system

Abstract

The system according to this embodiment aims to improve user convenience by automatically processing letters received in the mailbox. [Solution] The system according to the embodiment comprises an analysis unit, a notification unit, a shredder unit, and a forwarding unit. The analysis unit automatically opens letters that arrive in the mailbox and analyzes their contents. The notification unit notifies the user of the contents analyzed by the analysis unit. The shredder unit shreds unwanted letters based on the contents notified by the notification unit. The forwarding unit forwards important letters to another address based on the contents notified by the notification 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the processing of letters received at the post is performed manually, and there is a problem that it is difficult to manage letters, especially when the user is absent.

[0005] The system according to the embodiment aims to automatically process letters received at the post and improve the convenience of the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a notification unit, a shredder unit, and a forwarding unit. The analysis unit automatically opens letters received in the mailbox and analyzes their contents. The notification unit notifies the user of the contents analyzed by the analysis unit. The shredder unit shreds unwanted letters based on the contents notified by the notification unit. The forwarding unit forwards important letters to another address based on the contents notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically process letters received in the mailbox, thereby improving user convenience. [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 a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". 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 receiving 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 receiving 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 system for automatically processing the contents of a mailbox according to an embodiment of the present invention is a system that uses a generating AI to automatically open letters that arrive in the mailbox, analyze their contents, and notify the user. Based on the contents of the letters, this system can shred unwanted letters or forward important letters to another address. For example, when a letter arrives in the mailbox, the generating AI automatically opens the letter and analyzes its contents. The analyzed contents are notified to the user in real time. For example, if a letter arrives from the city hall, a summary of the letter is notified to the user's smartphone. The user can check the contents of the letter and, if necessary, open the letter or take a picture and send it. Furthermore, based on the contents of the letters, the generating AI can also automatically shred unwanted advertisements or forward important letters to another address. For example, if a flyer from a clothing store arrives, the contents of the flyer are summarized and notified to the user. If the user decides that the flyer is unnecessary, the generating AI automatically shreds it. The generating AI also has a function to detect potentially fraudulent letters and warn the user. For example, if a payment instruction from the National Tax Agency is deemed highly likely to be fraudulent, the generating AI will warn the user, allowing them to choose whether to shred the letter or contact the police. This system is extremely convenient for people who travel frequently, as it allows users to track the contents of letters in their mailbox in real time, even when they are far away. Furthermore, by automatically analyzing the contents of letters and taking necessary actions, it significantly reduces the user's workload.

[0029] The system for automatically processing the contents of a mailbox according to this embodiment comprises an analysis unit, a notification unit, a shredder unit, and a forwarding unit. The analysis unit automatically opens letters that arrive in the mailbox and analyzes their contents. The analysis unit opens letters using, for example, a device that mechanically opens letters, scans the contents, and converts them into digital data. The analysis unit uses a generation AI to analyze the contents of the letters and generates a summary. The generation AI can summarize the contents of the letters using, for example, a text generation AI (e.g., LLM). The analysis unit can also analyze the contents of the letters and determine whether they may be fraudulent. The notification unit notifies the user of the contents analyzed by the analysis unit. The notification unit can notify the user by, for example, email, SMS, or app notification. The notification unit can also use a generation AI to summarize the analyzed contents and notify the user. The shredder unit shreds unwanted letters based on the contents notified by the notification unit. The shredder unit can shred unwanted letters based on, for example, instructions from the user. The shredder unit can use generation AI to analyze the contents of letters and automatically shred unwanted letters. The forwarding unit forwards important letters to another address based on the information notified by the notification unit. The forwarding unit can, for example, forward important letters to another address based on user instructions. The forwarding unit can also use generation AI to analyze the contents of letters and automatically forward important letters to another address. As a result, the system for automatically processing the contents of a mailbox according to this embodiment can automatically process letters that arrive in the mailbox, notify the user, shred unwanted letters, and forward important letters.

[0030] The analysis unit automatically opens letters received in the mailbox and analyzes their contents. For example, the analysis unit uses a mechanical letter-opening device to open the letters, scans the contents, and converts them into digital data. Specifically, the letter-opening device is equipped with a precision cutter or laser to open the letter seal, ensuring that the contents are not damaged. The opened letters are scanned at high resolution by a scanner and converted into digital data using optical character recognition (OCR) technology. The generation AI can summarize the letter's contents using, for example, a text generation AI (e.g., LLM). The generation AI analyzes the letter's contents, extracts important information, and generates a summary. For example, it extracts key points and important messages from the letter's text to create a concise summary. The analysis unit can also analyze the letter's contents to determine if it is potentially fraudulent. The generation AI uses natural language processing technology to understand the context and content of the letter and detect signs of fraud. For example, it analyzes specific keywords, phrases, and stylistic patterns to identify letters that are likely to be fraudulent. This allows the analysis unit to quickly and accurately analyze the content of letters and provide important information to the user. Furthermore, the analysis unit can classify the content of letters and divide them into different categories. For example, it can classify them into business-related letters, personal letters, advertising and promotional letters, etc., allowing users to manage their letters efficiently. In this way, the analysis unit can analyze the content of letters from multiple perspectives and provide useful information to the user.

[0031] The notification unit notifies the user of the content analyzed by the analysis unit. The notification unit can notify the user through methods such as email, SMS, and app notifications. Specifically, it sends summaries and important information generated by the analysis unit using the notification method specified by the user. The notification unit can also use a generation AI to summarize the analyzed content and notify the user. The generation AI concisely summarizes the content of the letter so that the user can quickly understand it. For example, the letter summary can be included in the body of an email or sent as a short message via SMS. When using app notifications, a push notification can be sent to the user's smartphone, displaying the letter summary and important information. Furthermore, the notification unit can adjust the notification priority according to the user's settings. For example, it is possible to set it so that important or urgent letters are notified immediately, while advertising and promotional letters are notified in batches. In this way, the notification unit can notify the user of the content of the letter in the most optimal way and support efficient information management. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, by providing ratings and comments on notification content, the notification system can optimize notification methods and content based on that feedback. This allows the notification system to provide users with timely and accurate information and streamline the management of notifications.

[0032] The shredder unit shreds unwanted letters based on the information provided by the notification unit. For example, the shredder unit can shred unwanted letters based on user instructions. Specifically, after receiving a notification, the user can instruct the shredder unit to automatically shred letters they deem unnecessary. The shredder unit can also use generation AI to analyze the content of letters and automatically shred unwanted ones. The generation AI analyzes the content of letters and evaluates their importance and relevance. For example, it can identify unwanted letters such as advertisements, promotional letters, and spam emails and automatically shred them. The shredder unit is equipped with a high-performance shredder, allowing it to finely shred letters and prevent information leaks. Furthermore, the shredder unit has a mechanism for recycling the shredded paper fragments, enabling environmentally friendly processing. This allows the shredder unit to efficiently process unwanted letters and reduce the risk of information leaks. Furthermore, the shredder unit can also record the history of letter processing, allowing users to review it later. For example, it can record a list of shredded letters and the processing date and time, allowing users to check it as needed. This enables the shredder unit to process letters transparently and efficiently, increasing user confidence.

