system

Abstract

We provide the system. [Solution] An electronic means for detecting the address information of mail, A management system for storing the detected information in a database, An analytical means for determining the destination based on stored information, A reporting means for notifying the determined destination of transport, Information presentation methods used by workers, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to reduce human misdelivered letters in the process of accurately and efficiently sorting a large number of mails. The current manual sorting method has problems that it requires management of complex rules and is prone to mistakes. For this reason, an automated system for determining an appropriate sorting destination based on the destination information of mails is required.

Means for Solving the Problems

[0005] The present invention solves the above problem by providing a system comprising: an electronic means for detecting the destination information of mail; a management means for storing the detected information in a database; an analysis means for determining the destination based on the stored information; and a reporting means for notifying the determined destination. This system can significantly reduce misdeliveries by analyzing special sorting rules using a machine learning model and notifying the system via digital communication.

[0006] "Digitization means" refers to a device or system that converts the physical address information of a postal item into digital data.

[0007] "Management means" refers to a device or system for collecting, organizing, and storing digitized digital data in a database.

[0008] "Analysis means" refers to a device or mechanism that applies rules regarding destination and sender based on stored data, and determines the optimal destination based on these rules.

[0009] A "reporting means" is a device or mechanism for notifying relevant parties of destination information determined by the analysis means.

[0010] A "machine learning model" is an algorithm or mathematical model that uses data to analyze and learn complex patterns and rules.

[0011] "Digital communication methods" refer to technologies and protocols for transmitting information over the internet and networks. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system that automates the mail sorting process, enabling efficient and accurate delivery. This system is implemented by combining digitization, management, analysis, and reporting means. The program's processing is described below in natural language.

[0034] First, when mail arrives, the terminal scans it and uses OCR technology to recognize and extract the recipient information as digital data. This data is immediately stored in a database using a management system. The terminal separates information such as recipient, sender, and date into individual fields and stores it in the database as structured data, making it easier to search and analyze in subsequent processing.

[0035] Subsequently, the server analyzes the stored digital data using an analysis tool. This analysis tool utilizes machine learning models to determine the optimal destination based on vast amounts of historical data and specific operational rules. In particular, the server can automatically apply rules to sort the items into dedicated boxes, etc., if the destination department is involved in a special project.

[0036] These analysis results are automatically notified to the relevant personnel through reporting mechanisms on the server. Notifications are delivered via digital communication methods, such as email, messaging services, or dedicated internal business applications. Users can receive this information and perform quick and accurate sorting tasks.

[0037] For example, if mail is sent to a specific department and should be sorted into a project-specific box rather than a regular box, the system will automatically make this determination. This process allows users to sort mail smoothly without having to consider various special rules.

[0038] This system is expected to reduce errors and improve efficiency in mail sorting compared to traditional manual processes, contributing to increased productivity across the entire organization.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This digital data is then neatly categorized according to a pre-designed format.

[0042] Step 2:

[0043] The terminal extracts digital data and transmits it in real time to a database via a management system. By storing information such as destination, sender, and date in the database, an organized data structure is formed.

[0044] Step 3:

[0045] The server accesses the database and analyzes the stored data using analytical tools. Here, the server uses a machine learning model and, referencing past sorting patterns and departmental rules, identifies the optimal sorting destination.

[0046] Step 4:

[0047] Based on the analysis results, the server notifies relevant personnel of the determined destination via digital communication methods, such as email or messaging services. It also sends notifications to internal business applications as needed.

[0048] Step 5:

[0049] Users receive reports and place mail in designated sorting locations. Following the notification, users can work quickly and efficiently while preventing errors.

[0050] Step 6:

[0051] Users report misdeliveries and unusual cases to the feedback system. This allows the system to acquire data for further analysis and improve sorting accuracy in the future.

[0052] (Example 1)

[0053] 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."

[0054] Traditional mail sorting processes rely heavily on manual verification, limiting their efficiency and accuracy. This burden increases significantly when handling large volumes of mail. Such manual processes are prone to human error and delays, highlighting the need for efficient and accurate automated sorting systems.

[0055] 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.

[0056] In this invention, the server includes means for detecting mail destination information and performing character recognition; means for storing the detected information on an information recording medium; means for using a computational model to analyze the destination based on the stored information; means for notifying the determined destination via information communication; and means for the user to receive instructions and perform mail sorting. This enables automation and improved accuracy of mail sorting, as well as overall operational efficiency.

[0057] "Mail" refers to means of communication such as documents and parcels delivered through the postal system.

[0058] "Recipient information" refers to identifying information such as the address, name, and postal code that indicates the destination of the mail.

[0059] "Character recognition" is a technology that detects characters within an image and converts them into digital text.

[0060] An "information recording medium" refers to a database or storage system used to store digital data.

[0061] A "computational model" is a mathematical model constructed using algorithms for data analysis.

[0062] "Information and communication" refers to the means of transmitting information to a remote location using digital signals.

[0063] A "user" is a worker who uses the system to sort mail.

[0064] This system is designed to automate the mail sorting process. It primarily involves the collaboration of terminals and servers to perform efficient data processing.

[0065] First, when a piece of mail arrives, the terminal scans its surface with a high-resolution scanner. At this time, it uses OCR (Optical Character Recognition) software to extract the recipient and sender information written on the mail as digital data. The terminal organizes the extracted information into fields such as "Recipient," "Sender," and "Date," and saves it to an information storage medium.

[0066] Next, the server accesses the stored data and analyzes it using analytical tools. The server is equipped with machine learning algorithms that determine the optimal destination based on vast amounts of historical data and specific operational rules. This analysis enables a system where, for example, mail addressed to departments related to a specific project is automatically sorted into a dedicated box.

[0067] The analysis results are quickly notified to the user via a reporting mechanism on the server. This notification may be sent via email, a messaging service, or a proprietary internal business application. Users can then receive these notifications and properly sort their mail.

[0068] For example, if the system automatically sorts mail addressed to a specific destination into a project-specific box, users no longer need to manually check the rules, improving work efficiency. Furthermore, by utilizing a generative AI model, it is possible to give instructions to the system using prompts such as, "Tell me the sorting rules for mail addressed to a specific project."

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] When the terminal receives mail, it scans it using a high-resolution scanner. The input is the mail itself, and the output is a high-resolution scanned image of that mail. The terminal inputs this image into OCR software to extract recipient and sender information as digital text. Specifically, it analyzes each character in the image and classifies them into fields such as "address," "name," and "zip code" as text data.

[0072] Step 2:

[0073] The terminal uses the extracted digital text to save the data to an information storage medium. The input here is the structured text data from the previous step, and the output is the information stored in the database. The terminal organizes the data into fields according to attributes such as "recipient," "sender," and "date," and adds it to the database using SQL statements. Specifically, it generates and executes SQL commands to efficiently save the information.

[0074] Step 3:

[0075] The server retrieves data stored on an information recording medium and performs analysis using analytical tools. The input for this step is stored digital data, and the output is analyzed sorting instruction information. The server utilizes machine learning models to compare and classify the input data and determine the optimal destination. Specifically, it refers to past delivery history and selects the optimal transportation route in accordance with current laws and company rules.

[0076] Step 4:

[0077] The analysis results obtained on the server are notified to the user through a reporting mechanism. The input here is the analysis results from the server, and the output is a notification message sent to the user. Specifically, this involves quickly informing the user of mail delivery addresses and sorting instructions using email, messaging apps, or internal company applications.

[0078] Step 5:

[0079] The user receives a notification and performs the physical sorting of mail based on the instructions. The input for this step is the notified data, and the output is the properly sorted mail. Specifically, the user receives the mail in front of them and sorts it into the designated project box or regular delivery 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] Modern logistics centers handle the sorting of vast amounts of mail and packages, but they still rely on manual labor and partial automation, resulting in limitations in efficiency and accuracy. This is especially true when dealing with specialized sorting rules, which require significant time and human resources for decision-making. Furthermore, a lack of support for employees to quickly acquire information and take action is another challenge.

[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 an electronic means for detecting the destination information of mail, a management means for storing the detected information in a database, an analysis means for determining the destination based on the stored information, a reporting means for notifying the determined destination, and an information presentation means for use by workers. This enables efficient and accurate sorting of mail and quick information verification by workers.

[0085] "Digitization methods" refer to technologies that convert and recognize mailing address information as digital data, and typically involve using scanning or optical character recognition.

[0086] "Management means" refers to a function that structures digitized information, stores it in a database, and facilitates subsequent analysis and retrieval.

[0087] "Analysis means" refers to the process of determining the optimal destination based on stored data, and involves performing analysis using past data and rules with a learning algorithm.

[0088] The "reporting means" is a function for notifying personnel of the destination determined based on the analysis results, and this is done via information and communication means, such as email or messaging services.

[0089] "Information presentation means" refers to devices and technologies that display information in a way that employees can quickly understand and act upon, such as smart glasses or displays.

[0090] The system used to implement this application primarily aims to streamline the sorting process of mail. The specific procedures and necessary technologies for implementation are described below.

[0091] The server first scans incoming mail using a terminal, and then uses OCR technology to recognize and extract the recipient information as digital data. Examples of OCR technologies used include Google® Cloud Vision API. The digitized information is then stored in a database through a management system. For this process, a database management system such as MySQL® can be used.

[0092] The server then analyzes the stored digital data using analytical tools. This analysis utilizes machine learning libraries, such as TENSORFLOW®, to determine the optimal destination based on past data and operational rules. If the destination department meets specific criteria, the server can automatically apply sorting rules to place the items into dedicated boxes or other designated locations.

[0093] Notifications are made using information and communication means, informing workers of the destination determined by the reporting means. Users can check this information in real time through information display means such as smart glasses. The smart glasses' display uses, for example, Twilio's message delivery API to show the results.

[0094] As a concrete example, imagine a scenario in a logistics center where workers wearing smart glasses efficiently sort a massive number of packages. The glasses' camera scans the packages, which are immediately processed by OCR, and the analyzed results are displayed on a screen, significantly improving work efficiency.

[0095] Examples of prompt messages include the following:

[0096] "Please describe in detail the process of the automated package sorting system in a logistics center using smart glasses. In particular, please explain how OCR technology and machine learning models are combined."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The terminal scans the incoming mail and uses OCR technology to extract the recipient information as digital data. The input is the physical mail, and the output is the recipient information in digital format. This is the process by which OCR technology analyzes characters and generates digital data.

[0100] Step 2:

[0101] The server receives digitized destination information and uses management tools to store the data in a database. The input is digital data generated by OCR, and the output is structured and stored database entries. The database management system stores the data field by field.

[0102] Step 3:

[0103] The server analyzes stored data using analytical tools and determines the optimal destination through a machine learning model. The input is structured data in a database, and the output is instructions or suggestions regarding specific destinations. The model makes decisions using historical data and optimization algorithms.

[0104] Step 4:

[0105] The server notifies the user of the analysis results through a reporting mechanism. This notification is made via information and communication means (e.g., email or a dedicated application). The input is the analysis results from the server, and the output is the information notification received by the user. The user then quickly takes the next action based on this information.

