system

Abstract

The system according to this embodiment aims to provide an environment in which senior citizens can use smartphones with peace of mind. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a filtering unit, and a classification unit. The reception unit receives user questions and problems. The analysis unit analyzes the information received by the reception unit and provides solutions. The filtering unit filters out spam and fake emails based on the information analyzed by the analysis unit. The classification unit automatically classifies the emails filtered by the filtering unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, there is a problem that senior generations often feel uneasy about smartphone operations and there are insufficient countermeasures against spam and fake mails.

[0005] The system according to the embodiment aims to provide an environment in which senior generations can use smartphones with confidence.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a filtering unit, and a classification unit. The reception unit receives user questions and problems. The analysis unit analyzes the information received by the reception unit and provides solutions. The filtering unit filters out spam and fake emails based on the information analyzed by the analysis unit. The classification unit automatically classifies the emails filtered by the filtering unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an environment in which senior citizens can use smartphones with peace of mind. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also 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 Help Desk AI System according to an embodiment of the present invention is a 24-hour help desk AI system specifically designed for senior citizens who are unsure about using smartphones. This Help Desk AI System not only provides immediate responses to questions and problems regarding smartphone operation, but also offers functions such as filtering spam and fake emails and automatically classifying important emails, thereby reducing stress for seniors. This provides an environment where seniors can use smartphones with peace of mind and maximizes the convenience of digital devices. For example, a user inputs a question or problem regarding smartphone operation. The user can input the question by voice or text. For example, the user might input a question such as, "I don't know how to send an email." This information is entered into the Help Desk AI System. Next, the Help Desk AI System analyzes the input information and provides an appropriate solution. The Help Desk AI System not only provides immediate answers to the user's questions, but can also automatically complete setting operations as needed. For example, in addition to explaining how to send an email, it can automatically perform the settings to actually send an email if the user requests it. Furthermore, the Help Desk AI System provides a function to filter spam and fake emails. The Help Desk AI system analyzes received emails, automatically detects potentially spam and phishing emails, and provides users with risk information. This allows seniors to use email with peace of mind. The Help Desk AI system also provides an automatic email classification function. It analyzes received emails and automatically classifies important emails. For example, it prioritizes the display of important emails such as notifications from banks and communications from medical institutions, ensuring users don't miss important information. This system provides seniors with an environment where they can use smartphones with peace of mind, maximizing the convenience of digital devices. Furthermore, it enables a stress-free digital life and helps bridge the digital divide among seniors. In short, the Help Desk AI system provides seniors with an environment where they can use smartphones with peace of mind, maximizing the convenience of digital devices.

[0029] The AI ​​help desk system according to this embodiment comprises a reception unit, an analysis unit, a filtering unit, and a classification unit. The reception unit receives user questions and problems. The reception unit can accept input, for example, by voice or text. For example, a user can input a question such as "I don't know how to send an email" by voice. Alternatively, a user can input a question in text. The analysis unit analyzes the information received by the reception unit and provides solutions. The analysis unit analyzes the user's question using, for example, natural language processing technology and provides an appropriate answer. For example, the analysis unit can provide an immediate answer to a user's question. The analysis unit can also automatically complete setting operations as needed. For example, if the user requests it, the analysis unit can automatically configure email sending settings. The filtering unit filters spam and fake emails based on the information analyzed by the analysis unit. The filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. For example, the filtering unit can detect spam emails based on specific keywords or the trustworthiness of the sender. The classification unit automatically classifies emails that have been filtered by the filtering unit. For example, the classification unit can analyze received emails and automatically classify important emails. For instance, the classification unit can prioritize the display of important emails, such as notifications from banks or communications from medical institutions. As a result, the help desk AI system according to this embodiment can respond immediately to user questions and problems, filter spam and fake emails, and automatically classify important emails.

[0030] The reception desk receives user inquiries and problems. The reception desk can accept input via voice or text, for example. Specifically, users can input questions via voice, such as "I don't know how to send an email." Users can also input questions via text. The reception desk uses speech recognition technology to convert the user's voice input into text, and receives text input directly. The speech recognition technology accurately recognizes the user's speech, taking into account background noise and pronunciation differences to perform precise text conversion. Furthermore, the reception desk temporarily stores the user's input and performs simple pre-processing before sending it to the analysis department. For example, it performs grammar and spell checks on the input to enable efficient analysis by the analysis department. The reception desk also refers to the user's past inquiry history to ensure a quick response if similar problems occur again. This allows the reception desk to quickly and accurately receive user inquiries and problems and smoothly pass them on to the next processing step.

[0031] The analysis unit analyzes information received by the reception unit and provides solutions. For example, the analysis unit uses natural language processing technology to analyze user questions and provide appropriate answers. Specifically, the analysis unit tokenizes user questions and performs grammatical and semantic analysis. This allows it to accurately understand the user's intent and generate appropriate answers. The analysis unit refers to large databases and knowledge bases to provide optimal solutions based on similar past questions and their answers. Furthermore, the analysis unit uses machine learning algorithms to continuously improve the accuracy of its answers to user questions. For example, if a user asks, "I don't know how to send an email," the analysis unit can not only provide information on the email sending procedure but also automatically configure the user's email settings. In addition, the analysis unit collects user feedback and evaluates the quality of the answers to improve the overall system performance. This allows the analysis unit to respond quickly and accurately to user questions and problems, increasing user satisfaction.