[0033] The forwarding unit forwards important letters to a different address based on the information notified by the notification unit. For example, the forwarding unit can forward important letters to a different address based on user instructions. Specifically, after a user receives a notification, they can instruct the forwarding unit to forward letters they deem important to a specified address. The forwarding unit can also use generative AI to analyze the content of letters and automatically forward important letters to a different address. The generative AI analyzes the content of letters and evaluates their importance and relevance. For example, it can identify important business letters and important personal letters and automatically forward them to a specified address. The forwarding unit has a mechanism that allows users to pre-set the forwarding destination for letters, making it easy for users to change the forwarding destination. Furthermore, the forwarding unit can track the forwarding status of letters in real time and notify the user. For example, it can send a notification when a letter arrives at the forwarding destination, allowing the user to check the status of the letter's receipt. This ensures that the forwarding unit reliably forwards important letters and allows users to receive letters with peace of mind. In addition, the forwarding unit can record the forwarding history of letters so that users can review it later. For example, the system could record a list of forwarded letters, the forwarding date and time, and the forwarding address, allowing users to check them as needed. This would enable the forwarding unit to forward letters transparently and efficiently, increasing user confidence.

[0034] The fraud detection unit can detect potentially fraudulent letters. For example, the fraud detection unit analyzes the content of a letter to determine if it is potentially fraudulent. The fraud detection unit can also use generative AI to analyze the content of a letter and determine if it is potentially fraudulent. For example, the fraud detection unit can use keyword matching or machine learning models to detect potential fraud. This allows the system to detect potentially fraudulent letters and issue warnings to users.

[0035] The analysis unit can analyze the content of a letter and generate a summary. For example, the analysis unit can scan the content of a letter and convert it into digital data, then generate a summary using a generation AI. The generation AI can summarize the content of a letter using, for example, a text generation AI (e.g., LLM). The analysis unit can also analyze the content of a letter, extract important information, and generate a summary. This allows the user to quickly understand the content of a letter by summarizing it and notifying them.

[0036] The notification unit can notify the user of the summary generated by the analysis unit. The notification unit can notify the user by methods such as email, SMS, or app notifications. The notification unit can also use generation AI to summarize the analyzed content and notify the user. The notification unit can also analyze the content of a letter, extract important information, generate a summary, and notify the user. This allows the user to quickly grasp the content of the letter by notifying them of the letter summary.

[0037] The shredder unit can shred unwanted letters based on user instructions. For example, the shredder unit can shred unwanted letters based on user instructions. The shredder unit can also use generation AI to analyze the content of letters and automatically shred unwanted letters. This allows for the automatic shredding of unwanted letters, saving time and effort.

[0038] The forwarding unit can forward important letters to a different address based on user instructions. For example, the forwarding unit can forward important letters to a different address based on user instructions. The forwarding unit can also use generation AI to analyze the content of letters and automatically forward important letters to a different address. This allows for the automatic forwarding of important letters, saving time and effort.

[0039] The fraud detection unit can analyze the content of a letter and determine whether it is potentially fraudulent. For example, the fraud detection unit can scan the letter's content, convert it into digital data, and use generative AI to determine the likelihood of fraud. The generative AI can detect the likelihood of fraud using, for example, keyword matching or machine learning models. The fraud detection unit can also analyze the content of a letter and determine whether it is potentially fraudulent. This allows the system to analyze the letter's content and determine if it is potentially fraudulent, thereby issuing a warning to the user.

[0040] The fraud detection unit, if it determines that a letter may be fraudulent, will issue a warning to the user and allow them to choose whether to shred the letter or contact the police. For example, the fraud detection unit can analyze the content of a letter and, if it determines that there is a high probability of fraud, issue a warning to the user. The fraud detection unit can use generative AI to analyze the content of a letter and, if it determines that there is a high probability of fraud, issue a warning to the user. The fraud detection unit can also analyze the content of a letter and, if it determines that there is a high probability of fraud, issue a warning to the user and allow them to choose whether to shred the letter or contact the police. This allows for appropriate action to be taken regarding letters that may be fraudulent.

[0041] The analysis unit can evaluate the reliability of the letter sender when analyzing the content of a letter and reflect this in the analysis results. For example, the analysis unit can evaluate reliability by referring to the letter sender's past behavioral history. The generating AI can evaluate reliability by referring to the letter sender's official website or publicly available information. The analysis unit can also analyze the content of past letters from the letter sender, check for consistency, and evaluate reliability. By evaluating the reliability of the letter sender, the reliability of the analysis results can be improved.

[0042] The analysis unit can automatically generate a reply based on the content of a letter. For example, the analysis unit can scan the letter's content and convert it into digital data, then use a generation AI to generate a reply. The generation AI can, for example, analyze the letter's content and automatically generate an appropriate reply. The analysis unit can also select and customize a reply template based on the letter's content. Furthermore, the generation AI can analyze the letter's content and automatically insert necessary information into the reply. This reduces the user's effort by automatically generating a reply based on the letter's content.

[0043] The analysis unit can, when analyzing the content of a letter, refer to related past letters based on the content of the current letter and reflect this in the analysis results. For example, the analysis unit can scan the content of a letter and convert it into digital data, and then use a generative AI to search for related past letters. The generative AI can, for example, analyze the content of a letter and automatically search for and refer to related past letters. The analysis unit can also compare the content of past letters based on the content of the current letter, verify consistency, and reflect this in the analysis results. Furthermore, the generative AI can analyze the content of a letter, summarize the content of past letters, and reflect this in the analysis results. This allows for improved accuracy of the analysis results by referencing related past letters.

[0044] The analysis unit can automatically add appointments to the calendar based on the content of a letter. For example, the analysis unit can scan the letter content, convert it into digital data, and add appointments to the calendar using a generation AI. The generation AI can, for example, analyze the letter content and automatically add important events and deadlines to the calendar. The analysis unit can also suggest appointments relevant to the user's calendar based on the letter content. Furthermore, the generation AI can set reminders when analyzing the letter content and adding it to the calendar. This helps users manage their schedules by automatically adding appointments to the calendar based on the letter content.

[0045] The notification unit can select the notification format based on the content of the letter when it sends a notification. For example, the notification unit can scan the content of the letter and convert it into digital data, then use a generation AI to select the notification format. The generation AI can, for example, analyze the content of the letter and notify important information in text format. The notification unit can also select and deliver an audio notification to the user based on the content of the letter. Furthermore, the generation AI can analyze the content of the letter and generate a notification that includes images. This allows for user-friendly notifications by selecting the most appropriate notification format based on the content of the letter.

[0046] The notification unit can adjust the timing of notifications based on the content of the letter. For example, the notification unit can scan the letter's content, convert it into digital data, and use a generation AI to adjust the timing of the notification. The generation AI can, for example, analyze the letter's content and immediately deliver important notifications. The notification unit can also adjust the timing of notifications to match the user's schedule based on the letter's content. Furthermore, the generation AI can analyze the letter's content and deliver notifications at the time when the user is most likely to check them. This allows for the selection of the optimal notification timing based on the letter's content, ensuring that notifications are delivered at the most opportune time for the user.

[0047] The notification unit can adjust the frequency of notifications based on the content of the letter. For example, the notification unit can scan the letter's content and convert it into digital data, then use a generative AI to adjust the notification frequency. The generative AI can, for example, analyze the letter's content and send important notifications more frequently. The notification unit can also adjust the notification frequency to match the user's schedule based on the letter's content. Furthermore, the generative AI can analyze the letter's content and send notifications at a frequency that is most convenient for the user to check. This allows for notifications to be sent at the optimal frequency for the user by selecting the optimal notification frequency based on the letter's content.

[0048] The notification unit can select the notification method based on the content of the letter when a notification is sent. For example, the notification unit can scan the content of the letter and convert it into digital data, then use a generation AI to select the notification method. The generation AI can, for example, analyze the content of the letter and notify important information via email. The notification unit can also send a notification via SMS based on the content of the letter. Furthermore, the generation AI can analyze the content of the letter and select an app notification to convey to the user. This allows for user-friendly notifications by selecting the most appropriate notification method based on the content of the letter.

