display unit displays the priority and processing status of each email on the dashboard. The display unit also has a notification function and can send notifications when important emails are received. For example, the display unit sends a notification to the person in charge of processing based on the priority of the email. As a result, the corporate order receiving efficiency system according to the embodiment streamlines email management by automatically classifying received emails, scoring their priority, and displaying them. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display the priority using an AI model that takes the priority assigned by the scoring unit as input and displays the priority.

[0030] The classification unit automatically categorizes received emails. For example, it detects specific keywords or phrases and divides emails into categories. Specifically, it uses keyword matching technology to determine categories based on the content of the email. For example, it detects keywords such as "order," "inquiry," and "complaint" and classifies them into the respective categories. The classification unit can also use machine learning algorithms to analyze the content of emails and classify them into appropriate categories. For example, it uses natural language processing technology to analyze the content of emails and detect specific keywords or phrases. This enables classification that understands the context, not just simple keyword matching. Furthermore, the classification unit can perform more accurate classification by considering information such as the email sender and recipient, and past email exchange history. For example, it can be set to automatically classify emails from specific customers into the "important customer" category. In this way, the classification unit efficiently and accurately classifies received emails and provides a foundation for smooth subsequent processing.

[0031] The scoring unit assigns priority scores to emails classified by the classification unit. For example, the scoring unit assigns high scores to projects with approaching deadlines or requests from specific important clients. Specifically, it calculates scores based on the proximity of deadlines and the importance of the client. For instance, it assigns high scores to projects with deadlines within one week and low scores to projects with deadlines more than one month away. The scoring unit can also use machine learning algorithms to score email priorities. For example, it can build a model that learns from past data to predict email priorities. This model extracts features to predict priority based on past email processing results and customer feedback, and calculates a score. Furthermore, the scoring unit can perform more accurate scoring by considering the content of the email, sender information, and past communication history. For example, it assigns high scores to emails containing specific keywords or phrases and low scores to emails containing less important keywords. This allows the scoring unit to efficiently and accurately score email priorities, providing a foundation for smooth subsequent processing.

[0032] The display unit shows the priority scored by the scoring unit. The display unit provides a dashboard where, for example, priority and status can be checked. Specifically, it displays the priority and processing status of each email on the dashboard. For example, it provides a visually easy-to-understand interface, such as displaying high-priority emails in red and low-priority emails in blue. The display unit also has a notification function and can notify when important emails are received. For example, it sends a notification to the person in charge of processing based on the email's priority. Notifications can be sent in multiple ways, such as pop-up notifications, email notifications, and SMS notifications. Furthermore, the display unit updates the processing status of emails in real time, making it easier for the person in charge to understand the current status. For example, emails being processed are displayed in yellow and completed emails are displayed in green. The display unit also has a function to refer to the processing history of past emails, allowing users to check past handling status. In this way, the display unit streamlines email management and helps the person in charge to respond quickly and appropriately. Furthermore, some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can take the priority scored by the scoring unit as input and display the priority using an AI model that displays the priority. This allows the display unit to provide more advanced display functions and further streamline email management.

[0033] The reminder notification system comprises a collection unit with a calendar function linked to a customer database, and a generation unit that generates personalized renewal reminder emails based on the information collected by the collection unit. The collection unit links with the customer database to automatically determine each customer's contract renewal date. For example, the collection unit retrieves information about contract renewal dates from the customer database and registers it in the calendar. The collection unit manages contract renewal dates by linking with the calendar function. The generation unit generates personalized renewal reminder emails based on the information collected by the collection unit. The generation unit customizes the content of the reminders based on, for example, the customer's past behavior history and contract details. For example, the generation unit sends reminder emails to customers several weeks before the contract renewal month. The generation unit can also automatically generate the content of the reminders using, for example, AI. For example, the generation unit analyzes the customer's past behavior history and generates the optimal reminder content. This allows for a smooth customer contract renewal process by linking with the customer database and generating personalized renewal reminder emails. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information obtained from the customer database into the generation AI and have the generation AI execute the reminder content.

[0034] The customer portal comprises a reception section where customers can complete their orders themselves, and an answer section where a chatbot answers any questions. The reception section allows customers to complete their orders themselves. For example, the reception section provides an online form, allowing customers to complete their orders by entering the necessary information. The reception section can also, for example, integrate with a payment system, allowing customers to complete payments online. The reception section provides, for example, a function that allows customers to check the progress of their orders. The answer section uses a chatbot to answer any questions. The answer section provides appropriate answers to customer questions, for example, using natural language processing technology. The answer section provides FAQ-based answers to resolve customer doubts, for example. The answer section can also generate optimal answers to customer questions using AI, for example. For example, the answer section uses an AI model that analyzes customer questions and generates optimal answers. This improves customer convenience by allowing customers to complete their orders themselves and having a chatbot answer any questions. Some or all of the above-described processes in the answer section may be performed using AI, for example, or without AI. For example, the answering unit can input customer questions into a generation AI and have the AI ​​generate the optimal answer.

[0035] The classification unit can detect specific keywords and phrases and categorize emails. For example, the classification unit can determine a category based on the content of an email using keyword matching technology. The classification unit can also analyze the content of an email and classify it into an appropriate category using machine learning algorithms. For example, the classification unit can analyze the content of an email and detect specific keywords and phrases using natural language processing technology. For example, the classification unit can classify emails containing keywords such as "urgent" or "important" as having a high priority. This improves the accuracy of email classification by detecting specific keywords and phrases and categorizing emails. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of an email into a generating AI and have the generating AI perform keyword and phrase detection.

[0036] The scoring unit can assign high scores to projects with approaching deadlines or requests from specific important clients. The scoring unit calculates scores based, for example, on the proximity of deadlines or the importance of the client. The scoring unit can also score email priorities using, for example, machine learning algorithms. For example, the scoring unit learns from past data and builds a model to predict email priorities. For example, the scoring unit assigns high scores to projects with approaching deadlines and low scores to projects of low importance. This allows for quicker responses to high-priority emails by assigning high scores to projects with approaching deadlines or requests from specific important clients. Some or all of the above processing in the scoring unit may be performed using, for example, AI, or not using AI. For example, the scoring unit can input the content of an email into a generating AI and have the generating AI perform priority scoring.

[0037] The display unit can provide a dashboard that allows users to check priorities and statuses. For example, the display unit can display the priority and processing status of each email on the dashboard. The display unit can also have features such as graph display and filtering to facilitate email management. The display unit can also have a notification function to notify users when important emails are received. For example, the display unit can send notifications to the person in charge of processing based on the priority of the email. This makes email management easier by providing a dashboard that allows users to check priorities and statuses. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take the priority scored by the scoring unit as input and display the priority using an AI model that displays the priority.

