system

Abstract

The system according to this embodiment aims to automate the sorting of mail, making it efficient and accurate. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit captures image data of mail. The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail. The proposal unit proposes the relevant department and person in charge based on the content analyzed by the analysis unit. The provision unit builds a database of senders and provides recommended departments based on past information.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the sorting work of mail is often performed manually, which is inefficient and has a risk of misdistribution.

[0005] The system according to the embodiment aims to automate the sorting work of mail and perform it efficiently and accurately.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit captures image data of mail. The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail. The proposal unit proposes the appropriate department and person in charge based on the content analyzed by the analysis unit. The provision unit builds a database of senders and provides recommended departments based on past information. [Effects of the Invention]

[0007] The system according to this embodiment can automate the sorting of mail, enabling it to be performed efficiently and accurately. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] <0000The 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) An automated mail sorting system according to an embodiment of the present invention is a system that uses AI to automatically classify and sort mail according to complex sorting rules. This automated mail sorting system takes in image data of mail and the AI ​​scans its contents. Next, it uses natural language processing (NLP) technology to analyze the contents of the mail and automatically suggests the appropriate department and person in charge. For example, it can automatically convert old department names to new department names or match the names of retired and current employees. It also introduces a mechanism to distribute mail addressed to the president or executives to the appropriate department according to its importance and content. Furthermore, even for mail that only contains a company name or mail addressed to a department that does not have a corresponding address, the AI ​​analyzes the contents and suggests the appropriate department and person in charge. It also incorporates a mechanism to build a database of senders and provide recommended departments based on past information. For example, it takes in image data of mail. At this time, it uses OCR technology to read the address and recipient's name on the mail. For example, it extracts the address and recipient's name from the image of the mail and saves them in the database in separate columns. Next, the AI ​​scans the taken image data and analyzes the contents using natural language processing (NLP) technology. For example, the system can automatically convert old department names to new department names based on the content of mail, and match the names of former and current employees. Furthermore, mail addressed to the president or executives will be sorted to the appropriate department based on its importance and content. Even for mail containing only a company name or mail addressed to no specific department, the AI ​​will analyze the content and suggest the appropriate department or person. For instance, it can identify and suggest the relevant department or person based on the mail's content. The system will also incorporate a database of senders and provide recommended departments based on past information. This streamlines mail sorting and reduces workload. For example, even mail with incorrect addresses will have the AI ​​automatically determine its destination, eliminating the need for manual verification. Important mail will also be analyzed and sorted to the appropriate department, enabling a quick response. In short, the automated mail sorting system streamlines mail sorting and reduces workload.

[0029] The automated mail sorting system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit captures image data of mail. For example, the collection unit captures image data of mail using a scanner and saves it as digital data. The collection unit can read addresses and names on mail using OCR technology. For example, the collection unit extracts addresses and names from images of mail and saves them in a database in separate columns. The collection unit can also scan handwritten mail and convert it into digital data. For example, the collection unit reads handwritten mail with a scanner and converts it into text information using OCR technology. The collection unit can also directly read mail submitted in digital format. The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail. For example, the analysis unit analyzes the contents of mail using natural language processing (NLP) technology. The analysis unit can automatically convert old department names to new department names. For example, the analysis unit automatically converts old department names to new department names from the contents of mail. The analysis department can also match the names of former and current employees. For example, the analysis department can match the names of former and current employees based on the content of mail. The analysis department can also sort mail addressed to the president and executives to the appropriate department according to its importance and content. For example, the analysis department sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. The proposal department proposes the relevant department and person in charge based on the information analyzed by the analysis department. For example, the proposal department identifies and proposes the relevant department and person in charge based on the content of the mail. The proposal department can also analyze the content of mail that only contains the company name or mail addressed to a department that does not have a corresponding department, and propose the appropriate department and person in charge. For example, the proposal department identifies and proposes the relevant department and person in charge based on the content of the mail. The provision department builds a database of senders and provides recommended departments based on past information. For example, the provision department builds a database of senders and provides recommended departments based on past information. The provision department can provide recommended departments based on past mailing history and response results. For example, the service department provides recommended departments based on past mailing history and response results. This allows the automated mail sorting system according to the embodiment to streamline mail sorting operations and reduce man-hours.

[0030] The collection unit captures image data of mail. For example, the collection unit scans the image data of mail and saves it as digital data. Specifically, when mail is placed on a conveyor belt and passes through the scanner, a high-resolution image is taken. This image is immediately saved as digital data and used for subsequent processing. The collection unit can read addresses and names on mail using OCR technology. OCR technology is a technology that recognizes characters in an image and converts them into text data, and it supports both handwritten and printed characters. For example, the collection unit extracts addresses and names from images of mail and saves them in a database, separated into their respective columns. This structures the address and name information, making it easier to use for subsequent analysis and suggestions. The collection unit can also scan handwritten mail and convert it into digital data. Recognizing handwritten characters requires particularly advanced OCR technology, and algorithms are used to accurately recognize differences in character shape and handwriting. For example, the collection unit scans handwritten mail and converts it into text information using OCR technology. The collection unit can also directly read mail submitted in digital format. Digital mail, such as emails and online forms, is sent directly into the system and then sent to the analysis department. This allows the collection department to efficiently process not only physical mail but also digital mail.

[0031] The analysis unit analyzes image data captured by the collection unit to analyze the contents of mail. For example, the analysis unit uses natural language processing (NLP) technology to analyze the contents of mail. NLP technology is used to analyze text data and understand its meaning and context, and is used to accurately grasp the contents of mail. The analysis unit can automatically convert old department names to new department names. For example, the analysis unit automatically converts old department names to new department names based on the contents of mail. This is a function to accommodate organizational changes and changes in department names within a company, and is important to ensure that mail reaches the correct department. The analysis unit can also match the names of former and current employees. For example, the analysis unit matches the names of former and current employees based on the contents of mail. This prevents mail addressed to former employees from being mistakenly delivered to current employees. The analysis unit can also sort mail addressed to the president and executives to the appropriate department according to its importance and content. For example, the analysis unit sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the contents of the mail. This ensures that important mail reaches the appropriate department quickly, enabling prompt responses. Furthermore, the analysis unit can improve the accuracy of its analysis by using machine learning algorithms when analyzing the contents of mail. For example, it can learn from past mail data to build a model that more accurately analyzes the contents of new mail. This allows the analysis unit to always perform highly accurate analyses based on the latest information.

