system

Abstract

We provide the system. [Solution] An image analysis means that acquires images of mail and converts text information into electronic data, A data construction means for storing the aforementioned electronic data in a database, A data update mechanism for updating and analyzing past and new sorting rules, A sorting decision means that uses a pre-trained artificial intelligence model to determine the optimal sorting destination, A mail sorting system including a result notification means for outputting the results obtained by the sorting determination means.

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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 The task of sorting mail items has the problem that human errors are likely to occur because it involves a large number of mailboxes and complex sorting rules. Such work requires time and effort and is not efficient. Also, when there are undeliverable mail items or new department names, there is a problem that accurate sorting becomes difficult. Against this background, it is required to reduce misdelivered mail items and improve the efficiency and accuracy of the sorting work. 【Means for Solving the Problems】[[ID=三十八]] <000%028> 【0005】 This invention provides a system for automating the sorting of mail into mailboxes. Specifically, it includes an image analysis means for acquiring images of mail and converting textual information into electronic data, a data construction means for storing the electronic data in a database, a data update means for updating and analyzing past and new sorting rules, a sorting determination means for determining the optimal sorting destination using an artificial intelligence model, and a result notification means for notifying the sorting results. This reduces missorting of mail and enables efficient and accurate sorting by utilizing digital technology. 【0006】 "Image analysis means" refers to a technology that acquires images of mail and extracts textual information from those images as electronic data. 【0007】 A "data construction method" is a function for storing acquired electronic data and managing and organizing it through a database. 【0008】 A "data update mechanism" is a function that refers to past sorting rules and performs updates and analyses according to the situation, along with new rules. 【0009】 The "sorting decision means" is a function that uses a pre-trained artificial intelligence model to predict and determine the optimal sorting destination based on electronic data. 【0010】 A "result notification method" is a function that transmits sorting destination information obtained by artificial intelligence to the user. [Brief explanation of the drawing] 【0011】 [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0012】 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. 【0013】 First, let's explain the terminology used in the following explanation. 【0014】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0015】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0016】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0017】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0018】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0019】 [First Embodiment] 【0020】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0021】 As shown in Figure 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. 【0022】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0023】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0024】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0025】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0026】 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. 【0027】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0028】 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. 【0029】 The 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. 【0030】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0031】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0032】 This invention is a system for automating mail sorting. The system's program processing and operation are described below. 【0033】 First, the device takes a picture of the mail with its camera and analyzes the acquired image using OCR (Optical Character Recognition) technology. This extracts the mail's address and sender information as electronic data. This electronic data forms the basis for replacing the physical attributes of the mail with digital information. 【0034】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This database is built using Power Apps and SharePoint, and all mail information is managed uniformly. This management ensures information consistency and ease of access. 【0035】 Subsequently, the server uses Power Automate to call the API and analyze and update the sorting rules based on the information in the database. Here, it considers historical data and new department names, and updates the sorting rules as needed. This ensures that the system is always ready for optimal sorting. 【0036】 Next, the server utilizes Azure® OpenAI® and a pre-trained artificial intelligence model to determine the optimal mailbox number. This AI model learns from past sorting data and new information, enabling it to make accurate decisions. The resulting mailbox number serves as a crucial indicator for preventing mis-sorting. 【0037】 Finally, the user retrieves the destination box number provided by the server and sorts the mail into the appropriate box. Based on this information, the user can sort the mail efficiently and accurately. This improvement in the sorting process increases work efficiency and significantly reduces misdelivery of mail. 【0038】 The following describes the processing flow. 【0039】 Step 1: 【0040】 The terminal uses its camera to photograph mail items and acquire image data. Next, OCR technology is used to convert the text information of the recipient and sender from the image into electronic data. This process eliminates the need for manual data entry and enables rapid digitization. 【0041】 Step 2: 【0042】 The server receives electronic data sent from terminals. Based on this, it uses Power Apps and SharePoint to store and manage mail data in a database. This database plays a role in ensuring efficient access and data consistency by unifying the information for each piece of mail. 【0043】 Step 3: 【0044】 The server uses Power Automate to call APIs and analyze information in the database. At this stage, it compares existing sorting rules with new rules and updates the rules to the latest state if necessary. In this way, sorting is prepared that can flexibly adapt to changes in the organization. 【0045】 Step 4: 【0046】 The server runs an Azure OpenAI model to match electronic data with sorting rules. The artificial intelligence model learns from past data to predict the optimal mailbox number. This prediction can handle undeliverable mail and irregular rules. 【0047】 Step 5: 【0048】 The user verifies the appropriate BOX number provided by the server. Based on this information, the user can sort mail into the correct mailbox. Minimizing errors improves the overall efficiency and accuracy of the sorting process. 【0049】 (Example 1) 【0050】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0051】 In mail sorting, problems exist that make efficient and accurate sorting difficult due to misreading of address information, errors caused by manual work, and an increase in mail with unknown addresses. Furthermore, outdated sorting rules can hinder the accurate delivery of mail to new departments and addresses. 【0052】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0053】 In this invention, the server includes information analysis means for acquiring images of mail and converting textual information into electronic data, information management means for storing the electronic data in data storage, and information updating means for updating and analyzing past sorting rules and new sorting rules. This enables efficient and accurate automated sorting of mail and allows for quick response to new destinations. 【0054】 "Information analysis means" refers to a device or technology that has the function of acquiring images of mail and converting textual information into electronic data. 【0055】 "Information management means" refers to a device or technology intended to store electronic data in data storage and to structure and centrally manage the information. 【0056】 An "information update means" is a device or technology that has the function of updating and analyzing past and new sorting rules to keep them constantly up-to-date. 【0057】 A "sorting decision means" is a device or technology that uses a trained artificial intelligence model to determine and decide on the optimal sorting destination. 【0058】 "Information notification means" refers to a device or technology for outputting the results obtained by the sorting and decision means and notifying the user. 【0059】 "Data communication means" refers to devices or technologies that transmit and receive electronic data using security protocols to ensure data security. 【0060】 A "recommendation method" refers to a device or technology that uses a prompt message as input and has the function of recommending a sorting destination using a generated AI model. 【0061】 This invention is a comprehensive system for the automated sorting of mail. Specifically, the components work together as follows: 【0062】 The terminal first takes a picture of the mail using its camera. The captured image data is analyzed using OCR technology to extract text information. This OCR technology uses high-precision character recognition software, such as the "Tesseract" engine. In this process, the recipient and sender information written on the mail is converted into text format. 【0063】 The extracted electronic data is sent to a server. The server stores the received data in data storage built using platforms such as SharePoint and Power Apps. The data is securely transferred using security protocols and centrally managed. This management allows for efficient searching and information extraction. 【0064】 Next, the server uses Power Automate to update and analyze sorting rules based on the information in the database. This update creates rules that accommodate new departments and changed address information, based on past sorting data and newly added information. 【0065】 Furthermore, the server runs a generative AI model on Azure to determine the appropriate sorting destination. This model learns from past data and current trends and is used to predict the optimal box number. For example, if you input a prompt such as "What is the appropriate box number for mail addressed to the legal department?", the model will analyze the data and make the appropriate decision. 【0066】 The user receives the final sorting results provided by the server and performs the actual mail sorting. This sorting is done based on the designated box number, preventing misdelivery. The user can work smoothly as the overall process is streamlined and accurate sorting is continuously performed. In this way, the present invention realizes the rapid and accurate sorting of mail. 【0067】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0068】 Step 1: 【0069】 The device takes a picture of the mail with its camera. The input is a physical image of the mail. This image is acquired using a high-resolution camera. The output is image data, which is ready to be converted into a format that can be further processed using OCR technology. 【0070】 Step 2: 【0071】 The device applies OCR (Optical Character Recognition) technology using the captured image data. The input is the image data obtained in Step 1. The OCR engine analyzes this data and extracts character information. The output is text-based recipient and sender information. 【0072】 Step 3: 【0073】 The server receives text data from the terminal. The input is the text data generated in step 2. This data is securely sent using a security protocol and stored in "data storage". The output is the securely stored, organized data. 【0074】 Step 4: 【0075】 The server uses an "information update mechanism" to analyze and update sorting rules based on the information in the database. The input consists of stored text data and existing sorting rule information. This process considers new destination information, and the rules are revised as needed. The output is the generation of the latest sorting rules. 【0076】 Step 5: 【0077】 The server uses a generative AI model to determine the appropriate mailbox number. The input consists of the latest sorting rules and recipient information. This AI model utilizes machine learning algorithms and learns from past data to output the optimal mailbox number. 【0078】 Step 6: 【0079】 The user obtains a BOX number provided by the server and physically sorts the mail. The input is BOX number information, which is output from a generating AI model. Based on this information, the user sorts the mail to the designated location. The output is that the accurately sorted mail is neatly placed in each BOX. 【0080】 (Application Example 1) 【0081】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0082】 Mail sorting at logistics centers and similar facilities presented challenges with traditional manual methods, leading to missorting and reduced efficiency. Furthermore, the proper handling of mail with unknown addresses was difficult, creating a need for improved, rapid, and accurate sorting techniques. 【0083】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0084】 In this invention, the server includes optical character recognition means for photographing mail and converting textual information into digital data, data management means for storing the digital data in an information storage device, and information updating means for analyzing and revising past and current sorting rules. This enables efficient and accurate sorting of mail, reduces missorting, and allows for the rapid processing of mail with unknown destinations. 【0085】 "Mail" refers to the general term for documents and items such as letters and parcels that are sent to other places or individuals through any service. 【0086】 Optical character recognition (OCR) is a technology that analyzes characters and numbers contained in an image and converts them into digital data in a format that can be read by a machine. 【0087】 "Digital data" refers to a binary data format used to represent and process information electronically. 【0088】 An "information storage device" refers to a storage device or database that can store a wide variety of information in one place and retrieve it as needed. 【0089】 "Data management" is the process of efficiently and securely storing, maintaining, and managing information and data. 【0090】 "Information updating" refers to the act of revising existing data and rules to the latest state based on new information and conditions. 【0091】 A "machine learning model" is a system of algorithms that learn patterns based on given data and perform estimations and predictions. 【0092】 A "logistics system" is a collection of processes and tools designed to optimize and efficiently manage the flow of goods and information. 【0093】 The system implementing this invention utilizes a variety of hardware and software to achieve efficient sorting of mail. 【0094】 The terminal uses its camera to capture an image of the mail. The captured image is analyzed using optical character recognition (OCR) technology to extract textual information such as the mail's address as digital data. This data is then transmitted to an information storage device for data management. Image acquisition and digital conversion by the terminal are performed using a dedicated application installed on a device such as a smartphone or tablet. 【0095】 The server analyzes past and present sorting rules based on data stored in the information storage device, using information update mechanisms, and revises them as needed. Power Automate and other automation tools are utilized in this process. Furthermore, trained machine learning models are used to determine the optimal sorting location. Cloud-based AI services such as Azure are used. The server finally notifies the terminals of the sorting results. 【0096】 Users refer to the sorting results provided by the terminal and quickly sort the actual mail to its designated location. This entire process reduces missorting and improves work efficiency. 【0097】 For example, in logistics centers, this system can handle the sudden increase in mail during peak seasons such as Black Friday. It is highly effective in situations where large volumes of mail need to be processed accurately and quickly. 【0098】 Examples of prompts for a generative AI model: 【0099】 "Create updated sorting rules based on yesterday's shipping data and propose efficient mail processing methods." 【0100】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0101】 Step 1: 【0102】 The device acquires images of mail. It uses a smartphone camera to photograph the item and sends the image data to OCR software. The input is image data, and the output is an image ready for analysis. 【0103】 Step 2: 【0104】 The terminal uses OCR technology to analyze image data. The input is the image data obtained in step 1, and the OCR engine extracts the text information as digital data. The output is digital data of the recipient and sender information of the mail. 