system

Abstract

The system according to this embodiment aims to automatically analyze the recipient's and sender's information on mail and deliver it to the appropriate department. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a mailing unit, and a tracking unit. The acquisition unit acquires image data of the recipient and sender of the mail. The analysis unit analyzes the image data acquired by the acquisition unit and determines which department the mail is addressed to. The mailing unit determines the box to mail the mail based on the department determined by the analysis unit. The tracking unit attaches an RFID tag to the mail and records a log of who received it and when.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of manually checking the name and sender information of a postal item and delivering it to an appropriate department is complicated and inefficient.

[0005] The system according to the embodiment aims to automatically analyze the name and sender information of a postal item and deliver it to an appropriate department.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a mailing unit, and a tracking unit. The acquisition unit acquires image data of the recipient and sender of the mail. The analysis unit analyzes the image data acquired by the acquisition unit and determines which department the mail is addressed to. The mailing unit determines the box to mail the mail based on the department determined by the analysis unit. The tracking unit attaches an RFID tag to the mail and records a log of who received it and when. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze the recipient's and sender's information on mail and deliver it to the appropriate department. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The mail processing system according to an embodiment of the present invention is a system for streamlining the processing of mail arriving at the head office. This mail processing system reads image data of the recipient and sender of mail, uses a generating AI to determine which department it is addressed to, and finally decides which box to drop it into. In addition, an RFID system is introduced to enable mail tracking and to keep a log of who received it and when, thereby preventing problems such as loss. For example, when mail arrives at the head office, the image data of the recipient and sender of the mail is read. In this case, the image data is acquired using a scanner or camera. For example, the recipient and sender information written on the surface of the mail is scanned and saved as a digital image. Next, the generating AI analyzes the read image data to determine which department it is addressed to. Based on the recipient and sender information, the generating AI learns from past data and patterns to identify the optimal department. For example, if the recipient's name is written as "Sales Department," the generating AI will determine that the mail is addressed to the Sales Department. Subsequently, based on the department determined by the generating AI, the box to drop the mail into is decided. Each department has its own dedicated drop-off box, and the generating AI selects the optimal box. For example, mail addressed to the sales department is placed in a designated box for the sales department. Furthermore, an RFID system is implemented to allow for mail tracking. RFID tags are attached to mail to record who received it and when. For instance, when mail is retrieved from the drop box, an RFID reader reads the tag and records the recipient's information and the date and time of receipt. This helps prevent mail loss and other problems. This system streamlines mail processing and ensures faster delivery to each department. Additionally, the RFID system allows for mail tracking, preventing loss and other problems. This streamlines the mail processing system and ensures faster delivery to each department. Furthermore, the RFID system allows for mail tracking, preventing loss and other problems.

[0029] The mail processing system according to this embodiment comprises an acquisition unit, an analysis unit, a mailing unit, and a tracking unit. The acquisition unit acquires image data of the recipient and sender of the mail. The image data of the recipient and sender of the mail includes, but is not limited to, JPEG format, PNG format, and resolution. The acquisition unit can, for example, scan the surface of the mail using a scanner and acquire image data. Alternatively, the acquisition unit can also take a picture of the surface of the mail using a camera and acquire image data. For example, a scanner scans the surface of the mail at high resolution and acquires detailed image data. A camera takes a picture of the surface of the mail at high resolution and acquires image data. Furthermore, the acquisition unit can also extract text data from the image data using OCR technology. For example, OCR technology recognizes characters in the image data and extracts them as text data. The analysis unit analyzes the image data acquired by the acquisition unit and determines which department the mail is addressed to. The analysis unit, for example, uses OCR technology to recognize characters in the image data and extracts the recipient and sender information. Furthermore, the analysis unit uses machine learning algorithms to learn from past data and patterns based on the recipient and sender information to identify the optimal department. For example, if the recipient's name includes "Sales Department," the analysis unit determines that the mail is addressed to the Sales Department. The mailing unit determines the box to which the mail should be placed based on the department determined by the analysis unit. The mailing unit, for example, installs dedicated mailing boxes for each department and selects the most suitable box. For example, mail addressed to the Sales Department is placed in the box dedicated to the Sales Department. The tracking unit attaches RFID tags to the mail and records who received it and when. The tracking unit, for example, uses an RFID reader to read the tag and records the recipient's information and the date and time of receipt. For example, when mail is removed from the mailing box, the RFID reader reads the tag and records the recipient's information and the date and time of receipt. This helps to prevent mail loss and other problems. As a result, the mail processing system according to this embodiment can streamline mail processing and deliver mail to each department quickly. In addition, the RFID system enables mail tracking, preventing loss and other problems.

[0030] The acquisition unit can acquire image data using a scanner or a camera. For example, the acquisition unit can scan the surface of a piece of mail using a scanner to acquire image data. Scanners include, for example, high-resolution scanners, flatbed scanners, and document scanners. High-resolution scanners scan the surface of mail in detail and acquire clear image data. Flatbed scanners scan mail placed flat to acquire uniform image data. Document scanners scan multiple pieces of mail in succession to efficiently acquire image data. The acquisition unit can also acquire image data by photographing the surface of mail using a camera. Cameras include, for example, high-resolution cameras, digital SLR cameras, and smartphone cameras. High-resolution cameras photograph the surface of mail in high resolution and acquire detailed image data. Digital SLR cameras photograph the surface of mail in high quality and acquire clear image data. Smartphone cameras can easily photograph the surface of mail and acquire image data. This allows for accurate acquisition of image data of mail using scanners or cameras. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input image data acquired by the scanner into the generation AI, and have the generation AI perform the analysis of the image data.

[0031] The analysis unit can identify the appropriate department by learning from past data and patterns based on the recipient and sender information. For example, the analysis unit uses OCR technology to recognize characters in image data and extract the recipient and sender information. OCR technology includes, for example, optical character recognition, handwritten character recognition, and printed character recognition. Optical character recognition recognizes printed characters with high accuracy and extracts them as text data. Handwritten character recognition recognizes handwritten characters and extracts them as text data. Printed character recognition recognizes printed characters with high accuracy and extracts them as text data. In addition, the analysis unit uses machine learning algorithms to learn from past data and patterns based on the recipient and sender information and identify the optimal department. Machine learning algorithms include, for example, neural networks, support vector machines, and decision trees. Neural networks learn from large amounts of data and have advanced pattern recognition capabilities. Support vector machines perform well in data classification and regression analysis. Decision trees are used for data classification and prediction and have high interpretability. This allows for accurate determination of mail recipients by learning from past data and patterns. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input recipient and sender information into the generative AI and have the generative AI identify the optimal department.

[0032] The mail distribution department can install dedicated mail boxes for each department and select the appropriate box. For example, the mail distribution department can install dedicated mail boxes for each department and select the most suitable box. Dedicated mail boxes include, for example, a dedicated box for the sales department, a dedicated box for the general affairs department, and a dedicated box for the accounting department. The dedicated box for the sales department is for mail addressed to the sales department and is installed at the sales department's location. The dedicated box for the general affairs department is for mail addressed to the general affairs department and is installed at the general affairs department's location. The dedicated box for the accounting department is for mail addressed to the accounting department and is installed at the accounting department's location. The mail distribution department can also select the most suitable mail box considering the location and workload of each department. For example, if the sales department is in a busy period, the mail distribution department will prioritize mailing to the sales department's dedicated box. If the general affairs department is in a slow period, the mail distribution department will prioritize mailing to the general affairs department's dedicated box. In this way, by installing dedicated mail boxes for each department, mail delivery becomes more efficient. Some or all of the above-described processes in the mailing unit may be performed using AI, for example, or without AI. For example, the mailing unit can input department information determined by the analysis unit into a generating AI, and have the generating AI select the optimal mailing box.

[0033] The tracking unit can read tags using an RFID reader and record recipient information and the date and time of receipt. For example, the tracking unit can read an RFID tag attached to a piece of mail using an RFID reader and record the recipient information and the date and time of receipt. RFID readers include, for example, fixed RFID readers, handheld RFID readers, and portable RFID readers. Fixed RFID readers are installed in a specific location and automatically read tags as mail passes by. Handheld RFID readers are used by hand and read tags by bringing them close to the mail. Portable RFID readers are portable and can read tags in various locations. The tracking unit can also store the information read using the RFID reader in a database and manage tracking logs. For example, the tracking unit stores recipient information and the date and time of receipt in a database and manages tracking logs for mail. This allows for accurate recording of the recipient and the date and time of receipt of mail using an RFID reader. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input data acquired by the RFID reader into a generating AI, which can then record the recipient's information and the date and time of receipt.