[0032] The filtering unit filters out spam and fake emails based on information analyzed by the analysis unit. For example, the filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. Specifically, the filtering unit analyzes the content of emails and sender information to detect specific keywords and patterns. For example, it identifies emails containing keywords such as "free," "limited," and "urgent," or emails from unreliable senders, as spam. The filtering unit also uses machine learning algorithms to learn the characteristics of spam emails and improve its accuracy. Furthermore, the filtering unit refers to the user's past email receiving history and improves filtering accuracy based on the characteristics of emails that the user has previously marked as spam. As a result, the filtering unit can detect emails that may be spam or phishing scams with high accuracy, keeping the user's inbox clean.

[0033] The classification unit automatically categorizes emails filtered by the filtering unit. For example, the classification unit can analyze received emails and automatically categorize important emails. Specifically, the classification unit categorizes emails based on their content and sender information. For example, it prioritizes displaying important emails such as notifications from banks or communications from medical institutions. The classification unit can also refer to the user's past email classification history and categorize emails according to the user's preferences. Furthermore, the classification unit continuously improves the accuracy of email classification using machine learning algorithms. For example, if a user marks emails from a specific sender as important, it will automatically classify emails from the same sender as important. The classification unit also collects user feedback and evaluates the accuracy of the classification results to improve the overall system performance. As a result, the classification unit can efficiently manage the user's inbox and respond quickly without missing important emails.

[0034] The reception unit can accept input by voice or text. For example, the reception unit can accept questions from users by voice. For example, a user can enter a question by voice such as "I don't know how to send an email." The reception unit can also accept questions from users by text. For example, a user can enter a question using a smartphone keyboard. This allows users to input questions or problems by voice or text. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can use speech recognition technology to convert the user's voice input into text data and send that text data to the analysis unit.

[0035] The analysis unit can provide immediate answers to user questions. For example, the analysis unit can analyze user questions using natural language processing techniques and provide appropriate answers. The analysis unit can also provide answers quickly using pre-prepared answer templates. For example, the analysis unit can save answers to frequently asked questions as templates and provide immediate answers to user questions. This allows for immediate answers to user questions. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can use an AI model that analyzes user questions using natural language processing techniques and generates appropriate answers.

[0036] The analysis unit can automatically complete configuration operations as needed. For example, the analysis unit can automatically configure email sending settings if the user requests it. For example, if the user asks, "I don't know how to send emails," the analysis unit can automatically configure email sending settings. The analysis unit can also automatically complete other configuration operations, such as user account settings and notification settings. For example, the analysis unit can automatically configure the user's account settings and automatically complete notification settings. This allows the user's configuration operations to be completed automatically. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to automatically complete user configuration operations.

[0037] The filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. For example, the filtering unit can analyze received emails and detect spam emails based on specific keywords or the trustworthiness of the sender. For example, the filtering unit can automatically detect spam emails using an AI model that has learned the characteristics of spam emails. The filtering unit can also automatically detect emails that may be phishing scams. For example, the filtering unit can analyze the security of links in emails and detect emails that may be phishing scams. This enables the automatic detection of emails that may be spam or phishing scams. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can automatically detect spam emails using an AI model that has learned the characteristics of spam emails.

[0038] The classification unit can analyze received emails and automatically classify important emails. For example, the classification unit can analyze received emails and prioritize displaying important emails such as notifications from banks or communications from medical institutions. For example, the classification unit can automatically classify important emails based on their content and the importance of the sender. The classification unit can also prioritize displaying emails with high urgency. For example, the classification unit can automatically classify emails with high urgency and display them to the user with priority. This allows for the automatic classification of important 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 automatically classify important emails using an AI model that has learned the importance of email content and sender.

[0039] The reception desk can analyze a user's past inquiry history and select the most appropriate reception method. For example, the reception desk can prioritize providing relevant information based on the topics the user has frequently inquired about in the past. Furthermore, the reception desk can automatically suggest solutions to specific problems based on the user's past inquiry history. In addition, the reception desk can prioritize suggesting inquiry methods (voice, text, etc.) the user has used in the past. This allows the reception desk to select the most appropriate reception method based on the user's past inquiry history. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past inquiry history into an AI and have the AI ​​select the most appropriate reception method.

[0040] The reception desk can filter information based on the user's current situation and areas of interest at the time of reception. For example, if the user enters their current situation, the reception desk can provide relevant information corresponding to that situation. The reception desk can also prioritize displaying relevant inquiries based on the user's areas of interest. Furthermore, the reception desk can suggest the most suitable solution, taking into account the user's current situation and areas of interest. In this way, relevant information can be provided by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0041] The reception desk can prioritize receiving inquiries that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize providing information related to that region. The reception desk can also suggest solutions to region-specific problems based on the user's geographical location. Furthermore, if the user is traveling, the reception desk can prioritize providing information related to their travel destination. This allows the reception desk to prioritize receiving inquiries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into the AI ​​and have the AI ​​prioritize receiving inquiries that are highly relevant.

[0042] The reception desk can analyze a user's social media activity and receive relevant inquiries upon receiving an inquiry. For example, the reception desk can analyze a user's current interests from their social media activity and provide relevant information. It can also suggest solutions to problems the user has shared on social media. Furthermore, the reception desk can prioritize displaying relevant inquiries based on the user's social media activity. This allows the reception desk to receive relevant inquiries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI and have the AI ​​handle receiving relevant inquiries.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during the analysis. For example, for important inquiries, the analysis unit can perform a detailed analysis and provide a comprehensive solution. For general inquiries, the analysis unit can perform a concise analysis and provide a quick solution. Furthermore, for urgent inquiries, the analysis unit can perform a rapid analysis and provide an immediate solution. This allows the level of detail of the analysis to be adjusted based on the importance of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the inquiry into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical problems. It can also apply a simpler analysis algorithm to inquiries about general operations. Furthermore, it can apply a security-specific analysis algorithm to inquiries about security. This allows for the application of different analysis algorithms depending on the category of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the inquiry into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on when the inquiry was submitted. For example, the analysis unit can immediately analyze urgent inquiries and provide solutions quickly. It can also analyze regular inquiries sequentially based on when they were submitted. Furthermore, it can prioritize the analysis and provide solutions for inquiries that have been left unattended for a long time. This allows the analysis priority to be determined based on when the inquiry was submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the submission date of the inquiry into the AI ​​and have the AI ​​determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the queries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant queries and provide solutions quickly. It can also analyze less relevant queries in the normal order. Furthermore, if there are multiple highly relevant queries, the analysis unit can analyze them together and provide a comprehensive solution. This allows the order of analysis to be adjusted based on the relevance of the queries. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the queries into the AI ​​and have the AI ​​adjust the order of analysis.