[0106] Step 5:

[0107] The user receives information via smart glasses or other information display devices and sorts mail based on those instructions. The input is the content of the notification from the reporting device, and the output is the properly sorted mail. The user performs physical tasks based on the information presented.

[0108] 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.

[0109] This invention provides a system for efficient sorting of mail and optimizing user operations, comprising digitization means, management means, analysis means, reporting means, and an emotion engine. The following describes how this system operates and how it is specifically implemented.

[0110] First, upon arrival of mail, the terminal scans the mail and uses OCR technology to capture recipient and sender information as digital data. This information is then transmitted to a database using a management system and stored in a structured format. This allows for easy access to the information in subsequent processes.

[0111] The server analyzes the stored data using analytical tools. These analytical tools are trained by machine learning models and determine the optimal destination based on predefined rules and historical data. This determination also accommodates specific sorting rules for each department, as needed.

[0112] The analysis results are communicated to the relevant personnel through reporting mechanisms. The server sends the results via email or other messaging services using digital communication methods. The personnel receive the notification, enabling them to carry out their work efficiently.

[0113] Furthermore, the system incorporates an emotion engine that monitors the user's emotional state in real time. This engine analyzes data acquired from cameras and microphones to generate estimates of the user's stress level and work efficiency. Based on the results of the emotion analysis, the server can adjust the workload and issue alerts as needed.

[0114] For example, when a user is busy and stressed, the emotion engine detects this. Based on the analysis, the server notifies the user and recommends temporarily reducing low-priority tasks. This approach improves the user's ability to efficiently perform necessary tasks while avoiding excessive burden.

[0115] The implementation of this system will improve the accuracy and speed of mail sorting operations, while also optimizing the user's work environment. This will lead to increased efficiency in business processes across the entire organization.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This data is then structured in the appropriate format.

[0119] Step 2:

[0120] The terminal transmits the extracted digital data to a database in real time via a management system, where the information is stored and centrally managed. This makes the data easily accessible.

[0121] Step 3:

[0122] The server accesses the database and analyzes the stored data using analytical tools. A machine learning model uses the destination information and specific sorting rules to determine the appropriate destination for the mail.

[0123] Step 4:

[0124] Based on the analysis results, the server notifies the relevant personnel of the destination information via a reporting system. This notification is sent via digital communication methods, such as email or business applications.

[0125] Step 5:

[0126] The server activates an emotion engine and analyzes data from the camera and microphone to monitor the user's emotional state. It generates estimates of stress levels and work efficiency, and adjusts them as needed.

[0127] Step 6:

[0128] Users receive notifications based on the emotion engine's analysis results and adjust task priorities if necessary. Users receive guidance to continue working efficiently.

[0129] Step 7:

[0130] The system optimizes system settings and processing flows as needed, based on feedback from both the device and the user. This feedback is also used as data to help improve performance in the future.

[0131] (Example 2)

[0132] 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."

[0133] The challenge is to provide a system that efficiently determines the destination of mail based on digitized information, provides prompt notification, and improves operational efficiency and reduces stress by analyzing the emotional state of users and appropriately adjusting their workload.

[0134] 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.

[0135] In this invention, the server includes a device that converts mail address information into readable information, a device that records the converted information, a device that calculates the destination based on the recorded information, a device that notifies the user of the calculated destination, and a device that measures the user's emotional state and adjusts the workload. This enables efficient sorting of mail and optimization of the user's work state.

[0136] "Mail" refers to physical messages and packages such as letters and parcels that are sent through postal services.

[0137] "Recipient information" refers to the information, such as the address and name, that indicates the destination to which the mail will be sent.

[0138] The term "device" refers to a concept that includes hardware and software that constitute part or all of a machine or system, and perform a specific function or role.

[0139] "Conversion" refers to the process of changing data or information from one format or state to another.

[0140] "Recording" refers to the act of saving or storing data or information so that it can be referenced later.

[0141] "Calculation" refers to the process of performing calculations and evaluations based on specific input data to derive results.

[0142] "To inform" refers to the act of transmitting information or notification to a target person or group.

[0143] "Emotional state" refers to data such as facial expressions and tone of voice that indicate a person's emotions and psychological state at a given point in time.

[0144] "Adjusting the workload" refers to the act of reviewing the requirements and quantity of tasks and work and adjusting them to an optimal state.

[0145] This invention provides a multi-functional mail processing system that enables efficient management of mail and optimizes the user's work environment. This system leverages electronic data processing capabilities to solve problems through a multifaceted approach.

[0146] When mail arrives, the terminal first scans the physical mail and then uses OCR technology to convert the recipient and sender information into digital data. This process utilizes a scanner equipped with a high-precision image sensor and commonly used OCR software (e.g., Tesseract). The converted digital data is immediately transmitted by the terminal to the server using a secure protocol and stored in a database. The data is stored using a structured data format and managed in a relational database system (e.g., MySQL).

[0147] The server uses analytical tools to analyze information stored in the database and calculate the optimal destination for mail. The analysis utilizes machine learning models (e.g., models using scikit-learn or TensorFlow) to enable decisions based on historical data and pre-defined rules. Prioritization rules for specific departments are also appropriately reflected in this process.

[0148] The calculated results are notified to the relevant personnel via the server. Email and messaging services (e.g., Slack) are used for reporting to ensure quick and reliable information dissemination. Users can receive this information and develop efficient work plans.

[0149] Furthermore, the device is equipped with an emotion engine that monitors the user's emotional state in real time via the camera and microphone. It uses facial recognition technology (e.g., OpenCV) and voice analysis tools to estimate the user's stress level and work efficiency. Based on these analysis results, the server automatically adjusts the workload and issues alerts as needed. For example, if a certain metric becomes excessive, it might issue a notification such as, "Your current workload may be excessive; we recommend postponing some tasks."

[0150] As a concrete example, by inputting the prompt, "Please explain in detail the process of OCR scanning mail and storing it in a database," into the generating AI model, a more detailed explanation can be elicited. This approach allows companies implementing the system to achieve efficient mail sorting and improve the working environment for their users.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The terminal receives the mail. The mail is scanned, and high-resolution image data is obtained. The input is the physical mail, and the output is digitized image data. The scanner scans the surface of the mail and captures the image.

[0154] Step 2:

[0155] The terminal uses OCR technology to extract text information from scanned image data. The input is the image data obtained in step 1, and the output is destination and sender information in text format. The OCR software analyzes the video data and converts it into text.

[0156] Step 3:

[0157] The terminal sends the converted text data to the server. The input is text data generated by OCR, and the output is digital data sent to the server. The terminal securely transmits information over the network.

[0158] Step 4:

[0159] The server records the data it receives in a database. The input is text data sent from the terminal, and the output is organized information stored in the database. The server saves the received data to a relational database and creates an index.

[0160] Step 5:

[0161] The server analyzes the database information and calculates the optimal destination. The input is postal data stored in the database, and the output is optimal destination information. A machine learning model analyzes the data and estimates the destination.

[0162] Step 6:

[0163] The server calculates the destination and notifies the person in charge. The input is the destination information obtained in step 5, and the output is the notification sent to the person in charge. The notification is sent quickly via email or messaging service.

[0164] Step 7:

[0165] The device monitors the user's emotional state. Input is real-time data from the camera and microphone, and output is an evaluation of the user's emotional state. An emotion engine analyzes the video and audio data to measure the stress level.

[0166] Step 8:

[0167] The server adjusts the workload based on the emotional state. The input is the emotional state data obtained in step 7, and the output is the adjusted work instructions and alerts. The server uses the analysis results to suggest an appropriate work schedule.

[0168] (Application Example 2)

[0169] 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".

[0170] While efficient and accurate data management and destination determination are required in the sorting of mail and packages, there is a challenge in providing an appropriate work environment that takes into account the emotional state and workload of the workers.

[0171] 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.

[0172] In this invention, the server includes an information digitization means for detecting mail destination information, an information management means for storing the detected information in an information database, an information analysis means for determining the destination based on the stored information, an emotion evaluation means for evaluating the user's emotional state, and a work adjustment means for adjusting the workload based on the emotion evaluation. This makes it possible to achieve efficient mail sorting and reduce the burden on workers simultaneously.

[0173] "Information digitization means" refers to a means for optically identifying the address information of mail and converting it into a digital format.

[0174] "Information management means" refers to means for storing detected information in an information database in a structured format.

[0175] "Information analysis means" refers to a means of determining the optimal destination for transport using a machine learning algorithm based on stored information.

[0176] "Communication reporting means" refers to means including a digital communication interface for notifying the person in charge of the determined destination of transport.

[0177] "Emotional evaluation means" refers to a means for analyzing the emotional state of workers in real time via cameras or voice input devices.

[0178] "Work adjustment means" refers to means for appropriately adjusting the workload of workers based on the results of emotional evaluations.

[0179] In carrying out this invention, the server uses information digitization means to detect the address information of mail. The terminal scans the mail and captures the information as digital data using optical character recognition (OCR) software. The obtained digital data is stored in a database by information management means. The stored information is stored in a structured format that allows for easy access later.

[0180] Subsequently, the server analyzes the data using an information analysis tool. This tool is trained by a machine learning model and determines the optimal destination based on historical datasets and predefined rules. This analysis can utilize libraries such as Python's scikit-learn library.

[0181] The emotion assessment system analyzes data from the camera and voice input devices to monitor the user's emotional state. If the assessment determines that the user is in a high-stress state, the server adjusts tasks and workload through the work adjustment system. For example, it may postpone lower-priority tasks or issue an alert recommending a short break.

[0182] For example, if a user is detected to be in a high-stress state while sorting a large number of packages, the system may issue a notification recommending that they take a break to reduce stress. An example of this prompt message is: "Explain the function of adjusting the workload based on user emotion analysis as a work support application for a logistics center."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The terminal scans the mail. In this step, image data of the mail is captured by an optical sensor. The input is the mail, and the output is the digital image data of that mail.

[0186] Step 2:

[0187] The server uses OCR technology to extract text information from digital image data. This is a process that converts image data into text data; the input is digital image data of a postal item, and the output is the extracted text data of the recipient information.

[0188] Step 3:

[0189] The server uses information management tools to store the detected destination information in a database. The input is the text data of the destination information, and the output is the information recorded in the database. This step generates structured data that can be immediately accessed in subsequent processes.

[0190] Step 4:

[0191] The server uses information analysis tools to determine the optimal destination. A machine learning model analyzes the destination based on historical datasets and defined rules. The input is destination information read from a database, and the output is the determination of the optimal destination.

[0192] Step 5:

[0193] The server notifies the person in charge of the determined destination. Using a communication reporting means, the input is the destination determination result, and the output is the notification to the person in charge. This notification is made via a digital communication interface.

[0194] Step 6:

[0195] The user uses a camera and audio input device to collect real-time emotional states using an emotion assessment tool. The input is camera video and audio data, and the output is the emotion assessment result.