[0034] The acquisition unit can select an appropriate acquisition method according to the size and shape of the mail item. For example, in the case of a large mail item, the acquisition unit can use a wide-angle lens to acquire the entire item at once. A wide-angle lens can capture a wide area and is suitable for acquiring the entire image of a large mail item at once. In the case of a small mail item, the acquisition unit can also use a zoom function to acquire details. The zoom function can enlarge and photograph specific parts and is suitable for acquiring details of a small mail item. In the case of an irregularly shaped mail item, the acquisition unit can acquire images from multiple angles and reconstruct the overall image. Acquiring images from multiple angles is suitable for accurately grasping the overall image of an irregularly shaped mail item. This allows for the acquisition of accurate image data by selecting the optimal acquisition method according to the size and shape of the mail item. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the size and shape of the mail item into a generating AI and have the generating AI select the optimal acquisition method.

[0035] The acquisition unit can detect the surface condition of the mail item during acquisition and perform correction processing. For example, if the surface of the mail item is dirty, the acquisition unit can remove the dirt using image processing techniques to acquire a clear image. Image processing techniques include, for example, filtering, noise reduction, and edge detection. Filtering removes noise from the image and generates a clear image. Noise reduction removes unwanted noise from the image and generates a clear image. Edge detection enhances the edges of the image and generates a clear image. The acquisition unit can also repair the image using algorithms that complement the damaged parts if the surface of the mail item is damaged. Complementation of damaged parts includes, for example, inpainting, patch-based repair, and deep learning-based repair. Inpainting complements the damaged parts based on surrounding pixel information. Patch-based repair complements the damaged parts based on surrounding patch information. Deep learning-based repair complements the damaged parts using a deep learning model. The acquisition unit can also reproduce the original colors using color correction techniques if the surface of the mail item is faded. Color correction techniques include, for example, white balance adjustment, gamma correction, and hue adjustment. White balance adjustment adjusts the color temperature of the image to reproduce natural colors. Gamma correction adjusts the brightness of the image to reproduce natural colors. Hue adjustment adjusts the hue of the image to reproduce natural colors. This allows for the detection of the surface condition of the mail item and the acquisition of clear image data by performing appropriate correction processing. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the surface condition data of the mail item into a generating AI and have the generating AI perform the correction processing.

[0036] The acquisition unit can select an appropriate acquisition method considering the weight and material of the mail item during acquisition. For example, in the case of heavy mail, the acquisition unit can acquire an image by placing it on a stable stand. A stable stand is suitable for holding the mail item stably and acquiring a blur-free image. The acquisition unit can also easily acquire an image of light mail by hand. Handheld image acquisition is suitable for quickly photographing light mail items. Furthermore, in the case of mail made of special materials, the acquisition unit can acquire an image under optimal lighting conditions considering reflection and transmission. Lighting conditions that consider reflection and transmission are suitable for acquiring clear images of mail made of special materials. As a result, accurate image data can be obtained by selecting the optimal acquisition method considering the weight and material of the mail item. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the weight and material of the mail item into a generating AI and have the generating AI select the optimal acquisition method.

[0037] The acquisition unit can prioritize the acquisition of relevant data by considering the geographical location information of the sender of the mail item during acquisition. For example, if the sender is an important business partner, the acquisition unit will prioritize the acquisition of image data of that mail item. Mail from important business partners is of high business importance, so it is appropriate to prioritize its acquisition. The acquisition unit can also quickly acquire image data if the sender is far away, taking into account the time it will take to arrive. Mail from distant locations takes a long time to arrive, so it is appropriate to acquire it quickly. Furthermore, if the senders are concentrated in a particular region, the acquisition unit can prioritize the acquisition of mail from that region. Mail concentrated in a particular region is of high business relevance, so it is appropriate to prioritize its acquisition. In this way, by considering the geographical location information of the sender of the mail item, highly relevant data can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the geographical location information of the sender into a generating AI and have the generating AI prioritize the relevant data.

[0038] The analysis unit can adjust its analysis algorithm based on the type of contents of the mail during analysis. For example, if the contents of the mail are documents, the analysis unit uses a character recognition algorithm to analyze the contents. Character recognition algorithms include, for example, OCR technology, handwritten character recognition, and printed character recognition. OCR technology recognizes printed characters with high accuracy and extracts them as text data. Handwritten character recognition recognizes handwritten characters and extracts them as text data. Printed character recognition recognizes printed characters with high accuracy and extracts them as text data. In addition, if the contents of the mail are parcels, the analysis unit can also analyze the contents by reading barcodes or 2D codes (e.g., QR codes®). Examples of methods for reading barcodes and 2D codes include barcode readers, 2D code readers, and smartphone cameras. Barcode readers read barcodes with high accuracy and analyze their contents. 2D code readers read 2D codes with high accuracy and analyze their contents. Smartphone cameras can easily read barcodes and 2D codes and analyze their contents. Furthermore, if the contents of a mail item are unusual, the analysis unit can use a dedicated analysis algorithm to analyze the contents. Examples of such dedicated analysis algorithms include image recognition algorithms, speech recognition algorithms, and text analysis algorithms. The image recognition algorithm analyzes image data to identify its contents. The speech recognition algorithm analyzes audio data to identify its contents. The text analysis algorithm analyzes text data to identify its contents. This allows for improved analysis accuracy by adjusting the analysis algorithm based on the type of mail item. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the mail item's contents into a generative AI and have the generative AI adjust the analysis algorithm.

[0039] The analysis unit can improve the accuracy of its analysis by referring to past analysis results during the analysis process. For example, the analysis unit can improve the accuracy of analyzing mail with similar patterns based on past analysis results. Past analysis results include, for example, a database of past mail, the learning results of the analysis algorithm, and trends in misrecognition. The database of past mail stores information on mail that has been analyzed in the past and is useful for analyzing mail with similar patterns. The learning results of the analysis algorithm are the parameters and models of the algorithm learned based on past data and contribute to improving the accuracy of the analysis. Trends in misrecognition are patterns of misrecognition obtained from past analysis results and are useful for improving the analysis algorithm. The analysis unit can also improve the accuracy of its analysis by creating a database of past analysis results and referring to it in real time. Past analysis results stored in the database can be referred to in real time and used immediately during analysis. This improves the accuracy of the analysis by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past analysis results into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0040] The analysis unit can modify its analysis algorithm during analysis, taking into account the industry and business type of the sender of the mail. For example, if the sender is a financial institution, the analysis unit will use an analysis algorithm that prioritizes security. Mail from financial institutions often contains confidential information, so analysis that prioritizes security is necessary. The analysis unit can also use an analysis algorithm that prioritizes privacy if the sender is a medical institution. Mail from medical institutions often contains personal information, so analysis that prioritizes privacy is necessary. The analysis unit can also use an analysis algorithm that prioritizes technical content if the sender is a manufacturing company. Mail from manufacturing companies often contains technical information, so analysis that prioritizes technical content is necessary. In this way, the analysis algorithm can be optimized by taking into account the industry and business type of the sender of the mail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input data on the sender's industry and business type into the generative AI and have the generative AI execute the modification of the analysis algorithm.

[0041] The analysis unit can evaluate the reliability of the sender of mail during analysis and reflect this in the analysis results. For example, if the sender is a highly reliable company, the analysis unit can provide the analysis results quickly. Mail from highly reliable companies is of high importance, so it is appropriate to provide the analysis results quickly. The analysis unit can also provide the analysis results cautiously if the sender is a less reliable company. Mail from less reliable companies is of low reliability, so it is appropriate to provide the analysis results cautiously. The analysis unit can also refer to a database for evaluating the reliability of the sender and reflect this in the analysis results. Reliability evaluation includes, for example, past transaction history, evaluation scores, and evaluations by third-party organizations. Past transaction history evaluates reliability based on past transaction history with the sender. The evaluation score is a numerical representation of the sender's reliability and is useful for evaluating reliability. Evaluations by third-party organizations are evaluations of reliability by independent organizations and are useful for evaluating reliability. As a result, evaluating the reliability of the sender of mail improves the reliability of the analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input reliability data from the sender into the generating AI and have the generating AI perform a reliability evaluation.