[0038] The classification unit can analyze the sender's past behavior history to improve classification accuracy. For example, the classification unit analyzes the content of emails the sender has sent in the past and classifies emails with similar content into the same category. For example, the classification unit estimates the importance of the current email based on the importance of emails the sender has sent in the past. For example, the classification unit estimates the urgency of the current email based on the response speed of emails the sender has sent in the past. In this way, the classification accuracy of emails is improved by analyzing the sender's past behavior history. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the sender's past behavior history data into a generating AI and have the generating AI perform the task of improving classification accuracy.

[0039] The classification unit can automatically determine the urgency and importance of emails based on their content and classify them accordingly. For example, if an email contains keywords such as "urgent" or "immediate," the classification unit will classify it into a high-urgency category. For example, if an email contains keywords such as "important" or "priority," the classification unit will classify it into a high-importance category. For example, if an email contains a specific project name or customer name, the classification unit will classify it into a category related to that project or customer. This makes email classification more accurate by determining urgency and importance based on the content of the email. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of an email into a generating AI and have the generating AI perform the determination of urgency and importance.

[0040] The classification unit can prioritize the classification of emails based on their relevance, taking into account the geographical location information of the email sender. For example, the classification unit may prioritize displaying emails sent from nearby areas. For example, the classification unit may classify emails sent from a specific area into categories related to that area. For example, the classification unit may prioritize the display of emails based on the sender's geographical location information. In this way, by considering the geographical location information of the email sender, it is possible to prioritize the classification of emails based on their relevance. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit may input the sender's geographical location information into a generating AI and have the generating AI perform the classification of highly relevant emails.

[0041] The classification unit can automatically attach relevant literature and materials based on the content of the email. For example, if the email body contains a specific project name, the classification unit will automatically attach literature and materials related to that project. For example, if the email body contains a specific technical term, the classification unit will automatically attach literature and materials related to that technology. For example, if the email body contains a specific customer name, the classification unit will automatically attach literature and materials related to that customer. This enriches the information in emails by automatically attaching relevant literature and materials based on the content. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of the email into a generation AI and have the generation AI attach relevant literature and materials.

[0042] The scoring unit can analyze the sender's past behavioral history to improve the accuracy of scoring. For example, the scoring unit can analyze the content of emails the sender has sent in the past and assign higher scores to emails with similar content. For example, the scoring unit can adjust the score of the current email based on the importance of emails the sender has sent in the past. For example, the scoring unit can adjust the score of the current email based on the response speed of emails the sender has sent in the past. In this way, the accuracy of scoring is improved by analyzing the sender's past behavioral history. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the sender's past behavioral history data into a generating AI and have the generating AI perform the improvement of scoring accuracy.

[0043] The scoring unit can automatically determine the urgency and importance of emails based on their content and assign a score. For example, the scoring unit will assign a high score to emails that contain keywords such as "urgent" or "immediate." For example, the scoring unit will assign a high score to emails that contain keywords such as "important" or "priority." For example, the scoring unit will assign a high score to emails related to a specific project or customer if the email body contains that project or customer name. This makes the scoring more accurate by determining urgency and importance based on the content of the emails. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the content of emails into a generating AI and have the generating AI perform the determination of urgency and importance.

[0044] The scoring unit can prioritize scoring emails that are highly relevant by considering the geographical location information of the email sender. For example, the scoring unit may give a high score to emails sent by the sender from a nearby area. For example, the scoring unit may classify emails sent by the sender from a specific area into categories related to that area and give them a high score. For example, the scoring unit may give a high score to emails that are highly relevant based on the sender's geographical location information. In this way, by considering the geographical location information of the email sender, it is possible to prioritize scoring emails that are highly relevant. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit may input the sender's geographical location information into a generating AI and have the generating AI perform the scoring of highly relevant emails.

[0045] The scoring unit can automatically attach relevant literature and materials based on the content of the email and perform scoring. For example, if the email body contains a specific project name, the scoring unit will automatically attach literature and materials related to that project and assign a high score. For example, if the email body contains a specific technical term, the scoring unit will automatically attach literature and materials related to that technology and assign a high score. For example, if the email body contains a specific customer name, the scoring unit will automatically attach literature and materials related to that customer and assign a high score. This improves the accuracy of scoring by automatically attaching relevant literature and materials based on the content of the email. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the content of the email into a generating AI and have the generating AI attach relevant literature and materials.

[0046] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may suggest the optimal display method based on the display methods the user has used in the past. For example, the display unit may select a display method with high visibility from the user's past operation history. For example, the display unit may analyze the user's past operation history and suggest the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0047] The display unit can adjust the level of detail displayed based on the importance of the email. For example, the display unit can display detailed information for high-importance emails and concise information for low-importance emails. For example, the display unit can display detailed information for urgent emails and concise information for low-importance emails. For example, the display unit can display detailed information for emails related to a specific project or customer and concise information for other emails. This allows important information to be displayed preferentially by adjusting the level of detail based on the importance of the email. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input email importance data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0048] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit provides a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0049] The display unit can adjust the display order based on the relevance of emails during display. For example, the display unit may display highly relevant emails at the top and less relevant emails at the bottom. For example, the display unit may display emails related to a specific project or customer at the top and other emails at the bottom. For example, the display unit may display highly urgent emails at the top and less urgent emails at the bottom. In this way, by adjusting the display order based on the relevance of emails, highly relevant information can be displayed preferentially. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input email relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0050] The data collection unit can select the optimal data collection method by referring to the user's past behavior history during data collection. For example, the data collection unit may prioritize collecting information sources that the user has frequently accessed in the past. For example, the data collection unit may prioritize collecting highly relevant information from the user's past behavior history. For example, the data collection unit may analyze the user's past behavior history and propose the most efficient data collection method. In this way, the optimal data collection method can be provided by referring to the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0051] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, if the user is in a nearby area, the data collection unit will prioritize the collection of information related to that area. For example, if the user is in a specific area, the data collection unit will prioritize the collection of information related to that area. For example, the data collection unit will prioritize the collection of highly relevant information based on the user's geographical location information. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0052] The generation unit can select the optimal generation method by referring to the user's past behavior history during generation. For example, the generation unit can generate emails with similar content based on the content of emails previously sent by the user. For example, the generation unit can generate emails containing highly relevant information from the user's past behavior history. For example, the generation unit can analyze the user's past behavior history and propose the most efficient generation method. In this way, the optimal generation method can be provided by referring to the user's past behavior history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past behavior history data into a generation AI and have the generation AI select the optimal generation method.

[0053] The generation unit can adjust the level of detail in the generated emails based on their importance. For example, the generation unit can generate emails with detailed information for high-importance emails and emails with concise information for low-importance emails. For example, the generation unit can generate emails with detailed information for urgent emails and emails with concise information for low-importance emails. For example, the generation unit can generate emails with detailed information for emails related to specific projects or customers and emails with concise information for other emails. This allows for the priority generation of important information by adjusting the level of detail based on the importance of the emails. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input email importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the generated emails.