[0032] The Proposal Department proposes the appropriate department and person based on the analysis performed by the Analysis Department. For example, the Proposal Department identifies and proposes the appropriate department and person based on the content of a piece of mail. Specifically, an algorithm is used to identify the department and person best suited to the content of a piece of mail, based on the data provided by the Analysis Department. The Proposal Department can also analyze the content of mail that only contains a company name or mail addressed to a department that does not have a corresponding department, and propose the appropriate department and person. For example, the Proposal Department identifies and proposes the appropriate department and person based on the content of a piece of mail. This makes it possible to quickly deliver mail with an unclear address to the appropriate department and person. The Proposal Department can improve the accuracy of its proposals by utilizing past data. For example, it can make proposals for similar mail based on the delivery history and response results of past mail. This allows the Proposal Department to always make the best proposals. In addition, the Proposal Department can present the proposed content to users and collect feedback. Based on user feedback, the proposal algorithm is continuously improved to increase the accuracy of proposals. This allows the Proposal Department to respond flexibly to user needs and improve the overall efficiency of the system.

[0033] The service department builds a database of senders and provides recommended departments based on past information. Specifically, it centrally manages sender information and stores past sending history and response results in the database. Based on past sending history and response results, the service department can provide recommended departments. For example, if mail is sent again from the same sender, the appropriate department can be quickly identified based on past information. It is important for the service department to regularly update the sender database and maintain the latest information. This enables recommendations to always be based on the most up-to-date information. In addition, the service department can link the sender database with other systems and departments. For example, by linking with customer management systems and sales support systems and sharing sender information, more accurate recommendations can be made. Furthermore, the service department can not only provide recommended departments but also provide feedback to senders. For example, by encouraging senders to provide appropriate address information, the accuracy of mail sorting can be improved. This allows the service provider to offer value to both the sender and the recipient, and to streamline the mail sorting process.

[0034] The analysis unit can automatically convert old department names to new department names. For example, the analysis unit can automatically convert old department names to new department names based on the content of mail. The analysis unit can automatically convert old department names to new department names based on past department name lists and change history. For example, the analysis unit can automatically convert old department names to new department names based on past department name lists and change history. This improves the accuracy of mail sorting by automatically converting old department names to new department names. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of mail into a generating AI and have the generating AI perform the process of automatically converting old department names to new department names.

[0035] The analysis unit can match the names of retired and current employees. For example, the analysis unit can match the names of retired and current employees based on the contents of mail. The analysis unit can also match the names of retired and current employees based on a list of retired and current employees. For example, the analysis unit can match the names of retired and current employees based on a list of retired and current employees. This improves the accuracy of mail sorting by matching the names of retired and current employees. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the contents of mail into a generating AI and have the generating AI perform the process of matching the names of retired and current employees.

[0036] The proposal department can sort mail addressed to the president and executives to the appropriate department according to its importance and content. For example, the proposal department sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. The proposal department can sort mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. For example, the proposal department sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. This enables a swift response to important mail by sorting mail addressed to the president and executives to the appropriate department according to its importance and content. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the content of the mail into a generating AI and have the generating AI perform the processing of sorting it to the appropriate department according to its importance and content.

[0037] The proposal department can analyze the contents of mail containing only a company name or mail addressed to no applicable department and propose the appropriate department or person in charge. For example, the proposal department can identify and propose the appropriate department or person in charge based on the contents of the mail. The proposal department can also analyze the contents of mail containing only a company name or mail addressed to no applicable department and propose the appropriate department or person in charge. For example, the proposal department can identify and propose the appropriate department or person in charge based on the contents of the mail. This improves the accuracy of mail sorting by proposing the appropriate department or person in charge even for mail containing only a company name or mail addressed to no applicable department. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the contents of the mail into a generation AI and have the generation AI perform the process of proposing the appropriate department or person in charge.

[0038] The service department can build a database of senders and provide recommended departments based on past information. The service department can, for example, build a database of senders and provide recommended departments based on past information. The service department can provide recommended departments based on past sending history and response results. For example, the service department can provide recommended departments based on past sending history and response results. This improves the accuracy of mail sorting by building a database of senders and providing recommended departments based on past information. Some or all of the above processing in the service department may be performed using AI, for example, or without AI. For example, the service department can input the sender database into a generating AI and have the generating AI perform the process of providing recommended departments based on past information.

[0039] The collection unit can select a collection method based on the type or importance of the mail. For example, the collection unit may prioritize important mail and postpone regular mail. The collection unit may use dedicated collection equipment for large mail items. The collection unit may also implement special security measures for collecting highly confidential mail. This allows for efficient collection by selecting a collection method based on the type and importance of the mail. Some or all of the above processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit may input the type and importance of the mail into a generating AI and have the generating AI select the collection method.

[0040] The collection unit can automatically adjust the collection device according to the size or shape of the mail. For example, the collection unit can use a large collection device for large mail, a small collection device for small mail, and a flexible collection device for irregularly shaped mail. This allows for collection of various types of mail by automatically adjusting the collection device according to the size and shape of the mail. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the size and shape of the mail into a generating AI and have the generating AI perform the automatic adjustment of the collection device.

[0041] The collection unit can optimize the collection method based on the sender information of the mail. For example, the collection unit can prioritize mail from important senders, while delaying mail from regular senders. The collection unit can also apply special collection methods to mail from specific senders. This enables efficient collection by optimizing the collection method based on the sender information of the mail. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the sender information of the mail into a generating AI and have the generating AI perform the optimization of the collection method.