[0047] The filtering unit can improve the accuracy of filtering by considering the relationships between emails during the filtering process. For example, the filtering unit can analyze the relationship between the sender and recipient of an email and prioritize displaying highly reliable emails. It can also analyze the relationship between the content of an email and past emails to effectively eliminate spam. Furthermore, the filtering unit can automatically classify important emails based on their relationships. This improves the accuracy of filtering by considering the relationships between emails. Some or all of the above-described processes in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the relationships between emails into an AI and have the AI ​​perform the task of improving filtering accuracy.

[0048] The filtering unit can perform filtering while considering the attribute information of the email sender. For example, if the email sender is a trusted organization or individual, the filtering unit can prioritize displaying that email. The filtering unit can also filter emails as spam if the sender is unknown. Furthermore, the filtering unit can automatically classify important emails based on the attribute information of the email sender. This improves the accuracy of filtering by considering the attribute information of the email sender. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the attribute information of the email sender into AI and have the AI ​​perform the filtering.

[0049] The filtering unit can perform filtering while considering the geographical distribution of emails. For example, if an email is sent from a specific region, the filtering unit can prioritize displaying information related to that region. The filtering unit can also effectively eliminate spam emails based on the geographical distribution of emails. Furthermore, the filtering unit can automatically classify important emails while considering the geographical distribution of emails. This improves the accuracy of filtering by considering the geographical distribution of emails. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the geographical distribution of emails into AI and have the AI ​​perform the filtering.

[0050] The filtering unit can improve the accuracy of filtering by referring to related literature in the email during the filtering process. For example, the filtering unit can effectively eliminate spam emails by referring to literature related to the content of the email. The filtering unit can also automatically classify important emails based on the related literature in the email. Furthermore, the filtering unit can improve the accuracy of filtering by referring to related literature in the email. This allows for improved filtering accuracy by referring to related literature in the email. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input related literature in the email into AI and have AI perform the task of improving filtering accuracy.

[0051] The classification unit can improve the accuracy of classification by considering the relationships between emails during the classification process. For example, the classification unit can analyze the relationship between the sender and recipient of an email and prioritize the classification of highly reliable emails. It can also analyze the relationship between the content of an email and past emails to effectively classify important emails. Furthermore, the classification unit can effectively filter out spam emails based on their relationships. This improves the accuracy of classification by considering the relationships between emails. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the relationships between emails into an AI and have the AI ​​perform the task of improving classification accuracy.

[0052] The classification unit can classify emails while considering the sender's attribute information. For example, if the sender is a trusted organization or individual, the classification unit can prioritize classifying that email. The classification unit can also classify emails as spam if the sender is unknown. Furthermore, the classification unit can automatically classify important emails based on the sender's attribute information. This improves the accuracy of classification by considering the sender's attribute information. 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 attribute information into AI and have the AI ​​perform the classification.

[0053] The classification unit can classify emails while considering their geographical distribution. For example, if an email is sent from a specific region, the classification unit can prioritize classifying information related to that region. The classification unit can also effectively filter out spam emails based on their geographical distribution. Furthermore, the classification unit can automatically classify important emails while considering their geographical distribution. This improves the accuracy of classification by considering the geographical distribution of 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 geographical distribution of emails into AI and have the AI ​​perform the classification.

[0054] The classification unit can improve the accuracy of its classification by referring to related literature for emails during the classification process. For example, the classification unit can refer to literature related to the content of an email to effectively classify important emails. The classification unit can also effectively filter out spam emails based on the related literature for emails. Furthermore, the classification unit can improve the accuracy of its classification by referring to related literature for emails. This allows for improved classification accuracy by referring to related literature for 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 related literature for emails into AI and have the AI ​​perform the task of improving classification accuracy.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The AI ​​help desk system can analyze a user's past activity history and suggest the best solution based on their previous actions and settings. For example, if a user previously asked how to send an email, the system can quickly provide that solution when a similar question is entered again. It can also automatically apply similar settings based on the user's previous customizations. Furthermore, it can improve user convenience by prioritizing information about frequently used functions and applications. This allows for more personalized support based on the user's past activity history.

[0057] The analysis unit can analyze a user's device usage history and suggest optimal settings. For example, if a user frequently uses a particular application, it can prioritize suggesting settings related to that application. Furthermore, if a user tends to use a specific function during certain time periods, it can automatically apply the optimal settings for those times. It can also suggest similar settings based on past setting changes made by the user. This allows for more personalized settings based on the user's device usage history.

[0058] The filtering unit can analyze a user's email sending history to improve the accuracy of spam email detection. For example, it can analyze the content and recipients of emails a user has sent in the past and learn the characteristics of spam emails based on that analysis. It can also prioritize displaying emails from people the user frequently communicates with and filter out emails from unreliable senders as spam. Furthermore, it can effectively detect new spam emails based on the characteristics of emails that users have previously reported as spam. In this way, the accuracy of spam email detection can be improved based on the user's email sending history.