[0196] Step 7:

[0197] The server adjusts the workload based on the emotion assessment results. If the emotion assessment system identifies a high-stress state in the user, the workload adjustment system will re-evaluate task priorities or suggest a break. The input is the assessed emotion data, and the output is an alert or suggestion regarding the adjusted workload.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention provides a system that automates the mail sorting process, enabling efficient and accurate delivery. This system is implemented by combining digitization, management, analysis, and reporting means. The program's processing is described below in natural language.

[0215] First, when mail arrives, the terminal scans it and uses OCR technology to recognize and extract the recipient information as digital data. This data is immediately stored in a database using a management system. The terminal separates information such as recipient, sender, and date into individual fields and stores it in the database as structured data, making it easier to search and analyze in subsequent processing.

[0216] Subsequently, the server analyzes the stored digital data using an analysis tool. This analysis tool utilizes machine learning models to determine the optimal destination based on vast amounts of historical data and specific operational rules. In particular, the server can automatically apply rules to sort the items into dedicated boxes, etc., if the destination department is involved in a special project.

[0217] These analysis results are automatically notified to the relevant personnel through reporting mechanisms on the server. Notifications are delivered via digital communication methods, such as email, messaging services, or dedicated internal business applications. Users can receive this information and perform quick and accurate sorting tasks.

[0218] For example, if mail is sent to a specific department and should be sorted into a project-specific box rather than a regular box, the system will automatically make this determination. This process allows users to sort mail smoothly without having to consider various special rules.

[0219] This system is expected to reduce errors and improve efficiency in mail sorting compared to traditional manual processes, contributing to increased productivity across the entire organization.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This digital data is then neatly categorized according to a pre-designed format.

[0223] Step 2:

[0224] The terminal extracts digital data and transmits it in real time to a database via a management system. By storing information such as destination, sender, and date in the database, an organized data structure is formed.

[0225] Step 3:

[0226] The server accesses the database and analyzes the stored data using analytical tools. Here, the server uses a machine learning model and, referencing past sorting patterns and departmental rules, identifies the optimal sorting destination.

[0227] Step 4:

[0228] Based on the analysis results, the server notifies relevant personnel of the determined destination via digital communication methods, such as email or messaging services. It also sends notifications to internal business applications as needed.

[0229] Step 5:

[0230] Users receive reports and place mail in designated sorting locations. Following the notification, users can work quickly and efficiently while preventing errors.

[0231] Step 6:

[0232] Users report misdeliveries and unusual cases to the feedback system. This allows the system to acquire data for further analysis and improve sorting accuracy in the future.

[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] Traditional mail sorting processes rely heavily on manual verification, limiting their efficiency and accuracy. This burden increases significantly when handling large volumes of mail. Such manual processes are prone to human error and delays, highlighting the need for efficient and accurate automated sorting systems.

[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 means for detecting mail destination information and performing character recognition; means for storing the detected information on an information recording medium; means for using a computational model to analyze the destination based on the stored information; means for notifying the determined destination via information communication; and means for the user to receive instructions and perform mail sorting. This enables automation and improved accuracy of mail sorting, as well as overall operational efficiency.

[0238] "Mail" refers to means of communication such as documents and parcels delivered through the postal system.

[0239] "Recipient information" refers to identifying information such as the address, name, and postal code that indicates the destination of the mail.

[0240] "Character recognition" is a technology that detects characters within an image and converts them into digital text.

[0241] An "information recording medium" refers to a database or storage system used to store digital data.

[0242] A "computational model" is a mathematical model constructed using algorithms for data analysis.

[0243] "Information and communication" refers to the means of transmitting information to a remote location using digital signals.

[0244] A "user" is a worker who uses the system to sort mail.

[0245] This system is designed to automate the mail sorting process. It primarily involves the collaboration of terminals and servers to perform efficient data processing.

[0246] First, when a piece of mail arrives, the terminal scans its surface with a high-resolution scanner. At this time, it uses OCR (Optical Character Recognition) software to extract the recipient and sender information written on the mail as digital data. The terminal organizes the extracted information into fields such as "Recipient," "Sender," and "Date," and saves it to an information storage medium.

[0247] Next, the server accesses the stored data and analyzes it using analytical tools. The server is equipped with machine learning algorithms that determine the optimal destination based on vast amounts of historical data and specific operational rules. This analysis enables a system where, for example, mail addressed to departments related to a specific project is automatically sorted into a dedicated box.

[0248] The analysis results are quickly notified to the user via a reporting mechanism on the server. This notification may be sent via email, a messaging service, or a proprietary internal business application. Users can then receive these notifications and properly sort their mail.

[0249] For example, if the system automatically sorts mail addressed to a specific destination into a project-specific box, users no longer need to manually check the rules, improving work efficiency. Furthermore, by utilizing a generative AI model, it is possible to give instructions to the system using prompts such as, "Tell me the sorting rules for mail addressed to a specific project."

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] When the terminal receives mail, it scans it using a high-resolution scanner. The input is the mail itself, and the output is a high-resolution scanned image of that mail. The terminal inputs this image into OCR software to extract recipient and sender information as digital text. Specifically, it analyzes each character in the image and classifies them into fields such as "address," "name," and "zip code" as text data.

[0253] Step 2:

[0254] The terminal uses the extracted digital text to save the data to an information storage medium. The input here is the structured text data from the previous step, and the output is the information stored in the database. The terminal organizes the data into fields according to attributes such as "recipient," "sender," and "date," and adds it to the database using SQL statements. Specifically, it generates and executes SQL commands to efficiently save the information.

[0255] Step 3:

[0256] The server retrieves data stored on an information recording medium and performs analysis using analytical tools. The input for this step is stored digital data, and the output is analyzed sorting instruction information. The server utilizes machine learning models to compare and classify the input data and determine the optimal destination. Specifically, it refers to past delivery history and selects the optimal transportation route in accordance with current laws and company rules.

[0257] Step 4:

[0258] The analysis results obtained on the server are notified to the user through a reporting mechanism. The input here is the analysis results from the server, and the output is a notification message sent to the user. Specifically, this involves quickly informing the user of mail delivery addresses and sorting instructions using email, messaging apps, or internal company applications.

[0259] Step 5:

[0260] The user receives a notification and performs the physical sorting of mail based on the instructions. The input for this step is the notified data, and the output is the properly sorted mail. Specifically, the user receives the mail in front of them and sorts it into the designated project box or regular delivery box.

[0261] (Application Example 1)

[0262] 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."

[0263] Modern logistics centers handle the sorting of vast amounts of mail and packages, but they still rely on manual labor and partial automation, resulting in limitations in efficiency and accuracy. This is especially true when dealing with specialized sorting rules, which require significant time and human resources for decision-making. Furthermore, a lack of support for employees to quickly acquire information and take action is another challenge.

[0264] 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.

[0265] In this invention, the server includes an electronic means for detecting the destination information of mail, a management means for storing the detected information in a database, an analysis means for determining the destination based on the stored information, a reporting means for notifying the determined destination, and an information presentation means for use by workers. This enables efficient and accurate sorting of mail and quick information verification by workers.

[0266] "Digitization methods" refer to technologies that convert and recognize mailing address information as digital data, and typically involve using scanning or optical character recognition.

[0267] "Management means" refers to a function that structures digitized information, stores it in a database, and facilitates subsequent analysis and retrieval.

[0268] "Analysis means" refers to the process of determining the optimal destination based on stored data, and involves performing analysis using past data and rules with a learning algorithm.

[0269] The "reporting means" is a function for notifying personnel of the destination determined based on the analysis results, and this is done via information and communication means, such as email or messaging services.

[0270] "Information presentation means" refers to devices and technologies that display information in a way that employees can quickly understand and act upon, such as smart glasses or displays.

[0271] The system used to implement this application primarily aims to streamline the sorting process of mail. The specific procedures and necessary technologies for implementation are described below.

[0272] The server first scans incoming mail using a terminal, and then uses OCR technology to recognize and extract the recipient information as digital data. Examples of OCR technologies used include Google Cloud Vision API. The digitized information is then stored in a database through a management system. For this process, a database management system such as MySQL can be used.

[0273] The server then analyzes the stored digital data using analytical tools. This analysis utilizes machine learning libraries, such as TensorFlow, to determine the optimal destination based on historical data and operational rules. If the destination department meets specific criteria, the server can automatically apply sorting rules to place the items in a dedicated box or similar container.

[0274] Notifications are made using information and communication means, informing workers of the destination determined by the reporting means. Users can check this information in real time through information display means such as smart glasses. The smart glasses' display uses, for example, Twilio's message delivery API to show the results.

[0275] As a concrete example, imagine a scenario in a logistics center where workers wearing smart glasses efficiently sort a massive number of packages. The glasses' camera scans the packages, which are immediately processed by OCR, and the analyzed results are displayed on a screen, significantly improving work efficiency.

[0276] Examples of prompt messages include the following:

[0277] "Please describe in detail the process of the automated package sorting system in a logistics center using smart glasses. In particular, please explain how OCR technology and machine learning models are combined."

[0278] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0279] Step 1:

[0280] The terminal scans the received postal items and extracts the destination information as digital data using OCR technology. The input is the physical postal item, and the output is the destination information in digital form. This is the process where OCR technology analyzes the characters and generates digital data.

[0281] Step 2:

[0282] The server receives the digitized destination information and stores the data in a database using management means. The input is the digital data generated by OCR, and the output is the structured and stored database entry. The database management system stores the data by field.

[0283] Step 3:

[0284] The server analyzes the stored data using analysis means and determines the optimal delivery destination through a machine learning model. The input is the structured data in the database, and the output is instructions or suggestions regarding a specific delivery destination. The model makes a decision using past data and optimization algorithms.

[0285] Step 4:

[0286] The server notifies the user of the analysis results using reporting means. This notification is carried out via information communication means (such as email or a dedicated application). The input is the analysis result by the server, and the output is the information notification received by the user. The user takes the next action promptly based on this information.

[0287] Step 5:

[0288] The user receives the notified information using smart glasses or other information presentation means and sorts the postal items based on the instructions. The input is the content of the notification by the reporting means, and the output is the properly sorted postal items. The user performs a physical task based on the presented information.

[0289] 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.

[0290] This invention provides a system for efficient sorting of mail and optimizing user operations, comprising digitization means, management means, analysis means, reporting means, and an emotion engine. The following describes how this system operates and how it is specifically implemented.

[0291] First, upon arrival of mail, the terminal scans the mail and uses OCR technology to capture recipient and sender information as digital data. This information is then transmitted to a database using a management system and stored in a structured format. This allows for easy access to the information in subsequent processes.

[0292] The server analyzes the stored data using analytical tools. These analytical tools are trained by machine learning models and determine the optimal destination based on predefined rules and historical data. This determination also accommodates specific sorting rules for each department, as needed.

[0293] The analysis results are communicated to the relevant personnel through reporting mechanisms. The server sends the results via email or other messaging services using digital communication methods. The personnel receive the notification, enabling them to carry out their work efficiently.