【0105】 Step 3: 【0106】 The terminal transmits digital data to the server. The input is the digital information obtained in step 2, which is transmitted to the information storage device via the data management means. The output is the storage of the data in an integrated database. 【0107】 Step 4: 【0108】 The server analyzes and updates past and new sorting rules based on the received data. Input consists of existing and newly sent data in the integrated database, while output is the updated sorting rules. Automation tools such as Power Automate are used for data processing. 【0109】 Step 5: 【0110】 The server uses the updated data to determine the optimal sorting location. The input is the updated sorting rule obtained in step 4, and a machine learning model is used to determine the best sorting destination. The output is the sorting result as the optimal location. Azure's AI services are utilized here. 【0111】 Step 6: 【0112】 The server sends the sorting results to the terminal and notifies the user of the result. The input is the decision result from step 5, and the output is the sorting instructions displayed to the user. The user follows these instructions to sort the mail to the correct location. 【0113】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0114】 This invention is a system that automates the sorting of mail and further optimizes the work environment by recognizing the user's emotions. Specific embodiments are described below. 【0115】 First, the terminal takes a picture of the mail item with its camera and uses OCR technology to convert the recipient and sender information into electronic data. This electronic data is a digitized version of the physical information of the mail item and forms the basis for subsequent processing. 【0116】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This stored data is managed via Power Apps and SharePoint, ensuring that all mail information is centrally and systematically managed. 【0117】 The server uses Power Automate to call APIs and analyze sorting rules by referencing information in the database. By comparing both old and new rules and updating them as needed, it can always handle the latest sorting rules. 【0118】 Furthermore, the server utilizes Azure OpenAI, and a pre-trained AI model determines the optimal box number based on electronic data and sorting rules. This AI model has learned from past data and has the ability to flexibly handle complex cases. 【0119】 Furthermore, this system includes an emotion engine that recognizes the user's emotions in real time. Emotional data obtained from the user's facial expressions and voice is used as an indicator of stress level and satisfaction. For example, if stress levels are high during work, the system can adjust its judgment criteria and make suggestions to reduce the user's burden. 【0120】 For example, if a user frowns while sorting, the emotion engine will detect this, and the system will assist the user by adjusting the processing speed, etc. Furthermore, the emotion data is recorded and used as a database for future improvements to the work environment. 【0121】 Thus, the present invention aims to improve the efficiency of sorting mail and reduce the psychological burden on users. 【0122】 The following describes the processing flow. 【0123】 Step 1: 【0124】 The terminal takes a picture of the mail item with its camera and acquires image data. Then, it uses OCR technology to extract text information from the image and generates it as electronic data. This electronic data includes the recipient and sender information of the mail item and is entered into the system in digital format. 【0125】 Step 2: 【0126】 The server receives electronic data sent from the terminal. This received data is stored in a SharePoint database via Power Apps. Here, mail information is stored in a unified format and used for subsequent processing. 【0127】 Step 3: 【0128】 The server updates and analyzes sorting rules based on information in the database by calling an API through Power Automate. This provides the ability to check new and historical rules and update them as needed. This step ensures that the system is always up-to-date with the latest sorting rules. 【0129】 Step 4: 【0130】 The server runs an Azure OpenAI model, using electronic data and sorting rules to determine the optimal mailbox number. This artificial intelligence model has learned from past sorting data and is capable of handling complex cases and new information appropriately. 【0131】 Step 5: 【0132】 The user sorts mail into designated boxes based on box number information provided by the server. Here, the user is required to sort the mail quickly and accurately, following the instructions given by the server. 【0133】 Step 6: 【0134】 The device analyzes the user's facial expressions and voice using an emotion engine and evaluates their emotional state in real time. If negative emotions such as stress or dissatisfaction are detected, the system will notify the user and consider adjusting the task content or speed. 【0135】 Step 7: 【0136】 The server records emotional data and stores it in a database to help improve future sorting processes. This information is used as a valuable resource to optimize the user experience and reduce stress. 【0137】 (Example 2) 【0138】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0139】 Traditional mail sorting systems required manual sorting, which demanded significant effort and time from workers. Furthermore, the lack of measures to reduce the psychological burden on workers often led to increased stress and decreased work efficiency. This could result in misdelivery and sorting delays. Additionally, updating sorting rules was cumbersome with traditional methods, making rapid updates difficult. 【0140】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0141】 In this invention, the server includes information conversion means, information management means, and information processing means. This enables automated sorting of mail and reduces the psychological burden on workers. 【0142】 An "information conversion means" is a technological device that acquires images of mail and converts the text information within those images into electronic data. 【0143】 "Information management means" refers to a technological device that stores converted electronic data in an information recording device and manages it centrally. 【0144】 An "information processing means" is a technological device that compares and updates past and new sets of rules to generate the latest information. 【0145】 A "selection method" is a technical device that uses a pre-trained intelligent model to determine the optimal sorting destination. 【0146】 A "notification means" is a technical device that has the role of displaying or outputting the results obtained by the selection means. 【0147】 An "emotion recognition device" is a technological device that detects the emotional state of a worker and supports the optimization of the work environment. 【0148】 This invention provides a system that streamlines mail sorting and reduces the psychological burden on workers. A specific example of this system is described below. 【0149】 The terminal uses a camera to photograph mail and employs OCR (Optical Character Recognition) technology to convert the text information within the image into electronic data. This converts the recipient and sender information on the mail into digital data, forming the basis for processing. The terminal uses a standard image acquisition device and character recognition software. 【0150】 After the electronic data is converted, the server receives this data and stores it in an information storage device. Specifically, the data is centrally managed via data management software and made accessible at any time. A data storage platform is used in this process. 【0151】 The server then uses information processing technology to compare and update existing and new sorting rules. This involves the use of database management systems and automation tools. Based on the updated rules, the optimal sorting destination is determined. 【0152】 The server determines the appropriate sorting destination by utilizing a pre-trained intelligent model. This intelligent model is trained using machine learning algorithms based on past data and can handle complex sorting patterns. An example of a specific prompt message is, "Based on the latest sorting rules, please suggest the appropriate sorting destination for the entered mail data." 【0153】 Furthermore, this system uses an emotion recognition device to detect the user's emotional state in real time. By analyzing the worker's facial expressions and voice, it evaluates their stress level and satisfaction level during work and suggests adjustments to the work environment as needed. This process makes it possible to reduce the user's psychological burden and provide a comfortable work environment. 【0154】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0155】 Step 1: 【0156】 The device takes a picture of the mail with its camera and acquires image data. This image data is analyzed using OCR technology to extract the text information written on the mail. The input is image data of the mail, and the output is electronic data in text format. The OCR software recognizes and converts the text within the image. 【0157】 Step 2: 【0158】 The terminal sends the converted electronic data to the server. The server stores the received data in an information recording device. The input to this process is electronic data in text format, and the output is structured information in a database. The data management system records the electronic data in the appropriate format. 【0159】 Step 3: 【0160】 The server references information stored in the database and compares existing sorting rules with new information, thereby updating the rules. This process uses stored mail information and existing sorting rule data as input to generate updated sorting rules as output. An automated tool efficiently executes this process. 【0161】 Step 4: 【0162】 The server uses a trained intelligent model to determine the optimal sorting destination based on updated sorting rules. The input for this step is electronic data and updated sorting rules, and the output is information about the selected sorting destination. The intelligent model utilizes generative AI to make predictions based on past training data. Instructions can be given to this process via prompt messages. 【0163】 Step 5: 【0164】 To monitor the user's emotional state, the system uses a camera and microphone to capture facial expressions and voice. An emotion recognition device analyzes this data and extracts emotional information in real time. The input is real-time facial and voice data, and the output is numerical information such as stress levels and satisfaction levels. An emotion engine quantifies this data to provide information that contributes to optimizing the work environment. 【0165】 (Application Example 2) 【0166】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0167】 Mail sorting operations at logistics centers and similar facilities require increased efficiency, but the psychological burden on workers is also a significant challenge. While conventional sorting systems offer basic automation, they lack sufficient timely updates to sorting rules and adequate environmental adjustments based on workers' emotional states. As a result, work efficiency decreases and worker stress increases. This invention aims to solve these problems. 【0168】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0169】 In this invention, the server includes an analysis means for analyzing images of mail and converting them into electronic data, a data construction means for storing the electronic data in a storage device, a data management means for managing and analyzing past and new sorting rules, a judgment means for determining the optimal sorting destination using a trained intelligent model, and an emotion recognition means for acquiring worker emotion data and optimizing the work environment. This enables the automation of sorting work and the reduction of the psychological burden on workers. 【0170】 "Analysis means" refers to a device that has the function of acquiring images of mail and converting the textual information into electronic data. 【0171】 A "data construction means" is a device that has the function of storing acquired electronic data in a memory device. 【0172】 A "data management device" is a device that manages past and new sorting rules and has the function of updating and analyzing them as needed. 【0173】 A "decision-making device" is a device that uses a pre-trained intelligent model to determine the optimal sorting destination based on electronic data. 【0174】 A "notification means" is a device that has the function of outputting the sorting results obtained by the judgment means. 【0175】 An "emotion recognition device" is a device that has the function of capturing the emotional data of workers and analyzing their emotional state in real time. 【0176】 An "environmental optimization means" is a device that has the function of making suggestions for adjusting the work environment based on the analysis results obtained by an emotion recognition means. 【0177】 This invention is a system that streamlines mail sorting operations in a logistics center while optimizing the work environment by taking into account the emotional state of the workers. A specific embodiment of this system is described below. 【0178】 The device uses smart glasses or a smartphone camera to photograph mail and converts the text information into electronic data using OCR technology. The electronic data is then automatically sent to a storage device in the cloud. 【0179】 The server stores received electronic data in a database and manages existing and new sorting rules. Using Azure's machine learning services, a pre-trained AI model based on historical data effectively determines the optimal sorting destination. The results are notified to the worker in real time. 【0180】 Furthermore, sensors installed in the terminal collect emotional data from the worker (such as facial expressions and voice), and the server analyzes this data in real time using Azure Cognitive Services. Based on the analysis results, the system notifies the worker with suggestions for adjusting the work environment. Specifically, it automatically suggests speed adjustments to reduce workload and, if necessary, suggests breaks. 【0181】 As a concrete example, consider a worker at a logistics center during the busy year-end season. The system can detect when the worker begins to feel stressed and suggest adjusting the pace of work, thereby providing an efficient and comfortable working environment. 【0182】 Example prompt: "Consider an example of a user application that streamlines the sorting process in a logistics center, detects worker stress in real time, and suggests break times and speed adjustments." 【0183】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0184】 Step 1: 【0185】 The device takes a picture of the mail item using smart glasses or a smartphone camera. The input is the physical image data of the mail item, which is then converted into text information (such as recipient and sender information) using OCR technology and output as electronic data. 【0186】 Step 2: 【0187】 The terminal transmits electronic data converted using OCR technology to a storage device in the cloud. The input is converted character information, and this information is processed for transmission to the server via wireless communication (Wi-Fi or mobile data). The output is the data received by the server. 【0188】 Step 3: 【0189】 The server stores the received electronic data in its internal database. The input here is electronic data sent from the cloud, which is converted to an appropriate format according to the database structure before being output as stored data. This operation is performed using data construction tools. 【0190】 Step 4: 【0191】 The server uses Azure's machine learning services to determine the optimal sorting destination based on electronic data and past and new sorting rules. The input consists of sorting rules and electronic data retrieved from a database, and the output is the information of the optimal sorting destination. A generative AI model is used for this determination. 【0192】 Step 5: 【0193】 The server notifies the terminal in real time of the sorting destination information it has determined. The input is the sorting destination information, and the output is the instruction information displayed on the terminal. The server transmits this information to the terminal via a notification method. 【0194】 Step 6: 【0195】 The terminal collects the worker's facial expressions and voice using sensors and sends this data to a server for emotion recognition. The input is biosensor data acquired from the worker, and the output is data sent to the server as information necessary for analyzing the emotional state. 【0196】 Step 7: 【0197】 The server uses Azure Cognitive Services to analyze emotional data in real time. The input is emotional data sent from the terminal, and the output is analyzed emotional state information. This allows for understanding the worker's stress and comfort level. 