[0059] The reception desk can adjust its response based on the user's device's battery level. For example, if the user's device has a low battery level, it can respond quickly and provide a concise answer. If the battery level is sufficient, it can provide a detailed explanation. Furthermore, if the battery level is low, it can suggest battery-saving settings. This allows the system to provide a response tailored to the user's device's battery level.

[0060] The analysis unit can adjust the presentation method of analysis results based on the user's internet connection status. For example, if the user's internet connection is unstable, it can provide concise, text-based analysis results. If the internet connection is stable, it can also provide detailed analysis results including images and videos. Furthermore, if the internet connection is unstable, it can suggest offline solutions. This allows the system to provide analysis results in a way that suits the user's internet connection status.

[0061] The classification unit can analyze a user's email usage patterns and suggest the optimal classification method. For example, if a user frequently receives a specific type of email at a particular time, it can suggest the most suitable classification method for that time slot. Furthermore, if a user tends to prioritize emails from a specific sender, it can prioritize displaying emails from that sender. It can also suggest similar classification methods based on the user's past email classification patterns. This allows for a more personalized classification method based on the user's email usage patterns.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives user inquiries and problems. The reception desk can accept input via voice or text. For example, a user can voice-input a question such as "I don't know how to send an email." Users can also input questions in text. Step 2: The analysis unit analyzes the information received by the reception unit and provides solutions. The analysis unit uses natural language processing technology to analyze the user's questions and provide appropriate answers. For example, it can provide immediate answers to user questions. It can also automatically complete configuration operations as needed. For example, if the user requests it, the analysis unit can automatically configure email sending settings. Step 3: The filtering unit filters out spam and fake emails based on the information analyzed by the analysis unit. The filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. For example, it can detect spam emails based on specific keywords or the trustworthiness of the sender. Step 4: The classification unit automatically classifies emails filtered by the filtering unit. The classification unit can analyze received emails and automatically classify important emails. For example, it can prioritize displaying important emails such as notifications from banks or communications from medical institutions.

[0064] (Example of form 2) The Help Desk AI System according to an embodiment of the present invention is a 24-hour help desk AI system specifically designed for senior citizens who are unsure about using smartphones. This Help Desk AI System not only provides immediate responses to questions and problems regarding smartphone operation, but also offers functions such as filtering spam and fake emails and automatically classifying important emails, thereby reducing stress for seniors. This provides an environment where seniors can use smartphones with peace of mind and maximizes the convenience of digital devices. For example, a user inputs a question or problem regarding smartphone operation. The user can input the question by voice or text. For example, the user might input a question such as, "I don't know how to send an email." This information is entered into the Help Desk AI System. Next, the Help Desk AI System analyzes the input information and provides an appropriate solution. The Help Desk AI System not only provides immediate answers to the user's questions, but can also automatically complete setting operations as needed. For example, in addition to explaining how to send an email, it can automatically perform the settings to actually send an email if the user requests it. Furthermore, the Help Desk AI System provides a function to filter spam and fake emails. The Help Desk AI system analyzes received emails, automatically detects potentially spam and phishing emails, and provides users with risk information. This allows seniors to use email with peace of mind. The Help Desk AI system also provides an automatic email classification function. It analyzes received emails and automatically classifies important emails. For example, it prioritizes the display of important emails such as notifications from banks and communications from medical institutions, ensuring users don't miss important information. This system provides seniors with an environment where they can use smartphones with peace of mind, maximizing the convenience of digital devices. Furthermore, it enables a stress-free digital life and helps bridge the digital divide among seniors. In short, the Help Desk AI system provides seniors with an environment where they can use smartphones with peace of mind, maximizing the convenience of digital devices.

[0065] The AI ​​help desk system according to this embodiment comprises a reception unit, an analysis unit, a filtering unit, and a classification unit. The reception unit receives user questions and problems. The reception unit can accept input, for example, by voice or text. For example, a user can input a question such as "I don't know how to send an email" by voice. Alternatively, a user can input a question in text. The analysis unit analyzes the information received by the reception unit and provides solutions. The analysis unit analyzes the user's question using, for example, natural language processing technology and provides an appropriate answer. For example, the analysis unit can provide an immediate answer to a user's question. The analysis unit can also automatically complete setting operations as needed. For example, if the user requests it, the analysis unit can automatically configure email sending settings. The filtering unit filters spam and fake emails based on the information analyzed by the analysis unit. The filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. For example, the filtering unit can detect spam emails based on specific keywords or the trustworthiness of the sender. The classification unit automatically classifies emails that have been filtered by the filtering unit. For example, the classification unit can analyze received emails and automatically classify important emails. For instance, the classification unit can prioritize the display of important emails, such as notifications from banks or communications from medical institutions. As a result, the help desk AI system according to this embodiment can respond immediately to user questions and problems, filter spam and fake emails, and automatically classify important emails.

[0066] The reception desk receives user inquiries and problems. The reception desk can accept input via voice or text, for example. Specifically, users can input questions via voice, such as "I don't know how to send an email." Users can also input questions via text. The reception desk uses speech recognition technology to convert the user's voice input into text, and receives text input directly. The speech recognition technology accurately recognizes the user's speech, taking into account background noise and pronunciation differences to perform precise text conversion. Furthermore, the reception desk temporarily stores the user's input and performs simple pre-processing before sending it to the analysis department. For example, it performs grammar and spell checks on the input to enable efficient analysis by the analysis department. The reception desk also refers to the user's past inquiry history to ensure a quick response if similar problems occur again. This allows the reception desk to quickly and accurately receive user inquiries and problems and smoothly pass them on to the next processing step.