[0042] The collection unit can predict the arrival time of mail and adjust the collection schedule. For example, the collection unit can predict the arrival time of mail and set the optimal collection timing. If the arrival is delayed, the collection unit can change the collection schedule. If the arrival is earlier, the collection unit can also bring forward the collection schedule. This enables efficient collection by predicting the arrival time of mail and adjusting the collection schedule. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the arrival time of mail into a generating AI and have the generating AI perform the adjustment of the collection schedule.

[0043] The analysis unit can optimize its analysis algorithm based on the content of the mail. For example, the analysis unit can apply a detailed analysis algorithm to important mail. The analysis unit can apply a simplified analysis algorithm to ordinary mail. The analysis unit can also apply a special analysis algorithm to highly confidential mail. This allows for efficient analysis by optimizing the analysis algorithm based on the content of the mail. 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 the content of the mail into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can change its analysis method according to the language and format of the mail. For example, the analysis unit can apply an English-specific analysis method to English mail. The analysis unit can apply a Japanese-specific analysis method to Japanese mail. The analysis unit can also apply an analysis method suitable for a specific format of mail. This makes it possible to analyze various types of mail by changing the analysis method according to the language and format of the mail. 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 the language and format of the mail into a generating AI and have the generating AI execute the change in the analysis method.

[0045] The analysis unit can improve the accuracy of its analysis based on the sender information of the mail. For example, the analysis unit can perform a detailed analysis on mail from important senders. The analysis unit can perform a simplified analysis on mail from ordinary senders. The analysis unit can also apply special analysis methods to mail from specific senders. This allows for efficient analysis by improving the accuracy of the analysis based on the sender information of the mail. 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 the sender information of the mail into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0046] The analysis unit can supplement the analysis results by referring to an external database related to the contents of the mail. For example, the analysis unit can supplement the analysis results by referring to an external database related to the contents of the mail. The analysis unit can improve the accuracy of the analysis results based on the information obtained from the external database. The analysis unit can also supplement the analysis results based on the information from the external database. This improves the accuracy of the analysis by supplementing the analysis results by referring to an external database related to the contents of the mail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from the external database into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the mail item. For example, it can provide detailed proposals for important mail items, simplified proposals for regular mail items, and special proposals for highly confidential mail items. This allows for efficient proposals by adjusting the level of detail based on the importance of the mail item. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the mail item into a generating AI and have the generating AI perform the process of adjusting the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of mail. For example, the proposal unit can apply a detailed proposal algorithm to important mail. The proposal unit can apply a simplified proposal algorithm to ordinary mail. The proposal unit can also apply a special proposal algorithm to highly confidential mail. This allows for efficient proposals by applying different proposal algorithms depending on the category of mail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the mail category into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0049] The proposal unit can optimize the proposal content based on the sender information of the mail. For example, the proposal unit can provide detailed proposals for mail from important senders. For mail from regular senders, it can provide simplified proposals. The proposal unit can also provide special proposals for mail from specific senders. This enables efficient proposals by optimizing the proposal content based on the sender information of the mail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the sender information of the mail into a generating AI and have the generating AI perform the optimization of the proposal content.

[0050] The proposal unit can improve the accuracy of its proposals by referring to past proposal history related to the content of the mail. For example, the proposal unit can improve the accuracy of its proposals by referring to past proposal history. The proposal unit can make the most optimal proposal from past proposal history. The proposal unit can also improve the accuracy of its proposals based on past proposal history. This makes it possible to make efficient proposals by improving the accuracy of proposals by referring to past proposal history related to the content of the mail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input past proposal history into a generation AI and have the generation AI perform the improvement of proposal accuracy.

[0051] The service provider can improve the accuracy of recommended departments by referring to past source data. The service provider can improve the accuracy of recommended departments by referring to past source data. The service provider can provide the optimal recommended department from past source data. The service provider can also improve the accuracy of recommended departments based on past source data. This enables efficient service provision by improving the accuracy of recommended departments by referring to past source data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input past source data into a generating AI and have the generating AI perform the improvement of the accuracy of recommended departments.

[0052] The delivery unit can optimize the selection criteria for recommended departments based on the content of the mail. For example, the delivery unit can provide detailed department recommendations for important mail. For regular mail, it can provide simplified department recommendations. For highly confidential mail, it can also provide special department recommendations. This enables efficient delivery by optimizing the selection criteria for recommended departments based on the content of the mail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the content of the mail into a generating AI and have the generating AI optimize the selection criteria for recommended departments.

[0053] The delivery unit can optimize recommended departments based on the sender information of the mail. For example, the delivery unit can provide detailed recommended departments for mail from important senders. For mail from regular senders, it can provide simplified recommended departments. The delivery unit can also provide special recommended departments for mail from specific senders. This enables efficient delivery by optimizing recommended departments based on the sender information of the mail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the sender information of the mail into a generating AI and have the generating AI perform the optimization of recommended departments.

[0054] The delivery unit can improve the accuracy of recommended departments by referring to an external database related to the content of mail. The delivery unit can improve the accuracy of recommended departments by referring to an external database related to the content of mail. The delivery unit can optimize recommended departments based on information obtained from the external database. The delivery unit can also improve the accuracy of recommended departments based on information from the external database. This enables efficient delivery by improving the accuracy of recommended departments by referring to an external database related to the content of mail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information from the external database into a generating AI and have the generating AI perform the improvement of the accuracy of recommended departments.

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

[0056] The proposal department can adjust the way the proposal is presented based on the content of the mail. For example, the proposal department can provide detailed proposals for important mail, simplified proposals for regular mail, and special proposals for highly confidential mail. By adjusting the way the proposal is presented based on the content of the mail, efficient proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the content of the mail into a generating AI and have the generating AI perform the process of adjusting the way the proposal is presented.