【0198】 Step 8: 【0199】 Based on the results of the emotion analysis, the server sends suggestions for adjusting the work environment to the terminal. Specifically, these suggestions include adjusting the work pace or taking breaks. The input is information about the emotional state, and the output is instructional information notified to the worker as an adjustment suggestion. 【0200】 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. 【0201】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0202】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0203】 [Second Embodiment] 【0204】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0205】 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. 【0206】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0207】 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. 【0208】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0209】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0210】 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. 【0211】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0212】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0213】 The 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. 【0214】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0215】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0216】 This invention is a system for automating mail sorting. The system's program processing and operation are described below. 【0217】 First, the device takes a picture of the mail with its camera and analyzes the acquired image using OCR (Optical Character Recognition) technology. This extracts the mail's address and sender information as electronic data. This electronic data forms the basis for replacing the physical attributes of the mail with digital information. 【0218】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This database is built using Power Apps and SharePoint, and all mail information is managed uniformly. This management ensures information consistency and ease of access. 【0219】 Subsequently, the server uses Power Automate to call the API and analyze and update the sorting rules based on the information in the database. Here, it considers historical data and new department names, and updates the sorting rules as needed. This ensures that the system is always ready for optimal sorting. 【0220】 Next, the server leverages Azure OpenAI and uses a pre-trained artificial intelligence model to determine the optimal mailbox number. This AI model has the ability to learn from past sorting data and new information to make accurate decisions. The resulting mailbox number is an important indicator for preventing mis-sorting. 【0221】 Finally, the user retrieves the destination box number provided by the server and sorts the mail into the appropriate box. Based on this information, the user can sort the mail efficiently and accurately. This improvement in the sorting process increases work efficiency and significantly reduces misdelivery of mail. 【0222】 The following describes the processing flow. 【0223】 Step 1: 【0224】 The terminal uses its camera to photograph mail items and acquire image data. Next, OCR technology is used to convert the text information of the recipient and sender from the image into electronic data. This process eliminates the need for manual data entry and enables rapid digitization. 【0225】 Step 2: 【0226】 The server receives electronic data sent from terminals. Based on this, it uses Power Apps and SharePoint to store and manage mail data in a database. This database plays a role in ensuring efficient access and data consistency by unifying the information for each piece of mail. 【0227】 Step 3: 【0228】 The server uses Power Automate to call APIs and analyze information in the database. At this stage, it compares existing sorting rules with new rules and updates the rules to the latest state if necessary. In this way, sorting is prepared that can flexibly adapt to changes in the organization. 【0229】 Step 4: 【0230】 The server runs an Azure OpenAI model to match electronic data with sorting rules. The artificial intelligence model learns from past data to predict the optimal mailbox number. This prediction can handle undeliverable mail and irregular rules. 【0231】 Step 5: 【0232】 The user verifies the appropriate BOX number provided by the server. Based on this information, the user can sort mail into the correct mailbox. Minimizing errors improves the overall efficiency and accuracy of the sorting process. 【0233】 (Example 1) 【0234】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0235】 In mail sorting, problems exist that make efficient and accurate sorting difficult due to misreading of address information, errors caused by manual work, and an increase in mail with unknown addresses. Furthermore, outdated sorting rules can hinder the accurate delivery of mail to new departments and addresses. 【0236】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0237】 In this invention, the server includes information analysis means for acquiring images of mail and converting textual information into electronic data, information management means for storing the electronic data in data storage, and information updating means for updating and analyzing past sorting rules and new sorting rules. This enables efficient and accurate automated sorting of mail and allows for quick response to new destinations. 【0238】 "Information analysis means" refers to a device or technology that has the function of acquiring images of mail and converting textual information into electronic data. 【0239】 "Information management means" refers to a device or technology intended to store electronic data in data storage and to structure and centrally manage the information. 【0240】 An "information update means" is a device or technology that has the function of updating and analyzing past and new sorting rules to keep them constantly up-to-date. 【0241】 A "sorting decision means" is a device or technology that uses a trained artificial intelligence model to determine and decide on the optimal sorting destination. 【0242】 "Information notification means" refers to a device or technology for outputting the results obtained by the sorting and decision means and notifying the user. 【0243】 "Data communication means" refers to devices or technologies that transmit and receive electronic data using security protocols to ensure data security. 【0244】 A "recommendation method" refers to a device or technology that uses a prompt message as input and has the function of recommending a sorting destination using a generated AI model. 【0245】 This invention is a comprehensive system for the automated sorting of mail. Specifically, the components work together as follows: 【0246】 The terminal first takes a picture of the mail using its camera. The captured image data is analyzed using OCR technology to extract text information. This OCR technology uses high-precision character recognition software, such as the "Tesseract" engine. In this process, the recipient and sender information written on the mail is converted into text format. 【0247】 The extracted electronic data is sent to a server. The server stores the received data in data storage built using platforms such as SharePoint and Power Apps. The data is securely transferred using security protocols and centrally managed. This management allows for efficient searching and information extraction. 【0248】 Next, the server uses Power Automate to update and analyze sorting rules based on the information in the database. This update creates rules that accommodate new departments and changed address information, based on past sorting data and newly added information. 【0249】 Furthermore, the server runs a generative AI model on Azure to determine the appropriate sorting destination. This model learns from past data and current trends and is used to predict the optimal box number. For example, if you input a prompt such as "What is the appropriate box number for mail addressed to the legal department?", the model will analyze the data and make the appropriate decision. 【0250】 The user receives the final sorting results provided by the server and performs the actual mail sorting. This sorting is done based on the designated box number, preventing misdelivery. The user can work smoothly as the overall process is streamlined and accurate sorting is continuously performed. In this way, the present invention realizes the rapid and accurate sorting of mail. 【0251】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0252】 Step 1: 【0253】 The device takes a picture of the mail with its camera. The input is a physical image of the mail. This image is acquired using a high-resolution camera. The output is image data, which is ready to be converted into a format that can be further processed using OCR technology. 【0254】 Step 2: 【0255】 The device applies OCR (Optical Character Recognition) technology using the captured image data. The input is the image data obtained in Step 1. The OCR engine analyzes this data and extracts character information. The output is text-based recipient and sender information. 【0256】 Step 3: 【0257】 The server receives text data from the terminal. The input is the text data generated in step 2. This data is securely sent using a security protocol and stored in "data storage". The output is the securely stored, organized data. 【0258】 Step 4: 【0259】 The server uses an "information update mechanism" to analyze and update sorting rules based on the information in the database. The input consists of stored text data and existing sorting rule information. This process considers new destination information, and the rules are revised as needed. The output is the generation of the latest sorting rules. 【0260】 Step 5: 【0261】 The server uses a generative AI model to determine the appropriate mailbox number. The input consists of the latest sorting rules and recipient information. This AI model utilizes machine learning algorithms and learns from past data to output the optimal mailbox number. 【0262】 Step 6: 【0263】 The user obtains a BOX number provided by the server and physically sorts the mail. The input is BOX number information, which is output from a generating AI model. Based on this information, the user sorts the mail to the designated location. The output is that the accurately sorted mail is neatly placed in each BOX. 【0264】 (Application Example 1) 【0265】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0266】 Mail sorting at logistics centers and similar facilities presented challenges with traditional manual methods, leading to missorting and reduced efficiency. Furthermore, the proper handling of mail with unknown addresses was difficult, creating a need for improved, rapid, and accurate sorting techniques. 【0267】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0268】 In this invention, the server includes optical character recognition means for photographing mail and converting textual information into digital data, data management means for storing the digital data in an information storage device, and information updating means for analyzing and revising past and current sorting rules. This enables efficient and accurate sorting of mail, reduces missorting, and allows for the rapid processing of mail with unknown destinations. 【0269】 "Mail" refers to the general term for documents and items such as letters and parcels that are sent to other places or individuals through any service. 【0270】 Optical character recognition (OCR) is a technology that analyzes characters and numbers contained in an image and converts them into digital data in a format that can be read by a machine. 【0271】 "Digital data" refers to a binary data format used to represent and process information electronically. 【0272】 An "information storage device" refers to a storage device or database that can store a wide variety of information in one place and retrieve it as needed. 【0273】 "Data management" is the process of efficiently and securely storing, maintaining, and managing information and data. 【0274】 "Information updating" refers to the act of revising existing data and rules to the latest state based on new information and conditions. 【0275】 A "machine learning model" is a system of algorithms that learn patterns based on given data and perform estimations and predictions. 【0276】 A "logistics system" is a collection of processes and tools designed to optimize and efficiently manage the flow of goods and information. 【0277】 The system implementing this invention utilizes a variety of hardware and software to achieve efficient sorting of mail. 【0278】 The terminal uses its camera to capture an image of the mail. The captured image is analyzed using optical character recognition (OCR) technology to extract textual information such as the mail's address as digital data. This data is then transmitted to an information storage device for data management. Image acquisition and digital conversion by the terminal are performed using a dedicated application installed on a device such as a smartphone or tablet. 【0279】 The server analyzes past and present sorting rules based on data stored in the information storage device, using information update mechanisms, and revises them as needed. Power Automate and other automation tools are utilized in this process. Furthermore, trained machine learning models are used to determine the optimal sorting location. Cloud-based AI services such as Azure are used. The server finally notifies the terminals of the sorting results. 【0280】 Users refer to the sorting results provided by the terminal and quickly sort the actual mail to its designated location. This entire process reduces missorting and improves work efficiency. 【0281】 For example, in logistics centers, this system can handle the sudden increase in mail during peak seasons such as Black Friday. It is highly effective in situations where large volumes of mail need to be processed accurately and quickly. 【0282】 Examples of prompt sentences for the generated AI model: 【0283】 "Create the latest sorting rules based on yesterday's shipping data and propose efficient mail processing." 【0284】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0285】 Step 1: 【0286】 The terminal acquires an image of the mail. Use the camera of a smartphone to photograph the item and transmit the image data to the OCR software. The input is the image data, and the output is an image ready for analysis. 【0287】 Step 2: 【0288】 The terminal analyzes the image data using OCR technology. The input is the image data obtained in Step 1, and character information is extracted as digital data by the OCR engine. The output is digital data of the destination and sender information of the mail. 【0289】 Step 3: 【0290】 The terminal transmits the digital data to the server. The input is the digital information obtained in Step 2, and it is transmitted to the information storage device via the data management means. The output is the storage of the data in the integrated management database. 【0291】 Step 4: 【0292】 The server analyzes and updates the past and new sorting rules based on the received data. The input is the existing data and the newly sent data in the integrated database, and the output is the updated sorting rules. Automation tools such as Power Automate are used for data processing. 【0293】 Step 5: 【0294】 The server uses the updated data to determine the optimal sorting location. The input is the updated sorting rule obtained in step 4, and a machine learning model is used to determine the best sorting destination. The output is the sorting result as the optimal location. Azure's AI services are utilized here. 【0295】 Step 6: 【0296】 The server sends the sorting results to the terminal and notifies the user of the result. The input is the decision result from step 5, and the output is the sorting instructions displayed to the user. The user follows these instructions to sort the mail to the correct location. 【0297】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0298】 This invention is a system that automates the sorting of mail and further optimizes the work environment by recognizing the user's emotions. Specific embodiments are described below. 【0299】 First, the terminal takes a picture of the mail item with its camera and uses OCR technology to convert the recipient and sender information into electronic data. This electronic data is a digitized version of the physical information of the mail item and forms the basis for subsequent processing. 【0300】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This stored data is managed via Power Apps and SharePoint, ensuring that all mail information is centrally and systematically managed. 【0301】 The server uses Power Automate to call APIs and analyze sorting rules by referencing information in the database. By comparing both old and new rules and updating them as needed, it can always handle the latest sorting rules. 【0302】 In addition, the server utilizes Azure OpenAI to determine the optimal BOX number based on electronic data and sorting rules using a pre-trained AI model. This AI model has learned from past data and has the ability to flexibly handle complex cases. 【0303】 Furthermore, this system includes an emotion engine that recognizes the user's emotions in real-time. Emotion data obtained from the user's facial expressions, voice, etc. is utilized as an indicator of stress level and satisfaction. For example, when the stress during work is increasing, the system can adjust the judgment criteria and make proposals to reduce the user's burden. 