[0067] The analysis unit analyzes information received by the reception unit and provides solutions. For example, the analysis unit uses natural language processing technology to analyze user questions and provide appropriate answers. Specifically, the analysis unit tokenizes user questions and performs grammatical and semantic analysis. This allows it to accurately understand the user's intent and generate appropriate answers. The analysis unit refers to large databases and knowledge bases to provide optimal solutions based on similar past questions and their answers. Furthermore, the analysis unit uses machine learning algorithms to continuously improve the accuracy of its answers to user questions. For example, if a user asks, "I don't know how to send an email," the analysis unit can not only provide information on the email sending procedure but also automatically configure the user's email settings. In addition, the analysis unit collects user feedback and evaluates the quality of the answers to improve the overall system performance. This allows the analysis unit to respond quickly and accurately to user questions and problems, increasing user satisfaction.

[0068] The filtering unit filters out spam and fake emails based on information analyzed by the analysis unit. For example, the filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. Specifically, the filtering unit analyzes the content of emails and sender information to detect specific keywords and patterns. For example, it identifies emails containing keywords such as "free," "limited," and "urgent," or emails from unreliable senders, as spam. The filtering unit also uses machine learning algorithms to learn the characteristics of spam emails and improve its accuracy. Furthermore, the filtering unit refers to the user's past email receiving history and improves filtering accuracy based on the characteristics of emails that the user has previously marked as spam. As a result, the filtering unit can detect emails that may be spam or phishing scams with high accuracy, keeping the user's inbox clean.

[0069] The classification unit automatically categorizes emails filtered by the filtering unit. For example, the classification unit can analyze received emails and automatically categorize important emails. Specifically, the classification unit categorizes emails based on their content and sender information. For example, it prioritizes displaying important emails such as notifications from banks or communications from medical institutions. The classification unit can also refer to the user's past email classification history and categorize emails according to the user's preferences. Furthermore, the classification unit continuously improves the accuracy of email classification using machine learning algorithms. For example, if a user marks emails from a specific sender as important, it will automatically classify emails from the same sender as important. The classification unit also collects user feedback and evaluates the accuracy of the classification results to improve the overall system performance. As a result, the classification unit can efficiently manage the user's inbox and respond quickly without missing important emails.

[0070] The reception unit can accept input by voice or text. For example, the reception unit can accept questions from users by voice. For example, a user can enter a question by voice such as "I don't know how to send an email." The reception unit can also accept questions from users by text. For example, a user can enter a question using a smartphone keyboard. This allows users to input questions or problems by voice or text. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can use speech recognition technology to convert the user's voice input into text data and send that text data to the analysis unit.

[0071] The analysis unit can provide immediate answers to user questions. For example, the analysis unit can analyze user questions using natural language processing techniques and provide appropriate answers. The analysis unit can also provide answers quickly using pre-prepared answer templates. For example, the analysis unit can save answers to frequently asked questions as templates and provide immediate answers to user questions. This allows for immediate answers to user questions. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can use an AI model that analyzes user questions using natural language processing techniques and generates appropriate answers.

[0072] The analysis unit can automatically complete configuration operations as needed. For example, the analysis unit can automatically configure email sending settings if the user requests it. For example, if the user asks, "I don't know how to send emails," the analysis unit can automatically configure email sending settings. The analysis unit can also automatically complete other configuration operations, such as user account settings and notification settings. For example, the analysis unit can automatically configure the user's account settings and automatically complete notification settings. This allows the user's configuration operations to be completed automatically. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to automatically complete user configuration operations.

[0073] The filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. For example, the filtering unit can analyze received emails and detect spam emails based on specific keywords or the trustworthiness of the sender. For example, the filtering unit can automatically detect spam emails using an AI model that has learned the characteristics of spam emails. The filtering unit can also automatically detect emails that may be phishing scams. For example, the filtering unit can analyze the security of links in emails and detect emails that may be phishing scams. This enables the automatic detection of emails that may be spam or phishing scams. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can automatically detect spam emails using an AI model that has learned the characteristics of spam emails.

[0074] The classification unit can analyze received emails and automatically classify important emails. For example, the classification unit can analyze received emails and prioritize displaying important emails such as notifications from banks or communications from medical institutions. For example, the classification unit can automatically classify important emails based on their content and the importance of the sender. The classification unit can also prioritize displaying emails with high urgency. For example, the classification unit can automatically classify emails with high urgency and display them to the user with priority. This allows for the automatic classification of important 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 automatically classify important emails using an AI model that has learned the importance of email content and sender.

[0075] The reception desk can estimate the user's emotions and adjust its response based on those emotions. For example, if the user is feeling anxious, the reception desk can respond in a gentle tone and provide detailed explanations. If the user is in a hurry, the reception desk can respond quickly and provide concise answers. Furthermore, if the user is relaxed, the reception desk can offer detailed options and provide customizable responses. This allows the response to be adjusted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, 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 voice data or text data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can analyze a user's past inquiry history and select the most appropriate reception method. For example, the reception desk can prioritize providing relevant information based on the topics the user has frequently inquired about in the past. Furthermore, the reception desk can automatically suggest solutions to specific problems based on the user's past inquiry history. In addition, the reception desk can prioritize suggesting inquiry methods (voice, text, etc.) the user has used in the past. This allows the reception desk to select the most appropriate reception method based on the user's past inquiry history. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past inquiry history into an AI and have the AI ​​select the most appropriate reception method.