[0042] The mailing unit can select the appropriate mailing timing by considering the workload of each department. For example, the mailing unit can grasp the workload of each department in real time and select the optimal mailing timing. Understanding the workload includes, for example, the level of workload, schedules, and progress of work. The level of workload indicates the workload of each department and is useful for understanding busy and slow periods. The schedule indicates the plans of each department and is useful for understanding the timing of work. The progress of work indicates the progress of work in each department and is useful for understanding the timing of work. In addition, the mailing unit can mail mail to departments that are in a busy period when their workload has subsided. For departments in a busy period, mailing mail at a time when their workload has subsided is appropriate to reduce the workload. In addition, the mailing unit can mail mail quickly to departments in a slow period for efficient processing. For departments in a slow period, mailing mail quickly is appropriate to increase work efficiency. This allows mail to be delivered at the optimal time by considering the work status of each department. Some or all of the above-described processes in the mailing section may be performed using AI, for example, or without AI. For example, the mailing section can input work status data from each department into a generating AI and have the generating AI select the optimal mailing timing.

[0043] The mailing unit can set a priority for mailing based on the importance of the mail at the time of mailing. For example, the mailing unit will mail important mail with the highest priority. Important mail includes, for example, contracts, important notices, and urgent documents. Contracts are important documents with legal effect and need to be mailed promptly. Important notices are documents containing important business information and need to be mailed promptly. Urgent documents are documents that require a quick response and need to be mailed with the highest priority. The mailing unit can also mail general mail with normal priority. General mail includes, for example, daily communication documents, reports, and invitations. Daily communication documents are documents for business communication and are suitable for mailing with normal priority. Reports are documents that report the progress and results of work and are suitable for mailing with normal priority. Invitations are documents that announce events and meetings and are suitable for mailing with normal priority. The mailing unit can also mail highly urgent mail with special priority and promptly. Urgent mail includes, for example, documents requiring immediate attention and documents with approaching deadlines. Documents requiring immediate attention need to be handled quickly and should be mailed with special priority. Documents with approaching deadlines need to be handled within the deadline and should also be mailed with special priority. This allows important mail to be processed preferentially by determining the mailing priority based on the importance of the mail. Some or all of the above processing in the mailing unit may be performed using AI, for example, or not using AI. For example, the mailing unit can input mail importance data into a generating AI and have the generating AI set the mailing priority.

[0044] The mail delivery unit can select an appropriate delivery route by considering the physical location of each department when delivering mail. For example, the mail delivery unit can determine the physical location of each department and deliver the mail via the shortest route. Determining the physical location includes, for example, the location within the building, the floor layout, and the distance between departments. The location within the building indicates where each department is located within the building, which helps in selecting the shortest route. The floor layout indicates which floor each department is located on, which helps in selecting the shortest route. The distance between departments indicates the physical distance between each department, which helps in selecting the shortest route. Furthermore, when delivering mail to multiple departments, the mail delivery unit can select an efficient route and deliver the mail quickly. When delivering mail to multiple departments, selecting an efficient route shortens the delivery time and enables efficient processing. The mail delivery unit can also select the optimal delivery route by considering the distance between departments. By considering the distance between departments, the shortest route can be selected and mail delivered quickly. In this way, by considering the physical location of each department, mail can be delivered via the shortest route. Some or all of the above-described processes in the mailing unit may be performed using AI, for example, or without AI. For example, the mailing unit can input physical location data of each department into a generating AI and have the generating AI select the optimal mailing route.

[0045] The mail slot can select the appropriate mailing method based on the size and shape of the mail item at the time of mailing. For example, the mail slot will place large mail items into a dedicated box. Large mail items include, for example, large documents, large parcels, and large envelopes. Large documents will not fit in a regular box, so it is appropriate to place them in a dedicated box. Large parcels will not fit in a regular box, so it is appropriate to place them in a dedicated box. Large envelopes will not fit in a regular box, so it is appropriate to place them in a dedicated box. The mail slot can also place small mail items into a regular box. Small mail items include, for example, small documents, small parcels, and small envelopes. Small documents will fit in a regular box, so it is appropriate to place them in a regular box. Small parcels will fit in a regular box, so it is appropriate to place them in a regular box. Small envelopes will fit in a regular box, so it is appropriate to place them in a regular box. Furthermore, the mail slot can also accept irregularly shaped mail using special methods. Irregularly shaped mail includes, for example, irregularly shaped parcels, irregularly shaped envelopes, and irregularly shaped documents. Irregularly shaped parcels do not fit in regular boxes, so it is appropriate to accept them using special methods. Irregularly shaped envelopes do not fit in regular boxes, so it is appropriate to accept them using special methods. Irregularly shaped documents do not fit in regular boxes, so it is appropriate to accept them using special methods. This allows for efficient mail processing by selecting the optimal mailing method based on the size and shape of the mail. Some or all of the above processing in the mail slot may be performed using AI, for example, or without AI. For example, the mail slot can input data on the size and shape of the mail into a generating AI and have the generating AI select the optimal mailing method.

[0046] The tracking unit can adjust tracking data during tracking, taking into account the attribute information of the recipient of the mail. For example, if the recipient holds an important position, the tracking unit will provide detailed tracking data. Recipients with important positions include, for example, executives, department heads, and section chiefs. Executives hold positions that involve making important decisions for the company, so it is appropriate to provide detailed tracking data. Department heads hold positions that involve managing departments, so it is appropriate to provide detailed tracking data. Section chiefs hold positions that involve managing sections, so it is appropriate to provide detailed tracking data. The tracking unit can also provide concise tracking data if the recipient is a regular employee. Regular employees include, for example, staff, assistants, and so on. Staff are employees who are in charge of specific tasks, so it is appropriate to provide concise tracking data. Staff are employees who perform general tasks, so it is appropriate to provide concise tracking data. Assistants are employees who perform auxiliary tasks, so it is appropriate to provide concise tracking data. The tracking unit can also provide optimal tracking data based on the recipient's attribute information. Attribute information includes, for example, age, occupation, and position. Age indicates the recipient's age and is useful for providing tracking data. Occupation indicates the recipient's occupation and is useful for providing tracking data. Job title indicates the recipient's job title and is useful for providing tracking data. This allows for the provision of optimal tracking data by considering the recipient's attribute information of the mail. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input recipient attribute information data into a generating AI and have the generating AI perform adjustments to the tracking data.

[0047] The tracking unit can improve tracking accuracy by referring to past tracking data during tracking. For example, the tracking unit can improve the tracking accuracy of mail with similar patterns based on past tracking data. Past tracking data includes, for example, a database of past mail, the learning results of the tracking algorithm, and misrecognition trends. The database of past mail stores information on mail that has been tracked in the past and is useful for tracking mail with similar patterns. The learning results of the tracking algorithm are the parameters and models of the algorithm learned based on past data and contribute to improving tracking accuracy. Misrecognition trends are patterns of misrecognition obtained from past tracking results and are useful for improving the tracking algorithm. The tracking unit can also improve tracking accuracy by creating a database of past tracking data and referring to it in real time. The databased past tracking data can be referred to in real time and used immediately during tracking. This improves tracking accuracy by referring to past tracking data. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input past tracking data into a generative AI and have the generative AI perform the improvement of tracking accuracy.

[0048] The tracking unit can adjust tracking data while tracking mail, taking into account the recipient's work situation. For example, if the recipient is in an important meeting, the tracking unit will refrain from notifying them of tracking data. It is appropriate to refrain from notifying recipients in important meetings so as not to disrupt the meeting. The tracking unit can also provide concise tracking data if the recipient is working. It is appropriate to provide concise tracking data to recipients who are working to improve their work efficiency. The tracking unit can also understand the recipient's work situation in real time and provide optimal tracking data. Understanding work situation includes, for example, workload, schedule, and work progress. Work workload indicates the recipient's workload and helps to understand busy and slow periods. Schedule indicates the recipient's plans and helps to understand the timing of work. Work progress indicates the recipient's work progress and helps to understand the timing of work. This allows the tracking unit to provide optimal tracking data by taking into account the recipient's work situation. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input recipient's work status data into a generating AI and have the generating AI perform adjustments to the tracking data.