【0304】 Taking a specific example, when the user frowns during the sorting operation, the emotion engine detects this, and the system supports the user by, for example, adjusting the processing speed. Also, the emotion data is recorded and utilized as a database for future improvement of the working environment. 【0305】 In this way, the purpose of the present invention is to improve the sorting efficiency of postal items and reduce the psychological burden on the user. 【0306】 The processing flow will be described below. 【0307】 Step 1: 【0308】 The terminal takes a picture of the postal item with a camera to obtain image data. Then, using OCR technology, character information is extracted from the image and generated as electronic data. This electronic data includes the destination and sender information of the postal item and is input into the system in digital form. 【0309】 Step 2: 【0310】 The server receives electronic data sent from the terminal. This received data is stored in a SharePoint database via Power Apps. Here, mail information is stored in a unified format and used for subsequent processing. 【0311】 Step 3: 【0312】 The server updates and analyzes sorting rules based on information in the database by calling an API through Power Automate. This provides the ability to check new and historical rules and update them as needed. This step ensures that the system is always up-to-date with the latest sorting rules. 【0313】 Step 4: 【0314】 The server runs an Azure OpenAI model, using electronic data and sorting rules to determine the optimal mailbox number. This artificial intelligence model has learned from past sorting data and is capable of handling complex cases and new information appropriately. 【0315】 Step 5: 【0316】 The user sorts mail into designated boxes based on box number information provided by the server. Here, the user is required to sort the mail quickly and accurately, following the instructions given by the server. 【0317】 Step 6: 【0318】 The device analyzes the user's facial expressions and voice using an emotion engine and evaluates their emotional state in real time. If negative emotions such as stress or dissatisfaction are detected, the system will notify the user and consider adjusting the task content or speed. 【0319】 Step 7: 【0320】 The server records emotional data and stores it in a database to help improve future sorting processes. This information is used as a valuable resource to optimize the user experience and reduce stress. 【0321】 (Example 2) 【0322】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0323】 Traditional mail sorting systems required manual sorting, which demanded significant effort and time from workers. Furthermore, the lack of measures to reduce the psychological burden on workers often led to increased stress and decreased work efficiency. This could result in misdelivery and sorting delays. Additionally, updating sorting rules was cumbersome with traditional methods, making rapid updates difficult. 【0324】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0325】 In this invention, the server includes information conversion means, information management means, and information processing means. This enables automated sorting of mail and reduces the psychological burden on workers. 【0326】 An "information conversion means" is a technological device that acquires images of mail and converts the text information within those images into electronic data. 【0327】 "Information management means" refers to a technological device that stores converted electronic data in an information recording device and manages it centrally. 【0328】 An "information processing means" is a technological device that compares and updates past and new sets of rules to generate the latest information. 【0329】 A "selection method" is a technical device that uses a pre-trained intelligent model to determine the optimal sorting destination. 【0330】 A "notification means" is a technical device that has the role of displaying or outputting the results obtained by the selection means. 【0331】 An "emotion recognition device" is a technological device that detects the emotional state of a worker and supports the optimization of the work environment. 【0332】 This invention provides a system that streamlines mail sorting and reduces the psychological burden on workers. A specific example of this system is described below. 【0333】 The terminal uses a camera to photograph mail and employs OCR (Optical Character Recognition) technology to convert the text information within the image into electronic data. This converts the recipient and sender information on the mail into digital data, forming the basis for processing. The terminal uses a standard image acquisition device and character recognition software. 【0334】 After the electronic data is converted, the server receives this data and stores it in an information storage device. Specifically, the data is centrally managed via data management software and made accessible at any time. A data storage platform is used in this process. 【0335】 The server then uses information processing technology to compare and update existing and new sorting rules. This involves the use of database management systems and automation tools. Based on the updated rules, the optimal sorting destination is determined. 【0336】 The server determines the appropriate sorting destination by utilizing a pre-trained intelligent model. This intelligent model is trained using machine learning algorithms based on past data and can handle complex sorting patterns. An example of a specific prompt message is, "Based on the latest sorting rules, please suggest the appropriate sorting destination for the entered mail data." 【0337】 Furthermore, this system uses an emotion recognition device to detect the user's emotional state in real time. By analyzing the worker's facial expressions and voice, it evaluates their stress level and satisfaction level during work and suggests adjustments to the work environment as needed. This process makes it possible to reduce the user's psychological burden and provide a comfortable work environment. 【0338】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0339】 Step 1: 【0340】 The device takes a picture of the mail with its camera and acquires image data. This image data is analyzed using OCR technology to extract the text information written on the mail. The input is image data of the mail, and the output is electronic data in text format. The OCR software recognizes and converts the text within the image. 【0341】 Step 2: 【0342】 The terminal sends the converted electronic data to the server. The server stores the received data in an information recording device. The input to this process is electronic data in text format, and the output is structured information in a database. The data management system records the electronic data in the appropriate format. 【0343】 Step 3: 【0344】 The server references information stored in the database and compares existing sorting rules with new information, thereby updating the rules. This process uses stored mail information and existing sorting rule data as input to generate updated sorting rules as output. An automated tool efficiently executes this process. 【0345】 Step 4: 【0346】 The server uses a trained intelligent model to determine the optimal sorting destination based on updated sorting rules. The input for this step is electronic data and updated sorting rules, and the output is information about the selected sorting destination. The intelligent model utilizes generative AI to make predictions based on past training data. Instructions can be given to this process via prompt messages. 【0347】 Step 5: 【0348】 To monitor the user's emotional state, the system uses a camera and microphone to capture facial expressions and voice. An emotion recognition device analyzes this data and extracts emotional information in real time. The input is real-time facial and voice data, and the output is numerical information such as stress levels and satisfaction levels. An emotion engine quantifies this data to provide information that contributes to optimizing the work environment. 【0349】 (Application Example 2) 【0350】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0351】 Mail sorting operations at logistics centers and similar facilities require increased efficiency, but the psychological burden on workers is also a significant challenge. While conventional sorting systems offer basic automation, they lack sufficient timely updates to sorting rules and adequate environmental adjustments based on workers' emotional states. As a result, work efficiency decreases and worker stress increases. This invention aims to solve these problems. 【0352】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0353】 In this invention, the server includes an analysis means for analyzing images of mail and converting them into electronic data, a data construction means for storing the electronic data in a storage device, a data management means for managing and analyzing past and new sorting rules, a judgment means for determining the optimal sorting destination using a trained intelligent model, and an emotion recognition means for acquiring worker emotion data and optimizing the work environment. This enables the automation of sorting work and the reduction of the psychological burden on workers. 【0354】 "Analysis means" refers to a device that has the function of acquiring images of mail and converting the textual information into electronic data. 【0355】 A "data construction means" is a device that has the function of storing acquired electronic data in a memory device. 【0356】 A "data management device" is a device that manages past and new sorting rules and has the function of updating and analyzing them as needed. 【0357】 A "decision-making device" is a device that uses a pre-trained intelligent model to determine the optimal sorting destination based on electronic data. 【0358】 A "notification means" is a device that has the function of outputting the sorting results obtained by the judgment means. 【0359】 An "emotion recognition device" is a device that has the function of capturing the emotional data of workers and analyzing their emotional state in real time. 【0360】 An "environmental optimization means" is a device that has the function of making suggestions for adjusting the work environment based on the analysis results obtained by an emotion recognition means. 【0361】 This invention is a system that streamlines mail sorting operations in a logistics center while optimizing the work environment by taking into account the emotional state of the workers. A specific embodiment of this system is described below. 【0362】 The device uses smart glasses or a smartphone camera to photograph mail and converts the text information into electronic data using OCR technology. The electronic data is then automatically sent to a storage device in the cloud. 【0363】 The server stores received electronic data in a database and manages existing and new sorting rules. Using Azure's machine learning services, a pre-trained AI model based on historical data effectively determines the optimal sorting destination. The results are notified to the worker in real time. 【0364】 Furthermore, sensors installed in the terminal collect emotional data from the worker (such as facial expressions and voice), and the server analyzes this data in real time using Azure Cognitive Services. Based on the analysis results, the system notifies the worker with suggestions for adjusting the work environment. Specifically, it automatically suggests speed adjustments to reduce workload and, if necessary, suggests breaks. 【0365】 As a concrete example, consider a worker at a logistics center during the busy year-end season. The system can detect when the worker begins to feel stressed and suggest adjusting the pace of work, thereby providing an efficient and comfortable working environment. 【0366】 Example prompt: "Consider an example of a user application that streamlines the sorting process in a logistics center, detects worker stress in real time, and suggests break times and speed adjustments." 【0367】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0368】 Step 1: 【0369】 The device takes a picture of the mail item using smart glasses or a smartphone camera. The input is the physical image data of the mail item, which is then converted into text information (such as recipient and sender information) using OCR technology and output as electronic data. 【0370】 Step 2: 【0371】 The terminal transmits electronic data converted using OCR technology to a storage device in the cloud. The input is converted character information, and this information is processed for transmission to the server via wireless communication (Wi-Fi or mobile data). The output is the data received by the server. 【0372】 Step 3: 【0373】 The server stores the received electronic data in its internal database. The input here is electronic data sent from the cloud, which is converted to an appropriate format according to the database structure before being output as stored data. This operation is performed using data construction tools. 【0374】 Step 4: 【0375】 The server uses Azure's machine learning services to determine the optimal sorting destination based on electronic data and past and new sorting rules. The input consists of sorting rules and electronic data retrieved from a database, and the output is the information of the optimal sorting destination. A generative AI model is used for this determination. 【0376】 Step 5: 【0377】 The server notifies the terminal in real time of the sorting destination information it has determined. The input is the sorting destination information, and the output is the instruction information displayed on the terminal. The server transmits this information to the terminal via a notification method. 【0378】 Step 6: 【0379】 The terminal collects the worker's facial expressions and voice using sensors and sends this data to a server for emotion recognition. The input is biosensor data acquired from the worker, and the output is data sent to the server as information necessary for analyzing the emotional state. 【0380】 Step 7: 【0381】 The server uses Azure Cognitive Services to analyze emotional data in real time. The input is emotional data sent from the terminal, and the output is analyzed emotional state information. This allows for understanding the worker's stress and comfort level. 【0382】 Step 8: 【0383】 Based on the results of the emotion analysis, the server sends suggestions for adjusting the work environment to the terminal. Specifically, these suggestions include adjusting the work pace or taking breaks. The input is information about the emotional state, and the output is instructional information notified to the worker as an adjustment suggestion. 【0384】 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. 【0385】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0386】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0387】 [Third Embodiment] 【0388】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0389】 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. 【0390】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0391】 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. 【0392】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0393】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0394】 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. 【0395】 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. 【0396】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0397】 The 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. 【0398】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0399】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0400】 This invention is a system for automating mail sorting. The system's program processing and operation are described below. 【0401】 First, the device takes a picture of the mail with its camera and analyzes the acquired image using OCR (Optical Character Recognition) technology. This extracts the mail's address and sender information as electronic data. This electronic data forms the basis for replacing the physical attributes of the mail with digital information. 【0402】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This database is built using Power Apps and SharePoint, and all mail information is managed uniformly. This management ensures information consistency and ease of access. 【0403】 Subsequently, the server uses Power Automate to call the API and analyze and update the sorting rules based on the information in the database. Here, it considers historical data and new department names, and updates the sorting rules as needed. This ensures that the system is always ready for optimal sorting. 【0404】 Next, the server leverages Azure OpenAI and uses a pre-trained artificial intelligence model to determine the optimal mailbox number. This AI model has the ability to learn from past sorting data and new information to make accurate decisions. The resulting mailbox number is an important indicator for preventing mis-sorting. 