[0057] The collection unit can predict the arrival time of mail and adjust the collection schedule. For example, the collection unit predicts the arrival time of mail and sets the optimal collection timing. If the arrival is delayed, the collection unit can change the collection schedule. If the arrival is earlier, the collection unit can also bring the collection schedule forward. This enables efficient collection by predicting the arrival time of mail and adjusting the collection schedule. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the arrival time of mail into a generating AI and have the generating AI perform the adjustment of the collection schedule.

[0058] The analysis unit can supplement the analysis results by referring to an external database related to the contents of the mail. For example, the analysis unit can refer to an external database related to the contents of the mail and supplement the analysis results. The analysis unit can improve the accuracy of the analysis results based on the information obtained from the external database. The analysis unit can also supplement the analysis results based on the information from the external database. This improves the accuracy of the analysis by supplementing the analysis results by referring to an external database related to the contents of the mail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information from the external database into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0059] The collection unit can select a collection method based on the type or importance of the mail. For example, important mail can be collected urgently, while regular mail can be handled later. The collection unit can use dedicated collection equipment for large mail items. The collection unit can also collect highly confidential mail with special security measures. This allows for efficient collection by selecting a collection method based on the type and importance of the mail. Some or all of the above processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the type and importance of the mail into a generating AI and have the generating AI select the collection method.

[0060] The delivery department can optimize the selection criteria for recommended departments based on the content of the mail. For example, the delivery department can provide detailed department recommendations for important mail. For regular mail, it can provide simplified department recommendations. For highly confidential mail, it can also provide special department recommendations. This enables efficient delivery by optimizing the selection criteria for recommended departments based on the content of the mail. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the content of the mail into a generating AI and have the generating AI optimize the selection criteria for recommended departments.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit captures image data of mail. For example, the collection unit can use a scanner to save image data of mail as digital data and use OCR technology to read addresses and recipient names. Handwritten mail can also be scanned and converted into digital data. It can also directly read mail submitted in digital format. Step 2: The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail. For example, the analysis unit uses natural language processing (NLP) technology to analyze the contents of the mail, automatically converting old department names to new department names, and matching the names of former and current employees. It can also sort mail addressed to the president or executives to the appropriate department according to its importance and content. Step 3: The proposal department proposes the appropriate department and person based on the analysis conducted by the analysis department. For example, the proposal department identifies and proposes the appropriate department and person from the content of the mail. Even for mail containing only a company name or mail addressed to no specific department, the content can be analyzed and the appropriate department and person can be proposed. Step 4: The service department builds a database of senders and provides recommended departments based on past information. For example, the service department provides recommended departments based on past sending history and response results. This streamlines the mail sorting process and reduces man-hours.

[0063] (Example of form 2) An automated mail sorting system according to an embodiment of the present invention is a system that uses AI to automatically classify and sort mail according to complex sorting rules. This automated mail sorting system takes in image data of mail and the AI ​​scans its contents. Next, it uses natural language processing (NLP) technology to analyze the contents of the mail and automatically suggests the appropriate department and person in charge. For example, it can automatically convert old department names to new department names or match the names of retired and current employees. It also introduces a mechanism to distribute mail addressed to the president or executives to the appropriate department according to its importance and content. Furthermore, even for mail that only contains a company name or mail addressed to a department that does not have a corresponding address, the AI ​​analyzes the contents and suggests the appropriate department and person in charge. It also incorporates a mechanism to build a database of senders and provide recommended departments based on past information. For example, it takes in image data of mail. At this time, it uses OCR technology to read the address and recipient's name on the mail. For example, it extracts the address and recipient's name from the image of the mail and saves them in the database in separate columns. Next, the AI ​​scans the taken image data and analyzes the contents using natural language processing (NLP) technology. For example, the system can automatically convert old department names to new department names based on the content of mail, and match the names of former and current employees. Furthermore, mail addressed to the president or executives will be sorted to the appropriate department based on its importance and content. Even for mail containing only a company name or mail addressed to no specific department, the AI ​​will analyze the content and suggest the appropriate department or person. For instance, it can identify and suggest the relevant department or person based on the mail's content. The system will also incorporate a database of senders and provide recommended departments based on past information. This streamlines mail sorting and reduces workload. For example, even mail with incorrect addresses will have the AI ​​automatically determine its destination, eliminating the need for manual verification. Important mail will also be analyzed and sorted to the appropriate department, enabling a quick response. In short, the automated mail sorting system streamlines mail sorting and reduces workload.

[0064] The automated mail sorting system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit captures image data of mail. For example, the collection unit captures image data of mail using a scanner and saves it as digital data. The collection unit can read addresses and names on mail using OCR technology. For example, the collection unit extracts addresses and names from images of mail and saves them in a database in separate columns. The collection unit can also scan handwritten mail and convert it into digital data. For example, the collection unit reads handwritten mail with a scanner and converts it into text information using OCR technology. The collection unit can also directly read mail submitted in digital format. The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail. For example, the analysis unit analyzes the contents of mail using natural language processing (NLP) technology. The analysis unit can automatically convert old department names to new department names. For example, the analysis unit automatically converts old department names to new department names from the contents of mail. The analysis department can also match the names of former and current employees. For example, the analysis department can match the names of former and current employees based on the content of mail. The analysis department can also sort mail addressed to the president and executives to the appropriate department according to its importance and content. For example, the analysis department sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. The proposal department proposes the relevant department and person in charge based on the information analyzed by the analysis department. For example, the proposal department identifies and proposes the relevant department and person in charge based on the content of the mail. The proposal department can also analyze the content of mail that only contains the company name or mail addressed to a department that does not have a corresponding department, and propose the appropriate department and person in charge. For example, the proposal department identifies and proposes the relevant department and person in charge based on the content of the mail. The provision department builds a database of senders and provides recommended departments based on past information. For example, the provision department builds a database of senders and provides recommended departments based on past information. The provision department can provide recommended departments based on past mailing history and response results. For example, the service department provides recommended departments based on past mailing history and response results. This allows the automated mail sorting system according to the embodiment to streamline mail sorting operations and reduce man-hours.