[0054] The generation unit can prioritize generating highly relevant emails by considering the user's geographical location information during generation. For example, if the user is in a nearby area, the generation unit will prioritize generating emails related to that area. For example, if the user is in a specific area, the generation unit will prioritize generating emails related to that area. For example, the generation unit will prioritize generating highly relevant emails based on the user's geographical location information. This allows for the priority generation of highly relevant emails by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of highly relevant emails.

[0055] The generation unit can adjust the generation order based on the relevance of the emails during generation. For example, the generation unit can generate highly relevant emails at the top and less relevant emails at the bottom. For example, the generation unit can generate emails related to a specific project or customer at the top and other emails at the bottom. For example, the generation unit can generate highly urgent emails at the top and less urgent emails at the bottom. In this way, by adjusting the generation order based on the relevance of the emails, highly relevant emails can be generated preferentially. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input email relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0056] The reception unit can select the optimal reception method by referring to the user's past behavior history at the time of reception. For example, the reception unit may propose the optimal reception method based on the reception methods the user has used in the past. For example, the reception unit may select a highly relevant reception method from the user's past behavior history. For example, the reception unit may analyze the user's past behavior history and propose the most efficient reception method. In this way, the optimal reception method can be provided by referring to the user's past behavior history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's past behavior history data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0057] The reception unit can select a highly relevant reception method at the time of reception, taking into account the user's geographical location information. For example, if the user is in a nearby area, the reception unit will prioritize providing a reception method related to that area. For example, if the user is in a specific area, the reception unit will prioritize providing a reception method related to that area. For example, the reception unit will prioritize providing a highly relevant reception method based on the user's geographical location information. In this way, a highly relevant reception method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of a highly relevant reception method.

[0058] The response unit can select the optimal response method by referring to the user's past behavior history when responding. For example, the response unit may provide a similar response based on the content of responses the user has received in the past. For example, the response unit may provide a response containing highly relevant information from the user's past behavior history. For example, the response unit may analyze the user's past behavior history and propose the most efficient response method. In this way, the optimal response method can be provided by referring to the user's past behavior history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal response method.

[0059] The response unit can prioritize providing highly relevant answers by considering the user's geographical location information when a response is submitted. For example, if the user is in a nearby area, the response unit will prioritize providing answers related to that area. For example, if the user is in a specific area, the response unit will prioritize providing answers related to that area. For example, the response unit will prioritize providing highly relevant answers based on the user's geographical location information. In this way, by considering the user's geographical location information, highly relevant answers can be prioritized. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant answers.

[0060] The response unit can select the most appropriate response method when responding, taking into account the user's current situation. For example, if the user is on the move, the response unit provides a concise and easily readable response. If the user is using a desktop computer, the response unit provides a response containing detailed information. If the user is using a smartphone, the response unit provides a concise response adapted to the screen size. This allows the response unit to provide the most appropriate response method by considering the user's current situation. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's current situation data into a generating AI and have the generating AI select the most appropriate response method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The classification unit can analyze the sender's past behavior history to improve classification accuracy. For example, it can analyze the content of emails the sender has sent in the past and classify emails with similar content into the same category. It can estimate the importance of the current email based on the importance of emails the sender has sent in the past. It can estimate the urgency of the current email based on the response speed of emails the sender has sent in the past. In this way, analyzing the sender's past behavior history improves the accuracy of email classification. Some or all of the above processing in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the sender's past behavior history data into a generating AI and have the generating AI perform the task of improving classification accuracy.

[0063] The scoring unit can analyze the sender's past behavioral history to improve the accuracy of scoring. For example, it can analyze the content of emails the sender has sent in the past and assign higher scores to emails with similar content. It can adjust the score of the current email based on the importance of emails the sender has sent in the past. It can also adjust the score of the current email based on the response speed of emails the sender has sent in the past. In this way, the accuracy of scoring is improved by analyzing the sender's past behavioral history. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input the sender's past behavioral history data into a generating AI and have the generating AI perform the task of improving the accuracy of scoring.

[0064] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, it can suggest the optimal display method based on the display methods the user has used in the past. It can select a display method with high visibility from the user's past operation history. It can analyze the user's past operation history and suggest the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0065] The data collection unit can select the optimal data collection method by referring to the user's past behavior history during data collection. For example, it can prioritize collecting information sources that the user has frequently accessed in the past. It can also prioritize collecting highly relevant information from the user's past behavior history. It can analyze the user's past behavior history and propose the most efficient data collection method. In this way, the optimal data collection method can be provided by referring to the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0066] The generation unit can select the optimal generation method by referring to the user's past behavior history during generation. For example, it can generate emails with similar content based on the content of emails the user has sent in the past. It can also generate emails containing highly relevant information from the user's past behavior history. It can analyze the user's past behavior history and propose the most efficient generation method. In this way, the optimal generation method can be provided by referring to the user's past behavior history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past behavior history data into a generation AI and have the generation AI select the optimal generation method.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The classification unit automatically categorizes received emails. The classification unit detects specific keywords and phrases and divides emails into categories. For example, it uses keyword matching technology or machine learning algorithms to determine categories based on the content of the emails. It can also use natural language processing technology to analyze the content of emails and detect specific keywords and phrases. For example, it can classify emails containing keywords such as "urgent" or "important" as having a high priority. Step 2: The scoring unit scores priority based on the emails classified by the classification unit. The scoring unit assigns higher scores to projects with approaching deadlines and requests from specific important customers. The score is calculated based on the proximity of the deadline and the importance of the customer. Machine learning algorithms can also be used to build a model that learns from past data and predicts email priority. Step 3: The display unit shows the priority scored by the scoring unit. The display unit provides a dashboard where you can check the priority and status, showing the priority and processing status of each email. It also has a notification function and can notify you when an important email is received. For example, it can send a notification to the person in charge of processing based on the email's priority.

[0069] (Example of form 2) The corporate order receiving efficiency system according to an embodiment of the present invention is a system that streamlines corporate order receiving operations by utilizing natural language processing (NLP) and AI. This system consists of the following steps. First, a mail classification system utilizing natural language processing (NLP) is constructed. AI is used to automatically classify received mail and sort it by service and priority. By detecting specific keywords and phrases and dividing mail into categories, priorities are clarified. For example, mail containing keywords such as "urgent" or "important" is classified as having a high priority. Next, priority recommendations are made using a priority algorithm. An algorithm is designed to score priority for each mail and notify the person in charge of processing of the priority. For example, it is set to give a high score to cases with approaching deadlines or requests from specific important customers. This allows the person in charge of processing to quickly determine which mail should be prioritized. Furthermore, a dashboard is created that allows the priority and status to be checked. Management performs task management based on the priorities suggested by the AI. The dashboard displays the priority and processing status of each mail at a glance. This makes it easier for management to grasp the overall progress. In addition, a reminder notification system is introduced. Using a tool with a calendar function linked to the customer database, the system automatically tracks each customer's contract renewal date. A few weeks before the renewal month, AI generates and sends personalized renewal reminder emails. This ensures that customers do not forget their contract renewal and can complete the process smoothly. Finally, a customer portal is implemented. A system is built that allows customers to complete orders themselves, with chatbots answering any questions. This allows customers to complete the process themselves without the need for a representative. This system dramatically streamlines corporate order processing, reduces email response delays, and improves customer satisfaction. Thus, the corporate order processing efficiency system can streamline corporate order processing, reduce email response delays, and improve customer satisfaction.