[0049] The tracking unit can adjust tracking data during tracking, taking into account the recipient's geographical location. For example, if the recipient is at the company headquarters, the tracking unit provides detailed tracking data. Providing detailed tracking data is appropriate when the recipient is at the headquarters because mail can be received quickly. The tracking unit can also provide concise tracking data when the recipient is out. Providing concise tracking data is appropriate when the recipient is out because mail delivery may be delayed. The tracking unit can also provide optimal tracking data based on the recipient's geographical location. Geographical location information includes, for example, GPS data, map information, and location services. GPS data shows the recipient's current location and is useful for providing tracking data. Map information shows the recipient's location on a map and is useful for providing tracking data. Location services track the recipient's location in real time and are useful for providing tracking data. This allows for the provision of optimal tracking data by taking into account the recipient's geographical location. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input the recipient's geographical location data into the generating AI and have the generating AI perform adjustments to the tracking data.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The acquisition unit can read 2D codes and barcodes printed on the surface of mail and obtain additional information. For example, by scanning a 2D code, it can obtain detailed information about the sender and the contents of the mail. By reading a barcode, it can also obtain the mail's tracking number and delivery status. Furthermore, the acquisition unit can extract handwritten notes and annotations written on the surface of mail as text data using OCR technology. This allows for the efficient acquisition of additional information about mail, supporting processing in the analysis unit.

[0052] The analysis unit can adjust its analysis algorithm based on the type of contents of the mail. For example, if the contents of the mail are documents, it uses a character recognition algorithm to analyze the contents. Character recognition algorithms include OCR technology, handwriting recognition, and printed character recognition. If the contents of the mail are parcels, it can also read barcodes or 2D codes to analyze the contents. Furthermore, if the contents of the mail are special, the analysis unit can use a dedicated analysis algorithm to analyze the contents. This improves analysis accuracy by adjusting the analysis algorithm based on the type of contents of the mail.

[0053] The mailing department can monitor the workload of each department in real time and select the optimal mailing time. For example, it can consider the workload, schedule, and progress of tasks to mail mail when work is less busy. Conversely, it can quickly mail mail to departments during off-peak seasons for efficient processing. In this way, mail can be mailed at the optimal time by considering the workload of each department.

[0054] The acquisition unit can select the appropriate acquisition method according to the size and shape of the mail item. For example, for large mail items, a wide-angle lens can be used to capture the entire item at once. For smaller mail items, the zoom function can be used to capture detailed parts. For irregularly shaped mail items, images can be acquired from multiple angles to reconstruct the overall image. This allows for the selection of the optimal acquisition method according to the size and shape of the mail item, thereby obtaining accurate image data.

[0055] The analysis unit can modify its analysis algorithm during analysis, taking into account the industry and business type of the sender of the mail. For example, if the sender is a financial institution, an analysis algorithm that prioritizes security can be used. Similarly, if the sender is a medical institution, an analysis algorithm that prioritizes privacy can be used. If the sender is a manufacturing company, an analysis algorithm that prioritizes technical content can be used. This allows the analysis algorithm to be optimized by considering the industry and business type of the sender of the mail.

[0056] The tracking unit can adjust tracking data during tracking, taking into account the attribute information of the mail recipient. For example, if the recipient holds an important position, it can provide detailed tracking data. Important position recipients include executives, department heads, and section chiefs. Conversely, if the recipient is a regular employee, it can provide concise tracking data. Regular employees include staff, assistants, and other personnel. This allows for the provision of optimal tracking data by considering the attribute information of the mail recipient.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit acquires image data of the recipient and sender of the mail. For example, it can use a scanner to scan the surface of the mail and acquire high-resolution image data in JPEG or PNG format. Alternatively, it can use a camera to photograph the surface of the mail and acquire image data. Furthermore, it is possible to extract text data from the image data using OCR technology. Step 2: The analysis unit analyzes the image data acquired by the acquisition unit to determine which department the message is addressed to. For example, it uses OCR technology to recognize characters in the image data and extracts the recipient's name and sender's information. It also uses a machine learning algorithm to learn from past data and patterns based on the recipient's name and sender's information to identify the most suitable department. Step 3: The mailing department determines which box to use for mail based on the department identified by the analysis department. For example, each department may have its own dedicated mailing box, and the most suitable box is selected. Mail addressed to the sales department is placed in the sales department's dedicated box. Step 4: The tracking unit attaches RFID tags to the mail and logs who received it and when. For example, it uses an RFID reader to read the tag and record the recipient's information and the date and time of receipt. When the mail is removed from the drop box, the RFID reader reads the tag and records the recipient's information and the date and time of receipt.

[0059] (Example of form 2) The mail processing system according to an embodiment of the present invention is a system for streamlining the processing of mail arriving at the head office. This mail processing system reads image data of the recipient and sender of mail, uses a generating AI to determine which department it is addressed to, and finally decides which box to drop it into. In addition, an RFID system is introduced to enable mail tracking and to keep a log of who received it and when, thereby preventing problems such as loss. For example, when mail arrives at the head office, the image data of the recipient and sender of the mail is read. In this case, the image data is acquired using a scanner or camera. For example, the recipient and sender information written on the surface of the mail is scanned and saved as a digital image. Next, the generating AI analyzes the read image data to determine which department it is addressed to. Based on the recipient and sender information, the generating AI learns from past data and patterns to identify the optimal department. For example, if the recipient's name is written as "Sales Department," the generating AI will determine that the mail is addressed to the Sales Department. Subsequently, based on the department determined by the generating AI, the box to drop the mail into is decided. Each department has its own dedicated drop-off box, and the generating AI selects the optimal box. For example, mail addressed to the sales department is placed in a designated box for the sales department. Furthermore, an RFID system is implemented to allow for mail tracking. RFID tags are attached to mail to record who received it and when. For instance, when mail is retrieved from the drop box, an RFID reader reads the tag and records the recipient's information and the date and time of receipt. This helps prevent mail loss and other problems. This system streamlines mail processing and ensures faster delivery to each department. Additionally, the RFID system allows for mail tracking, preventing loss and other problems. This streamlines the mail processing system and ensures faster delivery to each department. Furthermore, the RFID system allows for mail tracking, preventing loss and other problems.

[0060] The mail processing system according to this embodiment comprises an acquisition unit, an analysis unit, a mailing unit, and a tracking unit. The acquisition unit acquires image data of the recipient and sender of the mail. The image data of the recipient and sender of the mail includes, but is not limited to, JPEG format, PNG format, and resolution. The acquisition unit can, for example, scan the surface of the mail using a scanner and acquire image data. Alternatively, the acquisition unit can also take a picture of the surface of the mail using a camera and acquire image data. For example, a scanner scans the surface of the mail at high resolution and acquires detailed image data. A camera takes a picture of the surface of the mail at high resolution and acquires image data. Furthermore, the acquisition unit can also extract text data from the image data using OCR technology. For example, OCR technology recognizes characters in the image data and extracts them as text data. The analysis unit analyzes the image data acquired by the acquisition unit and determines which department the mail is addressed to. The analysis unit, for example, uses OCR technology to recognize characters in the image data and extracts the recipient and sender information. Furthermore, the analysis unit uses machine learning algorithms to learn from past data and patterns based on the recipient and sender information to identify the optimal department. For example, if the recipient's name includes "Sales Department," the analysis unit determines that the mail is addressed to the Sales Department. The mailing unit determines the box to which the mail should be placed based on the department determined by the analysis unit. The mailing unit, for example, installs dedicated mailing boxes for each department and selects the most suitable box. For example, mail addressed to the Sales Department is placed in the box dedicated to the Sales Department. The tracking unit attaches RFID tags to the mail and records who received it and when. The tracking unit, for example, uses an RFID reader to read the tag and records the recipient's information and the date and time of receipt. For example, when mail is removed from the mailing box, the RFID reader reads the tag and records the recipient's information and the date and time of receipt. This helps to prevent mail loss and other problems. As a result, the mail processing system according to this embodiment can streamline mail processing and deliver mail to each department quickly. In addition, the RFID system enables mail tracking, preventing loss and other problems.