[0065] The collection unit captures image data of mail. For example, the collection unit scans the image data of mail and saves it as digital data. Specifically, when mail is placed on a conveyor belt and passes through the scanner, a high-resolution image is taken. This image is immediately saved as digital data and used for subsequent processing. The collection unit can read addresses and names on mail using OCR technology. OCR technology is a technology that recognizes characters in an image and converts them into text data, and it supports both handwritten and printed characters. For example, the collection unit extracts addresses and names from images of mail and saves them in a database, separated into their respective columns. This structures the address and name information, making it easier to use for subsequent analysis and suggestions. The collection unit can also scan handwritten mail and convert it into digital data. Recognizing handwritten characters requires particularly advanced OCR technology, and algorithms are used to accurately recognize differences in character shape and handwriting. For example, the collection unit scans handwritten mail and converts it into text information using OCR technology. The collection unit can also directly read mail submitted in digital format. Digital mail, such as emails and online forms, is sent directly into the system and then sent to the analysis department. This allows the collection department to efficiently process not only physical mail but also digital mail.

[0066] The analysis unit analyzes image data captured by the collection unit to analyze the contents of mail. For example, the analysis unit uses natural language processing (NLP) technology to analyze the contents of mail. NLP technology is used to analyze text data and understand its meaning and context, and is used to accurately grasp the contents of mail. The analysis unit can automatically convert old department names to new department names. For example, the analysis unit automatically converts old department names to new department names based on the contents of mail. This is a function to accommodate organizational changes and changes in department names within a company, and is important to ensure that mail reaches the correct department. The analysis unit can also match the names of former and current employees. For example, the analysis unit matches the names of former and current employees based on the contents of mail. This prevents mail addressed to former employees from being mistakenly delivered to current employees. The analysis unit can also sort mail addressed to the president and executives to the appropriate department according to its importance and content. For example, the analysis unit sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the contents of the mail. This ensures that important mail reaches the appropriate department quickly, enabling prompt responses. Furthermore, the analysis unit can improve the accuracy of its analysis by using machine learning algorithms when analyzing the contents of mail. For example, it can learn from past mail data to build a model that more accurately analyzes the contents of new mail. This allows the analysis unit to always perform highly accurate analyses based on the latest information.

[0067] The Proposal Department proposes the appropriate department and person based on the analysis performed by the Analysis Department. For example, the Proposal Department identifies and proposes the appropriate department and person based on the content of a piece of mail. Specifically, an algorithm is used to identify the department and person best suited to the content of a piece of mail, based on the data provided by the Analysis Department. The Proposal Department can also analyze the content of mail that only contains a company name or mail addressed to a department that does not have a corresponding department, and propose the appropriate department and person. For example, the Proposal Department identifies and proposes the appropriate department and person based on the content of a piece of mail. This makes it possible to quickly deliver mail with an unclear address to the appropriate department and person. The Proposal Department can improve the accuracy of its proposals by utilizing past data. For example, it can make proposals for similar mail based on the delivery history and response results of past mail. This allows the Proposal Department to always make the best proposals. In addition, the Proposal Department can present the proposed content to users and collect feedback. Based on user feedback, the proposal algorithm is continuously improved to increase the accuracy of proposals. This allows the Proposal Department to respond flexibly to user needs and improve the overall efficiency of the system.

[0068] The service department builds a database of senders and provides recommended departments based on past information. Specifically, it centrally manages sender information and stores past sending history and response results in the database. Based on past sending history and response results, the service department can provide recommended departments. For example, if mail is sent again from the same sender, the appropriate department can be quickly identified based on past information. It is important for the service department to regularly update the sender database and maintain the latest information. This enables recommendations to always be based on the most up-to-date information. In addition, the service department can link the sender database with other systems and departments. For example, by linking with customer management systems and sales support systems and sharing sender information, more accurate recommendations can be made. Furthermore, the service department can not only provide recommended departments but also provide feedback to senders. For example, by encouraging senders to provide appropriate address information, the accuracy of mail sorting can be improved. This allows the service provider to offer value to both the sender and the recipient, and to streamline the mail sorting process.

[0069] The analysis unit can automatically convert old department names to new department names. For example, the analysis unit can automatically convert old department names to new department names based on the content of mail. The analysis unit can automatically convert old department names to new department names based on past department name lists and change history. For example, the analysis unit can automatically convert old department names to new department names based on past department name lists and change history. This improves the accuracy of mail sorting by automatically converting old department names to new department names. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of mail into a generating AI and have the generating AI perform the process of automatically converting old department names to new department names.

[0070] The analysis unit can match the names of retired and current employees. For example, the analysis unit can match the names of retired and current employees based on the contents of mail. The analysis unit can also match the names of retired and current employees based on a list of retired and current employees. For example, the analysis unit can match the names of retired and current employees based on a list of retired and current employees. This improves the accuracy of mail sorting by matching the names of retired and current employees. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the contents of mail into a generating AI and have the generating AI perform the process of matching the names of retired and current employees.

[0071] The proposal department can sort mail addressed to the president and executives to the appropriate department according to its importance and content. For example, the proposal department sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. The proposal department can sort mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. For example, the proposal department sorts mail addressed to the president and executives to the appropriate department according to its importance and content based on the content of the mail. This enables a swift response to important mail by sorting mail addressed to the president and executives to the appropriate department according to its importance and content. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the content of the mail into a generating AI and have the generating AI perform the processing of sorting it to the appropriate department according to its importance and content.

[0072] The proposal department can analyze the contents of mail containing only a company name or mail addressed to no applicable department and propose the appropriate department or person in charge. For example, the proposal department can identify and propose the appropriate department or person in charge based on the contents of the mail. The proposal department can also analyze the contents of mail containing only a company name or mail addressed to no applicable department and propose the appropriate department or person in charge. For example, the proposal department can identify and propose the appropriate department or person in charge based on the contents of the mail. This improves the accuracy of mail sorting by proposing the appropriate department or person in charge even for mail containing only a company name or mail addressed to no applicable department. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the contents of the mail into a generation AI and have the generation AI perform the process of proposing the appropriate department or person in charge.