[0049] The shredder unit can adjust the shredding level based on the content of the letter being processed. For example, it can scan the letter's contents and convert them into digital data, then use generating AI to adjust the shredding level. The generating AI can, for example, analyze the letter's content and finely shred letters containing confidential information. The shredder unit can also process general letters at a normal shredding level based on their content. Furthermore, the generating AI can analyze the letter's content and easily shred unwanted advertisements. This allows for the protection of confidential information by adjusting the shredding level based on the letter's content.

[0050] The shredder unit can select the shredding mode based on the content of the letter when processing it. For example, the shredder unit can scan the letter's contents and convert them into digital data, then use a generating AI to select the operating mode. The generating AI can, for example, analyze the letter's contents and shred letters containing confidential information in continuous mode. The shredder unit can also process general letters in intermittent mode based on their contents. Furthermore, the generating AI can analyze the letter's contents and easily shred unwanted advertisements in intermittent mode. This allows for efficient processing by selecting the shredder's operating mode based on the letter's content.

[0051] The forwarding unit can select a forwarding method based on the content of the letter when forwarding it. For example, the forwarding unit can scan the content of the letter and convert it into digital data, then use a generating AI to select a forwarding method. The generating AI can, for example, analyze the content of the letter and forward important letters by postal mail. The forwarding unit can also forward letters by email based on the content of the letter. Furthermore, the generating AI can analyze the content of the letter and select the most suitable forwarding method. This enables efficient forwarding by selecting the most suitable forwarding method based on the content of the letter.

[0052] The forwarding unit can automatically update the forwarding address based on the content of the letter when forwarding it. For example, the forwarding unit can scan the content of the letter and convert it into digital data, and then update the forwarding address using a generating AI. For example, the generating AI can analyze the content of the letter and automatically update the forwarding address based on the user's current location. The forwarding unit can also forward the letter to an address specified by the user based on its content. Furthermore, the generating AI can analyze the content of the letter and automatically update the forwarding address by referring to the user's calendar information. This enables forwarding tailored to the user's current location by automatically updating the forwarding address based on the content of the letter.

[0053] The fraud detection unit can assess the likelihood of fraud by referring to the sender's past activity history when analyzing a letter. For example, the fraud detection unit can scan the letter's contents and convert them into digital data, then use a generating AI to refer to the sender's past activity history. The generating AI can, for example, analyze the sender's past activity history and assess the likelihood of fraud. The fraud detection unit can also refer to the sender's past letter content to check for consistency and assess the likelihood of fraud. Furthermore, the generating AI can also refer to the sender's official website and publicly available information to assess the likelihood of fraud. In this way, the likelihood of fraud can be assessed by referring to the sender's past activity history.

[0054] The fraud detection unit can have a function to automatically report to the police based on the content of the letter when analyzing it. For example, the fraud detection unit can scan the content of the letter, convert it into digital data, and report it to the police using a generating AI. The generating AI can, for example, analyze the content of the letter and automatically report to the police if there is a high probability of fraud. The fraud detection unit can also automatically report potentially fraudulent letters to the police based on their content. Furthermore, the generating AI can analyze the content of the letter and automatically generate a template for reporting to the police if there is a high probability of fraud. This enables a swift response by automatically reporting potentially fraudulent letters to the police.

[0055] The fraud detection unit can automatically isolate letters that are highly likely to be fraudulent based on their content when analyzing them. For example, the fraud detection unit scans the content of the letter, converts it into digital data, and isolates it using a generating AI. The generating AI can, for example, analyze the content of the letter and automatically isolate letters that are highly likely to be fraudulent. The fraud detection unit can also isolate potentially fraudulent letters based on their content and notify the user. Furthermore, the generating AI can analyze the content of the letter and automatically generate rules for isolating potentially fraudulent letters. This ensures user safety by automatically isolating potentially fraudulent letters.

[0056] The fraud detection unit can automatically forward potentially fraudulent letters to lawyers based on their content during the analysis process. For example, the fraud detection unit can scan the letter's content, convert it into digital data, and forward it to lawyers using a generating AI. The generating AI, for example, can analyze the letter's content and automatically forward letters with a high probability of being fraudulent to lawyers. The fraud detection unit can also report potentially fraudulent letters to lawyers based on their content. Furthermore, the generating AI can analyze the letter's content and automatically generate templates for forwarding potentially fraudulent letters to lawyers. This enables appropriate legal action by automatically forwarding potentially fraudulent letters to lawyers.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The analysis unit can automatically generate a reply based on the content of a letter. For example, the analysis unit can scan the letter's content and convert it into digital data, then use a generation AI to generate a reply. The generation AI can, for example, analyze the letter's content and automatically generate an appropriate reply. The analysis unit can also select and customize a reply template based on the letter's content. Furthermore, the generation AI can analyze the letter's content and automatically insert necessary information into the reply. This reduces the user's effort by automatically generating a reply based on the letter's content.

[0059] The analysis unit can, when analyzing the content of a letter, refer to related past letters based on the content of the current letter and reflect this in the analysis results. For example, the analysis unit can scan the content of a letter and convert it into digital data, and then use a generative AI to search for related past letters. The generative AI can, for example, analyze the content of a letter and automatically search for and refer to related past letters. The analysis unit can also compare the content of past letters based on the content of the current letter, verify consistency, and reflect this in the analysis results. Furthermore, the generative AI can analyze the content of a letter, summarize the content of past letters, and reflect this in the analysis results. This allows for improved accuracy of the analysis results by referencing related past letters.

[0060] The analysis unit can automatically add appointments to the calendar based on the content of a letter. For example, the analysis unit can scan the letter content, convert it into digital data, and add appointments to the calendar using a generation AI. The generation AI can, for example, analyze the letter content and automatically add important events and deadlines to the calendar. The analysis unit can also suggest appointments relevant to the user's calendar based on the letter content. Furthermore, the generation AI can set reminders when analyzing the letter content and adding it to the calendar. This helps users manage their schedules by automatically adding appointments to the calendar based on the letter content.

[0061] The notification unit can select the notification format based on the content of the letter when it sends a notification. For example, the notification unit can scan the content of the letter and convert it into digital data, then use a generation AI to select the notification format. The generation AI can, for example, analyze the content of the letter and notify important information in text format. The notification unit can also select and deliver an audio notification to the user based on the content of the letter. Furthermore, the generation AI can analyze the content of the letter and generate a notification that includes images. This allows for user-friendly notifications by selecting the most appropriate notification format based on the content of the letter.

[0062] The notification unit can adjust the timing of notifications based on the content of the letter. For example, the notification unit can scan the letter's content, convert it into digital data, and use a generation AI to adjust the timing of the notification. The generation AI can, for example, analyze the letter's content and immediately deliver important notifications. The notification unit can also adjust the timing of notifications to match the user's schedule based on the letter's content. Furthermore, the generation AI can analyze the letter's content and deliver notifications at the time when the user is most likely to check them. This allows for the selection of the optimal notification timing based on the letter's content, ensuring that notifications are delivered at the most opportune time for the user.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The analysis unit automatically opens letters received in the mailbox and analyzes their contents. The analysis unit uses a mechanical letter-opening device to open the letters, scans the contents, and converts them into digital data. Furthermore, it uses a generative AI to analyze the contents of the letters and generate a summary. The analysis unit can also analyze the contents of the letters and determine whether they may be fraudulent. Step 2: The notification unit notifies the user of the content analyzed by the analysis unit. The notification unit can notify the user via email, SMS, app notifications, etc. Furthermore, it can also summarize the analyzed content using generative AI and notify the user of that summary. Step 3: The shredder unit shreds unwanted letters based on the information provided by the notification unit. The shredder unit can shred unwanted letters based on user instructions. Furthermore, it can use generation AI to analyze the content of letters and automatically shred unwanted letters. Step 4: The forwarding unit forwards important letters to another address based on the information notified by the notification unit. The forwarding unit can forward important letters to another address based on user instructions. Furthermore, it can analyze the content of letters using generation AI and automatically forward important letters to another address.