[0294] Furthermore, the system incorporates an emotion engine that monitors the user's emotional state in real time. This engine analyzes data acquired from cameras and microphones to generate estimates of the user's stress level and work efficiency. Based on the results of the emotion analysis, the server can adjust the workload and issue alerts as needed.

[0295] For example, when a user is busy and stressed, the emotion engine detects this. Based on the analysis, the server notifies the user and recommends temporarily reducing low-priority tasks. This approach improves the user's ability to efficiently perform necessary tasks while avoiding excessive burden.

[0296] The implementation of this system will improve the accuracy and speed of mail sorting operations, while also optimizing the user's work environment. This will lead to increased efficiency in business processes across the entire organization.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This data is then structured in the appropriate format.

[0300] Step 2:

[0301] The terminal transmits the extracted digital data to a database in real time via a management system, where the information is stored and centrally managed. This makes the data easily accessible.

[0302] Step 3:

[0303] The server accesses the database and analyzes the stored data using analytical tools. A machine learning model uses the destination information and specific sorting rules to determine the appropriate destination for the mail.

[0304] Step 4:

[0305] Based on the analysis results, the server notifies the relevant personnel of the destination information via a reporting system. This notification is sent via digital communication methods, such as email or business applications.

[0306] Step 5:

[0307] The server starts the emotion engine, analyzes the data from the camera and microphone, and monitors the user's emotional state. It generates estimated values of stress levels and work efficiency and makes adjustments as necessary.

[0308] Step 6:

[0309] The user receives a notification based on the analysis results of the emotion engine and adjusts the task priority if necessary. The user receives guidance for continuing work efficiently.

[0310] Step 7:

[0311] The terminal and the user provide feedback and optimize the system settings and processing flow if necessary. The feedback is also used as data for future performance improvement.

[0312] (Example 2)

[0313] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0314] It is an issue to provide a system that efficiently determines the destination of mail items based on digitized information, quickly notifies, analyzes the user's emotional state, appropriately adjusts the work load, improves the efficiency of business operations, and reduces stress.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0316] In this invention, the server includes a device that converts mail address information into readable information, a device that records the converted information, a device that calculates the destination based on the recorded information, a device that notifies the user of the calculated destination, and a device that measures the user's emotional state and adjusts the workload. This enables efficient sorting of mail and optimization of the user's work state.

[0317] "Mail" refers to physical messages and packages such as letters and parcels that are sent through postal services.

[0318] "Recipient information" refers to the information, such as the address and name, that indicates the destination to which the mail will be sent.

[0319] The term "device" refers to a concept that includes hardware and software that constitute part or all of a machine or system, and perform a specific function or role.

[0320] "Conversion" refers to the process of changing data or information from one format or state to another.

[0321] "Recording" refers to the act of saving or storing data or information so that it can be referenced later.

[0322] "Calculation" refers to the process of performing calculations and evaluations based on specific input data to derive results.

[0323] "To inform" refers to the act of transmitting information or notification to a target person or group.

[0324] "Emotional state" refers to data such as facial expressions and tone of voice that indicate a person's emotions and psychological state at a given point in time.

[0325] "Adjusting the workload" refers to the act of reviewing the requirements and quantity of tasks and work and adjusting them to an optimal state.

[0326] This invention provides a multi-functional mail processing system that enables efficient management of mail and optimizes the user's work environment. This system leverages electronic data processing capabilities to solve problems through a multifaceted approach.

[0327] When mail arrives, the terminal first scans the physical mail and then uses OCR technology to convert the recipient and sender information into digital data. This process utilizes a scanner equipped with a high-precision image sensor and commonly used OCR software (e.g., Tesseract). The converted digital data is immediately transmitted by the terminal to the server using a secure protocol and stored in a database. The data is stored using a structured data format and managed in a relational database system (e.g., MySQL).

[0328] The server uses analytical tools to analyze information stored in the database and calculate the optimal destination for mail. The analysis utilizes machine learning models (e.g., models using scikit-learn or TensorFlow) to enable decisions based on historical data and pre-defined rules. Prioritization rules for specific departments are also appropriately reflected in this process.

[0329] The calculated results are notified to the relevant personnel via the server. Email and messaging services (e.g., Slack) are used for reporting to ensure quick and reliable information dissemination. Users can receive this information and develop efficient work plans.

[0330] Furthermore, the device is equipped with an emotion engine that monitors the user's emotional state in real time via the camera and microphone. It uses facial recognition technology (e.g., OpenCV) and voice analysis tools to estimate the user's stress level and work efficiency. Based on these analysis results, the server automatically adjusts the workload and issues alerts as needed. For example, if a certain metric becomes excessive, it might issue a notification such as, "Your current workload may be excessive; we recommend postponing some tasks."

[0331] As a concrete example, by inputting the prompt, "Please explain in detail the process of OCR scanning mail and storing it in a database," into the generating AI model, a more detailed explanation can be elicited. This approach allows companies implementing the system to achieve efficient mail sorting and improve the working environment for their users.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The terminal receives the mail. The mail is scanned, and high-resolution image data is obtained. The input is the physical mail, and the output is digitized image data. The scanner scans the surface of the mail and captures the image.

[0335] Step 2:

[0336] The terminal uses OCR technology to extract text information from scanned image data. The input is the image data obtained in step 1, and the output is destination and sender information in text format. The OCR software analyzes the video data and converts it into text.

[0337] Step 3:

[0338] The terminal sends the converted text data to the server. The input is text data generated by OCR, and the output is digital data sent to the server. The terminal securely transmits information over the network.

[0339] Step 4:

[0340] The server records the data it receives in a database. The input is text data sent from the terminal, and the output is organized information stored in the database. The server saves the received data to a relational database and creates an index.

[0341] Step 5:

[0342] The server analyzes the database information and calculates the optimal destination. The input is postal data stored in the database, and the output is optimal destination information. A machine learning model analyzes the data and estimates the destination.

[0343] Step 6:

[0344] The server calculates the destination and notifies the person in charge. The input is the destination information obtained in step 5, and the output is the notification sent to the person in charge. The notification is sent quickly via email or messaging service.

[0345] Step 7:

[0346] The device monitors the user's emotional state. Input is real-time data from the camera and microphone, and output is an evaluation of the user's emotional state. An emotion engine analyzes the video and audio data to measure the stress level.

[0347] Step 8:

[0348] The server adjusts the workload based on the emotional state. The input is the emotional state data obtained in step 7, and the output is the adjusted work instructions and alerts. The server uses the analysis results to suggest an appropriate work schedule.

[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] While efficient and accurate data management and destination determination are required in the sorting of mail and packages, there is a challenge in providing an appropriate work environment that takes into account the emotional state and workload of the workers.

[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 information digitization means for detecting mail destination information, an information management means for storing the detected information in an information database, an information analysis means for determining the destination based on the stored information, an emotion evaluation means for evaluating the user's emotional state, and a work adjustment means for adjusting the workload based on the emotion evaluation. This makes it possible to achieve efficient mail sorting and reduce the burden on workers simultaneously.

[0354] "Information digitization means" refers to a means for optically identifying the address information of mail and converting it into a digital format.

[0355] "Information management means" refers to means for storing detected information in an information database in a structured format.

[0356] "Information analysis means" refers to a means of determining the optimal destination for transport using a machine learning algorithm based on stored information.

[0357] "Communication reporting means" refers to means including a digital communication interface for notifying the person in charge of the determined destination of transport.

[0358] "Emotional evaluation means" refers to a means for analyzing the emotional state of workers in real time via cameras or voice input devices.

[0359] "Work adjustment means" refers to means for appropriately adjusting the workload of workers based on the results of emotional evaluations.

[0360] In carrying out this invention, the server uses information digitization means to detect the address information of mail. The terminal scans the mail and captures the information as digital data using optical character recognition (OCR) software. The obtained digital data is stored in a database by information management means. The stored information is stored in a structured format that allows for easy access later.

[0361] Subsequently, the server analyzes the data using an information analysis tool. This tool is trained by a machine learning model and determines the optimal destination based on historical datasets and predefined rules. This analysis can utilize libraries such as Python's scikit-learn library.

[0362] The emotion assessment system analyzes data from the camera and voice input devices to monitor the user's emotional state. If the assessment determines that the user is in a high-stress state, the server adjusts tasks and workload through the work adjustment system. For example, it may postpone lower-priority tasks or issue an alert recommending a short break.

[0363] For example, if a user is detected to be in a high-stress state while sorting a large number of packages, the system may issue a notification recommending that they take a break to reduce stress. An example of this prompt message is: "Explain the function of adjusting the workload based on user emotion analysis as a work support application for a logistics center."

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The terminal scans the mail. In this step, image data of the mail is captured by an optical sensor. The input is the mail, and the output is the digital image data of that mail.

[0367] Step 2:

[0368] The server uses OCR technology to extract text information from digital image data. This is a process that converts image data into text data; the input is digital image data of a postal item, and the output is the extracted text data of the recipient information.

[0369] Step 3:

[0370] The server uses information management tools to store the detected destination information in a database. The input is the text data of the destination information, and the output is the information recorded in the database. This step generates structured data that can be immediately accessed in subsequent processes.

[0371] Step 4:

[0372] The server uses information analysis tools to determine the optimal destination. A machine learning model analyzes the destination based on historical datasets and defined rules. The input is destination information read from a database, and the output is the determination of the optimal destination.

[0373] Step 5:

[0374] The server notifies the person in charge of the determined destination. Using a communication reporting means, the input is the destination determination result, and the output is the notification to the person in charge. This notification is made via a digital communication interface.

[0375] Step 6:

[0376] The user uses a camera and audio input device to collect real-time emotional states using an emotion assessment tool. The input is camera video and audio data, and the output is the emotion assessment result.

[0377] Step 7:

[0378] The server adjusts the workload based on the emotion assessment results. If the emotion assessment system identifies a high-stress state in the user, the workload adjustment system will re-evaluate task priorities or suggest a break. The input is the assessed emotion data, and the output is an alert or suggestion regarding the adjusted workload.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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".

[0395] This invention provides a system that automates the mail sorting process, enabling efficient and accurate delivery. This system is implemented by combining digitization, management, analysis, and reporting means. The program's processing is described below in natural language.

[0396] First, when mail arrives, the terminal scans it and uses OCR technology to recognize and extract the recipient information as digital data. This data is immediately stored in a database using a management system. The terminal separates information such as recipient, sender, and date into individual fields and stores it in the database as structured data, making it easier to search and analyze in subsequent processing.

[0397] Subsequently, the server analyzes the stored digital data using an analysis tool. This analysis tool utilizes machine learning models to determine the optimal destination based on vast amounts of historical data and specific operational rules. In particular, the server can automatically apply rules to sort the items into dedicated boxes, etc., if the destination department is involved in a special project.

[0398] These analysis results are automatically notified to the relevant personnel through reporting mechanisms on the server. Notifications are delivered via digital communication methods, such as email, messaging services, or dedicated internal business applications. Users can receive this information and perform quick and accurate sorting tasks.