[0077] The reception desk can filter information based on the user's current situation and areas of interest at the time of reception. For example, if the user enters their current situation, the reception desk can provide relevant information corresponding to that situation. The reception desk can also prioritize displaying relevant inquiries based on the user's areas of interest. Furthermore, the reception desk can suggest the most suitable solution, taking into account the user's current situation and areas of interest. In this way, relevant information can be provided by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0078] The reception desk can estimate the user's emotions and determine the priority of inquiries based on the estimated emotions. For example, if the user has an urgent problem, the reception desk can prioritize that inquiry. If the user is relaxed, the reception desk can also handle it with the normal priority. Furthermore, if the user is stressed, the reception desk can respond quickly and provide support to alleviate that stress. This allows for prioritizing inquiries 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 reception desk may be performed using AI or not. For example, the reception desk can input user voice data or text data into a generative AI and have the generative AI perform the user's emotion estimation.

[0079] The reception desk can prioritize receiving inquiries that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize providing information related to that region. The reception desk can also suggest solutions to region-specific problems based on the user's geographical location. Furthermore, if the user is traveling, the reception desk can prioritize providing information related to their travel destination. This allows the reception desk to prioritize receiving inquiries that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into the AI ​​and have the AI ​​prioritize receiving inquiries that are highly relevant.

[0080] The reception desk can analyze a user's social media activity and receive relevant inquiries upon receiving an inquiry. For example, the reception desk can analyze a user's current interests from their social media activity and provide relevant information. It can also suggest solutions to problems the user has shared on social media. Furthermore, the reception desk can prioritize displaying relevant inquiries based on the user's social media activity. This allows the reception desk to receive relevant inquiries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI and have the AI ​​handle receiving relevant inquiries.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide an explanation in a gentle tone. If the user is in a hurry, the analysis unit can provide a concise and quick explanation. Furthermore, if the user is relaxed, the analysis unit can provide a detailed explanation and suggest customizable options. This allows the presentation of the analysis to be adjusted 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-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user voice data or text data into a generative AI and have the generative AI perform the user's emotion estimation.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during the analysis. For example, for important inquiries, the analysis unit can perform a detailed analysis and provide a comprehensive solution. For general inquiries, the analysis unit can perform a concise analysis and provide a quick solution. Furthermore, for urgent inquiries, the analysis unit can perform a rapid analysis and provide an immediate solution. This allows the level of detail of the analysis to be adjusted based on the importance of the inquiry. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the inquiry into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical problems. It can also apply a simpler analysis algorithm to inquiries about general operations. Furthermore, it can apply a security-specific analysis algorithm to inquiries about security. This allows for the application of different analysis algorithms depending on the category of the inquiry. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the inquiry into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is feeling anxious, the analysis unit can provide a thorough analysis to reassure them. This allows the length of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user voice data or text data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The analysis unit can determine the priority of analysis based on when the inquiry was submitted. For example, the analysis unit can immediately analyze urgent inquiries and provide solutions quickly. It can also analyze regular inquiries sequentially based on when they were submitted. Furthermore, it can prioritize the analysis and provide solutions for inquiries that have been left unattended for a long time. This allows the analysis priority to be determined based on when the inquiry was submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the submission date of the inquiry into the AI ​​and have the AI ​​determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the queries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant queries and provide solutions quickly. It can also analyze less relevant queries in the normal order. Furthermore, if there are multiple highly relevant queries, the analysis unit can analyze them together and provide a comprehensive solution. This allows the order of analysis to be adjusted based on the relevance of the queries. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the queries into the AI ​​and have the AI ​​adjust the order of analysis.

[0087] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the user is feeling anxious, the filtering unit can apply strict filtering criteria and thoroughly eliminate risky emails. Conversely, if the user is relaxed, the filtering unit can apply normal filtering criteria. Furthermore, if the user is in a hurry, the filtering unit can perform rapid filtering and prioritize the display of important emails. This allows for adjustment of filtering 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-described processing in the filtering unit may be performed using AI or not. For example, the filtering unit can input user voice data or text data into a generative AI and have the generative AI perform the user's emotion estimation.

[0088] The filtering unit can improve the accuracy of filtering by considering the relationships between emails during the filtering process. For example, the filtering unit can analyze the relationship between the sender and recipient of an email and prioritize displaying highly reliable emails. It can also analyze the relationship between the content of an email and past emails to effectively eliminate spam. Furthermore, the filtering unit can automatically classify important emails based on their relationships. This improves the accuracy of filtering by considering the relationships between emails. Some or all of the above-described processes in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the relationships between emails into an AI and have the AI ​​perform the task of improving filtering accuracy.

[0089] The filtering unit can perform filtering while considering the attribute information of the email sender. For example, if the email sender is a trusted organization or individual, the filtering unit can prioritize displaying that email. The filtering unit can also filter emails as spam if the sender is unknown. Furthermore, the filtering unit can automatically classify important emails based on the attribute information of the email sender. This improves the accuracy of filtering by considering the attribute information of the email sender. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the attribute information of the email sender into AI and have the AI ​​perform the filtering.

[0090] The filtering unit can estimate the user's emotions and adjust the order in which filtering results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the filtering unit can prioritize important emails and postpone risky emails. Conversely, if the user is relaxed, the filtering unit can display filtering results in the normal order. Furthermore, if the user is in a hurry, the filtering unit can quickly display important emails and postpone spam emails. This allows the order in which filtering results are displayed to be adjusted 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 filtering unit may be performed using AI or not. For example, the filtering unit can input user voice data or text data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0091] The filtering unit can perform filtering while considering the geographical distribution of emails. For example, if an email is sent from a specific region, the filtering unit can prioritize displaying information related to that region. The filtering unit can also effectively eliminate spam emails based on the geographical distribution of emails. Furthermore, the filtering unit can automatically classify important emails while considering the geographical distribution of emails. This improves the accuracy of filtering by considering the geographical distribution of emails. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the geographical distribution of emails into AI and have the AI ​​perform the filtering.