【0405】 Finally, the user retrieves the destination box number provided by the server and sorts the mail into the appropriate box. Based on this information, the user can sort the mail efficiently and accurately. This improvement in the sorting process increases work efficiency and significantly reduces misdelivery of mail. 【0406】 The following describes the processing flow. 【0407】 Step 1: 【0408】 The terminal uses its camera to photograph mail items and acquire image data. Next, OCR technology is used to convert the text information of the recipient and sender from the image into electronic data. This process eliminates the need for manual data entry and enables rapid digitization. 【0409】 Step 2: 【0410】 The server receives electronic data sent from terminals. Based on this, it uses Power Apps and SharePoint to store and manage mail data in a database. This database plays a role in ensuring efficient access and data consistency by unifying the information for each piece of mail. 【0411】 Step 3: 【0412】 The server uses Power Automate to call APIs and analyze information in the database. At this stage, it compares existing sorting rules with new rules and updates the rules to the latest state if necessary. In this way, sorting is prepared that can flexibly adapt to changes in the organization. 【0413】 Step 4: 【0414】 The server runs an Azure OpenAI model to match electronic data with sorting rules. The artificial intelligence model learns from past data to predict the optimal mailbox number. This prediction can handle undeliverable mail and irregular rules. 【0415】 Step 5: 【0416】 The user verifies the appropriate BOX number provided by the server. Based on this information, the user can sort mail into the correct mailbox. Minimizing errors improves the overall efficiency and accuracy of the sorting process. 【0417】 (Example 1) 【0418】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0419】 In mail sorting, problems exist that make efficient and accurate sorting difficult due to misreading of address information, errors caused by manual work, and an increase in mail with unknown addresses. Furthermore, outdated sorting rules can hinder the accurate delivery of mail to new departments and addresses. 【0420】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0421】 In this invention, the server includes information analysis means for acquiring images of mail and converting textual information into electronic data, information management means for storing the electronic data in data storage, and information updating means for updating and analyzing past sorting rules and new sorting rules. This enables efficient and accurate automated sorting of mail and allows for quick response to new destinations. 【0422】 "Information analysis means" refers to a device or technology that has the function of acquiring images of mail and converting textual information into electronic data. 【0423】 "Information management means" refers to a device or technology intended to store electronic data in data storage and to structure and centrally manage the information. 【0424】 An "information update means" is a device or technology that has the function of updating and analyzing past and new sorting rules to keep them constantly up-to-date. 【0425】 A "sorting decision means" is a device or technology that uses a trained artificial intelligence model to determine and decide on the optimal sorting destination. 【0426】 "Information notification means" refers to a device or technology for outputting the results obtained by the sorting and decision means and notifying the user. 【0427】 "Data communication means" refers to devices or technologies that transmit and receive electronic data using security protocols to ensure data security. 【0428】 A "recommendation method" refers to a device or technology that uses a prompt message as input and has the function of recommending a sorting destination using a generated AI model. 【0429】 This invention is a comprehensive system for the automated sorting of mail. Specifically, the components work together as follows: 【0430】 The terminal first takes a picture of the mail using its camera. The captured image data is analyzed using OCR technology to extract text information. This OCR technology uses high-precision character recognition software, such as the "Tesseract" engine. In this process, the recipient and sender information written on the mail is converted into text format. 【0431】 The extracted electronic data is sent to a server. The server stores the received data in data storage built using platforms such as SharePoint and Power Apps. The data is securely transferred using security protocols and centrally managed. This management allows for efficient searching and information extraction. 【0432】 Next, the server uses Power Automate to update and analyze sorting rules based on the information in the database. This update creates rules that accommodate new departments and changed address information, based on past sorting data and newly added information. 【0433】 Furthermore, the server runs a generative AI model on Azure to determine the appropriate sorting destination. This model learns from past data and current trends and is used to predict the optimal box number. For example, if you input a prompt such as "What is the appropriate box number for mail addressed to the legal department?", the model will analyze the data and make the appropriate decision. 【0434】 The user receives the final sorting results provided by the server and performs the actual mail sorting. This sorting is done based on the designated box number, preventing misdelivery. The user can work smoothly as the overall process is streamlined and accurate sorting is continuously performed. In this way, the present invention realizes the rapid and accurate sorting of mail. 【0435】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0436】 Step 1: 【0437】 The device takes a picture of the mail with its camera. The input is a physical image of the mail. This image is acquired using a high-resolution camera. The output is image data, which is ready to be converted into a format that can be further processed using OCR technology. 【0438】 Step 2: 【0439】 The device applies OCR (Optical Character Recognition) technology using the captured image data. The input is the image data obtained in Step 1. The OCR engine analyzes this data and extracts character information. The output is text-based recipient and sender information. 【0440】 Step 3: 【0441】 The server receives text data from the terminal. The input is the text data generated in step 2. This data is securely sent using a security protocol and stored in "data storage". The output is the securely stored, organized data. 【0442】 Step 4: 【0443】 The server uses an "information update mechanism" to analyze and update sorting rules based on the information in the database. The input consists of stored text data and existing sorting rule information. This process considers new destination information, and the rules are revised as needed. The output is the generation of the latest sorting rules. 【0444】 Step 5: 【0445】 The server uses a generative AI model to determine the appropriate mailbox number. The input consists of the latest sorting rules and recipient information. This AI model utilizes machine learning algorithms and learns from past data to output the optimal mailbox number. 【0446】 Step 6: 【0447】 The user obtains a BOX number provided by the server and physically sorts the mail. The input is BOX number information, which is output from a generating AI model. Based on this information, the user sorts the mail to the designated location. The output is that the accurately sorted mail is neatly placed in each BOX. 【0448】 (Application Example 1) 【0449】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0450】 Mail sorting at logistics centers and similar facilities presented challenges with traditional manual methods, leading to missorting and reduced efficiency. Furthermore, the proper handling of mail with unknown addresses was difficult, creating a need for improved, rapid, and accurate sorting techniques. 【0451】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0452】 In this invention, the server includes optical character recognition means for photographing mail and converting textual information into digital data, data management means for storing the digital data in an information storage device, and information updating means for analyzing and revising past and current sorting rules. This enables efficient and accurate sorting of mail, reduces missorting, and allows for the rapid processing of mail with unknown destinations. 【0453】 "Mail" refers to the general term for documents and items such as letters and parcels that are sent to other places or individuals through any service. 【0454】 Optical character recognition (OCR) is a technology that analyzes characters and numbers contained in an image and converts them into digital data in a format that can be read by a machine. 【0455】 "Digital data" refers to a binary data format used to represent and process information electronically. 【0456】 An "information storage device" refers to a storage device or database that can store a wide variety of information in one place and retrieve it as needed. 【0457】 "Data management" is the process of efficiently and securely storing, maintaining, and managing information and data. 【0458】 "Information updating" refers to the act of revising existing data and rules to the latest state based on new information and conditions. 【0459】 A "machine learning model" is a system of algorithms that learn patterns based on given data and perform estimations and predictions. 【0460】 A "logistics system" is a collection of processes and tools designed to optimize and efficiently manage the flow of goods and information. 【0461】 The system implementing this invention utilizes a variety of hardware and software to achieve efficient sorting of mail. 【0462】 The terminal uses its camera to capture an image of the mail. The captured image is analyzed using optical character recognition (OCR) technology to extract textual information such as the mail's address as digital data. This data is then transmitted to an information storage device for data management. Image acquisition and digital conversion by the terminal are performed using a dedicated application installed on a device such as a smartphone or tablet. 【0463】 The server analyzes past and present sorting rules based on data stored in the information storage device, using information update mechanisms, and revises them as needed. Power Automate and other automation tools are utilized in this process. Furthermore, trained machine learning models are used to determine the optimal sorting location. Cloud-based AI services such as Azure are used. The server finally notifies the terminals of the sorting results. 【0464】 Users refer to the sorting results provided by the terminal and quickly sort the actual mail to its designated location. This entire process reduces missorting and improves work efficiency. 【0465】 For example, in logistics centers, this system can handle the sudden increase in mail during peak seasons such as Black Friday. It is highly effective in situations where large volumes of mail need to be processed accurately and quickly. 【0466】 Examples of prompts for a generative AI model: 【0467】 "Create updated sorting rules based on yesterday's shipping data and propose efficient mail processing methods." 【0468】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0469】 Step 1: 【0470】 The device acquires images of mail. It uses a smartphone camera to photograph the item and sends the image data to OCR software. The input is image data, and the output is an image ready for analysis. 【0471】 Step 2: 【0472】 The terminal uses OCR technology to analyze image data. The input is the image data obtained in step 1, and the OCR engine extracts the text information as digital data. The output is digital data of the recipient and sender information of the mail. 【0473】 Step 3: 【0474】 The terminal transmits digital data to the server. The input is the digital information obtained in step 2, which is transmitted to the information storage device via the data management means. The output is the storage of the data in an integrated database. 【0475】 Step 4: 【0476】 The server analyzes and updates past and new sorting rules based on the received data. Input consists of existing and newly sent data in the integrated database, while output is the updated sorting rules. Automation tools such as Power Automate are used for data processing. 【0477】 Step 5: 【0478】 The server uses the updated data to determine the optimal sorting location. The input is the updated sorting rule obtained in step 4, and a machine learning model is used to determine the best sorting destination. The output is the sorting result as the optimal location. Azure's AI services are utilized here. 【0479】 Step 6: 【0480】 The server sends the sorting results to the terminal and notifies the user of the result. The input is the decision result from step 5, and the output is the sorting instructions displayed to the user. The user follows these instructions to sort the mail to the correct location. 【0481】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0482】 This invention is a system that automates the sorting of mail and further optimizes the work environment by recognizing the user's emotions. Specific embodiments are described below. 【0483】 First, the terminal takes a picture of the mail item with its camera and uses OCR technology to convert the recipient and sender information into electronic data. This electronic data is a digitized version of the physical information of the mail item and forms the basis for subsequent processing. 【0484】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This stored data is managed via Power Apps and SharePoint, ensuring that all mail information is centrally and systematically managed. 【0485】 The server uses Power Automate to call APIs and analyze sorting rules by referencing information in the database. By comparing both old and new rules and updating them as needed, it can always handle the latest sorting rules. 【0486】 Furthermore, the server utilizes Azure OpenAI, and a pre-trained AI model determines the optimal box number based on electronic data and sorting rules. This AI model has learned from past data and has the ability to flexibly handle complex cases. 【0487】 Furthermore, this system includes an emotion engine that recognizes the user's emotions in real time. Emotional data obtained from the user's facial expressions and voice is used as an indicator of stress level and satisfaction. For example, if stress levels are high during work, the system can adjust its judgment criteria and make suggestions to reduce the user's burden. 【0488】 For example, if a user frowns while sorting, the emotion engine will detect this, and the system will assist the user by adjusting the processing speed, etc. Furthermore, the emotion data is recorded and used as a database for future improvements to the work environment. 【0489】 Thus, the present invention aims to improve the efficiency of sorting mail and reduce the psychological burden on users. 【0490】 The following describes the processing flow. 【0491】 Step 1: 【0492】 The terminal takes a picture of the mail item with its camera and acquires image data. Then, it uses OCR technology to extract text information from the image and generates it as electronic data. This electronic data includes the recipient and sender information of the mail item and is entered into the system in digital format. 【0493】 Step 2: 【0494】 The server receives electronic data sent from the terminal. This received data is stored in a SharePoint database via Power Apps. Here, mail information is stored in a unified format and used for subsequent processing. 【0495】 Step 3: 【0496】 The server updates and analyzes sorting rules based on information in the database by calling an API through Power Automate. This provides the ability to check new and historical rules and update them as needed. This step ensures that the system is always up-to-date with the latest sorting rules. 【0497】 Step 4: 【0498】 The server runs an Azure OpenAI model, using electronic data and sorting rules to determine the optimal mailbox number. This artificial intelligence model has learned from past sorting data and is capable of handling complex cases and new information appropriately. 【0499】 Step 5: 【0500】 The user sorts mail into designated boxes based on box number information provided by the server. Here, the user is required to sort the mail quickly and accurately, following the instructions given by the server. 【0501】 Step 6: 【0502】 The device analyzes the user's facial expressions and voice using an emotion engine and evaluates their emotional state in real time. If negative emotions such as stress or dissatisfaction are detected, the system will notify the user and consider adjusting the task content or speed. 【0503】 Step 7: 【0504】 The server records emotional data and stores it in a database to help improve future sorting processes. This information is used as a valuable resource to optimize the user experience and reduce stress. 