[0399] For example, if mail is sent to a specific department and should be sorted into a project-specific box rather than a regular box, the system will automatically make this determination. This process allows users to sort mail smoothly without having to consider various special rules.

[0400] This system is expected to reduce errors and improve efficiency in mail sorting compared to traditional manual processes, contributing to increased productivity across the entire organization.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This digital data is then neatly categorized according to a pre-designed format.

[0404] Step 2:

[0405] The terminal extracts digital data and transmits it in real time to a database via a management system. By storing information such as destination, sender, and date in the database, an organized data structure is formed.

[0406] Step 3:

[0407] The server accesses the database and analyzes the stored data using analytical tools. Here, the server uses a machine learning model and, referencing past sorting patterns and departmental rules, identifies the optimal sorting destination.

[0408] Step 4:

[0409] Based on the analysis results, the server notifies relevant personnel of the determined destination via digital communication methods, such as email or messaging services. It also sends notifications to internal business applications as needed.

[0410] Step 5:

[0411] Users receive reports and place mail in designated sorting locations. Following the notification, users can work quickly and efficiently while preventing errors.

[0412] Step 6:

[0413] Users report misdeliveries and unusual cases to the feedback system. This allows the system to acquire data for further analysis and improve sorting accuracy in the future.

[0414] (Example 1)

[0415] 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."

[0416] Traditional mail sorting processes rely heavily on manual verification, limiting their efficiency and accuracy. This burden increases significantly when handling large volumes of mail. Such manual processes are prone to human error and delays, highlighting the need for efficient and accurate automated sorting systems.

[0417] 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.

[0418] In this invention, the server includes means for detecting mail destination information and performing character recognition; means for storing the detected information on an information recording medium; means for using a computational model to analyze the destination based on the stored information; means for notifying the determined destination via information communication; and means for the user to receive instructions and perform mail sorting. This enables automation and improved accuracy of mail sorting, as well as overall operational efficiency.

[0419] "Mail" refers to means of communication such as documents and parcels delivered through the postal system.

[0420] "Recipient information" refers to identifying information such as the address, name, and postal code that indicates the destination of the mail.

[0421] "Character recognition" is a technology that detects characters within an image and converts them into digital text.

[0422] An "information recording medium" refers to a database or storage system used to store digital data.

[0423] A "computational model" is a mathematical model constructed using algorithms for data analysis.

[0424] "Information and communication" refers to the means of transmitting information to a remote location using digital signals.

[0425] A "user" is a worker who uses the system to sort mail.

[0426] This system is designed to automate the mail sorting process. It primarily involves the collaboration of terminals and servers to perform efficient data processing.

[0427] First, when a piece of mail arrives, the terminal scans its surface with a high-resolution scanner. At this time, it uses OCR (Optical Character Recognition) software to extract the recipient and sender information written on the mail as digital data. The terminal organizes the extracted information into fields such as "Recipient," "Sender," and "Date," and saves it to an information storage medium.

[0428] Next, the server accesses the stored data and analyzes it using analytical tools. The server is equipped with machine learning algorithms that determine the optimal destination based on vast amounts of historical data and specific operational rules. This analysis enables a system where, for example, mail addressed to departments related to a specific project is automatically sorted into a dedicated box.

[0429] The analysis results are quickly notified to the user via a reporting mechanism on the server. This notification may be sent via email, a messaging service, or a proprietary internal business application. Users can then receive these notifications and properly sort their mail.

[0430] For example, if the system automatically sorts mail addressed to a specific destination into a project-specific box, users no longer need to manually check the rules, improving work efficiency. Furthermore, by utilizing a generative AI model, it is possible to give instructions to the system using prompts such as, "Tell me the sorting rules for mail addressed to a specific project."

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] When the terminal receives mail, it scans it using a high-resolution scanner. The input is the mail itself, and the output is a high-resolution scanned image of that mail. The terminal inputs this image into OCR software to extract recipient and sender information as digital text. Specifically, it analyzes each character in the image and classifies them into fields such as "address," "name," and "zip code" as text data.

[0434] Step 2:

[0435] The terminal uses the extracted digital text to save the data to an information storage medium. The input here is the structured text data from the previous step, and the output is the information stored in the database. The terminal organizes the data into fields according to attributes such as "recipient," "sender," and "date," and adds it to the database using SQL statements. Specifically, it generates and executes SQL commands to efficiently save the information.

[0436] Step 3:

[0437] The server retrieves data stored on an information recording medium and performs analysis using analytical tools. The input for this step is stored digital data, and the output is analyzed sorting instruction information. The server utilizes machine learning models to compare and classify the input data and determine the optimal destination. Specifically, it refers to past delivery history and selects the optimal transportation route in accordance with current laws and company rules.

[0438] Step 4:

[0439] The analysis results obtained on the server are notified to the user through a reporting mechanism. The input here is the analysis results from the server, and the output is a notification message sent to the user. Specifically, this involves quickly informing the user of mail delivery addresses and sorting instructions using email, messaging apps, or internal company applications.

[0440] Step 5:

[0441] The user receives a notification and performs the physical sorting of mail based on the instructions. The input for this step is the notified data, and the output is the properly sorted mail. Specifically, the user receives the mail in front of them and sorts it into the designated project box or regular delivery box.

[0442] (Application Example 1)

[0443] 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."

[0444] Modern logistics centers handle the sorting of vast amounts of mail and packages, but they still rely on manual labor and partial automation, resulting in limitations in efficiency and accuracy. This is especially true when dealing with specialized sorting rules, which require significant time and human resources for decision-making. Furthermore, a lack of support for employees to quickly acquire information and take action is another challenge.

[0445] 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.

[0446] In this invention, the server includes an electronic means for detecting the destination information of mail, a management means for storing the detected information in a database, an analysis means for determining the destination based on the stored information, a reporting means for notifying the determined destination, and an information presentation means for use by workers. This enables efficient and accurate sorting of mail and quick information verification by workers.

[0447] "Digitization methods" refer to technologies that convert and recognize mailing address information as digital data, and typically involve using scanning or optical character recognition.

[0448] "Management means" refers to a function that structures digitized information, stores it in a database, and facilitates subsequent analysis and retrieval.

[0449] "Analysis means" refers to the process of determining the optimal destination based on stored data, and involves performing analysis using past data and rules with a learning algorithm.

[0450] The "reporting means" is a function for notifying personnel of the destination determined based on the analysis results, and this is done via information and communication means, such as email or messaging services.

[0451] "Information presentation means" refers to devices and technologies that display information in a way that employees can quickly understand and act upon, such as smart glasses or displays.

[0452] The system used to implement this application primarily aims to streamline the sorting process of mail. The specific procedures and necessary technologies for implementation are described below.

[0453] The server first scans incoming mail using a terminal, and then uses OCR technology to recognize and extract the recipient information as digital data. Examples of OCR technologies used include Google Cloud Vision API. The digitized information is then stored in a database through a management system. For this process, a database management system such as MySQL can be used.

[0454] The server then analyzes the stored digital data using analytical tools. This analysis utilizes machine learning libraries, such as TensorFlow, to determine the optimal destination based on historical data and operational rules. If the destination department meets specific criteria, the server can automatically apply sorting rules to place the items in a dedicated box or similar container.

[0455] Notifications are made using information and communication means, informing workers of the destination determined by the reporting means. Users can check this information in real time through information display means such as smart glasses. The smart glasses' display uses, for example, Twilio's message delivery API to show the results.

[0456] As a concrete example, imagine a scenario in a logistics center where workers wearing smart glasses efficiently sort a massive number of packages. The glasses' camera scans the packages, which are immediately processed by OCR, and the analyzed results are displayed on a screen, significantly improving work efficiency.

[0457] Examples of prompt messages include the following:

[0458] "Please describe in detail the process of the automated package sorting system in a logistics center using smart glasses. In particular, please explain how OCR technology and machine learning models are combined."

[0459] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0460] Step 1:

[0461] The terminal scans the incoming mail and uses OCR technology to extract the recipient information as digital data. The input is the physical mail, and the output is the recipient information in digital format. This is the process by which OCR technology analyzes characters and generates digital data.

[0462] Step 2:

[0463] The server receives digitized destination information and uses management tools to store the data in a database. The input is digital data generated by OCR, and the output is structured and stored database entries. The database management system stores the data field by field.

[0464] Step 3:

[0465] The server analyzes stored data using analytical tools and determines the optimal destination through a machine learning model. The input is structured data in a database, and the output is instructions or suggestions regarding specific destinations. The model makes decisions using historical data and optimization algorithms.

[0466] Step 4:

[0467] The server notifies the user of the analysis results through a reporting mechanism. This notification is made via information and communication means (e.g., email or a dedicated application). The input is the analysis results from the server, and the output is the information notification received by the user. The user then quickly takes the next action based on this information.

[0468] Step 5:

[0469] The user receives information via smart glasses or other information display devices and sorts mail based on those instructions. The input is the content of the notification from the reporting device, and the output is the properly sorted mail. The user performs physical tasks based on the information presented.

[0470] 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.

[0471] This invention provides a system for efficient sorting of mail and optimizing user operations, comprising digitization means, management means, analysis means, reporting means, and an emotion engine. The following describes how this system operates and how it is specifically implemented.

[0472] First, upon arrival of mail, the terminal scans the mail and uses OCR technology to capture recipient and sender information as digital data. This information is then transmitted to a database using a management system and stored in a structured format. This allows for easy access to the information in subsequent processes.

[0473] The server analyzes the stored data using analytical tools. These analytical tools are trained by machine learning models and determine the optimal destination based on predefined rules and historical data. This determination also accommodates specific sorting rules for each department, as needed.

[0474] The analysis results are communicated to the relevant personnel through reporting mechanisms. The server sends the results via email or other messaging services using digital communication methods. The personnel receive the notification, enabling them to carry out their work efficiently.

[0475] Furthermore, the system incorporates an emotion engine that monitors the user's emotional state in real time. This engine analyzes data acquired from cameras and microphones to generate estimates of the user's stress level and work efficiency. Based on the results of the emotion analysis, the server can adjust the workload and issue alerts as needed.

[0476] For example, when a user is busy and stressed, the emotion engine detects this. Based on the analysis, the server notifies the user and recommends temporarily reducing low-priority tasks. This approach improves the user's ability to efficiently perform necessary tasks while avoiding excessive burden.

[0477] The implementation of this system will improve the accuracy and speed of mail sorting operations, while also optimizing the user's work environment. This will lead to increased efficiency in business processes across the entire organization.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This data is then structured in the appropriate format.

[0481] Step 2:

[0482] The terminal transmits the extracted digital data to a database in real time via a management system, where the information is stored and centrally managed. This makes the data easily accessible.

[0483] Step 3:

[0484] The server accesses the database and analyzes the stored data using analytical tools. A machine learning model uses the destination information and specific sorting rules to determine the appropriate destination for the mail.