[0092] The filtering unit can improve the accuracy of filtering by referring to related literature in the email during the filtering process. For example, the filtering unit can effectively eliminate spam emails by referring to literature related to the content of the email. The filtering unit can also automatically classify important emails based on the related literature in the email. Furthermore, the filtering unit can improve the accuracy of filtering by referring to related literature in the email. This allows for improved filtering accuracy by referring to related literature in the email. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input related literature in the email into AI and have AI perform the task of improving filtering accuracy.

[0093] The classification unit can estimate the user's emotions and determine the priority of emails to classify based on the estimated emotions. For example, if the user is feeling anxious, the classification unit can prioritize important emails and postpone risky emails. If the user is relaxed, the classification unit can classify emails in the usual order. Furthermore, if the user is in a hurry, the classification unit can quickly display important emails and postpone spam emails. This allows for the determination of email prioritization 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 voice data or text data into a generative AI and have the generative AI perform the user's emotion estimation.

[0094] The classification unit can improve the accuracy of classification by considering the relationships between emails during the classification process. For example, the classification unit can analyze the relationship between the sender and recipient of an email and prioritize the classification of highly reliable emails. It can also analyze the relationship between the content of an email and past emails to effectively classify important emails. Furthermore, the classification unit can effectively filter out spam emails based on their relationships. This improves the accuracy of classification by considering the relationships between emails. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the relationships between emails into an AI and have the AI ​​perform the task of improving classification accuracy.

[0095] The classification unit can classify emails while considering the sender's attribute information. For example, if the sender is a trusted organization or individual, the classification unit can prioritize classifying that email. The classification unit can also classify emails as spam if the sender is unknown. Furthermore, the classification unit can automatically classify important emails based on the sender's attribute information. This improves the accuracy of classification by considering the sender's attribute information. 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 attribute information into AI and have the AI ​​perform the classification.

[0096] The classification unit can estimate the user's emotions and adjust how emails are displayed based on those emotions. For example, if the user is feeling anxious, the classification unit can make important emails more prominent and risky emails less prominent. Conversely, if the user is relaxed, the classification unit can display emails in the normal way. Furthermore, if the user is in a hurry, the classification unit can quickly display important emails and prioritize spam emails. This allows for adjustment of how emails are displayed 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 voice data or text data into a generative AI and have the generative AI perform the user emotion estimation.

[0097] The classification unit can classify emails while considering their geographical distribution. For example, if an email is sent from a specific region, the classification unit can prioritize classifying information related to that region. The classification unit can also effectively filter out spam emails based on their geographical distribution. Furthermore, the classification unit can automatically classify important emails while considering their geographical distribution. This improves the accuracy of classification by considering the geographical distribution of 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 geographical distribution of emails into AI and have the AI ​​perform the classification.

[0098] The classification unit can improve the accuracy of its classification by referring to related literature for emails during the classification process. For example, the classification unit can refer to literature related to the content of an email to effectively classify important emails. The classification unit can also effectively filter out spam emails based on the related literature for emails. Furthermore, the classification unit can improve the accuracy of its classification by referring to related literature for emails. This allows for improved classification accuracy by referring to related literature for 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 related literature for emails into AI and have the AI ​​perform the task of improving classification accuracy.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The AI ​​help desk system can analyze a user's past activity history and suggest the best solution based on their previous actions and settings. For example, if a user previously asked how to send an email, the system can quickly provide that solution when a similar question is entered again. It can also automatically apply similar settings based on the user's previous customizations. Furthermore, it can improve user convenience by prioritizing information about frequently used functions and applications. This allows for more personalized support based on the user's past activity history.

[0101] The reception desk can adjust its response based on the user's current health condition. For example, if a user enters information about their health, the response can be adjusted based on that information. If the user is tired, a concise and easy-to-understand explanation can be provided; if the user is relaxed, a detailed explanation can be provided. Furthermore, if the user is stressed, support to reduce stress can be offered. This allows for a response tailored to the user's health condition.

[0102] The analysis unit can adjust how it presents analysis results based on the user's learning style. For example, if the user is a visual learner, the analysis results can be presented using diagrams and graphs. If the user is an auditory learner, the analysis results can be explained verbally. Furthermore, if the user is a hands-on learner, step-by-step guidance can be provided, allowing the user to learn while actually performing operations. This allows the system to provide analysis results in a way that suits the user's learning style.

[0103] The analysis unit can analyze a user's device usage history and suggest optimal settings. For example, if a user frequently uses a particular application, it can prioritize suggesting settings related to that application. Furthermore, if a user tends to use a specific function during certain time periods, it can automatically apply the optimal settings for those times. It can also suggest similar settings based on past setting changes made by the user. This allows for more personalized settings based on the user's device usage history.

[0104] The filtering unit can analyze a user's email sending history to improve the accuracy of spam email detection. For example, it can analyze the content and recipients of emails a user has sent in the past and learn the characteristics of spam emails based on that analysis. It can also prioritize displaying emails from people the user frequently communicates with and filter out emails from unreliable senders as spam. Furthermore, it can effectively detect new spam emails based on the characteristics of emails that users have previously reported as spam. In this way, the accuracy of spam email detection can be improved based on the user's email sending history.

[0105] The classification unit can estimate the user's emotions and adjust how emails are classified based on those emotions. For example, if a user is feeling anxious, important emails can be displayed prominently, while risky emails can be displayed more discreetly. If a user is relaxed, emails can be classified using the normal display method. Furthermore, if a user is in a hurry, important emails can be displayed quickly, while spam emails are prioritized for later. This allows for the classification of emails to be adjusted based on the user's emotions.