[0070] The corporate order receiving efficiency system according to this embodiment comprises a classification unit, a scoring unit, and a display unit. The classification unit automatically classifies received emails. The classification unit, for example, detects specific keywords or phrases and divides emails into categories. The classification unit determines categories based on the content of emails, for example, using keyword matching technology. The classification unit can also analyze the content of emails using machine learning algorithms and classify them into appropriate categories. For example, the classification unit analyzes the content of emails using natural language processing technology and detects specific keywords or phrases. The classification unit classifies emails containing keywords such as "urgent" or "important" as having a high priority. The scoring unit scores the priority of emails based on the classification of the classification unit. The scoring unit, for example, assigns high scores to projects with approaching deadlines or requests from specific important customers. The scoring unit calculates scores based on factors such as proximity of deadlines and customer importance. The scoring unit can also score the priority of emails using machine learning algorithms. For example, the scoring unit learns from past data and builds a model to predict email priority. The scoring unit assigns high scores to cases with approaching deadlines and low scores to cases of low importance. The display unit displays the priority assigned by the scoring unit. The display unit provides a dashboard where priority and status can be checked. The display unit displays the priority and processing status of each email on the dashboard. The display unit also has a notification function and can send notifications when important emails are received. For example, the display unit sends a notification to the person in charge of processing based on the priority of the email. As a result, the corporate order receiving efficiency system according to the embodiment streamlines email management by automatically classifying received emails, scoring their priority, and displaying them. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display the priority using an AI model that takes the priority assigned by the scoring unit as input and displays the priority.

[0071] The classification unit automatically categorizes received emails. For example, it detects specific keywords or phrases and divides emails into categories. Specifically, it uses keyword matching technology to determine categories based on the content of the email. For example, it detects keywords such as "order," "inquiry," and "complaint" and classifies them into the respective categories. The classification unit can also use machine learning algorithms to analyze the content of emails and classify them into appropriate categories. For example, it uses natural language processing technology to analyze the content of emails and detect specific keywords or phrases. This enables classification that understands the context, not just simple keyword matching. Furthermore, the classification unit can perform more accurate classification by considering information such as the email sender and recipient, and past email exchange history. For example, it can be set to automatically classify emails from specific customers into the "important customer" category. In this way, the classification unit efficiently and accurately classifies received emails and provides a foundation for smooth subsequent processing.

[0072] The scoring unit assigns priority scores to emails classified by the classification unit. For example, the scoring unit assigns high scores to projects with approaching deadlines or requests from specific important clients. Specifically, it calculates scores based on the proximity of deadlines and the importance of the client. For instance, it assigns high scores to projects with deadlines within one week and low scores to projects with deadlines more than one month away. The scoring unit can also use machine learning algorithms to score email priorities. For example, it can build a model that learns from past data to predict email priorities. This model extracts features to predict priority based on past email processing results and customer feedback, and calculates a score. Furthermore, the scoring unit can perform more accurate scoring by considering the content of the email, sender information, and past communication history. For example, it assigns high scores to emails containing specific keywords or phrases and low scores to emails containing less important keywords. This allows the scoring unit to efficiently and accurately score email priorities, providing a foundation for smooth subsequent processing.

[0073] The display unit shows the priority scored by the scoring unit. The display unit provides a dashboard where, for example, priority and status can be checked. Specifically, it displays the priority and processing status of each email on the dashboard. For example, it provides a visually easy-to-understand interface, such as displaying high-priority emails in red and low-priority emails in blue. The display unit also has a notification function and can notify when important emails are received. For example, it sends a notification to the person in charge of processing based on the email's priority. Notifications can be sent in multiple ways, such as pop-up notifications, email notifications, and SMS notifications. Furthermore, the display unit updates the processing status of emails in real time, making it easier for the person in charge to understand the current status. For example, emails being processed are displayed in yellow and completed emails are displayed in green. The display unit also has a function to refer to the processing history of past emails, allowing users to check past handling status. In this way, the display unit streamlines email management and helps the person in charge to respond quickly and appropriately. Furthermore, some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can take the priority scored by the scoring unit as input and display the priority using an AI model that displays the priority. This allows the display unit to provide more advanced display functions and further streamline email management.

[0074] The reminder notification system comprises a collection unit with a calendar function linked to a customer database, and a generation unit that generates personalized renewal reminder emails based on the information collected by the collection unit. The collection unit links with the customer database to automatically determine each customer's contract renewal date. For example, the collection unit retrieves information about contract renewal dates from the customer database and registers it in the calendar. The collection unit manages contract renewal dates by linking with the calendar function. The generation unit generates personalized renewal reminder emails based on the information collected by the collection unit. The generation unit customizes the content of the reminders based on, for example, the customer's past behavior history and contract details. For example, the generation unit sends reminder emails to customers several weeks before the contract renewal month. The generation unit can also automatically generate the content of the reminders using, for example, AI. For example, the generation unit analyzes the customer's past behavior history and generates the optimal reminder content. This allows for a smooth customer contract renewal process by linking with the customer database and generating personalized renewal reminder emails. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information obtained from the customer database into the generation AI and have the generation AI execute the reminder content.

[0075] The customer portal comprises a reception section where customers can complete their orders themselves, and an answer section where a chatbot answers any questions. The reception section allows customers to complete their orders themselves. For example, the reception section provides an online form, allowing customers to complete their orders by entering the necessary information. The reception section can also, for example, integrate with a payment system, allowing customers to complete payments online. The reception section provides, for example, a function that allows customers to check the progress of their orders. The answer section uses a chatbot to answer any questions. The answer section provides appropriate answers to customer questions, for example, using natural language processing technology. The answer section provides FAQ-based answers to resolve customer doubts, for example. The answer section can also generate optimal answers to customer questions using AI, for example. For example, the answer section uses an AI model that analyzes customer questions and generates optimal answers. This improves customer convenience by allowing customers to complete their orders themselves and having a chatbot answer any questions. Some or all of the above-described processes in the answer section may be performed using AI, for example, or without AI. For example, the answering unit can input customer questions into a generation AI and have the AI ​​generate the optimal answer.