[0485] Step 4:

[0486] Based on the analysis results, the server notifies the relevant personnel of the destination information via a reporting system. This notification is sent via digital communication methods, such as email or business applications.

[0487] Step 5:

[0488] The server activates an emotion engine and analyzes data from the camera and microphone to monitor the user's emotional state. It generates estimates of stress levels and work efficiency, and adjusts them as needed.

[0489] Step 6:

[0490] Users receive notifications based on the emotion engine's analysis results and adjust task priorities if necessary. Users receive guidance to continue working efficiently.

[0491] Step 7:

[0492] The system optimizes system settings and processing flows as needed, based on feedback from both the device and the user. This feedback is also used as data to help improve performance in the future.

[0493] (Example 2)

[0494] 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."

[0495] The challenge is to provide a system that efficiently determines the destination of mail based on digitized information, provides prompt notification, and improves operational efficiency and reduces stress by analyzing the emotional state of users and appropriately adjusting their workload.

[0496] 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.

[0497] In this invention, the server includes a device that converts mail address information into readable information, a device that records the converted information, a device that calculates the destination based on the recorded information, a device that notifies the user of the calculated destination, and a device that measures the user's emotional state and adjusts the workload. This enables efficient sorting of mail and optimization of the user's work state.

[0498] "Mail" refers to physical messages and packages such as letters and parcels that are sent through postal services.

[0499] "Recipient information" refers to the information, such as the address and name, that indicates the destination to which the mail will be sent.

[0500] The term "device" refers to a concept that includes hardware and software that constitute part or all of a machine or system, and perform a specific function or role.

[0501] "Conversion" refers to the process of changing data or information from one format or state to another.

[0502] "Recording" refers to the act of saving or storing data or information so that it can be referenced later.

[0503] "Calculation" refers to the process of performing calculations and evaluations based on specific input data to derive results.

[0504] "To inform" refers to the act of transmitting information or notification to a target person or group.

[0505] "Emotional state" refers to data such as facial expressions and tone of voice that indicate a person's emotions and psychological state at a given point in time.

[0506] "Adjusting the workload" refers to the act of reviewing the requirements and quantity of tasks and work and adjusting them to an optimal state.

[0507] This invention provides a multi-functional mail processing system that enables efficient management of mail and optimizes the user's work environment. This system leverages electronic data processing capabilities to solve problems through a multifaceted approach.

[0508] When mail arrives, the terminal first scans the physical mail and then uses OCR technology to convert the recipient and sender information into digital data. This process utilizes a scanner equipped with a high-precision image sensor and commonly used OCR software (e.g., Tesseract). The converted digital data is immediately transmitted by the terminal to the server using a secure protocol and stored in a database. The data is stored using a structured data format and managed in a relational database system (e.g., MySQL).

[0509] The server uses analytical tools to analyze information stored in the database and calculate the optimal destination for mail. The analysis utilizes machine learning models (e.g., models using scikit-learn or TensorFlow) to enable decisions based on historical data and pre-defined rules. Prioritization rules for specific departments are also appropriately reflected in this process.

[0510] The calculated results are notified to the relevant personnel via the server. Email and messaging services (e.g., Slack) are used for reporting to ensure quick and reliable information dissemination. Users can receive this information and develop efficient work plans.

[0511] Furthermore, the device is equipped with an emotion engine that monitors the user's emotional state in real time via the camera and microphone. It uses facial recognition technology (e.g., OpenCV) and voice analysis tools to estimate the user's stress level and work efficiency. Based on these analysis results, the server automatically adjusts the workload and issues alerts as needed. For example, if a certain metric becomes excessive, it might issue a notification such as, "Your current workload may be excessive; we recommend postponing some tasks."

[0512] As a concrete example, by inputting the prompt, "Please explain in detail the process of OCR scanning mail and storing it in a database," into the generating AI model, a more detailed explanation can be elicited. This approach allows companies implementing the system to achieve efficient mail sorting and improve the working environment for their users.

[0513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0514] Step 1:

[0515] The terminal receives the mail. The mail is scanned, and high-resolution image data is obtained. The input is the physical mail, and the output is digitized image data. The scanner scans the surface of the mail and captures the image.

[0516] Step 2:

[0517] The terminal uses OCR technology to extract text information from scanned image data. The input is the image data obtained in step 1, and the output is destination and sender information in text format. The OCR software analyzes the video data and converts it into text.

[0518] Step 3:

[0519] The terminal sends the converted text data to the server. The input is text data generated by OCR, and the output is digital data sent to the server. The terminal securely transmits information over the network.

[0520] Step 4:

[0521] The server records the data it receives in a database. The input is text data sent from the terminal, and the output is organized information stored in the database. The server saves the received data to a relational database and creates an index.

[0522] Step 5:

[0523] The server analyzes the database information and calculates the optimal destination. The input is postal data stored in the database, and the output is optimal destination information. A machine learning model analyzes the data and estimates the destination.

[0524] Step 6:

[0525] The server calculates the destination and notifies the person in charge. The input is the destination information obtained in step 5, and the output is the notification sent to the person in charge. The notification is sent quickly via email or messaging service.

[0526] Step 7:

[0527] The device monitors the user's emotional state. Input is real-time data from the camera and microphone, and output is an evaluation of the user's emotional state. An emotion engine analyzes the video and audio data to measure the stress level.

[0528] Step 8:

[0529] The server adjusts the workload based on the emotional state. The input is the emotional state data obtained in step 7, and the output is the adjusted work instructions and alerts. The server uses the analysis results to suggest an appropriate work schedule.

[0530] (Application Example 2)

[0531] 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."

[0532] While efficient and accurate data management and destination determination are required in the sorting of mail and packages, there is a challenge in providing an appropriate work environment that takes into account the emotional state and workload of the workers.

[0533] 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.

[0534] In this invention, the server includes an information digitization means for detecting mail destination information, an information management means for storing the detected information in an information database, an information analysis means for determining the destination based on the stored information, an emotion evaluation means for evaluating the user's emotional state, and a work adjustment means for adjusting the workload based on the emotion evaluation. This makes it possible to achieve efficient mail sorting and reduce the burden on workers simultaneously.

[0535] "Information digitization means" refers to a means for optically identifying the address information of mail and converting it into a digital format.

[0536] "Information management means" refers to means for storing detected information in an information database in a structured format.

[0537] "Information analysis means" refers to a means of determining the optimal destination for transport using a machine learning algorithm based on stored information.

[0538] "Communication reporting means" refers to means including a digital communication interface for notifying the person in charge of the determined destination of transport.

[0539] "Emotional evaluation means" refers to a means for analyzing the emotional state of workers in real time via cameras or voice input devices.

[0540] "Work adjustment means" refers to means for appropriately adjusting the workload of workers based on the results of emotional evaluations.

[0541] In carrying out this invention, the server uses information digitization means to detect the address information of mail. The terminal scans the mail and captures the information as digital data using optical character recognition (OCR) software. The obtained digital data is stored in a database by information management means. The stored information is stored in a structured format that allows for easy access later.

[0542] Subsequently, the server analyzes the data using an information analysis tool. This tool is trained by a machine learning model and determines the optimal destination based on historical datasets and predefined rules. This analysis can utilize libraries such as Python's scikit-learn library.

[0543] The emotion assessment system analyzes data from the camera and voice input devices to monitor the user's emotional state. If the assessment determines that the user is in a high-stress state, the server adjusts tasks and workload through the work adjustment system. For example, it may postpone lower-priority tasks or issue an alert recommending a short break.

[0544] For example, if a user is detected to be in a high-stress state while sorting a large number of packages, the system may issue a notification recommending that they take a break to reduce stress. An example of this prompt message is: "Explain the function of adjusting the workload based on user emotion analysis as a work support application for a logistics center."

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] The terminal scans the mail. In this step, image data of the mail is captured by an optical sensor. The input is the mail, and the output is the digital image data of that mail.

[0548] Step 2:

[0549] The server uses OCR technology to extract text information from digital image data. This is a process that converts image data into text data; the input is digital image data of a postal item, and the output is the extracted text data of the recipient information.

[0550] Step 3:

[0551] The server uses information management tools to store the detected destination information in a database. The input is the text data of the destination information, and the output is the information recorded in the database. This step generates structured data that can be immediately accessed in subsequent processes.

[0552] Step 4:

[0553] The server uses information analysis tools to determine the optimal destination. A machine learning model analyzes the destination based on historical datasets and defined rules. The input is destination information read from a database, and the output is the determination of the optimal destination.

[0554] Step 5:

[0555] The server notifies the person in charge of the determined destination. Using a communication reporting means, the input is the destination determination result, and the output is the notification to the person in charge. This notification is made via a digital communication interface.

[0556] Step 6:

[0557] The user uses a camera and audio input device to collect real-time emotional states using an emotion assessment tool. The input is camera video and audio data, and the output is the emotion assessment result.

[0558] Step 7:

[0559] The server adjusts the workload based on the emotion assessment results. If the emotion assessment system identifies a high-stress state in the user, the workload adjustment system will re-evaluate task priorities or suggest a break. The input is the assessed emotion data, and the output is an alert or suggestion regarding the adjusted workload.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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".

[0577] This invention provides a system that automates the mail sorting process, enabling efficient and accurate delivery. This system is implemented by combining digitization, management, analysis, and reporting means. The program's processing is described below in natural language.

[0578] First, when mail arrives, the terminal scans it and uses OCR technology to recognize and extract the recipient information as digital data. This data is immediately stored in a database using a management system. The terminal separates information such as recipient, sender, and date into individual fields and stores it in the database as structured data, making it easier to search and analyze in subsequent processing.

[0579] Subsequently, the server analyzes the stored digital data using an analysis tool. This analysis tool utilizes machine learning models to determine the optimal destination based on vast amounts of historical data and specific operational rules. In particular, the server can automatically apply rules to sort the items into dedicated boxes, etc., if the destination department is involved in a special project.

[0580] These analysis results are automatically notified to the relevant personnel through reporting mechanisms on the server. Notifications are delivered via digital communication methods, such as email, messaging services, or dedicated internal business applications. Users can receive this information and perform quick and accurate sorting tasks.

[0581] For example, if mail is sent to a specific department and should be sorted into a project-specific box rather than a regular box, the system will automatically make this determination. This process allows users to sort mail smoothly without having to consider various special rules.

[0582] This system is expected to reduce errors and improve efficiency in mail sorting compared to traditional manual processes, contributing to increased productivity across the entire organization.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This digital data is then neatly categorized according to a pre-designed format.

[0586] Step 2:

[0587] The terminal extracts digital data and transmits it in real time to a database via a management system. By storing information such as destination, sender, and date in the database, an organized data structure is formed.

[0588] Step 3:

[0589] The server accesses the database and analyzes the stored data using analytical tools. Here, the server uses a machine learning model and, referencing past sorting patterns and departmental rules, identifies the optimal sorting destination.

[0590] Step 4:

[0591] Based on the analysis results, the server notifies relevant personnel of the determined destination via digital communication methods, such as email or messaging services. It also sends notifications to internal business applications as needed.

