system

Abstract

The system according to this embodiment aims to efficiently manage paper-based notices and enable parents to take appropriate necessary actions. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a management unit, and a proposal unit. The reception unit photographs and captures the letters. The analysis unit analyzes the content of the letters captured by the reception unit. The management unit creates summaries, manages to-do lists, and sends reminders based on the content analyzed by the analysis unit. The proposal unit proposes actions by referring to standard school events and events in society.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the management of paper-based letters is complicated, and there is a risk that guardians may miss important information or ToDos.

[0005] The system according to the embodiment aims to efficiently manage paper-based letters and enable guardians to appropriately perform necessary actions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a management unit, and a proposal unit. The reception unit photographs and captures the letters. The analysis unit analyzes the content of the letters captured by the reception unit. The management unit creates summaries, manages to-do lists, and sends reminders based on the content analyzed by the analysis unit. The proposal unit proposes actions by referring to standard school events and events in society. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage paper-based notices and enable parents to take appropriate necessary actions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The communication management system according to an embodiment of the present invention is a system in which an AI concierge manages "communications" issued by schools and daycare centers. When a parent takes a picture of a communication with their smartphone camera and uploads it, the AI ​​concierge analyzes the content and performs tasks such as summarizing, to-do list management, and reminders. Furthermore, it refers not only to the content of the communication but also to standard school events and current events, and proactively suggests necessary actions to the parent. This significantly reduces the burden of managing school-related information on parents. For example, if a parent requests, "What's for lunch tomorrow?", the AI ​​concierge will reply, "It's curry!" It also sends a reminder notification such as, "Swimming starts next week!" to encourage the preparation of swimsuits. It also sends notifications such as, "Make your hair and makeup appointment for the graduation ceremony early!" This communication management system uses OCR and NLP to digitize the content of communication, and the AI ​​manages the information. The notification interface is intended to use a general messaging app. As a result, the communication management system can significantly reduce the burden of managing school-related information on parents.

[0029] The communication management system according to this embodiment comprises a reception unit, an analysis unit, a management unit, and a proposal unit. The reception unit photographs and captures communication. For example, the reception unit can capture image data of communication taken by a parent using the camera of a smartphone. The reception unit can also directly capture communication provided in digital format. The analysis unit analyzes the content of communication captured by the reception unit. For example, the analysis unit converts the image data of communication into text data using OCR technology and analyzes its content using NLP technology. The analysis unit can also analyze the content of communication using image analysis technology. The management unit performs summaries, to-do management, and reminders based on the content analyzed by the analysis unit. For example, the management unit summarizes the content of the analyzed communication, extracts important information, and creates a summary. The management unit can also create a to-do list and remind parents of necessary actions. The management unit further includes a function to send reminder notifications. The proposal department suggests actions by referring to standard school events and general events. For example, the proposal department refers to the school's annual event schedule and local event information to suggest necessary actions for parents. The proposal department can also suggest actions at the optimal time based on parents' schedules. As a result, the communication management system according to the embodiment can streamline communication management and reduce the information management burden on parents.

[0030] The reception desk can take photos of notices and import them. For example, parents can take photos of notices using their smartphone cameras, and the reception desk can import the image data. Specifically, parents launch a dedicated app on their smartphone and use the camera function to take a picture of the notice. The captured image is automatically cropped within the app, so that only the necessary parts are cut out. Furthermore, distortion and tilt of the image are automatically corrected, making it easy to read. The reception desk can also directly import notices provided in digital format. For example, PDF files sent by email from the school or notices downloaded from the online portal can be uploaded to the app and imported as digital data. This makes it possible to centrally manage not only paper notices but also digital notices. The reception desk sends the imported image data and digital data to a cloud server, making it accessible to other departments. This allows parents to easily import notices and efficiently share data across the entire system.

[0031] The analysis unit analyzes the content of letters received by the reception unit. For example, the analysis unit uses OCR technology to convert image data of letters into text data and then uses NLP technology to analyze its content. Specifically, it uses OCR technology to recognize characters in images and extracts them as text data. The extracted text data is then subjected to grammatical and semantic analysis using NLP technology to identify important information and keywords. For example, the date, time, and location of events, and lists of items to bring are extracted and classified. The analysis unit can also analyze the content of letters using image analysis technology. For example, it recognizes diagrams and illustrations in images and analyzes what they represent. This enables comprehensive analysis that includes not only textual information but also visual information. Furthermore, the analysis unit has a function to learn past data and patterns and automatically classify similar letters. This allows for efficient processing even when letters with similar content are sent multiple times. The analysis unit saves the analysis results to a database, making it accessible to the management unit and the proposal unit. This allows the analysis unit to quickly and accurately analyze the content of the collected messages, supporting the overall information processing of the system.