[0076] The classification unit can detect specific keywords and phrases and categorize emails. For example, the classification unit can determine a category based on the content of an email using keyword matching technology. The classification unit can also analyze the content of an email and classify it into an appropriate category using machine learning algorithms. For example, the classification unit can analyze the content of an email and detect specific keywords and phrases using natural language processing technology. For example, the classification unit can classify emails containing keywords such as "urgent" or "important" as having a high priority. This improves the accuracy of email classification by detecting specific keywords and phrases and categorizing emails. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of an email into a generating AI and have the generating AI perform keyword and phrase detection.

[0077] The scoring unit can assign high scores to projects with approaching deadlines or requests from specific important clients. The scoring unit calculates scores based, for example, on the proximity of deadlines or the importance of the client. The scoring unit can also score email priorities using, for example, machine learning algorithms. For example, the scoring unit learns from past data and builds a model to predict email priorities. For example, the scoring unit assigns high scores to projects with approaching deadlines and low scores to projects of low importance. This allows for quicker responses to high-priority emails by assigning high scores to projects with approaching deadlines or requests from specific important clients. Some or all of the above processing in the scoring unit may be performed using, for example, AI, or not using AI. For example, the scoring unit can input the content of an email into a generating AI and have the generating AI perform priority scoring.

[0078] The display unit can provide a dashboard that allows users to check priorities and statuses. For example, the display unit can display the priority and processing status of each email on the dashboard. The display unit can also have features such as graph display and filtering to facilitate email management. The display unit can also have a notification function to notify users when important emails are received. For example, the display unit can send notifications to the person in charge of processing based on the priority of the email. This makes email management easier by providing a dashboard that allows users to check priorities and statuses. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take the priority scored by the scoring unit as input and display the priority using an AI model that displays the priority.

[0079] The classification unit can estimate the user's emotions and adjust the email classification criteria based on the estimated emotions. For example, if the user is stressed, the classification unit will prioritize important emails and filter out unnecessary ones. If the user is relaxed, the classification unit will display all emails equally, allowing the user to choose freely. If the user is in a hurry, the classification unit will prioritize urgent emails and postpone other emails. This allows for more appropriate email classification by adjusting the email classification criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The classification unit can analyze the sender's past behavior history to improve classification accuracy. For example, the classification unit analyzes the content of emails the sender has sent in the past and classifies emails with similar content into the same category. For example, the classification unit estimates the importance of the current email based on the importance of emails the sender has sent in the past. For example, the classification unit estimates the urgency of the current email based on the response speed of emails the sender has sent in the past. In this way, the classification accuracy of emails is improved by analyzing the sender's past behavior history. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the sender's past behavior history data into a generating AI and have the generating AI perform the task of improving classification accuracy.

[0081] The classification unit can automatically determine the urgency and importance of emails based on their content and classify them accordingly. For example, if an email contains keywords such as "urgent" or "immediate," the classification unit will classify it into a high-urgency category. For example, if an email contains keywords such as "important" or "priority," the classification unit will classify it into a high-importance category. For example, if an email contains a specific project name or customer name, the classification unit will classify it into a category related to that project or customer. This makes email classification more accurate by determining urgency and importance based on the content of the email. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of an email into a generating AI and have the generating AI perform the determination of urgency and importance.

[0082] The classification unit can estimate the user's emotions and adjust the display order of classified emails based on the estimated emotions. For example, if the user is stressed, the classification unit will display important emails at the top and unnecessary emails at the bottom. If the user is relaxed, the classification unit will display all emails in chronological order, allowing the user to choose freely. If the user is in a hurry, the classification unit will display urgent emails at the top and other emails later. By adjusting the display order of emails based on the user's emotions, emails can be displayed in the most optimal order for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The classification unit can prioritize the classification of emails based on their relevance, taking into account the geographical location information of the email sender. For example, the classification unit may prioritize displaying emails sent from nearby areas. For example, the classification unit may classify emails sent from a specific area into categories related to that area. For example, the classification unit may prioritize the display of emails based on the sender's geographical location information. In this way, by considering the geographical location information of the email sender, it is possible to prioritize the classification of emails based on their relevance. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit may input the sender's geographical location information into a generating AI and have the generating AI perform the classification of highly relevant emails.

[0084] The classification unit can automatically attach relevant literature and materials based on the content of the email. For example, if the email body contains a specific project name, the classification unit will automatically attach literature and materials related to that project. For example, if the email body contains a specific technical term, the classification unit will automatically attach literature and materials related to that technology. For example, if the email body contains a specific customer name, the classification unit will automatically attach literature and materials related to that customer. This enriches the information in emails by automatically attaching relevant literature and materials based on the content. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of the email into a generation AI and have the generation AI attach relevant literature and materials.

[0085] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated emotions. For example, if the user is stressed, the scoring unit will give a high score to urgent emails. If the user is relaxed, the scoring unit will give an equal score to all emails. If the user is in a hurry, the scoring unit will give a high score to important emails. By adjusting the scoring criteria based on the user's emotions, more appropriate scoring becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scoring unit may be performed using AI or not using AI. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The scoring unit can analyze the sender's past behavioral history to improve the accuracy of scoring. For example, the scoring unit can analyze the content of emails the sender has sent in the past and assign higher scores to emails with similar content. For example, the scoring unit can adjust the score of the current email based on the importance of emails the sender has sent in the past. For example, the scoring unit can adjust the score of the current email based on the response speed of emails the sender has sent in the past. In this way, the accuracy of scoring is improved by analyzing the sender's past behavioral history. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the sender's past behavioral history data into a generating AI and have the generating AI perform the improvement of scoring accuracy.

[0087] The scoring unit can automatically determine the urgency and importance of emails based on their content and assign a score. For example, the scoring unit will assign a high score to emails that contain keywords such as "urgent" or "immediate." For example, the scoring unit will assign a high score to emails that contain keywords such as "important" or "priority." For example, the scoring unit will assign a high score to emails related to a specific project or customer if the email body contains that project or customer name. This makes the scoring more accurate by determining urgency and importance based on the content of the emails. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the content of emails into a generating AI and have the generating AI perform the determination of urgency and importance.

[0088] The scoring unit can estimate the user's emotions and adjust the display order of emails scored based on the estimated emotions. For example, if the user is stressed, the scoring unit will display emails with high scores at the top. For example, if the user is relaxed, the scoring unit will display all emails in chronological order. For example, if the user is in a hurry, the scoring unit will display emails of high urgency at the top. In this way, by adjusting the display order of emails based on the user's emotions, emails can be displayed in the optimal order for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The scoring unit can prioritize scoring emails that are highly relevant by considering the geographical location information of the email sender. For example, the scoring unit may give a high score to emails sent by the sender from a nearby area. For example, the scoring unit may classify emails sent by the sender from a specific area into categories related to that area and give them a high score. For example, the scoring unit may give a high score to emails that are highly relevant based on the sender's geographical location information. In this way, by considering the geographical location information of the email sender, it is possible to prioritize scoring emails that are highly relevant. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit may input the sender's geographical location information into a generating AI and have the generating AI perform the scoring of highly relevant emails.