[0061] The acquisition unit can acquire image data using a scanner or a camera. For example, the acquisition unit can scan the surface of a piece of mail using a scanner to acquire image data. Scanners include, for example, high-resolution scanners, flatbed scanners, and document scanners. High-resolution scanners scan the surface of mail in detail and acquire clear image data. Flatbed scanners scan mail placed flat to acquire uniform image data. Document scanners scan multiple pieces of mail in succession to efficiently acquire image data. The acquisition unit can also acquire image data by photographing the surface of mail using a camera. Cameras include, for example, high-resolution cameras, digital SLR cameras, and smartphone cameras. High-resolution cameras photograph the surface of mail in high resolution and acquire detailed image data. Digital SLR cameras photograph the surface of mail in high quality and acquire clear image data. Smartphone cameras can easily photograph the surface of mail and acquire image data. This allows for accurate acquisition of image data of mail using scanners or cameras. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input image data acquired by the scanner into the generation AI, and have the generation AI perform the analysis of the image data.

[0062] The analysis unit can identify the appropriate department by learning from past data and patterns based on the recipient and sender information. For example, the analysis unit uses OCR technology to recognize characters in image data and extract the recipient and sender information. OCR technology includes, for example, optical character recognition, handwritten character recognition, and printed character recognition. Optical character recognition recognizes printed characters with high accuracy and extracts them as text data. Handwritten character recognition recognizes handwritten characters and extracts them as text data. Printed character recognition recognizes printed characters with high accuracy and extracts them as text data. In addition, the analysis unit uses machine learning algorithms to learn from past data and patterns based on the recipient and sender information and identify the optimal department. Machine learning algorithms include, for example, neural networks, support vector machines, and decision trees. Neural networks learn from large amounts of data and have advanced pattern recognition capabilities. Support vector machines perform well in data classification and regression analysis. Decision trees are used for data classification and prediction and have high interpretability. This allows for accurate determination of mail recipients by learning from past data and patterns. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input recipient and sender information into the generative AI and have the generative AI identify the optimal department.

[0063] The mail distribution department can install dedicated mail boxes for each department and select the appropriate box. For example, the mail distribution department can install dedicated mail boxes for each department and select the most suitable box. Dedicated mail boxes include, for example, a dedicated box for the sales department, a dedicated box for the general affairs department, and a dedicated box for the accounting department. The dedicated box for the sales department is for mail addressed to the sales department and is installed at the sales department's location. The dedicated box for the general affairs department is for mail addressed to the general affairs department and is installed at the general affairs department's location. The dedicated box for the accounting department is for mail addressed to the accounting department and is installed at the accounting department's location. The mail distribution department can also select the most suitable mail box considering the location and workload of each department. For example, if the sales department is in a busy period, the mail distribution department will prioritize mailing to the sales department's dedicated box. If the general affairs department is in a slow period, the mail distribution department will prioritize mailing to the general affairs department's dedicated box. In this way, by installing dedicated mail boxes for each department, mail delivery becomes more efficient. Some or all of the above-described processes in the mailing unit may be performed using AI, for example, or without AI. For example, the mailing unit can input department information determined by the analysis unit into a generating AI, and have the generating AI select the optimal mailing box.

[0064] The tracking unit can read tags using an RFID reader and record recipient information and the date and time of receipt. For example, the tracking unit can read an RFID tag attached to a piece of mail using an RFID reader and record the recipient information and the date and time of receipt. RFID readers include, for example, fixed RFID readers, handheld RFID readers, and portable RFID readers. Fixed RFID readers are installed in a specific location and automatically read tags as mail passes by. Handheld RFID readers are used by hand and read tags by bringing them close to the mail. Portable RFID readers are portable and can read tags in various locations. The tracking unit can also store the information read using the RFID reader in a database and manage tracking logs. For example, the tracking unit stores recipient information and the date and time of receipt in a database and manages tracking logs for mail. This allows for accurate recording of the recipient and the date and time of receipt of mail using an RFID reader. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input data acquired by the RFID reader into a generating AI, which can then record the recipient's information and the date and time of receipt.

[0065] The acquisition unit can estimate the user's emotions and adjust the timing of image data acquisition based on the estimated emotions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the acquisition unit can calculate an emotion score based on changes in facial expressions and adjust the timing of image data acquisition. The acquisition unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the acquisition unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of image data acquisition. The acquisition unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the acquisition unit can calculate an emotion score based on fluctuations in heart rate and adjust the timing of image data acquisition. This reduces the burden on the user by adjusting the timing of image data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user image data captured by a camera into a generating AI and have the generating AI perform emotion estimation.

[0066] The acquisition unit can select an appropriate acquisition method according to the size and shape of the mail item. For example, in the case of a large mail item, the acquisition unit can use a wide-angle lens to acquire the entire item at once. A wide-angle lens can capture a wide area and is suitable for acquiring the entire image of a large mail item at once. In the case of a small mail item, the acquisition unit can also use a zoom function to acquire details. The zoom function can enlarge and photograph specific parts and is suitable for acquiring details of a small mail item. In the case of an irregularly shaped mail item, the acquisition unit can acquire images from multiple angles and reconstruct the overall image. Acquiring images from multiple angles is suitable for accurately grasping the overall image of an irregularly shaped mail item. This allows for the acquisition of accurate image data by selecting the optimal acquisition method according to the size and shape of the mail item. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the size and shape of the mail item into a generating AI and have the generating AI select the optimal acquisition method.

[0067] The acquisition unit can detect the surface condition of the mail item during acquisition and perform correction processing. For example, if the surface of the mail item is dirty, the acquisition unit can remove the dirt using image processing techniques to acquire a clear image. Image processing techniques include, for example, filtering, noise reduction, and edge detection. Filtering removes noise from the image and generates a clear image. Noise reduction removes unwanted noise from the image and generates a clear image. Edge detection enhances the edges of the image and generates a clear image. The acquisition unit can also repair the image using algorithms that complement the damaged parts if the surface of the mail item is damaged. Complementation of damaged parts includes, for example, inpainting, patch-based repair, and deep learning-based repair. Inpainting complements the damaged parts based on surrounding pixel information. Patch-based repair complements the damaged parts based on surrounding patch information. Deep learning-based repair complements the damaged parts using a deep learning model. The acquisition unit can also reproduce the original colors using color correction techniques if the surface of the mail item is faded. Color correction techniques include, for example, white balance adjustment, gamma correction, and hue adjustment. White balance adjustment adjusts the color temperature of the image to reproduce natural colors. Gamma correction adjusts the brightness of the image to reproduce natural colors. Hue adjustment adjusts the hue of the image to reproduce natural colors. This allows for the detection of the surface condition of the mail item and the acquisition of clear image data by performing appropriate correction processing. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the surface condition data of the mail item into a generating AI and have the generating AI perform the correction processing.

[0068] The acquisition unit can estimate the user's emotions and determine the priority of image data to acquire based on the estimated emotions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the acquisition unit can calculate an emotion score based on changes in facial expressions and determine the priority of image data to acquire. The acquisition unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the acquisition unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of image data to acquire. Furthermore, the acquisition unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the acquisition unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of image data to acquire. This allows for the priority of image data according to the user's emotions, enabling the acquisition of important mail images with priority. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user image data captured by a camera into the generating AI and have the generating AI perform emotion estimation.

[0069] The acquisition unit can select an appropriate acquisition method considering the weight and material of the mail item during acquisition. For example, in the case of heavy mail, the acquisition unit can acquire an image by placing it on a stable stand. A stable stand is suitable for holding the mail item stably and acquiring a blur-free image. The acquisition unit can also easily acquire an image of light mail by hand. Handheld image acquisition is suitable for quickly photographing light mail items. Furthermore, in the case of mail made of special materials, the acquisition unit can acquire an image under optimal lighting conditions considering reflection and transmission. Lighting conditions that consider reflection and transmission are suitable for acquiring clear images of mail made of special materials. As a result, accurate image data can be obtained by selecting the optimal acquisition method considering the weight and material of the mail item. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the weight and material of the mail item into a generating AI and have the generating AI select the optimal acquisition method.