【0505】 (Example 2) 【0506】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0507】 Traditional mail sorting systems required manual sorting, which demanded significant effort and time from workers. Furthermore, the lack of measures to reduce the psychological burden on workers often led to increased stress and decreased work efficiency. This could result in misdelivery and sorting delays. Additionally, updating sorting rules was cumbersome with traditional methods, making rapid updates difficult. 【0508】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0509】 In this invention, the server includes information conversion means, information management means, and information processing means. This enables automated sorting of mail and reduces the psychological burden on workers. 【0510】 An "information conversion means" is a technological device that acquires images of mail and converts the text information within those images into electronic data. 【0511】 "Information management means" refers to a technological device that stores converted electronic data in an information recording device and manages it centrally. 【0512】 An "information processing means" is a technological device that compares and updates past and new sets of rules to generate the latest information. 【0513】 A "selection method" is a technical device that uses a pre-trained intelligent model to determine the optimal sorting destination. 【0514】 A "notification means" is a technical device that has the role of displaying or outputting the results obtained by the selection means. 【0515】 An "emotion recognition device" is a technological device that detects the emotional state of a worker and supports the optimization of the work environment. 【0516】 This invention provides a system that streamlines mail sorting and reduces the psychological burden on workers. A specific example of this system is described below. 【0517】 The terminal uses a camera to photograph mail and employs OCR (Optical Character Recognition) technology to convert the text information within the image into electronic data. This converts the recipient and sender information on the mail into digital data, forming the basis for processing. The terminal uses a standard image acquisition device and character recognition software. 【0518】 After the electronic data is converted, the server receives this data and stores it in an information storage device. Specifically, the data is centrally managed via data management software and made accessible at any time. A data storage platform is used in this process. 【0519】 The server then uses information processing technology to compare and update existing and new sorting rules. This involves the use of database management systems and automation tools. Based on the updated rules, the optimal sorting destination is determined. 【0520】 The server determines the appropriate sorting destination by utilizing a pre-trained intelligent model. This intelligent model is trained using machine learning algorithms based on past data and can handle complex sorting patterns. An example of a specific prompt message is, "Based on the latest sorting rules, please suggest the appropriate sorting destination for the entered mail data." 【0521】 Furthermore, this system uses an emotion recognition device to detect the user's emotional state in real time. By analyzing the worker's facial expressions and voice, it evaluates their stress level and satisfaction level during work and suggests adjustments to the work environment as needed. This process makes it possible to reduce the user's psychological burden and provide a comfortable work environment. 【0522】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0523】 Step 1: 【0524】 The device takes a picture of the mail with its camera and acquires image data. This image data is analyzed using OCR technology to extract the text information written on the mail. The input is image data of the mail, and the output is electronic data in text format. The OCR software recognizes and converts the text within the image. 【0525】 Step 2: 【0526】 The terminal sends the converted electronic data to the server. The server stores the received data in an information recording device. The input to this process is electronic data in text format, and the output is structured information in a database. The data management system records the electronic data in the appropriate format. 【0527】 Step 3: 【0528】 The server references information stored in the database and compares existing sorting rules with new information, thereby updating the rules. This process uses stored mail information and existing sorting rule data as input to generate updated sorting rules as output. An automated tool efficiently executes this process. 【0529】 Step 4: 【0530】 The server uses a trained intelligent model to determine the optimal sorting destination based on updated sorting rules. The input for this step is electronic data and updated sorting rules, and the output is information about the selected sorting destination. The intelligent model utilizes generative AI to make predictions based on past training data. Instructions can be given to this process via prompt messages. 【0531】 Step 5: 【0532】 To monitor the user's emotional state, the system uses a camera and microphone to capture facial expressions and voice. An emotion recognition device analyzes this data and extracts emotional information in real time. The input is real-time facial and voice data, and the output is numerical information such as stress levels and satisfaction levels. An emotion engine quantifies this data to provide information that contributes to optimizing the work environment. 【0533】 (Application Example 2) 【0534】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0535】 Mail sorting operations at logistics centers and similar facilities require increased efficiency, but the psychological burden on workers is also a significant challenge. While conventional sorting systems offer basic automation, they lack sufficient timely updates to sorting rules and adequate environmental adjustments based on workers' emotional states. As a result, work efficiency decreases and worker stress increases. This invention aims to solve these problems. 【0536】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0537】 In this invention, the server includes an analysis means for analyzing images of mail and converting them into electronic data, a data construction means for storing the electronic data in a storage device, a data management means for managing and analyzing past and new sorting rules, a judgment means for determining the optimal sorting destination using a trained intelligent model, and an emotion recognition means for acquiring worker emotion data and optimizing the work environment. This enables the automation of sorting work and the reduction of the psychological burden on workers. 【0538】 "Analysis means" refers to a device that has the function of acquiring images of mail and converting the textual information into electronic data. 【0539】 A "data construction means" is a device that has the function of storing acquired electronic data in a memory device. 【0540】 A "data management device" is a device that manages past and new sorting rules and has the function of updating and analyzing them as needed. 【0541】 A "decision-making device" is a device that uses a pre-trained intelligent model to determine the optimal sorting destination based on electronic data. 【0542】 A "notification means" is a device that has the function of outputting the sorting results obtained by the judgment means. 【0543】 An "emotion recognition device" is a device that has the function of capturing the emotional data of workers and analyzing their emotional state in real time. 【0544】 An "environmental optimization means" is a device that has the function of making suggestions for adjusting the work environment based on the analysis results obtained by an emotion recognition means. 【0545】 This invention is a system that streamlines mail sorting operations in a logistics center while optimizing the work environment by taking into account the emotional state of the workers. A specific embodiment of this system is described below. 【0546】 The device uses smart glasses or a smartphone camera to photograph mail and converts the text information into electronic data using OCR technology. The electronic data is then automatically sent to a storage device in the cloud. 【0547】 The server stores received electronic data in a database and manages existing and new sorting rules. Using Azure's machine learning services, a pre-trained AI model based on historical data effectively determines the optimal sorting destination. The results are notified to the worker in real time. 【0548】 Furthermore, sensors installed in the terminal collect emotional data from the worker (such as facial expressions and voice), and the server analyzes this data in real time using Azure Cognitive Services. Based on the analysis results, the system notifies the worker with suggestions for adjusting the work environment. Specifically, it automatically suggests speed adjustments to reduce workload and, if necessary, suggests breaks. 【0549】 As a concrete example, consider a worker at a logistics center during the busy year-end season. The system can detect when the worker begins to feel stressed and suggest adjusting the pace of work, thereby providing an efficient and comfortable working environment. 【0550】 Example prompt: "Consider an example of a user application that streamlines the sorting process in a logistics center, detects worker stress in real time, and suggests break times and speed adjustments." 【0551】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0552】 Step 1: 【0553】 The device takes a picture of the mail item using smart glasses or a smartphone camera. The input is the physical image data of the mail item, which is then converted into text information (such as recipient and sender information) using OCR technology and output as electronic data. 【0554】 Step 2: 【0555】 The terminal transmits electronic data converted using OCR technology to a storage device in the cloud. The input is converted character information, and this information is processed for transmission to the server via wireless communication (Wi-Fi or mobile data). The output is the data received by the server. 【0556】 Step 3: 【0557】 The server stores the received electronic data in its internal database. The input here is electronic data sent from the cloud, which is converted to an appropriate format according to the database structure before being output as stored data. This operation is performed using data construction tools. 【0558】 Step 4: 【0559】 The server uses Azure's machine learning services to determine the optimal sorting destination based on electronic data and past and new sorting rules. The input consists of sorting rules and electronic data retrieved from a database, and the output is the information of the optimal sorting destination. A generative AI model is used for this determination. 【0560】 Step 5: 【0561】 The server notifies the terminal in real time of the sorting destination information it has determined. The input is the sorting destination information, and the output is the instruction information displayed on the terminal. The server transmits this information to the terminal via a notification method. 【0562】 Step 6: 【0563】 The terminal collects the worker's facial expressions and voice using sensors and sends this data to a server for emotion recognition. The input is biosensor data acquired from the worker, and the output is data sent to the server as information necessary for analyzing the emotional state. 【0564】 Step 7: 【0565】 The server uses Azure Cognitive Services to analyze emotional data in real time. The input is emotional data sent from the terminal, and the output is analyzed emotional state information. This allows for understanding the worker's stress and comfort level. 【0566】 Step 8: 【0567】 Based on the results of the emotion analysis, the server sends suggestions for adjusting the work environment to the terminal. Specifically, these suggestions include adjusting the work pace or taking breaks. The input is information about the emotional state, and the output is instructional information notified to the worker as an adjustment suggestion. 【0568】 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. 【0569】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0570】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0571】 [Fourth Embodiment] 【0572】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0573】 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. 【0574】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0575】 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. 【0576】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0577】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0578】 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. 【0579】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0580】 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. 【0581】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0582】 The 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. 【0583】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0584】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0585】 This invention is a system for automating mail sorting. The system's program processing and operation are described below. 【0586】 First, the device takes a picture of the mail with its camera and analyzes the acquired image using OCR (Optical Character Recognition) technology. This extracts the mail's address and sender information as electronic data. This electronic data forms the basis for replacing the physical attributes of the mail with digital information. 【0587】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This database is built using Power Apps and SharePoint, and all mail information is managed uniformly. This management ensures information consistency and ease of access. 【0588】 Subsequently, the server uses Power Automate to call the API and analyze and update the sorting rules based on the information in the database. Here, it considers historical data and new department names, and updates the sorting rules as needed. This ensures that the system is always ready for optimal sorting. 【0589】 Next, the server leverages Azure OpenAI and uses a pre-trained artificial intelligence model to determine the optimal mailbox number. This AI model has the ability to learn from past sorting data and new information to make accurate decisions. The resulting mailbox number is an important indicator for preventing mis-sorting. 【0590】 Finally, the user retrieves the destination box number provided by the server and sorts the mail into the appropriate box. Based on this information, the user can sort the mail efficiently and accurately. This improvement in the sorting process increases work efficiency and significantly reduces misdelivery of mail. 【0591】 The following describes the processing flow. 【0592】 Step 1: 【0593】 The terminal uses its camera to photograph mail items and acquire image data. Next, OCR technology is used to convert the text information of the recipient and sender from the image into electronic data. This process eliminates the need for manual data entry and enables rapid digitization. 【0594】 Step 2: 【0595】 The server receives electronic data sent from terminals. Based on this, it uses Power Apps and SharePoint to store and manage mail data in a database. This database plays a role in ensuring efficient access and data consistency by unifying the information for each piece of mail. 【0596】 Step 3: 【0597】 The server uses Power Automate to call APIs and analyze information in the database. At this stage, it compares existing sorting rules with new rules and updates the rules to the latest state if necessary. In this way, sorting is prepared that can flexibly adapt to changes in the organization. 【0598】 Step 4: 【0599】 The server runs an Azure OpenAI model to match electronic data with sorting rules. The artificial intelligence model learns from past data to predict the optimal mailbox number. This prediction can handle undeliverable mail and irregular rules. 【0600】 Step 5: 【0601】 The user verifies the appropriate BOX number provided by the server. Based on this information, the user can sort mail into the correct mailbox. Minimizing errors improves the overall efficiency and accuracy of the sorting process. 【0602】 (Example 1) 【0603】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0604】 In mail sorting, problems exist that make efficient and accurate sorting difficult due to misreading of address information, errors caused by manual work, and an increase in mail with unknown addresses. Furthermore, outdated sorting rules can hinder the accurate delivery of mail to new departments and addresses. 【0605】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0606】 In this invention, the server includes information analysis means for acquiring images of mail and converting textual information into electronic data, information management means for storing the electronic data in data storage, and information updating means for updating and analyzing past sorting rules and new sorting rules. This enables efficient and accurate automated sorting of mail and allows for quick response to new destinations. 【0607】 "Information analysis means" refers to a device or technology that has the function of acquiring images of mail and converting textual information into electronic data. 