[0592] Step 5:

[0593] Users receive reports and place mail in designated sorting locations. Following the notification, users can work quickly and efficiently while preventing errors.

[0594] Step 6:

[0595] Users report misdeliveries and unusual cases to the feedback system. This allows the system to acquire data for further analysis and improve sorting accuracy in the future.

[0596] (Example 1)

[0597] 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".

[0598] Traditional mail sorting processes rely heavily on manual verification, limiting their efficiency and accuracy. This burden increases significantly when handling large volumes of mail. Such manual processes are prone to human error and delays, highlighting the need for efficient and accurate automated sorting systems.

[0599] 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.

[0600] In this invention, the server includes means for detecting mail destination information and performing character recognition; means for storing the detected information on an information recording medium; means for using a computational model to analyze the destination based on the stored information; means for notifying the determined destination via information communication; and means for the user to receive instructions and perform mail sorting. This enables automation and improved accuracy of mail sorting, as well as overall operational efficiency.

[0601] "Mail" refers to means of communication such as documents and parcels delivered through the postal system.

[0602] "Recipient information" refers to identifying information such as the address, name, and postal code that indicates the destination of the mail.

[0603] "Character recognition" is a technology that detects characters within an image and converts them into digital text.

[0604] An "information recording medium" refers to a database or storage system used to store digital data.

[0605] A "computational model" is a mathematical model constructed using algorithms for data analysis.

[0606] "Information and communication" refers to the means of transmitting information to a remote location using digital signals.

[0607] A "user" is a worker who uses the system to sort mail.

[0608] This system is designed to automate the mail sorting process. It primarily involves the collaboration of terminals and servers to perform efficient data processing.

[0609] First, when a piece of mail arrives, the terminal scans its surface with a high-resolution scanner. At this time, it uses OCR (Optical Character Recognition) software to extract the recipient and sender information written on the mail as digital data. The terminal organizes the extracted information into fields such as "Recipient," "Sender," and "Date," and saves it to an information storage medium.

[0610] Next, the server accesses the stored data and analyzes it using analytical tools. The server is equipped with machine learning algorithms that determine the optimal destination based on vast amounts of historical data and specific operational rules. This analysis enables a system where, for example, mail addressed to departments related to a specific project is automatically sorted into a dedicated box.

[0611] The analysis results are quickly notified to the user via a reporting mechanism on the server. This notification may be sent via email, a messaging service, or a proprietary internal business application. Users can then receive these notifications and properly sort their mail.

[0612] For example, if the system automatically sorts mail addressed to a specific destination into a project-specific box, users no longer need to manually check the rules, improving work efficiency. Furthermore, by utilizing a generative AI model, it is possible to give instructions to the system using prompts such as, "Tell me the sorting rules for mail addressed to a specific project."

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] When the terminal receives mail, it scans it using a high-resolution scanner. The input is the mail itself, and the output is a high-resolution scanned image of that mail. The terminal inputs this image into OCR software to extract recipient and sender information as digital text. Specifically, it analyzes each character in the image and classifies them into fields such as "address," "name," and "zip code" as text data.

[0616] Step 2:

[0617] The terminal uses the extracted digital text to save the data to an information storage medium. The input here is the structured text data from the previous step, and the output is the information stored in the database. The terminal organizes the data into fields according to attributes such as "recipient," "sender," and "date," and adds it to the database using SQL statements. Specifically, it generates and executes SQL commands to efficiently save the information.

[0618] Step 3:

[0619] The server retrieves data stored on an information recording medium and performs analysis using analytical tools. The input for this step is stored digital data, and the output is analyzed sorting instruction information. The server utilizes machine learning models to compare and classify the input data and determine the optimal destination. Specifically, it refers to past delivery history and selects the optimal transportation route in accordance with current laws and company rules.

[0620] Step 4:

[0621] The analysis results obtained on the server are notified to the user through a reporting mechanism. The input here is the analysis results from the server, and the output is a notification message sent to the user. Specifically, this involves quickly informing the user of mail delivery addresses and sorting instructions using email, messaging apps, or internal company applications.

[0622] Step 5:

[0623] The user receives a notification and performs the physical sorting of mail based on the instructions. The input for this step is the notified data, and the output is the properly sorted mail. Specifically, the user receives the mail in front of them and sorts it into the designated project box or regular delivery box.

[0624] (Application Example 1)

[0625] 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".

[0626] Modern logistics centers handle the sorting of vast amounts of mail and packages, but they still rely on manual labor and partial automation, resulting in limitations in efficiency and accuracy. This is especially true when dealing with specialized sorting rules, which require significant time and human resources for decision-making. Furthermore, a lack of support for employees to quickly acquire information and take action is another challenge.

[0627] 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.

[0628] In this invention, the server includes an electronic means for detecting the destination information of mail, a management means for storing the detected information in a database, an analysis means for determining the destination based on the stored information, a reporting means for notifying the determined destination, and an information presentation means for use by workers. This enables efficient and accurate sorting of mail and quick information verification by workers.

[0629] "Digitization methods" refer to technologies that convert and recognize mailing address information as digital data, and typically involve using scanning or optical character recognition.

[0630] "Management means" refers to a function that structures digitized information, stores it in a database, and facilitates subsequent analysis and retrieval.

[0631] "Analysis means" refers to the process of determining the optimal destination based on stored data, and involves performing analysis using past data and rules with a learning algorithm.

[0632] The "reporting means" is a function for notifying personnel of the destination determined based on the analysis results, and this is done via information and communication means, such as email or messaging services.

[0633] "Information presentation means" refers to devices and technologies that display information in a way that employees can quickly understand and act upon, such as smart glasses or displays.

[0634] The system used to implement this application primarily aims to streamline the sorting process of mail. The specific procedures and necessary technologies for implementation are described below.

[0635] The server first scans incoming mail using a terminal, and then uses OCR technology to recognize and extract the recipient information as digital data. Examples of OCR technologies used include Google Cloud Vision API. The digitized information is then stored in a database through a management system. For this process, a database management system such as MySQL can be used.

[0636] The server then analyzes the stored digital data using analytical tools. This analysis utilizes machine learning libraries, such as TensorFlow, to determine the optimal destination based on historical data and operational rules. If the destination department meets specific criteria, the server can automatically apply sorting rules to place the items in a dedicated box or similar container.

[0637] Notifications are made using information and communication means, informing workers of the destination determined by the reporting means. Users can check this information in real time through information display means such as smart glasses. The smart glasses' display uses, for example, Twilio's message delivery API to show the results.

[0638] As a concrete example, imagine a scenario in a logistics center where workers wearing smart glasses efficiently sort a massive number of packages. The glasses' camera scans the packages, which are immediately processed by OCR, and the analyzed results are displayed on a screen, significantly improving work efficiency.

[0639] Examples of prompt messages include the following:

[0640] "Please describe in detail the process of the automated package sorting system in a logistics center using smart glasses. In particular, please explain how OCR technology and machine learning models are combined."

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The terminal scans the incoming mail and uses OCR technology to extract the recipient information as digital data. The input is the physical mail, and the output is the recipient information in digital format. This is the process by which OCR technology analyzes characters and generates digital data.

[0644] Step 2:

[0645] The server receives digitized destination information and uses management tools to store the data in a database. The input is digital data generated by OCR, and the output is structured and stored database entries. The database management system stores the data field by field.

[0646] Step 3:

[0647] The server analyzes stored data using analytical tools and determines the optimal destination through a machine learning model. The input is structured data in a database, and the output is instructions or suggestions regarding specific destinations. The model makes decisions using historical data and optimization algorithms.

[0648] Step 4:

[0649] The server notifies the user of the analysis results through a reporting mechanism. This notification is made via information and communication means (e.g., email or a dedicated application). The input is the analysis results from the server, and the output is the information notification received by the user. The user then quickly takes the next action based on this information.

[0650] Step 5:

[0651] The user receives information via smart glasses or other information display devices and sorts mail based on those instructions. The input is the content of the notification from the reporting device, and the output is the properly sorted mail. The user performs physical tasks based on the information presented.

[0652] 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.

[0653] This invention provides a system for efficient sorting of mail and optimizing user operations, comprising digitization means, management means, analysis means, reporting means, and an emotion engine. The following describes how this system operates and how it is specifically implemented.

[0654] First, upon arrival of mail, the terminal scans the mail and uses OCR technology to capture recipient and sender information as digital data. This information is then transmitted to a database using a management system and stored in a structured format. This allows for easy access to the information in subsequent processes.

[0655] The server analyzes the stored data using analytical tools. These analytical tools are trained by machine learning models and determine the optimal destination based on predefined rules and historical data. This determination also accommodates specific sorting rules for each department, as needed.

[0656] The analysis results are communicated to the relevant personnel through reporting mechanisms. The server sends the results via email or other messaging services using digital communication methods. The personnel receive the notification, enabling them to carry out their work efficiently.

[0657] Furthermore, the system incorporates an emotion engine that monitors the user's emotional state in real time. This engine analyzes data acquired from cameras and microphones to generate estimates of the user's stress level and work efficiency. Based on the results of the emotion analysis, the server can adjust the workload and issue alerts as needed.

[0658] For example, when a user is busy and stressed, the emotion engine detects this. Based on the analysis, the server notifies the user and recommends temporarily reducing low-priority tasks. This approach improves the user's ability to efficiently perform necessary tasks while avoiding excessive burden.

[0659] The implementation of this system will improve the accuracy and speed of mail sorting operations, while also optimizing the user's work environment. This will lead to increased efficiency in business processes across the entire organization.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The terminal scans the mail and uses OCR technology to extract recipient and sender information as digital data. This data is then structured in the appropriate format.

[0663] Step 2:

[0664] The terminal transmits the extracted digital data to a database in real time via a management system, where the information is stored and centrally managed. This makes the data easily accessible.

[0665] Step 3:

[0666] The server accesses the database and analyzes the stored data using analytical tools. A machine learning model uses the destination information and specific sorting rules to determine the appropriate destination for the mail.

[0667] Step 4:

[0668] Based on the analysis results, the server notifies the relevant personnel of the destination information via a reporting system. This notification is sent via digital communication methods, such as email or business applications.

[0669] Step 5:

[0670] The server activates an emotion engine and analyzes data from the camera and microphone to monitor the user's emotional state. It generates estimates of stress levels and work efficiency, and adjusts them as needed.

[0671] Step 6:

[0672] Users receive notifications based on the emotion engine's analysis results and adjust task priorities if necessary. Users receive guidance to continue working efficiently.

[0673] Step 7:

[0674] The system optimizes system settings and processing flows as needed, based on feedback from both the device and the user. This feedback is also used as data to help improve performance in the future.

[0675] (Example 2)

[0676] 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".

[0677] The challenge is to provide a system that efficiently determines the destination of mail based on digitized information, provides prompt notification, and improves operational efficiency and reduces stress by analyzing the emotional state of users and appropriately adjusting their workload.