[0090] The scoring unit can automatically attach relevant literature and materials based on the content of the email and perform scoring. For example, if the email body contains a specific project name, the scoring unit will automatically attach literature and materials related to that project and assign a high score. For example, if the email body contains a specific technical term, the scoring unit will automatically attach literature and materials related to that technology and assign a high score. For example, if the email body contains a specific customer name, the scoring unit will automatically attach literature and materials related to that customer and assign a high score. This improves the accuracy of scoring by automatically attaching relevant literature and materials based on the content of the email. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the content of the email into a generating AI and have the generating AI attach relevant literature and materials.

[0091] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is stressed, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is in a hurry, the display unit provides a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, the optimal display method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may suggest the optimal display method based on the display methods the user has used in the past. For example, the display unit may select a display method with high visibility from the user's past operation history. For example, the display unit may analyze the user's past operation history and suggest the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0093] The display unit can adjust the level of detail displayed based on the importance of the email. For example, the display unit can display detailed information for high-importance emails and concise information for low-importance emails. For example, the display unit can display detailed information for urgent emails and concise information for low-importance emails. For example, the display unit can display detailed information for emails related to a specific project or customer and concise information for other emails. This allows important information to be displayed preferentially by adjusting the level of detail based on the importance of the email. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input email importance data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0094] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is stressed, the display unit will display important emails at the top. If the user is relaxed, the display unit will display all emails in chronological order. If the user is in a hurry, the display unit will display urgent emails at the top. In this way, by determining the display priority based on the user's emotions, information can be displayed in the optimal order for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit provides a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0096] The display unit can adjust the display order based on the relevance of emails during display. For example, the display unit may display highly relevant emails at the top and less relevant emails at the bottom. For example, the display unit may display emails related to a specific project or customer at the top and other emails at the bottom. For example, the display unit may display highly urgent emails at the top and less urgent emails at the bottom. In this way, by adjusting the display order based on the relevance of emails, highly relevant information can be displayed preferentially. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input email relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0097] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information and filter out unnecessary information. For example, if the user is relaxed, the data collection unit will collect all information equally, allowing the user to choose freely. For example, if the user is in a hurry, the data collection unit will prioritize collecting information of high urgency and postpone other information. This allows for the priority collection of important information by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The data collection unit can select the optimal data collection method by referring to the user's past behavior history during data collection. For example, the data collection unit may prioritize collecting information sources that the user has frequently accessed in the past. For example, the data collection unit may prioritize collecting highly relevant information from the user's past behavior history. For example, the data collection unit may analyze the user's past behavior history and propose the most efficient data collection method. In this way, the optimal data collection method can be provided by referring to the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0099] The data collection unit can estimate the user's emotions and adjust how the collected information is displayed based on the estimated emotions. For example, if the user is stressed, the data collection unit can display important information at the top and unnecessary information at the bottom. If the user is relaxed, the data collection unit can display all information in chronological order, allowing the user to choose freely. If the user is in a hurry, the data collection unit can display highly urgent information at the top and other information at a later stage. By adjusting how information is displayed based on the user's emotions, the system can provide the user with the most optimal display method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, if the user is in a nearby area, the data collection unit will prioritize the collection of information related to that area. For example, if the user is in a specific area, the data collection unit will prioritize the collection of information related to that area. For example, the data collection unit will prioritize the collection of highly relevant information based on the user's geographical location information. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0101] The generation unit can estimate the user's emotions and adjust the content of the email it generates based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a concise and to-the-point email. For example, if the user is relaxed, the generation unit will generate an email containing detailed information. For example, if the user is in a hurry, the generation unit will generate a concise email that allows for a quick response. In this way, by adjusting the content of the email based on the user's emotions, it is possible to generate the most suitable email for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0102] The generation unit can select the optimal generation method by referring to the user's past behavior history during generation. For example, the generation unit can generate emails with similar content based on the content of emails previously sent by the user. For example, the generation unit can generate emails containing highly relevant information from the user's past behavior history. For example, the generation unit can analyze the user's past behavior history and propose the most efficient generation method. In this way, the optimal generation method can be provided by referring to the user's past behavior history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past behavior history data into a generation AI and have the generation AI select the optimal generation method.

[0103] The generation unit can adjust the level of detail in the generated emails based on their importance. For example, the generation unit can generate emails with detailed information for high-importance emails and emails with concise information for low-importance emails. For example, the generation unit can generate emails with detailed information for urgent emails and emails with concise information for low-importance emails. For example, the generation unit can generate emails with detailed information for emails related to specific projects or customers and emails with concise information for other emails. This allows for the priority generation of important information by adjusting the level of detail based on the importance of the emails. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input email importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the generated emails.

[0104] The generation unit can estimate the user's emotions and determine the priority of emails to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will generate important emails at the top. If the user is relaxed, the generation unit will generate all emails in chronological order. If the user is in a hurry, the generation unit will generate urgent emails at the top. This allows for the priority generation of important emails by determining the priority of emails based 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 is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0105] The generation unit can prioritize generating highly relevant emails by considering the user's geographical location information during generation. For example, if the user is in a nearby area, the generation unit will prioritize generating emails related to that area. For example, if the user is in a specific area, the generation unit will prioritize generating emails related to that area. For example, the generation unit will prioritize generating highly relevant emails based on the user's geographical location information. This allows for the priority generation of highly relevant emails by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of highly relevant emails.

[0106] The generation unit can adjust the generation order based on the relevance of the emails during generation. For example, the generation unit can generate highly relevant emails at the top and less relevant emails at the bottom. For example, the generation unit can generate emails related to a specific project or customer at the top and other emails at the bottom. For example, the generation unit can generate highly urgent emails at the top and less urgent emails at the bottom. In this way, by adjusting the generation order based on the relevance of the emails, highly relevant emails can be generated preferentially. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input email relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0107] The reception desk can estimate the user's emotions and adjust the reception process based on the estimated emotions. For example, if the user is stressed, the reception desk may provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk may provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk may prioritize voice input and process the request quickly. This allows the reception desk to provide the optimal reception experience for the user by adjusting the reception process based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The reception unit can select the optimal reception method by referring to the user's past behavior history at the time of reception. For example, the reception unit may propose the optimal reception method based on the reception methods the user has used in the past. For example, the reception unit may select a highly relevant reception method from the user's past behavior history. For example, the reception unit may analyze the user's past behavior history and propose the most efficient reception method. In this way, the optimal reception method can be provided by referring to the user's past behavior history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's past behavior history data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0109] The reception desk can estimate the user's emotions and determine the priority of requests based on those emotions. For example, if the user is stressed, the reception desk will display important requests at the top. If the user is relaxed, the reception desk will display all requests in chronological order. If the user is in a hurry, the reception desk will display the most urgent requests at the top. This allows important requests to be prioritized by determining the priority of requests based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The reception unit can select a highly relevant reception method at the time of reception, taking into account the user's geographical location information. For example, if the user is in a nearby area, the reception unit will prioritize providing a reception method related to that area. For example, if the user is in a specific area, the reception unit will prioritize providing a reception method related to that area. For example, the reception unit will prioritize providing a highly relevant reception method based on the user's geographical location information. In this way, a highly relevant reception method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of a highly relevant reception method.