[0073] The service department can build a database of senders and provide recommended departments based on past information. The service department can, for example, build a database of senders and provide recommended departments based on past information. The service department can provide recommended departments based on past sending history and response results. For example, the service department can provide recommended departments based on past sending history and response results. This improves the accuracy of mail sorting by building a database of senders and providing recommended departments based on past information. Some or all of the above processing in the service department may be performed using AI, for example, or without AI. For example, the service department can input the sender database into a generating AI and have the generating AI perform the process of providing recommended departments based on past information.

[0074] The collection unit can estimate the user's emotions and adjust the timing of mail collection based on the estimated emotions. For example, if the user is feeling busy, the collection unit may postpone mail collection. If the user is relaxed, the collection unit can collect the mail immediately. If the user is feeling stressed, the collection unit may also temporarily stop mail collection. This allows for flexible collection tailored to the user's situation by adjusting the timing of mail collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The collection unit can select a collection method based on the type or importance of the mail. For example, the collection unit may prioritize important mail and postpone regular mail. The collection unit may use dedicated collection equipment for large mail items. The collection unit may also implement special security measures for collecting highly confidential mail. This allows for efficient collection by selecting a collection method based on the type and importance of the mail. Some or all of the above processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit may input the type and importance of the mail into a generating AI and have the generating AI select the collection method.

[0076] The collection unit can automatically adjust the collection device according to the size or shape of the mail. For example, the collection unit can use a large collection device for large mail, a small collection device for small mail, and a flexible collection device for irregularly shaped mail. This allows for collection of various types of mail by automatically adjusting the collection device according to the size and shape of the mail. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the size and shape of the mail into a generating AI and have the generating AI perform the automatic adjustment of the collection device.

[0077] The collection unit can estimate the user's emotions and determine the priority of mail to collect based on the estimated emotions. For example, if the user is in a hurry, the collection unit will prioritize collecting important mail. If the user is relaxed, the collection unit can collect regular mail as well. If the user is stressed, the collection unit can collect only important mail. This allows for flexible collection tailored to the user's situation by determining the priority of mail to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform the process of determining the priority of mail to collect based on emotions.

[0078] The collection unit can optimize the collection method based on the sender information of the mail. For example, the collection unit can prioritize mail from important senders, while delaying mail from regular senders. The collection unit can also apply special collection methods to mail from specific senders. This enables efficient collection by optimizing the collection method based on the sender information of the mail. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the sender information of the mail into a generating AI and have the generating AI perform the optimization of the collection method.

[0079] The collection unit can predict the arrival time of mail and adjust the collection schedule. For example, the collection unit can predict the arrival time of mail and set the optimal collection timing. If the arrival is delayed, the collection unit can change the collection schedule. If the arrival is earlier, the collection unit can also bring forward the collection schedule. This enables efficient collection by predicting the arrival time of mail and adjusting the collection schedule. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the arrival time of mail into a generating AI and have the generating AI perform the adjustment of the collection schedule.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a simplified analysis. If the user is stressed, the analysis unit can also temporarily stop the analysis. This allows for flexible analysis tailored to the user's situation by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the process of adjusting the accuracy of the analysis based on the emotions.

[0081] The analysis unit can optimize its analysis algorithm based on the content of the mail. For example, the analysis unit can apply a detailed analysis algorithm to important mail. The analysis unit can apply a simplified analysis algorithm to ordinary mail. The analysis unit can also apply a special analysis algorithm to highly confidential mail. This allows for efficient analysis by optimizing the analysis algorithm based on the content of the mail. 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 the content of the mail into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0082] The analysis unit can change its analysis method according to the language and format of the mail. For example, the analysis unit can apply an English-specific analysis method to English mail. The analysis unit can apply a Japanese-specific analysis method to Japanese mail. The analysis unit can also apply an analysis method suitable for a specific format of mail. This makes it possible to analyze various types of mail by changing the analysis method according to the language and format of the mail. 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 the language and format of the mail into a generating AI and have the generating AI execute the change in the analysis method.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows for flexible display tailored to the user's situation by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 emotion data into the generative AI and have the generative AI perform the process of adjusting the display method of the analysis results based on the emotions.

[0084] The analysis unit can improve the accuracy of its analysis based on the sender information of the mail. For example, the analysis unit can perform a detailed analysis on mail from important senders. The analysis unit can perform a simplified analysis on mail from ordinary senders. The analysis unit can also apply special analysis methods to mail from specific senders. This allows for efficient analysis by improving the accuracy of the analysis based on the sender information of the mail. 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 the sender information of the mail into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0085] The analysis unit can supplement the analysis results by referring to an external database related to the contents of the mail. For example, the analysis unit can supplement the analysis results by referring to an external database related to the contents of the mail. The analysis unit can improve the accuracy of the analysis results based on the information obtained from the external database. The analysis unit can also supplement the analysis results based on the information from the external database. This improves the accuracy of the analysis by supplementing the analysis results by referring to an external database related to the contents of the mail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from the external database into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can make simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can make suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also make suggestions that get straight to the point. This allows for flexible suggestions tailored to the user's situation by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the way suggestions are presented based on the emotions.

[0087] The proposal unit can adjust the level of detail of its proposals based on the importance of the mail item. For example, it can provide detailed proposals for important mail items, simplified proposals for regular mail items, and special proposals for highly confidential mail items. This allows for efficient proposals by adjusting the level of detail based on the importance of the mail item. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the mail item into a generating AI and have the generating AI perform the process of adjusting the level of detail of the proposals.