[0678] 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.

[0679] In this invention, the server includes a device that converts mail address information into readable information, a device that records the converted information, a device that calculates the destination based on the recorded information, a device that notifies the user of the calculated destination, and a device that measures the user's emotional state and adjusts the workload. This enables efficient sorting of mail and optimization of the user's work state.

[0680] "Mail" refers to physical messages and packages such as letters and parcels that are sent through postal services.

[0681] "Recipient information" refers to the information, such as the address and name, that indicates the destination to which the mail will be sent.

[0682] The term "device" refers to a concept that includes hardware and software that constitute part or all of a machine or system, and perform a specific function or role.

[0683] "Conversion" refers to the process of changing data or information from one format or state to another.

[0684] "Recording" refers to the act of saving or storing data or information so that it can be referenced later.

[0685] "Calculation" refers to the process of performing calculations and evaluations based on specific input data to derive results.

[0686] "To inform" refers to the act of transmitting information or notification to a target person or group.

[0687] "Emotional state" refers to data such as facial expressions and tone of voice that indicate a person's emotions and psychological state at a given point in time.

[0688] "Adjusting the workload" refers to the act of reviewing the requirements and quantity of tasks and work and adjusting them to an optimal state.

[0689] This invention provides a multi-functional mail processing system that enables efficient management of mail and optimizes the user's work environment. This system leverages electronic data processing capabilities to solve problems through a multifaceted approach.

[0690] When mail arrives, the terminal first scans the physical mail and then uses OCR technology to convert the recipient and sender information into digital data. This process utilizes a scanner equipped with a high-precision image sensor and commonly used OCR software (e.g., Tesseract). The converted digital data is immediately transmitted by the terminal to the server using a secure protocol and stored in a database. The data is stored using a structured data format and managed in a relational database system (e.g., MySQL).

[0691] The server uses analytical tools to analyze information stored in the database and calculate the optimal destination for mail. The analysis utilizes machine learning models (e.g., models using scikit-learn or TensorFlow) to enable decisions based on historical data and pre-defined rules. Prioritization rules for specific departments are also appropriately reflected in this process.

[0692] The calculated results are notified to the relevant personnel via the server. Email and messaging services (e.g., Slack) are used for reporting to ensure quick and reliable information dissemination. Users can receive this information and develop efficient work plans.

[0693] Furthermore, the device is equipped with an emotion engine that monitors the user's emotional state in real time via the camera and microphone. It uses facial recognition technology (e.g., OpenCV) and voice analysis tools to estimate the user's stress level and work efficiency. Based on these analysis results, the server automatically adjusts the workload and issues alerts as needed. For example, if a certain metric becomes excessive, it might issue a notification such as, "Your current workload may be excessive; we recommend postponing some tasks."

[0694] As a concrete example, by inputting the prompt, "Please explain in detail the process of OCR scanning mail and storing it in a database," into the generating AI model, a more detailed explanation can be elicited. This approach allows companies implementing the system to achieve efficient mail sorting and improve the working environment for their users.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The terminal receives the mail. The mail is scanned, and high-resolution image data is obtained. The input is the physical mail, and the output is digitized image data. The scanner scans the surface of the mail and captures the image.

[0698] Step 2:

[0699] The terminal uses OCR technology to extract text information from scanned image data. The input is the image data obtained in step 1, and the output is destination and sender information in text format. The OCR software analyzes the video data and converts it into text.

[0700] Step 3:

[0701] The terminal sends the converted text data to the server. The input is text data generated by OCR, and the output is digital data sent to the server. The terminal securely transmits information over the network.

[0702] Step 4:

[0703] The server records the data it receives in a database. The input is text data sent from the terminal, and the output is organized information stored in the database. The server saves the received data to a relational database and creates an index.

[0704] Step 5:

[0705] The server analyzes the database information and calculates the optimal destination. The input is postal data stored in the database, and the output is optimal destination information. A machine learning model analyzes the data and estimates the destination.

[0706] Step 6:

[0707] The server calculates the destination and notifies the person in charge. The input is the destination information obtained in step 5, and the output is the notification sent to the person in charge. The notification is sent quickly via email or messaging service.

[0708] Step 7:

[0709] The device monitors the user's emotional state. Input is real-time data from the camera and microphone, and output is an evaluation of the user's emotional state. An emotion engine analyzes the video and audio data to measure the stress level.

[0710] Step 8:

[0711] The server adjusts the workload based on the emotional state. The input is the emotional state data obtained in step 7, and the output is the adjusted work instructions and alerts. The server uses the analysis results to suggest an appropriate work schedule.

[0712] (Application Example 2)

[0713] 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".

[0714] While efficient and accurate data management and destination determination are required in the sorting of mail and packages, there is a challenge in providing an appropriate work environment that takes into account the emotional state and workload of the workers.

[0715] 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.

[0716] In this invention, the server includes an information digitization means for detecting mail destination information, an information management means for storing the detected information in an information database, an information analysis means for determining the destination based on the stored information, an emotion evaluation means for evaluating the user's emotional state, and a work adjustment means for adjusting the workload based on the emotion evaluation. This makes it possible to achieve efficient mail sorting and reduce the burden on workers simultaneously.

[0717] "Information digitization means" refers to a means for optically identifying the address information of mail and converting it into a digital format.

[0718] "Information management means" refers to means for storing detected information in an information database in a structured format.

[0719] "Information analysis means" refers to a means of determining the optimal destination for transport using a machine learning algorithm based on stored information.

[0720] "Communication reporting means" refers to means including a digital communication interface for notifying the person in charge of the determined destination of transport.

[0721] "Emotional evaluation means" refers to a means for analyzing the emotional state of workers in real time via cameras or voice input devices.

[0722] "Work adjustment means" refers to means for appropriately adjusting the workload of workers based on the results of emotional evaluations.

[0723] In carrying out this invention, the server uses information digitization means to detect the address information of mail. The terminal scans the mail and captures the information as digital data using optical character recognition (OCR) software. The obtained digital data is stored in a database by information management means. The stored information is stored in a structured format that allows for easy access later.

[0724] Subsequently, the server analyzes the data using an information analysis tool. This tool is trained by a machine learning model and determines the optimal destination based on historical datasets and predefined rules. This analysis can utilize libraries such as Python's scikit-learn library.

[0725] The emotion assessment system analyzes data from the camera and voice input devices to monitor the user's emotional state. If the assessment determines that the user is in a high-stress state, the server adjusts tasks and workload through the work adjustment system. For example, it may postpone lower-priority tasks or issue an alert recommending a short break.

[0726] For example, if a user is detected to be in a high-stress state while sorting a large number of packages, the system may issue a notification recommending that they take a break to reduce stress. An example of this prompt message is: "Explain the function of adjusting the workload based on user emotion analysis as a work support application for a logistics center."

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The terminal scans the mail. In this step, image data of the mail is captured by an optical sensor. The input is the mail, and the output is the digital image data of that mail.

[0730] Step 2:

[0731] The server uses OCR technology to extract text information from digital image data. This is a process that converts image data into text data; the input is digital image data of a postal item, and the output is the extracted text data of the recipient information.

[0732] Step 3:

[0733] The server uses information management tools to store the detected destination information in a database. The input is the text data of the destination information, and the output is the information recorded in the database. This step generates structured data that can be immediately accessed in subsequent processes.

[0734] Step 4:

[0735] The server uses information analysis tools to determine the optimal destination. A machine learning model analyzes the destination based on historical datasets and defined rules. The input is destination information read from a database, and the output is the determination of the optimal destination.

[0736] Step 5:

[0737] The server notifies the person in charge of the determined destination. Using a communication reporting means, the input is the destination determination result, and the output is the notification to the person in charge. This notification is made via a digital communication interface.

[0738] Step 6:

[0739] The user uses a camera and audio input device to collect real-time emotional states using an emotion assessment tool. The input is camera video and audio data, and the output is the emotion assessment result.

[0740] Step 7:

[0741] The server adjusts the workload based on the emotion assessment results. If the emotion assessment system identifies a high-stress state in the user, the workload adjustment system will re-evaluate task priorities or suggest a break. The input is the assessed emotion data, and the output is an alert or suggestion regarding the adjusted workload.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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."

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] An electronic means for detecting the address information of mail,

[0766] A management system for storing the detected information in a database,

[0767] An analytical means for determining the destination based on stored information,

[0768] A reporting means for notifying the determined destination of transport,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, wherein the analysis means analyzes destination information and special sorting rules using a machine learning model.

[0772] (Claim 3)

[0773] The system according to claim 1, wherein the reporting means provides notification via digital communication means.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A device that detects the address information of mail and performs character recognition,

[0777] A device for storing detected information on an information recording medium,

[0778] A device that uses a computational model to analyze the destination based on stored information,

[0779] A device that notifies the determined destination via information and communication,

[0780] A device for users to sort mail according to instructions,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, wherein the computational model uses a machine learning algorithm to analyze destination information and specific sorting rules.

[0784] (Claim 3)

[0785] The system according to claim 1, wherein information and communication is performed using an electronic communication medium.

[0786] "Application Example 1"

[0787] (Claim 1)

[0788] An electronic means for detecting the address information of mail,

[0789] A management system for storing the detected information in a database,

[0790] An analytical means for determining the destination based on stored information,

[0791] A reporting means for notifying the determined destination of transport,

[0792] Information presentation methods used by workers,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the analysis means analyzes destination information and special sorting rules using a learning algorithm.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein a reporting means provides notification via an information and communication means, and an information presentation means displays the information to the user.

[0798] "Example 2 of combining an emotion engine"

[0799] (Claim 1)

[0800] A device that converts mailing address information into readable information,

[0801] A device for recording the converted information,

[0802] A device that calculates a destination based on recorded information,

[0803] A device that notifies the calculated destination,

[0804] A device that measures the emotional state of the user and adjusts the workload,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the device analyzes destination information and individual sorting rules using a learning algorithm and generates a notification.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the device transmits information through electrical communication.

[0810] "Application example 2 when combining with an emotional engine"

[0811] (Claim 1)

[0812] A means for digitizing information to detect the address information of mail,

[0813] An information management means for storing the detected information in an information database,

[0814] Information analysis means for determining the destination based on stored information,

[0815] A means of communication and reporting for notifying the determined destination of transport,

[0816] A means for evaluating the emotional state of a user,

[0817] A work adjustment mechanism for adjusting workload based on emotional evaluation,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the information analysis means analyzes destination information and special sorting rules using a machine learning model.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the communication reporting means provides notification via digital communication means. [Explanation of symbols]

[0823] 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

1. An electronic means for detecting the address information of mail, A management system for storing the detected information in a database, An analytical means for determining the destination based on stored information, A reporting means for notifying the determined destination of transport, Information presentation methods used by workers, A system that includes this.

2. The system according to claim 1, wherein the analysis means analyzes destination information and special sorting rules using a learning algorithm.

3. The system according to claim 1, wherein a reporting means provides notification via an information and communication means, and an information presentation means displays the information to the user.

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