[0111] The response unit can estimate the user's emotions and adjust the content of its response based on the estimated emotions. For example, if the user is stressed, the response unit will provide a concise and to-the-point response. For example, if the user is relaxed, the response unit will provide a detailed response. For example, if the user is in a hurry, the response unit will provide a concise response that allows for a quick response. In this way, by adjusting the content of the response based on the user's emotions, the response unit can provide the most appropriate response for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The response unit can select the optimal response method by referring to the user's past behavior history when responding. For example, the response unit may provide a similar response based on the content of responses the user has received in the past. For example, the response unit may provide a response containing highly relevant information from the user's past behavior history. For example, the response unit may analyze the user's past behavior history and propose the most efficient response method. In this way, the optimal response method can be provided by referring to the user's past behavior history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal response method.

[0113] The response unit can estimate the user's emotions and prioritize responses based on the estimated emotions. For example, if the user is stressed, the response unit will display important responses at the top. If the user is relaxed, the response unit will display all responses in chronological order. If the user is in a hurry, the response unit will display responses of highest urgency at the top. This allows important responses to be provided preferentially by prioritizing responses based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The response unit can prioritize providing highly relevant answers by considering the user's geographical location information when a response is submitted. For example, if the user is in a nearby area, the response unit will prioritize providing answers related to that area. For example, if the user is in a specific area, the response unit will prioritize providing answers related to that area. For example, the response unit will prioritize providing highly relevant answers based on the user's geographical location information. In this way, by considering the user's geographical location information, highly relevant answers can be prioritized. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant answers.

[0115] The response unit can select the most appropriate response method when responding, taking into account the user's current situation. For example, if the user is on the move, the response unit provides a concise and easily readable response. If the user is using a desktop computer, the response unit provides a response containing detailed information. If the user is using a smartphone, the response unit provides a concise response adapted to the screen size. This allows the response unit to provide the most appropriate response method by considering the user's current situation. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's current situation data into a generating AI and have the generating AI select the most appropriate response method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The classification unit can estimate the user's emotions and adjust the email classification criteria based on the estimated emotions. For example, if the user is stressed, important emails are displayed preferentially and unnecessary emails are filtered out. If the user is relaxed, all emails are displayed equally, allowing the user to choose freely. If the user is in a hurry, urgent emails are displayed first, and other emails are put on hold. This allows for more appropriate email classification by adjusting the email classification criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated emotions. For example, if the user is stressed, it may give a high score to urgent emails. If the user is relaxed, it may give an equal score to all emails. If the user is in a hurry, it may give a high score to important emails. By adjusting the scoring criteria based on the user's emotions, more appropriate scoring becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scoring unit may be performed using AI or not. For example, the scoring unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, important information is prioritized and unnecessary information is filtered out. If the user is relaxed, all information is collected equally, allowing the user to choose freely. If the user is in a hurry, highly urgent information is collected first, and other information is postponed. This ensures that important information is collected preferentially by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The generation unit can estimate the user's emotions and adjust the content of the email it generates based on the estimated emotions. For example, if the user is stressed, it can generate a concise and to-the-point email. If the user is relaxed, it can generate an email containing detailed information. If the user is in a hurry, it can generate a concise email that allows for a quick response. In this way, by adjusting the content of the email based on the user's emotions, it is possible to generate the most suitable email for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0122] The classification unit can analyze the sender's past behavior history to improve classification accuracy. For example, it can analyze the content of emails the sender has sent in the past and classify emails with similar content into the same category. It can estimate the importance of the current email based on the importance of emails the sender has sent in the past. It can estimate the urgency of the current email based on the response speed of emails the sender has sent in the past. In this way, analyzing the sender's past behavior history improves the accuracy of email classification. Some or all of the above processing in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the sender's past behavior history data into a generating AI and have the generating AI perform the task of improving classification accuracy.

[0123] The scoring unit can analyze the sender's past behavioral history to improve the accuracy of scoring. For example, it can analyze the content of emails the sender has sent in the past and assign higher scores to emails with similar content. It can adjust the score of the current email based on the importance of emails the sender has sent in the past. It can also adjust the score of the current email based on the response speed of emails the sender has sent in the past. In this way, the accuracy of scoring is improved by analyzing the sender's past behavioral history. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input the sender's past behavioral history data into a generating AI and have the generating AI perform the task of improving the accuracy of scoring.