[0070] The acquisition unit can prioritize the acquisition of relevant data by considering the geographical location information of the sender of the mail item during acquisition. For example, if the sender is an important business partner, the acquisition unit will prioritize the acquisition of image data of that mail item. Mail from important business partners is of high business importance, so it is appropriate to prioritize its acquisition. The acquisition unit can also quickly acquire image data if the sender is far away, taking into account the time it will take to arrive. Mail from distant locations takes a long time to arrive, so it is appropriate to acquire it quickly. Furthermore, if the senders are concentrated in a particular region, the acquisition unit can prioritize the acquisition of mail from that region. Mail concentrated in a particular region is of high business relevance, so it is appropriate to prioritize its acquisition. In this way, by considering the geographical location information of the sender of the mail item, highly relevant data can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the geographical location information of the sender into a generating AI and have the generating AI prioritize the relevant data.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the presentation of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the presentation of the analysis. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the presentation of the analysis. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform emotion estimation.

[0072] The analysis unit can adjust its analysis algorithm based on the type of contents of the mail during analysis. For example, if the contents of the mail are documents, the analysis unit uses a character recognition algorithm to analyze the contents. Character recognition algorithms include, for example, OCR technology, handwriting recognition, and printed character recognition. OCR technology recognizes printed characters with high accuracy and extracts them as text data. Handwriting recognition recognizes handwritten characters and extracts them as text data. Printed character recognition recognizes printed characters with high accuracy and extracts them as text data. In addition, if the contents of the mail are parcels, the analysis unit can also analyze the contents by reading barcodes or 2D codes (e.g., QR codes). Examples of barcode and 2D code reading include barcode readers, 2D code readers, and smartphone cameras. Barcode readers read barcodes with high accuracy and analyze their contents. 2D code readers read 2D codes with high accuracy and analyze their contents. Smartphone cameras can easily read barcodes and 2D codes and analyze their contents. Furthermore, if the contents of a mail item are unusual, the analysis unit can use a dedicated analysis algorithm to analyze the contents. Examples of such dedicated analysis algorithms include image recognition algorithms, speech recognition algorithms, and text analysis algorithms. The image recognition algorithm analyzes image data to identify its contents. The speech recognition algorithm analyzes audio data to identify its contents. The text analysis algorithm analyzes text data to identify its contents. This allows for improved analysis accuracy by adjusting the analysis algorithm based on the type of mail item. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the mail item's contents into a generative AI and have the generative AI adjust the analysis algorithm.

[0073] The analysis unit can improve the accuracy of its analysis by referring to past analysis results during the analysis process. For example, the analysis unit can improve the accuracy of analyzing mail with similar patterns based on past analysis results. Past analysis results include, for example, a database of past mail, the learning results of the analysis algorithm, and trends in misrecognition. The database of past mail stores information on mail that has been analyzed in the past and is useful for analyzing mail with similar patterns. The learning results of the analysis algorithm are the parameters and models of the algorithm learned based on past data and contribute to improving the accuracy of the analysis. Trends in misrecognition are patterns of misrecognition obtained from past analysis results and are useful for improving the analysis algorithm. The analysis unit can also improve the accuracy of its analysis by creating a database of past analysis results and referring to it in real time. Past analysis results stored in the database can be referred to in real time and used immediately during analysis. This improves the accuracy of the analysis by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past analysis results into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0074] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and determine the priority of analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of analysis. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of analysis. This allows for prioritizing the analysis of important mail according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform emotion estimation.

[0075] The analysis unit can modify its analysis algorithm during analysis, taking into account the industry and business type of the sender of the mail. For example, if the sender is a financial institution, the analysis unit will use an analysis algorithm that prioritizes security. Mail from financial institutions often contains confidential information, so analysis that prioritizes security is necessary. The analysis unit can also use an analysis algorithm that prioritizes privacy if the sender is a medical institution. Mail from medical institutions often contains personal information, so analysis that prioritizes privacy is necessary. The analysis unit can also use an analysis algorithm that prioritizes technical content if the sender is a manufacturing company. Mail from manufacturing companies often contains technical information, so analysis that prioritizes technical content is necessary. In this way, the analysis algorithm can be optimized by taking into account the industry and business type of the sender of the mail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input data on the sender's industry and business type into the generative AI and have the generative AI execute the modification of the analysis algorithm.

[0076] The analysis unit can evaluate the reliability of the sender of mail during analysis and reflect this in the analysis results. For example, if the sender is a highly reliable company, the analysis unit can provide the analysis results quickly. Mail from highly reliable companies is of high importance, so it is appropriate to provide the analysis results quickly. The analysis unit can also provide the analysis results cautiously if the sender is a less reliable company. Mail from less reliable companies is of low reliability, so it is appropriate to provide the analysis results cautiously. The analysis unit can also refer to a database for evaluating the reliability of the sender and reflect this in the analysis results. Reliability evaluation includes, for example, past transaction history, evaluation scores, and evaluations by third-party organizations. Past transaction history evaluates reliability based on past transaction history with the sender. The evaluation score is a numerical representation of the sender's reliability and is useful for evaluating reliability. Evaluations by third-party organizations are evaluations of reliability by independent organizations and are useful for evaluating reliability. As a result, evaluating the reliability of the sender of mail improves the reliability of the analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input reliability data from the sender into the generating AI and have the generating AI perform a reliability evaluation.

[0077] The mail delivery unit can estimate the user's emotions and adjust the delivery timing based on the estimated emotions. For example, the mail delivery unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the mail delivery unit can calculate an emotion score based on changes in facial expressions and adjust the delivery timing. The mail delivery unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the mail delivery unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the delivery timing. Furthermore, the mail delivery unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the mail delivery unit can calculate an emotion score based on fluctuations in heart rate and adjust the delivery timing. This reduces the burden on the user by adjusting the delivery timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the mailing unit may be performed using AI, or not using AI. For example, the mailing unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation.

[0078] The mailing unit can select the appropriate mailing timing by considering the workload of each department. For example, the mailing unit can grasp the workload of each department in real time and select the optimal mailing timing. Understanding the workload includes, for example, the level of workload, schedules, and progress of work. The level of workload indicates the workload of each department and is useful for understanding busy and slow periods. The schedule indicates the plans of each department and is useful for understanding the timing of work. The progress of work indicates the progress of work in each department and is useful for understanding the timing of work. In addition, the mailing unit can mail mail to departments that are in a busy period when their workload has subsided. For departments in a busy period, mailing mail at a time when their workload has subsided is appropriate to reduce the workload. In addition, the mailing unit can mail mail quickly to departments in a slow period for efficient processing. For departments in a slow period, mailing mail quickly is appropriate to increase work efficiency. This allows mail to be delivered at the optimal time by considering the work status of each department. Some or all of the above-described processes in the mailing section may be performed using AI, for example, or without AI. For example, the mailing section can input work status data from each department into a generating AI and have the generating AI select the optimal mailing timing.

[0079] The mailing unit can set a priority for mailing based on the importance of the mail at the time of mailing. For example, the mailing unit will mail important mail with the highest priority. Important mail includes, for example, contracts, important notices, and urgent documents. Contracts are important documents with legal effect and need to be mailed promptly. Important notices are documents containing important business information and need to be mailed promptly. Urgent documents are documents that require a quick response and need to be mailed with the highest priority. The mailing unit can also mail general mail with normal priority. General mail includes, for example, daily communication documents, reports, and invitations. Daily communication documents are documents for business communication and are suitable for mailing with normal priority. Reports are documents that report the progress and results of work and are suitable for mailing with normal priority. Invitations are documents that announce events and meetings and are suitable for mailing with normal priority. The mailing unit can also mail highly urgent mail with special priority and promptly. Urgent mail includes, for example, documents requiring immediate attention and documents with approaching deadlines. Documents requiring immediate attention need to be handled quickly and should be mailed with special priority. Documents with approaching deadlines need to be handled within the deadline and should also be mailed with special priority. This allows important mail to be processed preferentially by determining the mailing priority based on the importance of the mail. Some or all of the above processing in the mailing unit may be performed using AI, for example, or not using AI. For example, the mailing unit can input mail importance data into a generating AI and have the generating AI set the mailing priority.