[0088] The proposal unit can apply different proposal algorithms depending on the category of mail. For example, the proposal unit can apply a detailed proposal algorithm to important mail. The proposal unit can apply a simplified proposal algorithm to ordinary mail. The proposal unit can also apply a special proposal algorithm to highly confidential mail. This allows for efficient proposals by applying different proposal algorithms depending on the category of mail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the mail category into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0089] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit can also provide regular suggestions. If the user is stressed, the suggestion unit can also provide only important suggestions. This allows for flexible suggestions tailored to the user's situation by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the process of determining the priority of suggestions based on the emotions.

[0090] The proposal unit can optimize the proposal content based on the sender information of the mail. For example, the proposal unit can provide detailed proposals for mail from important senders. For mail from regular senders, it can provide simplified proposals. The proposal unit can also provide special proposals for mail from specific senders. This enables efficient proposals by optimizing the proposal content based on the sender information of the mail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the sender information of the mail into a generating AI and have the generating AI perform the optimization of the proposal content.

[0091] The proposal unit can improve the accuracy of its proposals by referring to past proposal history related to the content of the mail. For example, the proposal unit can improve the accuracy of its proposals by referring to past proposal history. The proposal unit can make the most optimal proposal from past proposal history. The proposal unit can also improve the accuracy of its proposals based on past proposal history. This makes it possible to make efficient proposals by improving the accuracy of proposals by referring to past proposal history related to the content of the mail. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input past proposal history into a generation AI and have the generation AI perform the improvement of proposal accuracy.

[0092] The service provider can estimate the user's emotions and determine the priority of recommended departments to provide based on the estimated emotions. For example, if the user is in a hurry, the service provider will prioritize providing important recommended departments. If the user is relaxed, the service provider can provide a range of recommended departments, including standard ones. If the user is stressed, the service provider can provide only important recommended departments. This allows for flexible service tailored to the user's situation by determining the priority of recommended departments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the process of determining the priority of recommended departments to provide based on the emotions.

[0093] The service provider can improve the accuracy of recommended departments by referring to past source data. The service provider can improve the accuracy of recommended departments by referring to past source data. The service provider can provide the optimal recommended department from past source data. The service provider can also improve the accuracy of recommended departments based on past source data. This enables efficient service provision by improving the accuracy of recommended departments by referring to past source data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input past source data into a generating AI and have the generating AI perform the improvement of the accuracy of recommended departments.

[0094] The delivery unit can optimize the selection criteria for recommended departments based on the content of the mail. For example, the delivery unit can provide detailed department recommendations for important mail. For regular mail, it can provide simplified department recommendations. For highly confidential mail, it can also provide special department recommendations. This enables efficient delivery by optimizing the selection criteria for recommended departments based on the content of the mail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the content of the mail into a generating AI and have the generating AI optimize the selection criteria for recommended departments.

[0095] The service provider can estimate the user's emotions and adjust how recommended departments are displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can provide a display that includes detailed information. If the user is in a hurry, the service provider can also provide a concise display. This allows for flexible display tailored to the user's situation by adjusting how recommended departments are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the process of adjusting how recommended departments are displayed based on the emotions.

[0096] The delivery unit can optimize recommended departments based on the sender information of the mail. For example, the delivery unit can provide detailed recommended departments for mail from important senders. For mail from regular senders, it can provide simplified recommended departments. The delivery unit can also provide special recommended departments for mail from specific senders. This enables efficient delivery by optimizing recommended departments based on the sender information of the mail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the sender information of the mail into a generating AI and have the generating AI perform the optimization of recommended departments.

[0097] The delivery unit can improve the accuracy of recommended departments by referring to an external database related to the content of mail. The delivery unit can improve the accuracy of recommended departments by referring to an external database related to the content of mail. The delivery unit can optimize recommended departments based on information obtained from the external database. The delivery unit can also improve the accuracy of recommended departments based on information from the external database. This enables efficient delivery by improving the accuracy of recommended departments by referring to an external database related to the content of mail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information from the external database into a generating AI and have the generating AI perform the improvement of the accuracy of recommended departments.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate emotions based on the content of mail and determine the priority of analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will prioritize the analysis of important mail. If the user is relaxed, the analysis unit can analyze all mail, including regular mail. If the user is stressed, the analysis unit can analyze only important mail. This allows for flexible analysis tailored to the user's situation by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform the process of determining the priority of analysis based on emotions.

[0100] The proposal department can adjust the way the proposal is presented based on the content of the mail. For example, the proposal department can provide detailed proposals for important mail, simplified proposals for regular mail, and special proposals for highly confidential mail. By adjusting the way the proposal is presented based on the content of the mail, efficient proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the content of the mail into a generating AI and have the generating AI perform the process of adjusting the way the proposal is presented.

[0101] The service provider can estimate the user's emotions and adjust how recommended departments are displayed based on those emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can provide a display that includes detailed information. If the user is in a hurry, the service provider can also provide a concise display. By adjusting how recommended departments are displayed based on the user's emotions, flexible displays tailored to the user's situation are possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the process of adjusting how recommended departments are displayed based on the emotions.

[0102] The collection unit can predict the arrival time of mail and adjust the collection schedule. For example, the collection unit predicts the arrival time of mail and sets the optimal collection timing. If the arrival is delayed, the collection unit can change the collection schedule. If the arrival is earlier, the collection unit can also bring the collection schedule forward. This enables efficient collection by predicting the arrival time of mail and adjusting the collection schedule. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the arrival time of mail into a generating AI and have the generating AI perform the adjustment of the collection schedule.

[0103] The analysis unit can supplement the analysis results by referring to an external database related to the contents of the mail. For example, the analysis unit can refer to an external database related to the contents of the mail and supplement the analysis results. The analysis unit can improve the accuracy of the analysis results based on the information obtained from the external database. The analysis unit can also supplement the analysis results based on the information from the external database. This improves the accuracy of the analysis by supplementing the analysis results by referring to an external database related to the contents of the mail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information from the external database into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0104] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit can offer a range of suggestions, including standard ones. If the user is stressed, the suggestion unit can offer only important suggestions. This allows for flexible suggestions tailored to the user's situation by prioritizing suggestions based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the process of prioritizing suggestions based on those emotions.