[0065] (Example of form 2) The system for automatically processing the contents of a mailbox according to an embodiment of the present invention is a system that uses a generating AI to automatically open letters that arrive in the mailbox, analyze their contents, and notify the user. Based on the contents of the letters, this system can shred unwanted letters or forward important letters to another address. For example, when a letter arrives in the mailbox, the generating AI automatically opens the letter and analyzes its contents. The analyzed contents are notified to the user in real time. For example, if a letter arrives from the city hall, a summary of the letter is notified to the user's smartphone. The user can check the contents of the letter and, if necessary, open the letter or take a picture and send it. Furthermore, based on the contents of the letters, the generating AI can also automatically shred unwanted advertisements or forward important letters to another address. For example, if a flyer from a clothing store arrives, the contents of the flyer are summarized and notified to the user. If the user decides that the flyer is unnecessary, the generating AI automatically shreds it. The generating AI also has a function to detect potentially fraudulent letters and warn the user. For example, if a payment instruction from the National Tax Agency is deemed highly likely to be fraudulent, the generating AI will warn the user, allowing them to choose whether to shred the letter or contact the police. This system is extremely convenient for people who travel frequently, as it allows users to track the contents of letters in their mailbox in real time, even when they are far away. Furthermore, by automatically analyzing the contents of letters and taking necessary actions, it significantly reduces the user's workload.

[0066] The system for automatically processing the contents of a mailbox according to this embodiment comprises an analysis unit, a notification unit, a shredder unit, and a forwarding unit. The analysis unit automatically opens letters that arrive in the mailbox and analyzes their contents. The analysis unit opens letters using, for example, a device that mechanically opens letters, scans the contents, and converts them into digital data. The analysis unit uses a generation AI to analyze the contents of the letters and generates a summary. The generation AI can summarize the contents of the letters using, for example, a text generation AI (e.g., LLM). The analysis unit can also analyze the contents of the letters and determine whether they may be fraudulent. The notification unit notifies the user of the contents analyzed by the analysis unit. The notification unit can notify the user by, for example, email, SMS, or app notification. The notification unit can also use a generation AI to summarize the analyzed contents and notify the user. The shredder unit shreds unwanted letters based on the contents notified by the notification unit. The shredder unit can shred unwanted letters based on, for example, instructions from the user. The shredder unit can use generation AI to analyze the contents of letters and automatically shred unwanted letters. The forwarding unit forwards important letters to another address based on the information notified by the notification unit. The forwarding unit can, for example, forward important letters to another address based on user instructions. The forwarding unit can also use generation AI to analyze the contents of letters and automatically forward important letters to another address. As a result, the system for automatically processing the contents of a mailbox according to this embodiment can automatically process letters that arrive in the mailbox, notify the user, shred unwanted letters, and forward important letters.

[0067] The analysis unit automatically opens letters received in the mailbox and analyzes their contents. For example, the analysis unit uses a mechanical letter-opening device to open the letters, scans the contents, and converts them into digital data. Specifically, the letter-opening device is equipped with a precision cutter or laser to open the letter seal, ensuring that the contents are not damaged. The opened letters are scanned at high resolution by a scanner and converted into digital data using optical character recognition (OCR) technology. The generation AI can summarize the letter's contents using, for example, a text generation AI (e.g., LLM). The generation AI analyzes the letter's contents, extracts important information, and generates a summary. For example, it extracts key points and important messages from the letter's text to create a concise summary. The analysis unit can also analyze the letter's contents to determine if it is potentially fraudulent. The generation AI uses natural language processing technology to understand the context and content of the letter and detect signs of fraud. For example, it analyzes specific keywords, phrases, and stylistic patterns to identify letters that are likely to be fraudulent. This allows the analysis unit to quickly and accurately analyze the content of letters and provide important information to the user. Furthermore, the analysis unit can classify the content of letters and divide them into different categories. For example, it can classify them into business-related letters, personal letters, advertising and promotional letters, etc., allowing users to manage their letters efficiently. In this way, the analysis unit can analyze the content of letters from multiple perspectives and provide useful information to the user.

[0068] The notification unit notifies the user of the content analyzed by the analysis unit. The notification unit can notify the user through methods such as email, SMS, and app notifications. Specifically, it sends summaries and important information generated by the analysis unit using the notification method specified by the user. The notification unit can also use a generation AI to summarize the analyzed content and notify the user. The generation AI concisely summarizes the content of the letter so that the user can quickly understand it. For example, the letter summary can be included in the body of an email or sent as a short message via SMS. When using app notifications, a push notification can be sent to the user's smartphone, displaying the letter summary and important information. Furthermore, the notification unit can adjust the notification priority according to the user's settings. For example, it is possible to set it so that important or urgent letters are notified immediately, while advertising and promotional letters are notified in batches. In this way, the notification unit can notify the user of the content of the letter in the most optimal way and support efficient information management. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, by providing ratings and comments on notification content, the notification system can optimize notification methods and content based on that feedback. This allows the notification system to provide users with timely and accurate information and streamline the management of notifications.

[0069] The shredder unit shreds unwanted letters based on the information provided by the notification unit. For example, the shredder unit can shred unwanted letters based on user instructions. Specifically, after receiving a notification, the user can instruct the shredder unit to automatically shred letters they deem unnecessary. The shredder unit can also use generation AI to analyze the content of letters and automatically shred unwanted ones. The generation AI analyzes the content of letters and evaluates their importance and relevance. For example, it can identify unwanted letters such as advertisements, promotional letters, and spam emails and automatically shred them. The shredder unit is equipped with a high-performance shredder, allowing it to finely shred letters and prevent information leaks. Furthermore, the shredder unit has a mechanism for recycling the shredded paper fragments, enabling environmentally friendly processing. This allows the shredder unit to efficiently process unwanted letters and reduce the risk of information leaks. Furthermore, the shredder unit can also record the history of letter processing, allowing users to review it later. For example, it can record a list of shredded letters and the processing date and time, allowing users to check it as needed. This enables the shredder unit to process letters transparently and efficiently, increasing user confidence.

[0070] The forwarding unit forwards important letters to a different address based on the information notified by the notification unit. For example, the forwarding unit can forward important letters to a different address based on user instructions. Specifically, after a user receives a notification, they can instruct the forwarding unit to forward letters they deem important to a specified address. The forwarding unit can also use generative AI to analyze the content of letters and automatically forward important letters to a different address. The generative AI analyzes the content of letters and evaluates their importance and relevance. For example, it can identify important business letters and important personal letters and automatically forward them to a specified address. The forwarding unit has a mechanism that allows users to pre-set the forwarding destination for letters, making it easy for users to change the forwarding destination. Furthermore, the forwarding unit can track the forwarding status of letters in real time and notify the user. For example, it can send a notification when a letter arrives at the forwarding destination, allowing the user to check the status of the letter's receipt. This ensures that the forwarding unit reliably forwards important letters and allows users to receive letters with peace of mind. In addition, the forwarding unit can record the forwarding history of letters so that users can review it later. For example, the system could record a list of forwarded letters, the forwarding date and time, and the forwarding address, allowing users to check them as needed. This would enable the forwarding unit to forward letters transparently and efficiently, increasing user confidence.