[0080] The mail delivery unit can estimate the user's emotions and select a box to deliver the mail to based on those emotions. For example, the unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on changes in facial expressions and select a box to deliver the mail to. The mail delivery unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the unit can analyze the tone and speed of the voice, calculate an emotion score, and select a box to deliver the mail to. Furthermore, the mail delivery unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on fluctuations in heart rate and select a box to deliver the mail to. This allows for quick and efficient processing of mail by selecting a box according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the mailing unit may be performed using AI, or not using AI. For example, the mailing unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation.

[0081] The mail delivery unit can select an appropriate delivery route by considering the physical location of each department when delivering mail. For example, the mail delivery unit can determine the physical location of each department and deliver the mail via the shortest route. Determining the physical location includes, for example, the location within the building, the floor layout, and the distance between departments. The location within the building indicates where each department is located within the building, which helps in selecting the shortest route. The floor layout indicates which floor each department is located on, which helps in selecting the shortest route. The distance between departments indicates the physical distance between each department, which helps in selecting the shortest route. Furthermore, when delivering mail to multiple departments, the mail delivery unit can select an efficient route and deliver the mail quickly. When delivering mail to multiple departments, selecting an efficient route shortens the delivery time and enables efficient processing. The mail delivery unit can also select the optimal delivery route by considering the distance between departments. By considering the distance between departments, the shortest route can be selected and mail delivered quickly. In this way, by considering the physical location of each department, mail can be delivered via the shortest route. Some or all of the above-described processes in the mailing unit may be performed using AI, for example, or without AI. For example, the mailing unit can input physical location data of each department into a generating AI and have the generating AI select the optimal mailing route.

[0082] The mail slot can select the appropriate mailing method based on the size and shape of the mail item at the time of mailing. For example, the mail slot will place large mail items into a dedicated box. Large mail items include, for example, large documents, large parcels, and large envelopes. Large documents will not fit in a regular box, so it is appropriate to place them in a dedicated box. Large parcels will not fit in a regular box, so it is appropriate to place them in a dedicated box. Large envelopes will not fit in a regular box, so it is appropriate to place them in a dedicated box. The mail slot can also place small mail items into a regular box. Small mail items include, for example, small documents, small parcels, and small envelopes. Small documents will fit in a regular box, so it is appropriate to place them in a regular box. Small parcels will fit in a regular box, so it is appropriate to place them in a regular box. Small envelopes will fit in a regular box, so it is appropriate to place them in a regular box. Furthermore, the mail slot can also accept irregularly shaped mail using special methods. Irregularly shaped mail includes, for example, irregularly shaped parcels, irregularly shaped envelopes, and irregularly shaped documents. Irregularly shaped parcels do not fit in regular boxes, so it is appropriate to accept them using special methods. Irregularly shaped envelopes do not fit in regular boxes, so it is appropriate to accept them using special methods. Irregularly shaped documents do not fit in regular boxes, so it is appropriate to accept them using special methods. This allows for efficient mail processing by selecting the optimal mailing method based on the size and shape of the mail. Some or all of the above processing in the mail slot may be performed using AI, for example, or without AI. For example, the mail slot can input data on the size and shape of the mail into a generating AI and have the generating AI select the optimal mailing method.

[0083] The tracking unit can estimate the user's emotions and adjust the display method of tracking information based on the estimated emotions. For example, the tracking unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the tracking unit can calculate an emotion score based on changes in facial expressions and adjust the display method of tracking information. The tracking unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the tracking unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the display method of tracking information. The tracking unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the tracking unit can calculate an emotion score based on fluctuations in heart rate and adjust the display method of tracking information. By adjusting the display method of tracking information according to the user's emotions, it is possible to provide tracking information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the tracking unit may be performed using AI, or not using AI. For example, the tracking unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation.

[0084] The tracking unit can adjust tracking data during tracking, taking into account the attribute information of the recipient of the mail. For example, if the recipient holds an important position, the tracking unit will provide detailed tracking data. Recipients with important positions include, for example, executives, department heads, and section chiefs. Executives hold positions that involve making important decisions for the company, so it is appropriate to provide detailed tracking data. Department heads hold positions that involve managing departments, so it is appropriate to provide detailed tracking data. Section chiefs hold positions that involve managing sections, so it is appropriate to provide detailed tracking data. The tracking unit can also provide concise tracking data if the recipient is a regular employee. Regular employees include, for example, staff, assistants, and so on. Staff are employees who are in charge of specific tasks, so it is appropriate to provide concise tracking data. Staff are employees who perform general tasks, so it is appropriate to provide concise tracking data. Assistants are employees who perform auxiliary tasks, so it is appropriate to provide concise tracking data. The tracking unit can also provide optimal tracking data based on the recipient's attribute information. Attribute information includes, for example, age, occupation, and position. Age indicates the recipient's age and is useful for providing tracking data. Occupation indicates the recipient's occupation and is useful for providing tracking data. Job title indicates the recipient's job title and is useful for providing tracking data. This allows for the provision of optimal tracking data by considering the recipient's attribute information of the mail. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input recipient attribute information data into a generating AI and have the generating AI perform adjustments to the tracking data.

[0085] The tracking unit can improve tracking accuracy by referring to past tracking data during tracking. For example, the tracking unit can improve the tracking accuracy of mail with similar patterns based on past tracking data. Past tracking data includes, for example, a database of past mail, the learning results of the tracking algorithm, and misrecognition trends. The database of past mail stores information on mail that has been tracked in the past and is useful for tracking mail with similar patterns. The learning results of the tracking algorithm are the parameters and models of the algorithm learned based on past data and contribute to improving tracking accuracy. Misrecognition trends are patterns of misrecognition obtained from past tracking results and are useful for improving the tracking algorithm. The tracking unit can also improve tracking accuracy by creating a database of past tracking data and referring to it in real time. The databased past tracking data can be referred to in real time and used immediately during tracking. This improves tracking accuracy by referring to past tracking data. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input past tracking data into a generative AI and have the generative AI perform the improvement of tracking accuracy.

[0086] The tracking unit can estimate the user's emotions and prioritize tracking information based on the estimated emotions. For example, the tracking unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the tracking unit can calculate an emotion score based on changes in facial expressions and determine the priority of tracking information. The tracking unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the tracking unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of tracking information. The tracking unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the tracking unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of tracking information. This allows tracking information for important mail to be displayed preferentially by prioritizing tracking information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user image data captured by a camera into a generating AI and have the generating AI perform emotion estimation.

[0087] The tracking unit can adjust tracking data while tracking mail, taking into account the recipient's work situation. For example, if the recipient is in an important meeting, the tracking unit will refrain from notifying them of tracking data. It is appropriate to refrain from notifying recipients in important meetings so as not to disrupt the meeting. The tracking unit can also provide concise tracking data if the recipient is working. It is appropriate to provide concise tracking data to recipients who are working to improve their work efficiency. The tracking unit can also understand the recipient's work situation in real time and provide optimal tracking data. Understanding work situation includes, for example, workload, schedule, and work progress. Work workload indicates the recipient's workload and helps to understand busy and slow periods. Schedule indicates the recipient's plans and helps to understand the timing of work. Work progress indicates the recipient's work progress and helps to understand the timing of work. This allows the tracking unit to provide optimal tracking data by taking into account the recipient's work situation. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input recipient's work status data into a generating AI and have the generating AI perform adjustments to the tracking data.