【0608】 "Information management means" refers to a device or technology intended to store electronic data in data storage and to structure and centrally manage the information. 【0609】 An "information update means" is a device or technology that has the function of updating and analyzing past and new sorting rules to keep them constantly up-to-date. 【0610】 A "sorting decision means" is a device or technology that uses a trained artificial intelligence model to determine and decide on the optimal sorting destination. 【0611】 "Information notification means" refers to a device or technology for outputting the results obtained by the sorting and decision means and notifying the user. 【0612】 "Data communication means" refers to devices or technologies that transmit and receive electronic data using security protocols to ensure data security. 【0613】 A "recommendation method" refers to a device or technology that uses a prompt message as input and has the function of recommending a sorting destination using a generated AI model. 【0614】 This invention is a comprehensive system for the automated sorting of mail. Specifically, the components work together as follows: 【0615】 The terminal first takes a picture of the mail using its camera. The captured image data is analyzed using OCR technology to extract text information. This OCR technology uses high-precision character recognition software, such as the "Tesseract" engine. In this process, the recipient and sender information written on the mail is converted into text format. 【0616】 The extracted electronic data is sent to a server. The server stores the received data in data storage built using platforms such as SharePoint and Power Apps. The data is securely transferred using security protocols and centrally managed. This management allows for efficient searching and information extraction. 【0617】 Next, the server uses Power Automate to update and analyze sorting rules based on the information in the database. This update creates rules that accommodate new departments and changed address information, based on past sorting data and newly added information. 【0618】 Furthermore, the server runs a generative AI model on Azure to determine the appropriate sorting destination. This model learns from past data and current trends and is used to predict the optimal box number. For example, if you input a prompt such as "What is the appropriate box number for mail addressed to the legal department?", the model will analyze the data and make the appropriate decision. 【0619】 The user receives the final sorting results provided by the server and performs the actual mail sorting. This sorting is done based on the designated box number, preventing misdelivery. The user can work smoothly as the overall process is streamlined and accurate sorting is continuously performed. In this way, the present invention realizes the rapid and accurate sorting of mail. 【0620】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0621】 Step 1: 【0622】 The device takes a picture of the mail with its camera. The input is a physical image of the mail. This image is acquired using a high-resolution camera. The output is image data, which is ready to be converted into a format that can be further processed using OCR technology. 【0623】 Step 2: 【0624】 The device applies OCR (Optical Character Recognition) technology using the captured image data. The input is the image data obtained in Step 1. The OCR engine analyzes this data and extracts character information. The output is text-based recipient and sender information. 【0625】 Step 3: 【0626】 The server receives text data from the terminal. The input is the text data generated in step 2. This data is securely sent using a security protocol and stored in "data storage". The output is the securely stored, organized data. 【0627】 Step 4: 【0628】 The server uses an "information update mechanism" to analyze and update sorting rules based on the information in the database. The input consists of stored text data and existing sorting rule information. This process considers new destination information, and the rules are revised as needed. The output is the generation of the latest sorting rules. 【0629】 Step 5: 【0630】 The server uses a generative AI model to determine the appropriate mailbox number. The input consists of the latest sorting rules and recipient information. This AI model utilizes machine learning algorithms and learns from past data to output the optimal mailbox number. 【0631】 Step 6: 【0632】 The user obtains a BOX number provided by the server and physically sorts the mail. The input is BOX number information, which is output from a generating AI model. Based on this information, the user sorts the mail to the designated location. The output is that the accurately sorted mail is neatly placed in each BOX. 【0633】 (Application Example 1) 【0634】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0635】 Mail sorting at logistics centers and similar facilities presented challenges with traditional manual methods, leading to missorting and reduced efficiency. Furthermore, the proper handling of mail with unknown addresses was difficult, creating a need for improved, rapid, and accurate sorting techniques. 【0636】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0637】 In this invention, the server includes optical character recognition means for photographing mail and converting textual information into digital data, data management means for storing the digital data in an information storage device, and information updating means for analyzing and revising past and current sorting rules. This enables efficient and accurate sorting of mail, reduces missorting, and allows for the rapid processing of mail with unknown destinations. 【0638】 "Mail" refers to the general term for documents and items such as letters and parcels that are sent to other places or individuals through any service. 【0639】 Optical character recognition (OCR) is a technology that analyzes characters and numbers contained in an image and converts them into digital data in a format that can be read by a machine. 【0640】 "Digital data" refers to a binary data format used to represent and process information electronically. 【0641】 An "information storage device" refers to a storage device or database that can store a wide variety of information in one place and retrieve it as needed. 【0642】 "Data management" is the process of efficiently and securely storing, maintaining, and managing information and data. 【0643】 "Information updating" refers to the act of revising existing data and rules to the latest state based on new information and conditions. 【0644】 A "machine learning model" is a system of algorithms that learn patterns based on given data and perform estimations and predictions. 【0645】 A "logistics system" is a collection of processes and tools designed to optimize and efficiently manage the flow of goods and information. 【0646】 The system implementing this invention utilizes a variety of hardware and software to achieve efficient sorting of mail. 【0647】 The terminal uses its camera to capture an image of the mail. The captured image is analyzed using optical character recognition (OCR) technology to extract textual information such as the mail's address as digital data. This data is then transmitted to an information storage device for data management. Image acquisition and digital conversion by the terminal are performed using a dedicated application installed on a device such as a smartphone or tablet. 【0648】 The server analyzes past and present sorting rules based on data stored in the information storage device, using information update mechanisms, and revises them as needed. Power Automate and other automation tools are utilized in this process. Furthermore, trained machine learning models are used to determine the optimal sorting location. Cloud-based AI services such as Azure are used. The server finally notifies the terminals of the sorting results. 【0649】 Users refer to the sorting results provided by the terminal and quickly sort the actual mail to its designated location. This entire process reduces missorting and improves work efficiency. 【0650】 For example, in logistics centers, this system can handle the sudden increase in mail during peak seasons such as Black Friday. It is highly effective in situations where large volumes of mail need to be processed accurately and quickly. 【0651】 Examples of prompts for a generative AI model: 【0652】 "Create updated sorting rules based on yesterday's shipping data and propose efficient mail processing methods." 【0653】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0654】 Step 1: 【0655】 The device acquires images of mail. It uses a smartphone camera to photograph the item and sends the image data to OCR software. The input is image data, and the output is an image ready for analysis. 【0656】 Step 2: 【0657】 The terminal uses OCR technology to analyze image data. The input is the image data obtained in step 1, and the OCR engine extracts the text information as digital data. The output is digital data of the recipient and sender information of the mail. 【0658】 Step 3: 【0659】 The terminal transmits digital data to the server. The input is the digital information obtained in step 2, which is transmitted to the information storage device via the data management means. The output is the storage of the data in an integrated database. 【0660】 Step 4: 【0661】 The server analyzes and updates past and new sorting rules based on the received data. Input consists of existing and newly sent data in the integrated database, while output is the updated sorting rules. Automation tools such as Power Automate are used for data processing. 【0662】 Step 5: 【0663】 The server uses the updated data to determine the optimal sorting location. The input is the updated sorting rule obtained in step 4, and a machine learning model is used to determine the best sorting destination. The output is the sorting result as the optimal location. Azure's AI services are utilized here. 【0664】 Step 6: 【0665】 The server sends the sorting results to the terminal and notifies the user of the result. The input is the decision result from step 5, and the output is the sorting instructions displayed to the user. The user follows these instructions to sort the mail to the correct location. 【0666】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0667】 This invention is a system that automates the sorting of mail and further optimizes the work environment by recognizing the user's emotions. Specific embodiments are described below. 【0668】 First, the terminal takes a picture of the mail item with its camera and uses OCR technology to convert the recipient and sender information into electronic data. This electronic data is a digitized version of the physical information of the mail item and forms the basis for subsequent processing. 【0669】 Next, the server receives the electronic data sent from the terminal and stores it in a database. This stored data is managed via Power Apps and SharePoint, ensuring that all mail information is centrally and systematically managed. 【0670】 The server uses Power Automate to call APIs and analyze sorting rules by referencing information in the database. By comparing both old and new rules and updating them as needed, it can always handle the latest sorting rules. 【0671】 Furthermore, the server utilizes Azure OpenAI, and a pre-trained AI model determines the optimal box number based on electronic data and sorting rules. This AI model has learned from past data and has the ability to flexibly handle complex cases. 【0672】 Furthermore, this system includes an emotion engine that recognizes the user's emotions in real time. Emotional data obtained from the user's facial expressions and voice is used as an indicator of stress level and satisfaction. For example, if stress levels are high during work, the system can adjust its judgment criteria and make suggestions to reduce the user's burden. 【0673】 For example, if a user frowns while sorting, the emotion engine will detect this, and the system will assist the user by adjusting the processing speed, etc. Furthermore, the emotion data is recorded and used as a database for future improvements to the work environment. 【0674】 Thus, the present invention aims to improve the efficiency of sorting mail and reduce the psychological burden on users. 【0675】 The following describes the processing flow. 【0676】 Step 1: 【0677】 The terminal takes a picture of the mail item with its camera and acquires image data. Then, it uses OCR technology to extract text information from the image and generates it as electronic data. This electronic data includes the recipient and sender information of the mail item and is entered into the system in digital format. 【0678】 Step 2: 【0679】 The server receives electronic data sent from the terminal. This received data is stored in a SharePoint database via Power Apps. Here, mail information is stored in a unified format and used for subsequent processing. 【0680】 Step 3: 【0681】 The server updates and analyzes sorting rules based on information in the database by calling an API through Power Automate. This provides the ability to check new and historical rules and update them as needed. This step ensures that the system is always up-to-date with the latest sorting rules. 【0682】 Step 4: 【0683】 The server runs an Azure OpenAI model, using electronic data and sorting rules to determine the optimal mailbox number. This artificial intelligence model has learned from past sorting data and is capable of handling complex cases and new information appropriately. 【0684】 Step 5: 【0685】 The user sorts mail into designated boxes based on box number information provided by the server. Here, the user is required to sort the mail quickly and accurately, following the instructions given by the server. 【0686】 Step 6: 【0687】 The device analyzes the user's facial expressions and voice using an emotion engine and evaluates their emotional state in real time. If negative emotions such as stress or dissatisfaction are detected, the system will notify the user and consider adjusting the task content or speed. 【0688】 Step 7: 【0689】 The server records emotional data and stores it in a database to help improve future sorting processes. This information is used as a valuable resource to optimize the user experience and reduce stress. 【0690】 (Example 2) 【0691】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0692】 Traditional mail sorting systems required manual sorting, which demanded significant effort and time from workers. Furthermore, the lack of measures to reduce the psychological burden on workers often led to increased stress and decreased work efficiency. This could result in misdelivery and sorting delays. Additionally, updating sorting rules was cumbersome with traditional methods, making rapid updates difficult. 【0693】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0694】 In this invention, the server includes information conversion means, information management means, and information processing means. This enables automated sorting of mail and reduces the psychological burden on workers. 【0695】 An "information conversion means" is a technological device that acquires images of mail and converts the text information within those images into electronic data. 【0696】 "Information management means" refers to a technological device that stores converted electronic data in an information recording device and manages it centrally. 【0697】 An "information processing means" is a technological device that compares and updates past and new sets of rules to generate the latest information. 【0698】 A "selection method" is a technical device that uses a pre-trained intelligent model to determine the optimal sorting destination. 【0699】 A "notification means" is a technical device that has the role of displaying or outputting the results obtained by the selection means. 【0700】 An "emotion recognition device" is a technological device that detects the emotional state of a worker and supports the optimization of the work environment. 【0701】 This invention provides a system that streamlines mail sorting and reduces the psychological burden on workers. A specific example of this system is described below. 【0702】 The terminal uses a camera to photograph mail and employs OCR (Optical Character Recognition) technology to convert the text information within the image into electronic data. This converts the recipient and sender information on the mail into digital data, forming the basis for processing. The terminal uses a standard image acquisition device and character recognition software. 【0703】 After the electronic data is converted, the server receives this data and stores it in an information storage device. Specifically, the data is centrally managed via data management software and made accessible at any time. A data storage platform is used in this process. 【0704】 The server then uses information processing technology to compare and update existing and new sorting rules. This involves the use of database management systems and automation tools. Based on the updated rules, the optimal sorting destination is determined. 