[0071] The fraud detection unit can detect potentially fraudulent letters. For example, the fraud detection unit analyzes the content of a letter to determine if it is potentially fraudulent. The fraud detection unit can also use generative AI to analyze the content of a letter and determine if it is potentially fraudulent. For example, the fraud detection unit can use keyword matching or machine learning models to detect potential fraud. This allows the system to detect potentially fraudulent letters and issue warnings to users.

[0072] The analysis unit can analyze the content of a letter and generate a summary. For example, the analysis unit can scan the content of a letter and convert it into digital data, then generate a summary using a generation AI. The generation AI can summarize the content of a letter using, for example, a text generation AI (e.g., LLM). The analysis unit can also analyze the content of a letter, extract important information, and generate a summary. This allows the user to quickly understand the content of a letter by summarizing it and notifying them.

[0073] The notification unit can notify the user of the summary generated by the analysis unit. The notification unit can notify the user by methods such as email, SMS, or app notifications. The notification unit can also use generation AI to summarize the analyzed content and notify the user. The notification unit can also analyze the content of a letter, extract important information, generate a summary, and notify the user. This allows the user to quickly grasp the content of the letter by notifying them of the letter summary.

[0074] The shredder unit can shred unwanted letters based on user instructions. For example, the shredder unit can shred unwanted letters based on user instructions. The shredder unit can also use generation AI to analyze the content of letters and automatically shred unwanted letters. This allows for the automatic shredding of unwanted letters, saving time and effort.

[0075] The forwarding unit can forward important letters to a different address based on user instructions. For example, the forwarding unit can forward important letters to a different address based on user instructions. The forwarding unit can also use generation AI to analyze the content of letters and automatically forward important letters to a different address. This allows for the automatic forwarding of important letters, saving time and effort.

[0076] The fraud detection unit can analyze the content of a letter and determine whether it is potentially fraudulent. For example, the fraud detection unit can scan the letter's content, convert it into digital data, and use generative AI to determine the likelihood of fraud. The generative AI can detect the likelihood of fraud using, for example, keyword matching or machine learning models. The fraud detection unit can also analyze the content of a letter and determine whether it is potentially fraudulent. This allows the system to analyze the letter's content and determine if it is potentially fraudulent, thereby issuing a warning to the user.

[0077] The fraud detection unit, if it determines that a letter may be fraudulent, will issue a warning to the user and allow them to choose whether to shred the letter or contact the police. For example, the fraud detection unit can analyze the content of a letter and, if it determines that there is a high probability of fraud, issue a warning to the user. The fraud detection unit can use generative AI to analyze the content of a letter and, if it determines that there is a high probability of fraud, issue a warning to the user. The fraud detection unit can also analyze the content of a letter and, if it determines that there is a high probability of fraud, issue a warning to the user and allow them to choose whether to shred the letter or contact the police. This allows for appropriate action to be taken regarding letters that may be fraudulent.

[0078] The analysis unit can estimate the user's emotions and adjust the level of detail in the letter analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The analysis unit estimates the user's emotions and adjusts the level of detail in the letter analysis results based on the estimated emotions. For example, if the user is stressed, the generative AI will concisely summarize the contents of the letter and notify only the important points. If the user is relaxed, the generative AI can analyze the contents of the letter in detail and notify all the information. Furthermore, if the user is in a hurry, the generative AI can narrow down the contents of the letter to only the most important parts. In this way, by adjusting the level of detail in the letter analysis results according to the user's emotions, the system can provide the user with the most optimal information.

[0079] The analysis unit can evaluate the reliability of the letter sender when analyzing the content of a letter and reflect this in the analysis results. For example, the analysis unit can evaluate reliability by referring to the letter sender's past behavioral history. The generating AI can evaluate reliability by referring to the letter sender's official website or publicly available information. The analysis unit can also analyze the content of past letters from the letter sender, check for consistency, and evaluate reliability. By evaluating the reliability of the letter sender, the reliability of the analysis results can be improved.

[0080] The analysis unit can automatically generate a reply based on the content of a letter. For example, the analysis unit can scan the letter's content and convert it into digital data, then use a generation AI to generate a reply. The generation AI can, for example, analyze the letter's content and automatically generate an appropriate reply. The analysis unit can also select and customize a reply template based on the letter's content. Furthermore, the generation AI can analyze the letter's content and automatically insert necessary information into the reply. This reduces the user's effort by automatically generating a reply based on the letter's content.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotions using a generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the generative AI displays the analysis results in a simple and easy-to-read format. If the user is relaxed, the generative AI can display the analysis results in detail and provide additional information. Furthermore, if the user is in a hurry, the generative AI can display only the essential points of the analysis results for quick review. In this way, by adjusting the display method of the analysis results according to the user's emotions, the system can provide the user with the most optimal information.

[0082] The analysis unit can, when analyzing the content of a letter, refer to related past letters based on the content of the current letter and reflect this in the analysis results. For example, the analysis unit can scan the content of a letter and convert it into digital data, and then use a generative AI to search for related past letters. The generative AI can, for example, analyze the content of a letter and automatically search for and refer to related past letters. The analysis unit can also compare the content of past letters based on the content of the current letter, verify consistency, and reflect this in the analysis results. Furthermore, the generative AI can analyze the content of a letter, summarize the content of past letters, and reflect this in the analysis results. This allows for improved accuracy of the analysis results by referencing related past letters.

[0083] The analysis unit can automatically add appointments to the calendar based on the content of a letter. For example, the analysis unit can scan the letter content, convert it into digital data, and add appointments to the calendar using a generation AI. The generation AI can, for example, analyze the letter content and automatically add important events and deadlines to the calendar. The analysis unit can also suggest appointments relevant to the user's calendar based on the letter content. Furthermore, the generation AI can set reminders when analyzing the letter content and adding it to the calendar. This helps users manage their schedules by automatically adding appointments to the calendar based on the letter content.

[0084] The notification unit can estimate the user's emotions and adjust the priority of notifications based on those emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The notification unit estimates the user's emotions and adjusts the priority of notifications based on those emotions. For example, if the user is stressed, the generative AI will prioritize displaying only important notifications. If the user is relaxed, the generative AI can display all notifications. Furthermore, if the user is in a hurry, the generative AI can prioritize displaying the most important notifications. In this way, important information can be prioritized by adjusting the priority of notifications according to the user's emotions.

[0085] The notification unit can select the notification format based on the content of the letter when it sends a notification. For example, the notification unit can scan the content of the letter and convert it into digital data, then use a generation AI to select the notification format. The generation AI can, for example, analyze the content of the letter and notify important information in text format. The notification unit can also select and deliver an audio notification to the user based on the content of the letter. Furthermore, the generation AI can analyze the content of the letter and generate a notification that includes images. This allows for user-friendly notifications by selecting the most appropriate notification format based on the content of the letter.

[0086] The notification unit can adjust the timing of notifications based on the content of the letter. For example, the notification unit can scan the letter's content, convert it into digital data, and use a generation AI to adjust the timing of the notification. The generation AI can, for example, analyze the letter's content and immediately deliver important notifications. The notification unit can also adjust the timing of notifications to match the user's schedule based on the letter's content. Furthermore, the generation AI can analyze the letter's content and deliver notifications at the time when the user is most likely to check them. This allows for the selection of the optimal notification timing based on the letter's content, ensuring that notifications are delivered at the most opportune time for the user.