[0088] The tracking unit can adjust tracking data during tracking, taking into account the recipient's geographical location. For example, if the recipient is at the company headquarters, the tracking unit provides detailed tracking data. Providing detailed tracking data is appropriate when the recipient is at the headquarters because mail can be received quickly. The tracking unit can also provide concise tracking data when the recipient is out. Providing concise tracking data is appropriate when the recipient is out because mail delivery may be delayed. The tracking unit can also provide optimal tracking data based on the recipient's geographical location. Geographical location information includes, for example, GPS data, map information, and location services. GPS data shows the recipient's current location and is useful for providing tracking data. Map information shows the recipient's location on a map and is useful for providing tracking data. Location services track the recipient's location in real time and are useful for providing tracking data. This allows for the provision of optimal tracking data by taking into account the recipient's geographical location. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input the recipient's geographical location data into the generating AI and have the generating AI perform adjustments to the tracking data. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, analysis unit, mailing unit, and tracking unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit uses the camera 42 or scanner of the smart device 14 to acquire image data of the recipient and sender of the mail. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired image data to determine which department the mail is addressed to. The mailing unit is implemented, for example, by the control unit 46A of the smart device 14, which determines the box to mail the mail into based on the analysis results. The tracking unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses an RFID system to record the recipient information and the date and time of receipt of the mail. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, analysis unit, mailing unit, and tracking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit uses the camera 42 or scanner of the smart glasses 214 to acquire image data of the recipient and sender of the mail. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired image data to determine which department the mail is addressed to. The mailing unit is implemented, for example, by the control unit 46A of the smart glasses 214, which determines the box to mail the mail into based on the analysis results. The tracking unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses an RFID system to record the recipient information and the date and time of receipt of the mail. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, analysis unit, mailing unit, and tracking unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and scanner of the headset terminal 314 to acquire image data of the recipient and sender of the mail. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired image data to determine which department the mail is addressed to. The mailing unit is implemented, for example, by the control unit 46A of the headset terminal 314, which determines the box to mail the mail into based on the analysis results. The tracking unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses an RFID system to record the recipient information and the date and time of receipt of the mail. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, analysis unit, mailing unit, and tracking unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and scanner of the robot 414 to acquire image data of the recipient and sender of the mail. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired image data to determine which department the mail is addressed to. The mailing unit is implemented, for example, by the control unit 46A of the robot 414, which determines the box to mail the mail into based on the analysis results. The tracking unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses an RFID system to record the recipient information and the date and time of receipt of the mail.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The acquisition unit can read 2D codes and barcodes printed on the surface of mail and obtain additional information. For example, by scanning a 2D code, it can obtain detailed information about the sender and the contents of the mail. By reading a barcode, it can also obtain the mail's tracking number and delivery status. Furthermore, the acquisition unit can extract handwritten notes and annotations written on the surface of mail as text data using OCR technology. This allows for the efficient acquisition of additional information about mail, supporting processing in the analysis unit.

[0091] The analysis unit can adjust its analysis algorithm based on the type of contents of the mail. For example, if the contents of the mail are documents, it uses a character recognition algorithm to analyze the contents. Character recognition algorithms include OCR technology, handwriting recognition, and printed character recognition. If the contents of the mail are parcels, it can also read barcodes or 2D codes to analyze the contents. Furthermore, if the contents of the mail are special, the analysis unit can use a dedicated analysis algorithm to analyze the contents. This improves analysis accuracy by adjusting the analysis algorithm based on the type of contents of the mail.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It calculates an emotion score based on changes in facial expressions and adjusts the presentation of the analysis. It can also record the user's voice and estimate their emotions using voice analysis technology. It analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the presentation of the analysis. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0093] The mailing department can monitor the workload of each department in real time and select the optimal mailing time. For example, it can consider the workload, schedule, and progress of tasks to mail mail when work is less busy. Conversely, it can quickly mail mail to departments during off-peak seasons for efficient processing. In this way, mail can be mailed at the optimal time by considering the workload of each department.

[0094] The tracking unit can estimate the user's emotions and adjust the display method of tracking information based on the estimated emotions. For example, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can then calculate an emotion score based on changes in facial expressions and adjust the display method of tracking information. It can also record the user's voice and estimate their emotions using voice analysis technology. It can analyze the tone and speed of their voice, calculate an emotion score, and adjust the display method of tracking information. By adjusting the display method of tracking information according to the user's emotions, it is possible to provide tracking information that is easy for the user to understand.

[0095] The acquisition unit can select the appropriate acquisition method according to the size and shape of the mail item. For example, for large mail items, a wide-angle lens can be used to capture the entire item at once. For smaller mail items, the zoom function can be used to capture detailed parts. For irregularly shaped mail items, images can be acquired from multiple angles to reconstruct the overall image. This allows for the selection of the optimal acquisition method according to the size and shape of the mail item, thereby obtaining accurate image data.

[0096] The analysis unit can modify its analysis algorithm during analysis, taking into account the industry and business type of the sender of the mail. For example, if the sender is a financial institution, an analysis algorithm that prioritizes security can be used. Similarly, if the sender is a medical institution, an analysis algorithm that prioritizes privacy can be used. If the sender is a manufacturing company, an analysis algorithm that prioritizes technical content can be used. This allows the analysis algorithm to be optimized by considering the industry and business type of the sender of the mail.

[0097] The mail delivery unit can estimate the user's emotions and adjust the mail delivery timing based on those emotions. For example, it can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. It can then calculate an emotion score based on changes in facial expression and adjust the mail delivery timing accordingly. It can also record the user's voice and estimate their emotions using voice analysis technology. By analyzing the tone and speed of their voice, it can calculate an emotion score and adjust the mail delivery timing accordingly. This allows the system to reduce the burden on the user by adjusting the mail delivery timing according to their emotions.

[0098] The tracking unit can adjust tracking data during tracking, taking into account the attribute information of the mail recipient. For example, if the recipient holds an important position, it can provide detailed tracking data. Important position recipients include executives, department heads, and section chiefs. Conversely, if the recipient is a regular employee, it can provide concise tracking data. Regular employees include staff, assistants, and other personnel. This allows for the provision of optimal tracking data by considering the attribute information of the mail recipient.

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It calculates an emotion score based on changes in facial expressions and determines the priority of analysis. It can also record the user's voice and estimate their emotions using voice analysis technology. It analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of analysis. By determining the priority of analysis according to the user's emotions, it is possible to prioritize the analysis of important mail.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The acquisition unit acquires image data of the recipient and sender of the mail. For example, it can use a scanner to scan the surface of the mail and acquire high-resolution image data in JPEG or PNG format. Alternatively, it can use a camera to photograph the surface of the mail and acquire image data. Furthermore, it is possible to extract text data from the image data using OCR technology. Step 2: The analysis unit analyzes the image data acquired by the acquisition unit to determine which department the message is addressed to. For example, it uses OCR technology to recognize characters in the image data and extracts the recipient's name and sender's information. It also uses a machine learning algorithm to learn from past data and patterns based on the recipient's name and sender's information to identify the most suitable department. Step 3: The mailing department determines which box to use for mail based on the department identified by the analysis department. For example, each department may have its own dedicated mailing box, and the most suitable box is selected. Mail addressed to the sales department is placed in the sales department's dedicated box. Step 4: The tracking unit attaches RFID tags to the mail and logs who received it and when. For example, it uses an RFID reader to read the tag and record the recipient's information and the date and time of receipt. When the mail is removed from the drop box, the RFID reader reads the tag and records the recipient's information and the date and time of receipt.

[0102] 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include 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 inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] 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.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] 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.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] 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.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] 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.

[0129] 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.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] 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.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] 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.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] 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.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] 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.

[0158] 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.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] 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.

[0173] [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires image data of the recipient's name and sender's name on the mail, An analysis unit analyzes the image data acquired by the acquisition unit and determines which department it is addressed to, Based on the department determined by the aforementioned analysis unit, the mailing unit determines the box to which the mail should be placed, It includes an RFID tag attached to mail and a tracking unit that logs who received it and when. A system characterized by the following features.

2. The acquisition unit is, Acquire image data using a scanner or camera. The system according to feature 1.

3. The aforementioned analysis unit, Based on the recipient's and sender's information, the system learns from past data and patterns to identify the appropriate department. The system according to feature 1.

4. The aforementioned mailing section is, Each department will have its own designated drop box, and the appropriate box will be selected. The system according to feature 1.

5. The aforementioned tracking unit is Use an RFID reader to read the tag and record the recipient's information and the date and time of receipt. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image data acquisition based on those estimated emotions. The system according to feature 1.

7. The acquisition unit is, Select the appropriate method of obtaining mail according to its size and shape. The system according to feature 1.

8. The acquisition unit is, Upon acquisition, the condition of the mail's surface is detected and correction processing is performed. The system according to feature 1.

Images