[0105] The collection unit can select a collection method based on the type or importance of the mail. For example, important mail can be collected urgently, while regular mail can be handled later. The collection unit can use dedicated collection equipment for large mail items. The collection unit can also collect highly confidential mail with special security measures. This allows for efficient collection by selecting a collection method based on the type and importance of the mail. Some or all of the above processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the type and importance of the mail into a generating AI and have the generating AI select the collection method.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, flexible display tailored to the user's situation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the process of adjusting the display method of the analysis results based on the emotions.

[0107] The delivery department can optimize the selection criteria for recommended departments based on the content of the mail. For example, the delivery department can provide detailed department recommendations for important mail. For regular mail, it can provide simplified department recommendations. For highly confidential mail, it can also provide special department recommendations. This enables efficient delivery by optimizing the selection criteria for recommended departments based on the content of the mail. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the content of the mail into a generating AI and have the generating AI optimize the selection criteria for recommended departments.

[0108] The collection unit can estimate the user's emotions and determine the priority of mail to collect based on the estimated emotions. For example, if the user is in a hurry, the collection unit will prioritize collecting important mail. If the user is relaxed, the collection unit can collect regular mail as well. If the user is stressed, the collection unit can collect only important mail. This allows for flexible collection tailored to the user's situation by determining the priority of mail to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform the process of determining the priority of mail to collect based on emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit captures image data of mail. For example, the collection unit can use a scanner to save image data of mail as digital data and use OCR technology to read addresses and recipient names. Handwritten mail can also be scanned and converted into digital data. It can also directly read mail submitted in digital format. Step 2: The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail. For example, the analysis unit uses natural language processing (NLP) technology to analyze the contents of the mail, automatically converting old department names to new department names, and matching the names of former and current employees. It can also sort mail addressed to the president or executives to the appropriate department according to its importance and content. Step 3: The proposal department proposes the appropriate department and person based on the analysis conducted by the analysis department. For example, the proposal department identifies and proposes the appropriate department and person from the content of the mail. Even for mail containing only a company name or mail addressed to no specific department, the content can be analyzed and the appropriate department and person can be proposed. Step 4: The service department builds a database of senders and provides recommended departments based on past information. For example, the service department provides recommended departments based on past sending history and response results. This streamlines the mail sorting process and reduces man-hours.

[0111] 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to capture image data of mail and reads the address and recipient's name using OCR technology. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the contents of the mail using natural language processing (NLP) technology. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes the relevant department and person in charge based on the analyzed content. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and builds a database of senders and provides recommended departments based on past information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to capture image data of mail and reads the address and recipient's name using OCR technology. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the contents of the mail using natural language processing (NLP) technology. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes the relevant department and person in charge based on the analyzed content. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and builds a database of senders and provides recommended departments based on past information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 of the headset terminal 314 to capture image data of mail and reads the address and recipient's name using OCR technology. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the contents of mail using natural language processing (NLP) technology. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes the relevant department and person in charge based on the analyzed content. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and builds a database of senders and provides recommended departments based on past information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to capture image data of mail and reads the address and recipient's name using OCR technology. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the contents of the mail using natural language processing (NLP) technology. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes the relevant department and person in charge based on the analyzed content. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and builds a database of senders and provides recommended departments based on past information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit that captures image data of mail, The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail, Based on the analysis performed by the aforementioned analysis unit, the proposal unit proposes the relevant department and person in charge. It includes a provisioning department that builds a database of senders and provides recommended departments based on past information. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Automatically convert the old department name to the new department name. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Match the names of retired and current employees. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Mail addressed to the president and executives is sorted to the appropriate department according to its importance and content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, For mail containing only a company name or mail addressed to a department that does not apply, we analyze the content and suggest the appropriate department or person to contact. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We build a database of senders and provide recommended departments based on past information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of mail collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The collection method is selected based on the type or importance of the mail. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The collection device automatically adjusts according to the size or shape of the mail. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of mail to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Optimize collection methods based on the sender information of mail. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Predict mail arrival times and adjust collection schedules accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Optimize the analysis algorithm based on the contents of the mail. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The analysis method is changed depending on the language and format of the mail. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Improve the accuracy of analysis based on the sender information of mail. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Supplement the analysis results by referring to external databases related to the contents of the mail. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the mail item. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Apply different suggestion algorithms depending on the category of mail. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Optimize suggestions based on the sender information of the mail item. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Improve the accuracy of proposals by referring to past proposal history related to the content of mail. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of recommended departments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, Improve the accuracy of recommended departments by referring to past sender data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, Optimize the selection criteria for recommended departments based on the content of mail. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We estimate the user's emotions and adjust how recommended departments are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, Optimize recommended departments based on the sender information of mail. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We improve the accuracy of recommended departments by referring to external databases related to the content of mail. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that captures image data of mail, The analysis unit analyzes the image data captured by the collection unit and analyzes the contents of the mail, Based on the analysis performed by the aforementioned analysis unit, the proposal unit proposes the relevant department and person in charge. It includes a provisioning department that builds a database of senders and provides recommended departments based on past information. A system characterized by the following features.

2. The aforementioned analysis unit, Automatically convert the old department name to the new department name. The system according to feature 1.

3. The aforementioned analysis unit, Match the names of retired and current employees. The system according to feature 1.

4. The aforementioned proposal section is, Mail addressed to the president and executives is sorted to the appropriate department according to its importance and content. The system according to feature 1.

5. The aforementioned proposal section is, For mail containing only a company name or mail addressed to a department that does not apply, we analyze the content and suggest the appropriate department or person to contact. The system according to feature 1.

6. The aforementioned supply unit is, We build a database of senders and provide recommended departments based on past information. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of mail collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is The collection method is selected based on the type or importance of the mail. The system according to feature 1.

Images