[0087] The notification unit can estimate the user's emotions and customize the content of notifications based on those emotions. For example, the notification unit can capture the user's facial expression with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The notification unit estimates the user's emotions and customizes the content of notifications based on those emotions. For example, if the user is nervous, the generative AI can make the notification concise and convey only the important points. If the user is relaxed, the generative AI can make the notification more detailed and convey all the information. Furthermore, if the user is in a hurry, the generative AI can convey only the essentials. In this way, by customizing the content of notifications according to the user's emotions, the system can provide the user with the most appropriate information.

[0088] The notification unit can adjust the frequency of notifications based on the content of the letter. For example, the notification unit can scan the letter's content and convert it into digital data, then use a generative AI to adjust the notification frequency. The generative AI can, for example, analyze the letter's content and send important notifications more frequently. The notification unit can also adjust the notification frequency to match the user's schedule based on the letter's content. Furthermore, the generative AI can analyze the letter's content and send notifications at a frequency that is most convenient for the user to check. This allows for notifications to be sent at the optimal frequency for the user by selecting the optimal notification frequency based on the letter's content.

[0089] The notification unit can select the notification method based on the content of the letter when a notification is sent. For example, the notification unit can scan the content of the letter and convert it into digital data, then use a generation AI to select the notification method. The generation AI can, for example, analyze the content of the letter and notify important information via email. The notification unit can also send a notification via SMS based on the content of the letter. Furthermore, the generation AI can analyze the content of the letter and select an app notification to convey to the user. This allows for user-friendly notifications by selecting the most appropriate notification method based on the content of the letter.

[0090] The shredder unit can estimate the user's emotions and adjust its operation based on those emotions. For example, the shredder unit can capture the user's facial expression with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The shredder unit estimates the user's emotions and adjusts its operation based on those emotions. For example, if the user is stressed, the generative AI will operate the shredder quickly. If the user is relaxed, the generative AI can operate the shredder at a normal speed. Furthermore, if the user is in a hurry, the generative AI can operate the shredder at the fastest possible speed. In this way, by adjusting the shredder's operation according to the user's emotions, optimal processing can be achieved for the user.

[0091] The shredder unit can adjust the shredding level based on the content of the letter being processed. For example, it can scan the letter's contents and convert them into digital data, then use generating AI to adjust the shredding level. The generating AI can, for example, analyze the letter's content and finely shred letters containing confidential information. The shredder unit can also process general letters at a normal shredding level based on their content. Furthermore, the generating AI can analyze the letter's content and easily shred unwanted advertisements. This allows for the protection of confidential information by adjusting the shredding level based on the letter's content.

[0092] The shredder unit can estimate the user's emotions and adjust the timing of its operation based on those emotions. For example, the shredder unit can capture the user's facial expression with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The shredder unit estimates the user's emotions and adjusts the timing of its operation based on those emotions. For example, if the user is stressed, the generative AI will start the shredder operation immediately. If the user is relaxed, the generative AI can start the shredder operation at a normal pace. Furthermore, if the user is in a hurry, the generative AI can start the shredder operation as quickly as possible. In this way, by adjusting the timing of the shredder operation according to the user's emotions, optimal processing can be achieved for the user.

[0093] The shredder unit can select the shredding mode based on the content of the letter when processing it. For example, the shredder unit can scan the letter's contents and convert them into digital data, then use a generating AI to select the operating mode. The generating AI can, for example, analyze the letter's contents and shred letters containing confidential information in continuous mode. The shredder unit can also process general letters in intermittent mode based on their contents. Furthermore, the generating AI can analyze the letter's contents and easily shred unwanted advertisements in intermittent mode. This allows for efficient processing by selecting the shredder's operating mode based on the letter's content.

[0094] The forwarding unit can estimate the user's emotions and adjust the priority of forwarding destinations based on the estimated emotions. For example, the forwarding unit can capture the user's facial expression with a camera and estimate the emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The forwarding unit estimates the user's emotions and adjusts the priority of forwarding destinations based on the estimated emotions. For example, if the user is feeling stressed, the generative AI will forward important letters with the highest priority. Also, if the user is relaxed, the generative AI can forward all letters. Furthermore, if the user is in a hurry, the generative AI can forward the most important letters with the highest priority. In this way, important letters can be forwarded preferentially by adjusting the priority of forwarding destinations according to the user's emotions.

[0095] The forwarding unit can select a forwarding method based on the content of the letter when forwarding it. For example, the forwarding unit can scan the content of the letter and convert it into digital data, then use a generating AI to select a forwarding method. The generating AI can, for example, analyze the content of the letter and forward important letters by postal mail. The forwarding unit can also forward letters by email based on the content of the letter. Furthermore, the generating AI can analyze the content of the letter and select the most suitable forwarding method. This enables efficient forwarding by selecting the most suitable forwarding method based on the content of the letter.

[0096] The forwarding unit can estimate the user's emotions and adjust the forwarding timing based on the estimated emotions. For example, the forwarding unit can capture the user's facial expression with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The forwarding unit estimates the user's emotions and adjusts the forwarding timing based on the estimated emotions. For example, if the user is feeling stressed, the generative AI will forward the letter immediately. If the user is relaxed, the generative AI can also forward the letter at the normal timing. Furthermore, if the user is in a hurry, the generative AI can forward the letter as quickly as possible. In this way, by adjusting the forwarding timing according to the user's emotions, important letters can be forwarded at the appropriate time.

[0097] The forwarding unit can automatically update the forwarding address based on the content of the letter when forwarding it. For example, the forwarding unit can scan the content of the letter and convert it into digital data, and then update the forwarding address using a generating AI. For example, the generating AI can analyze the content of the letter and automatically update the forwarding address based on the user's current location. The forwarding unit can also forward the letter to an address specified by the user based on its content. Furthermore, the generating AI can analyze the content of the letter and automatically update the forwarding address by referring to the user's calendar information. This enables forwarding tailored to the user's current location by automatically updating the forwarding address based on the content of the letter.

[0098] The fraud detection unit can estimate the user's emotions and adjust the fraud warning level based on the estimated emotions. For example, the fraud detection unit can capture the user's facial expressions with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The fraud detection unit estimates the user's emotions and adjusts the fraud warning level based on the estimated emotions. For example, if the user is stressed, the generative AI can set a high fraud warning level and provide a detailed warning. If the user is relaxed, the generative AI can set a normal fraud warning level and provide a standard warning. Furthermore, if the user is in a hurry, the generative AI can set the fraud warning level to the highest level and provide a quick warning. In this way, appropriate warnings can be provided by adjusting the fraud warning level according to the user's emotions.

[0099] The fraud detection unit can assess the likelihood of fraud by referring to the sender's past activity history when analyzing a letter. For example, the fraud detection unit can scan the letter's contents and convert them into digital data, then use a generating AI to refer to the sender's past activity history. The generating AI can, for example, analyze the sender's past activity history and assess the likelihood of fraud. The fraud detection unit can also refer to the sender's past letter content to check for consistency and assess the likelihood of fraud. Furthermore, the generating AI can also refer to the sender's official website and publicly available information to assess the likelihood of fraud. In this way, the likelihood of fraud can be assessed by referring to the sender's past activity history.

[0100] The fraud detection unit can have a function to automatically report to the police based on the content of the letter when analyzing it. For example, the fraud detection unit can scan the content of the letter, convert it into digital data, and report it to the police using a generating AI. The generating AI can, for example, analyze the content of the letter and automatically report to the police if there is a high probability of fraud. The fraud detection unit can also automatically report potentially fraudulent letters to the police based on their content. Furthermore, the generating AI can analyze the content of the letter and automatically generate a template for reporting to the police if there is a high probability of fraud. This enables a swift response by automatically reporting potentially fraudulent letters to the police.