【0705】 The server determines the appropriate sorting destination by utilizing a pre-trained intelligent model. This intelligent model is trained using machine learning algorithms based on past data and can handle complex sorting patterns. An example of a specific prompt message is, "Based on the latest sorting rules, please suggest the appropriate sorting destination for the entered mail data." 【0706】 Furthermore, this system uses an emotion recognition device to detect the user's emotional state in real time. By analyzing the worker's facial expressions and voice, it evaluates their stress level and satisfaction level during work and suggests adjustments to the work environment as needed. This process makes it possible to reduce the user's psychological burden and provide a comfortable work environment. 【0707】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0708】 Step 1: 【0709】 The device takes a picture of the mail with its camera and acquires image data. This image data is analyzed using OCR technology to extract the text information written on the mail. The input is image data of the mail, and the output is electronic data in text format. The OCR software recognizes and converts the text within the image. 【0710】 Step 2: 【0711】 The terminal sends the converted electronic data to the server. The server stores the received data in an information recording device. The input to this process is electronic data in text format, and the output is structured information in a database. The data management system records the electronic data in the appropriate format. 【0712】 Step 3: 【0713】 The server references information stored in the database and compares existing sorting rules with new information, thereby updating the rules. This process uses stored mail information and existing sorting rule data as input to generate updated sorting rules as output. An automated tool efficiently executes this process. 【0714】 Step 4: 【0715】 The server uses a trained intelligent model to determine the optimal sorting destination based on updated sorting rules. The input for this step is electronic data and updated sorting rules, and the output is information about the selected sorting destination. The intelligent model utilizes generative AI to make predictions based on past training data. Instructions can be given to this process via prompt messages. 【0716】 Step 5: 【0717】 To monitor the user's emotional state, the system uses a camera and microphone to capture facial expressions and voice. An emotion recognition device analyzes this data and extracts emotional information in real time. The input is real-time facial and voice data, and the output is numerical information such as stress levels and satisfaction levels. An emotion engine quantifies this data to provide information that contributes to optimizing the work environment. 【0718】 (Application Example 2) 【0719】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0720】 Mail sorting operations at logistics centers and similar facilities require increased efficiency, but the psychological burden on workers is also a significant challenge. While conventional sorting systems offer basic automation, they lack sufficient timely updates to sorting rules and adequate environmental adjustments based on workers' emotional states. As a result, work efficiency decreases and worker stress increases. This invention aims to solve these problems. 【0721】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0722】 In this invention, the server includes an analysis means for analyzing images of mail and converting them into electronic data, a data construction means for storing the electronic data in a storage device, a data management means for managing and analyzing past and new sorting rules, a judgment means for determining the optimal sorting destination using a trained intelligent model, and an emotion recognition means for acquiring worker emotion data and optimizing the work environment. This enables the automation of sorting work and the reduction of the psychological burden on workers. 【0723】 "Analysis means" refers to a device that has the function of acquiring images of mail and converting the textual information into electronic data. 【0724】 A "data construction means" is a device that has the function of storing acquired electronic data in a memory device. 【0725】 A "data management device" is a device that manages past and new sorting rules and has the function of updating and analyzing them as needed. 【0726】 A "decision-making device" is a device that uses a pre-trained intelligent model to determine the optimal sorting destination based on electronic data. 【0727】 A "notification means" is a device that has the function of outputting the sorting results obtained by the judgment means. 【0728】 An "emotion recognition device" is a device that has the function of capturing the emotional data of workers and analyzing their emotional state in real time. 【0729】 An "environmental optimization means" is a device that has the function of making suggestions for adjusting the work environment based on the analysis results obtained by an emotion recognition means. 【0730】 This invention is a system that streamlines mail sorting operations in a logistics center while optimizing the work environment by taking into account the emotional state of the workers. A specific embodiment of this system is described below. 【0731】 The device uses smart glasses or a smartphone camera to photograph mail and converts the text information into electronic data using OCR technology. The electronic data is then automatically sent to a storage device in the cloud. 【0732】 The server stores received electronic data in a database and manages existing and new sorting rules. Using Azure's machine learning services, a pre-trained AI model based on historical data effectively determines the optimal sorting destination. The results are notified to the worker in real time. 【0733】 Furthermore, sensors installed in the terminal collect emotional data from the worker (such as facial expressions and voice), and the server analyzes this data in real time using Azure Cognitive Services. Based on the analysis results, the system notifies the worker with suggestions for adjusting the work environment. Specifically, it automatically suggests speed adjustments to reduce workload and, if necessary, suggests breaks. 【0734】 As a concrete example, consider a worker at a logistics center during the busy year-end season. The system can detect when the worker begins to feel stressed and suggest adjusting the pace of work, thereby providing an efficient and comfortable working environment. 【0735】 Example prompt: "Consider an example of a user application that streamlines the sorting process in a logistics center, detects worker stress in real time, and suggests break times and speed adjustments." 【0736】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0737】 Step 1: 【0738】 The device takes a picture of the mail item using smart glasses or a smartphone camera. The input is the physical image data of the mail item, which is then converted into text information (such as recipient and sender information) using OCR technology and output as electronic data. 【0739】 Step 2: 【0740】 The terminal transmits electronic data converted using OCR technology to a storage device in the cloud. The input is converted character information, and this information is processed for transmission to the server via wireless communication (Wi-Fi or mobile data). The output is the data received by the server. 【0741】 Step 3: 【0742】 The server stores the received electronic data in its internal database. The input here is electronic data sent from the cloud, which is converted to an appropriate format according to the database structure before being output as stored data. This operation is performed using data construction tools. 【0743】 Step 4: 【0744】 The server uses Azure's machine learning services to determine the optimal sorting destination based on electronic data and past and new sorting rules. The input consists of sorting rules and electronic data retrieved from a database, and the output is the information of the optimal sorting destination. A generative AI model is used for this determination. 【0745】 Step 5: 【0746】 The server notifies the terminal in real time of the sorting destination information it has determined. The input is the sorting destination information, and the output is the instruction information displayed on the terminal. The server transmits this information to the terminal via a notification method. 【0747】 Step 6: 【0748】 The terminal collects the worker's facial expressions and voice using sensors and sends this data to a server for emotion recognition. The input is biosensor data acquired from the worker, and the output is data sent to the server as information necessary for analyzing the emotional state. 【0749】 Step 7: 【0750】 The server uses Azure Cognitive Services to analyze emotional data in real time. The input is emotional data sent from the terminal, and the output is analyzed emotional state information. This allows for understanding the worker's stress and comfort level. 【0751】 Step 8: 【0752】 Based on the results of the emotion analysis, the server sends suggestions for adjusting the work environment to the terminal. Specifically, these suggestions include adjusting the work pace or taking breaks. The input is information about the emotional state, and the output is instructional information notified to the worker as an adjustment suggestion. 【0753】 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. 【0754】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0755】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0756】 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. 【0757】 Figure 9 shows an 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. 【0758】 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. 【0759】 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. 【0760】 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, motorcycles, etc., 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, for example, based 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. 【0761】 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." 【0762】 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. 【0763】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0764】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0765】 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. 【0766】 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. 【0767】 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. 【0768】 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. 【0769】 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. 【0770】 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. 【0771】 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. 【0772】 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 the like 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. 【0773】 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 as being incorporated by reference. 【0774】 The following is further disclosed regarding the embodiments described above. 【0775】 (Claim 1) 【0776】 An image analysis means that acquires images of mail and converts text information into electronic data, 【0777】 A data construction means for storing the aforementioned electronic data in a database, 【0778】 A data update mechanism for updating and analyzing past and new sorting rules, 【0779】 A sorting decision means that uses a pre-trained artificial intelligence model to determine the optimal sorting destination, 【0780】 A mail sorting system including a result notification means for outputting the results obtained by the sorting determination means. 【0781】 (Claim 2) 【0782】 The system according to claim 1, which checks against past sorting rules and selects an appropriate sorting destination for mail with an unknown address. 【0783】 (Claim 3) 【0784】 The system according to claim 1, wherein the artificial intelligence model for making sorting decisions uses a machine learning algorithm. 【0785】 "Example 1" 【0786】 (Claim 1) 【0787】 An information analysis means that acquires images of mail and converts textual information into electronic data, 【0788】 Information management means for storing the aforementioned electronic data in data storage, 【0789】 Information update means for updating and analyzing past and new sorting rules, 【0790】 A sorting decision means that uses a pre-trained artificial intelligence model to determine the optimal sorting destination, 【0791】 Information notification means for outputting the results obtained by the sorting determination means, 【0792】 A data communication means that transmits and receives electronic data using a security protocol, 【0793】 A recommendation method that uses a generative AI model to recommend sorting destinations based on prompt text as input, 【0794】 A system that includes this. 【0795】 (Claim 2) 【0796】 The system according to claim 1, which checks against past sorting rules and selects an appropriate sorting destination for mail with an unknown address. 【0797】 (Claim 3) 【0798】 The system according to claim 1, wherein the artificial intelligence model for making sorting decisions uses a learning algorithm. 【0799】 "Application Example 1" 【0800】 (Claim 1) 【0801】 An optical character recognition means that photographs mail and converts the text information into digital data, 【0802】 A data management means for storing the aforementioned digital data in an information storage device, 【0803】 Information update means for analyzing and revising past and current sorting rules, 【0804】 A sorting decision means that uses a pre-trained machine learning model to determine the optimal sorting location, 【0805】 A logistics system including a result output means for displaying the result determined by the sorting determination means. 【0806】 (Claim 2) 【0807】 The logistics system according to claim 1, which selects the optimal sorting location for mail with unknown destinations by comparing it with past sorting rules. 【0808】 (Claim 3) 【0809】 The logistics system according to claim 1, which uses algorithmic learning in a machine learning model for sorting decisions. 【0810】 "Example 2 of combining an emotion engine" 【0811】 (Claim 1) 【0812】 An information conversion means that acquires images of mail and converts text information into electronic data, 【0813】 Information management means for storing the aforementioned electronic data in an information recording device, 【0814】 Information processing means for comparing and updating past and new sets of rules, 【0815】 A selection method that uses a pre-trained intelligent model to determine the optimal sorting destination, 【0816】 A notification means that outputs the results obtained by the selection means, 【0817】 A system including emotion recognition means for detecting the emotional state of workers and optimizing the work environment. 【0818】 (Claim 2) 【0819】 The system according to claim 1, which checks against a set of past rules and selects an appropriate sorting destination for mail with an unknown address. 【0820】 (Claim 3) 【0821】 The system according to claim 1, wherein the intelligent model for making selections uses machine learning techniques. 【0822】 "Application example 2 when combining with an emotional engine" 【0823】 (Claim 1) 【0824】 An analysis means that acquires images of mail and converts text information into electronic data, 【0825】 A data construction means for storing the aforementioned electronic data in a storage device, 【0826】 A data management system for updating and analyzing past and new sorting rules, 【0827】 A decision-making method that uses a pre-trained intelligent model to determine the optimal sorting destination, 【0828】 A notification means that outputs the result obtained by the aforementioned determination means, 【0829】 A means for acquiring worker emotional data and optimizing the work environment through emotion recognition, 【0830】 A system including an environment optimization means that proposes work adjustments based on the results of the emotion recognition means. 【0831】 (Claim 2) 【0832】 The system according to claim 1, which checks against past sorting rules and selects an appropriate sorting destination for mail with an unknown address. 【0833】 (Claim 3) 【0834】 The system according to claim 1, wherein the intelligent model for making sorting decisions uses a learning algorithm. [Explanation of Symbols] 【0835】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] An image analysis means that acquires images of mail and converts text information into electronic data, A data construction means for storing the aforementioned electronic data in a database, A data update mechanism for updating and analyzing past and new sorting rules, A sorting decision means that uses a pre-trained artificial intelligence model to determine the optimal sorting destination, A mail sorting system including a result notification means for outputting the results obtained by the sorting determination means. [Claim 2] The system according to claim 1, which checks against past sorting rules and selects an appropriate sorting destination for mail with an unknown address. [Claim 3] The system according to claim 1, wherein the artificial intelligence model for making sorting decisions uses a machine learning algorithm.

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