[0124] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, it can suggest the optimal display method based on the display methods the user has used in the past. It can select a display method with high visibility from the user's past operation history. It can analyze the user's past operation history and suggest the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0125] The data collection unit can select the optimal data collection method by referring to the user's past behavior history during data collection. For example, it can prioritize collecting information sources that the user has frequently accessed in the past. It can also prioritize collecting highly relevant information from the user's past behavior history. It can analyze the user's past behavior history and propose the most efficient data collection method. In this way, the optimal data collection method can be provided by referring to the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0126] The generation unit can select the optimal generation method by referring to the user's past behavior history during generation. For example, it can generate emails with similar content based on the content of emails the user has sent in the past. It can also generate emails containing highly relevant information from the user's past behavior history. It can analyze the user's past behavior history and propose the most efficient generation method. In this way, the optimal generation method can be provided by referring to the user's past behavior history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past behavior history data into a generation AI and have the generation AI select the optimal generation method.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The classification unit automatically categorizes received emails. The classification unit detects specific keywords and phrases and divides emails into categories. For example, it uses keyword matching technology or machine learning algorithms to determine categories based on the content of the emails. It can also use natural language processing technology to analyze the content of emails and detect specific keywords and phrases. For example, it can classify emails containing keywords such as "urgent" or "important" as having a high priority. Step 2: The scoring unit scores priority based on the emails classified by the classification unit. The scoring unit assigns higher scores to projects with approaching deadlines and requests from specific important customers. The score is calculated based on the proximity of the deadline and the importance of the customer. Machine learning algorithms can also be used to build a model that learns from past data and predicts email priority. Step 3: The display unit shows the priority scored by the scoring unit. The display unit provides a dashboard where you can check the priority and status, showing the priority and processing status of each email. It also has a notification function and can notify you when an important email is received. For example, it can send a notification to the person in charge of processing based on the email's priority.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the classification unit, scoring unit, display unit, collection unit, generation unit, reception unit, and response unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the classification unit is implemented by the control unit 46A of the smart device 14 and automatically classifies received emails. The scoring unit is implemented by the identification processing unit 290 of the data processing device 12 and scores priority based on the classified emails. The display unit is implemented by the display 40A of the smart device 14 and displays priority and status. The collection unit is implemented by the identification processing unit 290 of the data processing device 12 and determines the contract renewal date in cooperation with the customer database. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates personalized renewal reminder emails. The reception unit is implemented by the control unit 46A of the smart device 14 and enables customers to complete orders themselves. The response unit is implemented by the control unit 46A of the smart device 14 and a chatbot answers customer questions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the classification unit, scoring unit, display unit, collection unit, generation unit, reception unit, and response unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the smart glasses 214 and automatically classifies received emails. The scoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and scores priority based on the classified emails. The display unit is implemented by the display of the smart glasses 214 and displays priority and status. The collection unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the contract renewal date in cooperation with the customer database. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates personalized renewal reminder emails. The reception unit is implemented by the control unit 46A of the smart glasses 214 and allows customers to complete their orders themselves. The response unit is implemented by the control unit 46A of the smart glasses 214 and a chatbot answers customer questions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the classification unit, scoring unit, display unit, collection unit, generation unit, reception unit, and response unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the headset terminal 314 and automatically classifies received emails. The scoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and scores priority based on the classified emails. The display unit is implemented by the display 343 of the headset terminal 314 and displays priority and status. The collection unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the contract renewal date in cooperation with the customer database. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates personalized renewal reminder emails. The reception unit is implemented by the control unit 46A of the headset terminal 314 and enables customers to complete their orders themselves. The response function is implemented by the control unit 46A of the headset terminal 314, and the chatbot answers the customer's questions. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the classification unit, scoring unit, display unit, collection unit, generation unit, reception unit, and response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the robot 414 and automatically classifies received emails. The scoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and scores priority based on the classified emails. The display unit is implemented by the display of the robot 414 and displays priority and status. The collection unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the contract renewal date in cooperation with the customer database. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates personalized renewal reminder emails. The reception unit is implemented by the control unit 46A of the robot 414 and allows customers to complete their orders themselves. The response unit is implemented by the control unit 46A of the robot 414 and the chatbot answers customer questions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A classification unit that automatically sorts received emails, A scoring unit that scores priority based on emails classified by the aforementioned classification unit, The system includes a display unit that displays the priority scored by the scoring unit. A system characterized by the following features. (Note 2) A collection unit with a calendar function linked to the customer database, The system comprises a generation unit that generates personalized update reminder emails based on the information collected by the collection unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) A reception area where customers can complete their orders themselves, It includes a section where a chatbot answers questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned classification unit is Detect specific keywords or phrases and categorize emails accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The scoring unit is, We give high scores to projects with approaching deadlines and requests from specific, important clients. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Provides a dashboard where you can check priorities and status. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned classification unit is It estimates the user's sentiment and adjusts email classification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned classification unit is Analyze the past behavior history of email senders to improve classification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned classification unit is The system automatically determines and categorizes emails based on their urgency and importance. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned classification unit is It estimates the user's sentiment and adjusts the display order of emails categorized based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned classification unit is The system prioritizes classifying emails based on their relevance, taking into account the sender's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned classification unit is Based on the content of the email, relevant literature and materials will be automatically attached. The system described in Appendix 1, characterized by the features described herein. (Note 13) The scoring unit is, The system estimates the user's emotions and adjusts the scoring criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The scoring unit is, Analyze the email sender's past behavior history to improve scoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The scoring unit is, Based on the content of the email, the system automatically determines the urgency and importance and assigns a score. The system described in Appendix 1, characterized by the features described herein. (Note 16) The scoring unit is, It estimates the user's emotions and adjusts the display order of emails based on the scored emotions of the estimated user. The system described in Appendix 1, characterized by the features described herein. (Note 17) The scoring unit is, The system prioritizes scoring emails based on their relevance, taking into account the sender's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The scoring unit is, Based on the content of the email, relevant literature and materials are automatically attached and scored. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying, adjust the level of detail based on the importance of the email. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying emails, adjust the display order based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is During data collection, the system selects the optimal collection method by referring to the user's past behavioral history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned collection unit is It estimates the user's emotions and adjusts how the collected information is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The generating unit is It estimates the user's emotions and adjusts the content of the emails generated based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The generating unit is During generation, the system selects the optimal generation method by referring to the user's past behavior history. The system described in Appendix 2, characterized by the features described herein. (Note 31) The generating unit is During generation, adjust the level of detail based on the importance of the email. The system described in Appendix 2, characterized by the features described herein. (Note 32) The generating unit is It estimates the user's emotions and determines the priority of emails to generate based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The generating unit is During generation, the system prioritizes generating highly relevant emails by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The generating unit is During generation, the generation order is adjusted based on the relevance of the emails. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned reception unit is The system estimates the user's emotions and adjusts the reception process based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned reception unit is During registration, the system selects the most suitable registration method by referring to the user's past behavioral history. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the reception process based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned reception unit is During registration, the system selects the most relevant registration method by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned response section is, The system estimates the user's emotions and adjusts the content of the response based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned response section is, When a user responds, the system selects the most appropriate response method by referring to the user's past behavior history. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned response section is, The system estimates the user's emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned response section is, When users respond, the system prioritizes providing highly relevant answers by considering their geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned response section is, When responding, the system selects the most appropriate response method, taking into account the user's current situation. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A classification unit that automatically sorts received emails, A scoring unit that scores priority based on emails classified by the aforementioned classification unit, The system includes a display unit that displays the priority scored by the scoring unit. A system characterized by the following features.

2. A collection unit with a calendar function linked to the customer database, The system comprises a generation unit that generates personalized update reminder emails based on the information collected by the collection unit. The system according to feature 1.

3. A reception area where customers can complete their orders themselves, It includes a section where a chatbot answers questions. The system according to feature 1.

4. The aforementioned classification unit is Detect specific keywords or phrases and categorize emails accordingly. The system according to feature 1.

5. The scoring unit is, We give high scores to projects with approaching deadlines and requests from specific, important clients. The system according to feature 1.

6. The aforementioned display unit is Provides a dashboard where you can check priorities and status. The system according to feature 1.

7. The aforementioned classification unit is It estimates the user's sentiment and adjusts email classification criteria based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned classification unit is Analyze the past behavior history of email senders to improve classification accuracy. The system according to feature 1.

9. The aforementioned classification unit is The system automatically determines and categorizes emails based on their urgency and importance. The system according to feature 1.

10. The aforementioned classification unit is It estimates the user's sentiment and adjusts the display order of emails categorized based on the estimated user sentiment. The system according to feature 1.

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