[0106] The reception desk can adjust its response based on the user's device's battery level. For example, if the user's device has a low battery level, it can respond quickly and provide a concise answer. If the battery level is sufficient, it can provide a detailed explanation. Furthermore, if the battery level is low, it can suggest battery-saving settings. This allows the system to provide a response tailored to the user's device's battery level.

[0107] The analysis unit can adjust the presentation method of analysis results based on the user's internet connection status. For example, if the user's internet connection is unstable, it can provide concise, text-based analysis results. If the internet connection is stable, it can also provide detailed analysis results including images and videos. Furthermore, if the internet connection is unstable, it can suggest offline solutions. This allows the system to provide analysis results in a way that suits the user's internet connection status.

[0108] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on those emotions. For example, if the user is feeling anxious, strict filtering criteria can be applied to thoroughly eliminate risky emails. Conversely, if the user is relaxed, normal filtering criteria can be applied. Furthermore, if the user is in a hurry, filtering can be performed quickly to prioritize the display of important emails. In this way, filtering criteria can be adjusted based on the user's emotions.

[0109] The classification unit can analyze a user's email usage patterns and suggest the optimal classification method. For example, if a user frequently receives a specific type of email at a particular time, it can suggest the most suitable classification method for that time slot. Furthermore, if a user tends to prioritize emails from a specific sender, it can prioritize displaying emails from that sender. It can also suggest similar classification methods based on the user's past email classification patterns. This allows for a more personalized classification method based on the user's email usage patterns.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives user inquiries and problems. The reception desk can accept input via voice or text. For example, a user can voice-input a question such as "I don't know how to send an email." Users can also input questions in text. Step 2: The analysis unit analyzes the information received by the reception unit and provides solutions. The analysis unit uses natural language processing technology to analyze the user's questions and provide appropriate answers. For example, it can provide immediate answers to user questions. It can also automatically complete configuration operations as needed. For example, if the user requests it, the analysis unit can automatically configure email sending settings. Step 3: The filtering unit filters out spam and fake emails based on the information analyzed by the analysis unit. The filtering unit can analyze received emails and automatically detect emails that may be spam or phishing scams. For example, it can detect spam emails based on specific keywords or the trustworthiness of the sender. Step 4: The classification unit automatically classifies emails filtered by the filtering unit. The classification unit can analyze received emails and automatically classify important emails. For example, it can prioritize displaying important emails such as notifications from banks or communications from medical institutions.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, and classification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts voice or text input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's question using natural language processing technology and provides an appropriate answer. The filtering unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically detects emails that may be spam or phishing scams. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically classifies important emails. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, and classification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts voice or text input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's question using natural language processing technology and provides an appropriate answer. The filtering unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically detects emails that may be spam or phishing scams. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically classifies important emails. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

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

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

[0142] In 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.

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

[0144] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0146] The data processing system 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, and classification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts voice or text input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's question using natural language processing technology and provides an appropriate answer. The filtering unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically detects emails that may be spam or phishing scams. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically classifies important emails. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[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 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.

[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 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.

[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 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, and classification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice or text input from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the user's question using natural language processing technology and provides an appropriate answer. The filtering unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically detects emails that may be spam or phishing scams. The classification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes received emails and automatically classifies important emails. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that handles user inquiries and problems, An analysis unit analyzes the information received by the reception unit and provides a solution, A filtering unit filters out spam and fake emails based on the information analyzed by the aforementioned analysis unit, The system includes a classification unit that automatically classifies emails filtered by the filtering unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts voice or text input. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Provides immediate answers to user questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Complete the setup process automatically as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The filtering unit is It analyzes received emails and automatically detects emails that may be spam or phishing scams. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned classification unit is It analyzes received emails and automatically categorizes important emails. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the receptionist's response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable contact method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inquiries to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving an inquiry, the system prioritizes inquiries that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and receives related inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the query. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the query category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the queries. The system described in Appendix 1, characterized by the features described herein. (Note 19) The filtering unit is It estimates the user's sentiment and adjusts the filtering criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The filtering unit is When filtering, consider the relationships between emails to improve the accuracy of the filtering process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The filtering unit is When filtering, the sender's attribute information of the email is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The filtering unit is It estimates the user's sentiment and adjusts the order in which filtering results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The filtering unit is When filtering, consider the geographical distribution of emails. The system described in Appendix 1, characterized by the features described herein. (Note 24) The filtering unit is When filtering, we improve the accuracy of the filtering by referring to related literature in the email. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned classification unit is It estimates the user's emotions and determines the priority of emails based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned classification unit is When classifying emails, consider the relationships between them to improve classification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned classification unit is When classifying emails, the sender's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned classification unit is We estimate the user's emotions and adjust how emails are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned classification unit is When classifying emails, the geographical distribution of the emails should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned classification unit is During classification, we refer to related literature in emails to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 reception desk that handles user inquiries and problems, An analysis unit analyzes the information received by the reception unit and provides a solution, A filtering unit filters out spam and fake emails based on the information analyzed by the aforementioned analysis unit, The system includes a classification unit that automatically classifies emails filtered by the filtering unit. A system characterized by the following features.

2. The aforementioned reception unit is Accepts voice or text input. The system according to feature 1.

3. The aforementioned analysis unit, Provides immediate answers to user questions. The system according to feature 1.

4. The aforementioned analysis unit, Complete the setup process automatically as needed. The system according to feature 1.

5. The filtering unit is It analyzes received emails and automatically detects emails that may be spam or phishing scams. The system according to feature 1.

6. The aforementioned classification unit is It analyzes received emails and automatically categorizes important emails. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the receptionist's response based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable contact method. The system according to feature 1.

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