[0101] The fraud detection unit can estimate the user's emotions and adjust the fraud warning method based on the estimated emotions. For example, the fraud detection unit can capture the user's facial expressions with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The fraud detection unit estimates the user's emotions and adjusts the fraud warning method based on the estimated emotions. For example, if the user is nervous, the generative AI can provide a detailed warning and suggest specific countermeasures. If the user is relaxed, the generative AI can provide a standard warning and suggest general countermeasures. Furthermore, if the user is in a hurry, the generative AI can provide a rapid warning and suggest the most important countermeasures. In this way, appropriate warnings can be provided by adjusting the fraud warning method according to the user's emotions.

[0102] The fraud detection unit can automatically isolate letters that are highly likely to be fraudulent based on their content when analyzing them. For example, the fraud detection unit scans the content of the letter, converts it into digital data, and isolates it using a generating AI. The generating AI can, for example, analyze the content of the letter and automatically isolate letters that are highly likely to be fraudulent. The fraud detection unit can also isolate potentially fraudulent letters based on their content and notify the user. Furthermore, the generating AI can analyze the content of the letter and automatically generate rules for isolating potentially fraudulent letters. This ensures user safety by automatically isolating potentially fraudulent letters.

[0103] The fraud detection unit can automatically forward potentially fraudulent letters to lawyers based on their content during the analysis process. For example, the fraud detection unit can scan the letter's content, convert it into digital data, and forward it to lawyers using a generating AI. The generating AI, for example, can analyze the letter's content and automatically forward letters with a high probability of being fraudulent to lawyers. The fraud detection unit can also report potentially fraudulent letters to lawyers based on their content. Furthermore, the generating AI can analyze the letter's content and automatically generate templates for forwarding potentially fraudulent letters to lawyers. This enables appropriate legal action by automatically forwarding potentially fraudulent letters to lawyers.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis unit can automatically generate a reply based on the content of a letter. For example, the analysis unit can scan the letter's content and convert it into digital data, then use a generation AI to generate a reply. The generation AI can, for example, analyze the letter's content and automatically generate an appropriate reply. The analysis unit can also select and customize a reply template based on the letter's content. Furthermore, the generation AI can analyze the letter's content and automatically insert necessary information into the reply. This reduces the user's effort by automatically generating a reply based on the letter's content.

[0106] The analysis unit can, when analyzing the content of a letter, refer to related past letters based on the content of the current letter and reflect this in the analysis results. For example, the analysis unit can scan the content of a letter and convert it into digital data, and then use a generative AI to search for related past letters. The generative AI can, for example, analyze the content of a letter and automatically search for and refer to related past letters. The analysis unit can also compare the content of past letters based on the content of the current letter, verify consistency, and reflect this in the analysis results. Furthermore, the generative AI can analyze the content of a letter, summarize the content of past letters, and reflect this in the analysis results. This allows for improved accuracy of the analysis results by referencing related past letters.

[0107] The analysis unit can automatically add appointments to the calendar based on the content of a letter. For example, the analysis unit can scan the letter content, convert it into digital data, and add appointments to the calendar using a generation AI. The generation AI can, for example, analyze the letter content and automatically add important events and deadlines to the calendar. The analysis unit can also suggest appointments relevant to the user's calendar based on the letter content. Furthermore, the generation AI can set reminders when analyzing the letter content and adding it to the calendar. This helps users manage their schedules by automatically adding appointments to the calendar based on the letter content.

[0108] The notification unit can select the notification format based on the content of the letter when it sends a notification. For example, the notification unit can scan the content of the letter and convert it into digital data, then use a generation AI to select the notification format. The generation AI can, for example, analyze the content of the letter and notify important information in text format. The notification unit can also select and deliver an audio notification to the user based on the content of the letter. Furthermore, the generation AI can analyze the content of the letter and generate a notification that includes images. This allows for user-friendly notifications by selecting the most appropriate notification format based on the content of the letter.

[0109] The notification unit can adjust the timing of notifications based on the content of the letter. For example, the notification unit can scan the letter's content, convert it into digital data, and use a generation AI to adjust the timing of the notification. The generation AI can, for example, analyze the letter's content and immediately deliver important notifications. The notification unit can also adjust the timing of notifications to match the user's schedule based on the letter's content. Furthermore, the generation AI can analyze the letter's content and deliver notifications at the time when the user is most likely to check them. This allows for the selection of the optimal notification timing based on the letter's content, ensuring that notifications are delivered at the most opportune time for the user.

[0110] The analysis unit can estimate the user's emotions and adjust the level of detail in the letter analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The analysis unit estimates the user's emotions and adjusts the level of detail in the letter analysis results based on the estimated emotions. For example, if the user is stressed, the generative AI will concisely summarize the contents of the letter and notify only the important points. If the user is relaxed, the generative AI can analyze the contents of the letter in detail and notify all the information. Furthermore, if the user is in a hurry, the generative AI can narrow down the contents of the letter to only the most important parts. In this way, by adjusting the level of detail in the letter analysis results according to the user's emotions, the system can provide the user with the most optimal information.

[0111] The notification unit can estimate the user's emotions and adjust the priority of notifications based on those emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The notification unit estimates the user's emotions and adjusts the priority of notifications based on those emotions. For example, if the user is stressed, the generative AI will prioritize displaying only important notifications. If the user is relaxed, the generative AI can display all notifications. Furthermore, if the user is in a hurry, the generative AI can prioritize displaying the most important notifications. In this way, important information can be prioritized by adjusting the priority of notifications according to the user's emotions.

[0112] The notification unit can estimate the user's emotions and customize the content of notifications based on those emotions. For example, the notification unit can capture the user's facial expression with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The notification unit estimates the user's emotions and customizes the content of notifications based on those emotions. For example, if the user is nervous, the generative AI can make the notification concise and convey only the important points. If the user is relaxed, the generative AI can make the notification more detailed and convey all the information. Furthermore, if the user is in a hurry, the generative AI can convey only the essentials. In this way, by customizing the content of notifications according to the user's emotions, the system can provide the user with the most appropriate information.

[0113] The shredder unit can estimate the user's emotions and adjust its operation based on those emotions. For example, the shredder unit can capture the user's facial expression with a camera and estimate their emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The shredder unit estimates the user's emotions and adjusts its operation based on those emotions. For example, if the user is stressed, the generative AI will operate the shredder quickly. If the user is relaxed, the generative AI can operate the shredder at a normal speed. Furthermore, if the user is in a hurry, the generative AI can operate the shredder at the fastest possible speed. In this way, by adjusting the shredder's operation according to the user's emotions, optimal processing can be achieved for the user.

[0114] The forwarding unit can estimate the user's emotions and adjust the priority of forwarding destinations based on the estimated emotions. For example, the forwarding unit can capture the user's facial expression with a camera and estimate the emotions using generative AI. The generative AI can estimate the user's emotions using, for example, facial recognition technology. The forwarding unit estimates the user's emotions and adjusts the priority of forwarding destinations based on the estimated emotions. For example, if the user is feeling stressed, the generative AI will forward important letters with the highest priority. Also, if the user is relaxed, the generative AI can forward all letters. Furthermore, if the user is in a hurry, the generative AI can forward the most important letters with the highest priority. In this way, important letters can be forwarded preferentially by adjusting the priority of forwarding destinations according to the user's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The analysis unit automatically opens letters received in the mailbox and analyzes their contents. The analysis unit uses a mechanical letter-opening device to open the letters, scans the contents, and converts them into digital data. Furthermore, it uses a generative AI to analyze the contents of the letters and generate a summary. The analysis unit can also analyze the contents of the letters and determine whether they may be fraudulent. Step 2: The notification unit notifies the user of the content analyzed by the analysis unit. The notification unit can notify the user via email, SMS, app notifications, etc. Furthermore, it can also summarize the analyzed content using generative AI and notify the user of that summary. Step 3: The shredder unit shreds unwanted letters based on the information provided by the notification unit. The shredder unit can shred unwanted letters based on user instructions. Furthermore, it can use generation AI to analyze the content of letters and automatically shred unwanted letters. Step 4: The forwarding unit forwards important letters to another address based on the information notified by the notification unit. The forwarding unit can forward important letters to another address based on user instructions. Furthermore, it can analyze the content of letters using generation AI and automatically forward important letters to another address.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the analysis unit, notification unit, shredder unit, forwarding unit, and fraud detection unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit scans the letter using the camera 42 and processor 46 of the smart device 14 and analyzes its contents using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user of the analysis results, for example, using the control unit 46A of the smart device 14. The shredder unit issues an instruction to shred unwanted letters, for example, using the control unit 46A of the smart device 14. The forwarding unit issues an instruction to forward important letters to another address, for example, using the identification processing unit 290 of the data processing unit 12. The fraud detection unit analyzes the contents of the letter, for example, using the identification processing unit 290 of the data processing unit 12 and determines the possibility of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the analysis unit, notification unit, shredder unit, forwarding unit, and fraud detection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit scans the letter using the camera 42 and processor 46 of the smart glasses 214 and analyzes its contents using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user of the analysis results, for example, using the control unit 46A of the smart glasses 214. The shredder unit, for example, issues an instruction to shred unwanted letters using the control unit 46A of the smart glasses 214. The forwarding unit, for example, issues an instruction to forward important letters to another address using the identification processing unit 290 of the data processing unit 12. The fraud detection unit, for example, analyzes the contents of the letter using the identification processing unit 290 of the data processing unit 12 and determines the possibility of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the analysis unit, notification unit, shredder unit, forwarding unit, and fraud detection unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit scans the letter using the camera 42 and processor 46 of the headset terminal 314 and analyzes its contents using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user of the analysis results, for example, using the control unit 46A of the headset terminal 314. The shredder unit, for example, issues an instruction to shred unwanted letters using the control unit 46A of the headset terminal 314. The forwarding unit, for example, issues an instruction to forward important letters to another address using the identification processing unit 290 of the data processing unit 12. The fraud detection unit, for example, analyzes the contents of the letter using the identification processing unit 290 of the data processing unit 12 and determines the possibility of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the analysis unit, notification unit, shredder unit, transfer unit, and fraud detection unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit scans the letter using the camera 42 and processor 46 of the robot 414 and analyzes its contents using the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user of the analysis results, for example, by the control unit 46A of the robot 414. The shredder unit issues an instruction to shred unwanted letters, for example, by the control unit 46A of the robot 414. The transfer unit issues an instruction to transfer important letters to another address, for example, by the identification processing unit 290 of the data processing unit 12. The fraud detection unit analyzes the contents of the letter, for example, by the identification processing unit 290 of the data processing unit 12 and determines the possibility of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) An analysis unit that automatically opens letters delivered to the mailbox and analyzes their contents, A notification unit that notifies the user of the results of the analysis performed by the aforementioned analysis unit, A shredder unit that shreds unwanted letters based on the content notified by the aforementioned notification unit, The system includes a forwarding unit that forwards important letters to another address based on the contents notified by the notification unit. A system characterized by the following features. (Note 2) It is equipped with a fraud detection unit that detects potentially fraudulent letters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the contents of the letter and generate a summary. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, The user is notified of the summary generated by the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shredder unit is, Shred unwanted letters based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The transfer unit is, Forward important letters to a different address based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned fraud detection unit, Analyze the contents of the letter to determine if it is potentially fraudulent. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned fraud detection unit, If a potential scam is detected, the user will be warned and given the option to shred the letter or contact the police. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the level of detail in the letter analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing the content of a letter, the reliability of the sender is evaluated and reflected in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing the contents of a letter, a reply message is automatically generated based on the letter's content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing the content of a letter, the system references relevant past letters based on the content of the current letter and incorporates this information into the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing the contents of a letter, an event will be automatically added to the calendar based on the letter's content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, It estimates the user's emotions and adjusts the priority of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, When sending a notification, the format of the notification will be selected based on the content of the letter. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending a notification, we will adjust the timing of the notification based on the content of the letter. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and customizes the content of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending a notification, we will adjust the frequency of notifications based on the content of the letter. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When notifying, the method of notification will be selected based on the content of the letter. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned shredder unit is, The system estimates the user's emotions and adjusts the shredder's operation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned shredder unit is, When processing letters, the shredder's shredding level is adjusted based on the content of the letter. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned shredder unit is, The system estimates the user's emotions and adjusts the shredder's operating timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shredder unit is, When processing letters, the shredder's operating mode is selected based on the content of the letter. The system described in Appendix 1, characterized by the features described herein. (Note 25) The transfer unit is, It estimates the user's emotions and adjusts the priority of destinations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The transfer unit is, When forwarding a letter, select the forwarding method based on the content of the letter. The system described in Appendix 1, characterized by the features described herein. (Note 27) The transfer unit is, It estimates the user's emotions and adjusts the timing of transfers based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The transfer unit is, When forwarding a letter, the forwarding address is automatically updated based on the letter's content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned fraud detection unit, It estimates the user's emotions and adjusts the fraud warning level based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned fraud detection unit, When analyzing a letter, we refer to the sender's past activity history to assess the likelihood of fraud. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned fraud detection unit, When analyzing a letter, it has a function that automatically notifies the police based on the content of the letter. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned fraud detection unit, We estimate the user's emotions and adjust the fraud warning method based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned fraud detection unit, When analyzing letters, the system automatically isolates letters that are highly likely to be fraudulent based on their content. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned fraud detection unit, When analyzing letters, the system automatically forwards potentially fraudulent letters to lawyers based on their content. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0189] 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. An analysis unit that automatically opens letters delivered to the mailbox and analyzes their contents, A notification unit that notifies the user of the results of the analysis performed by the aforementioned analysis unit, A shredder unit that shreds unwanted letters based on the content notified by the aforementioned notification unit, The system includes a forwarding unit that forwards important letters to another address based on the contents notified by the notification unit. A system characterized by the following features.

2. It is equipped with a fraud detection unit that detects potentially fraudulent letters. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the contents of the letter and generate a summary. The system according to feature 1.

4. The aforementioned notification unit, The user is notified of the summary generated by the aforementioned analysis unit. The system according to feature 1.

5. The aforementioned shredder unit is, Shred unwanted letters based on user instructions. The system according to feature 1.

6. The transfer unit is, Forward important letters to a different address based on user instructions. The system according to feature 1.

7. The aforementioned fraud detection unit, Analyze the contents of the letter to determine if it is potentially fraudulent. The system according to feature 2.

8. The aforementioned fraud detection unit, If a potential scam is detected, the user will be warned and given the option to shred the letter or contact the police. The system according to feature 2.

9. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the level of detail in the letter analysis results based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing the content of a letter, the reliability of the sender is evaluated and reflected in the analysis results. The system according to feature 1.

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