[0032] The management department creates summaries, manages to-do lists, and sends reminders based on the data analyzed by the analysis department. For example, the management department summarizes the content of analyzed notices, extracts important information, and creates summaries. Specifically, it extracts important information such as the date, time, and location of events, and lists of items to bring, and provides these to parents as concise summaries. The management department can also create to-do lists and remind parents of necessary actions. For example, it can send reminder notifications the day before an event to encourage preparation of belongings, or notify parents of departure times on the day of the event, providing timely reminders. The management department also has a function to send reminder notifications. Reminder notifications are sent through multiple means, such as smartphone push notifications, email, and integration with calendar apps. This allows parents to take action at the appropriate time without missing important information. The management department can also save past reminder history and analyze parents' behavior patterns to provide more effective reminders. This allows the management department to reduce the information management burden on parents and support efficient information management.

[0033] The suggestion department proposes actions by referring to standard school events and general events. For example, it refers to the school's annual event schedule and local event information to suggest necessary actions for parents. Specifically, it registers the school's annual event schedule in a database and notifies parents of necessary preparations and points to note before events. For example, before a sports day, it notifies parents of a list of things to bring and the day's schedule to encourage necessary preparations. It can also refer to local event information and suggest activities to encourage parents to participate. The suggestion department can also suggest actions at the optimal time based on parents' schedules. For example, it can link with parents' calendar apps and send notifications prompting them to prepare during their free time. This allows parents to efficiently prepare even in their busy daily lives. Furthermore, the suggestion department can analyze past suggestion history and provide personalized suggestions based on parents' preferences and behavioral patterns. This allows the suggestion department to propose the most suitable actions for parents and support the efficiency of information management.

[0034] The management unit includes a data conversion unit that uses OCR and NLP to convert the contents of the letters into data. The data conversion unit, for example, uses OCR technology to convert the image data of the letters into text data. The data conversion unit can also use NLP technology to analyze the content of the text data and extract important information. Furthermore, the data conversion unit can also use image analysis technology to convert the contents of the letters into data. This makes management easier by converting the contents of the letters into data. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input text data acquired using OCR technology into a generating AI and have the generating AI perform analysis of the text data.

[0035] The management unit includes a response unit that provides information in response to requests from parents. For example, if a parent requests, "What's for lunch tomorrow?", the response unit will respond, "It's curry!" based on the analyzed contents of the school newsletter. The response unit can also respond, if a parent requests, "What events are happening next week?", it can respond, "There's a sports day!" based on the school's annual event schedule. Furthermore, if a parent requests, "What about graduation preparations?", the response unit can provide information regarding graduation preparations. This enables the provision of information in response to parents' requests. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input a request from a parent into a generating AI and have the generating AI execute the optimal response to the request.

[0036] The management unit includes a notification unit that sends reminder notifications. The notification unit can, for example, send a reminder notification saying, "Swimming starts next week!" to prompt students to prepare their swimsuits. The notification unit can also send notifications such as, "Make your hair and makeup appointment for graduation early!" Furthermore, the notification unit can respond to requests such as, "What do I need to bring tomorrow?" with, "Gym clothes!" This ensures that parents do not miss important information by sending reminder notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the content of the reminder notification into a generating AI and have the generating AI execute the most appropriate notification content.

[0037] The suggestion department proposes actions by referring to standard school events and societal events. For example, it refers to the school's annual event schedule and local event information to propose necessary actions for parents. The suggestion department can also propose actions at the optimal time based on the parents' schedules. Furthermore, the suggestion department can propose relevant actions based on the parents' areas of interest. This allows parents to proactively take necessary actions by proposing actions by referring to standard school events and societal events. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input school event and event information into a generating AI and have the generating AI execute the content of the optimal action proposal.

[0038] The notification unit sends notifications using a common messaging app. The notification unit can also notify parents of important information using a messaging app they are familiar with. Furthermore, the notification unit can provide information requested by parents through the messaging app. This allows parents to receive notifications using an app they are familiar with. Some or all of the above processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input the notification content to be sent via the messaging app into a generating AI and have the generating AI execute the optimal notification method.

[0039] The reception desk analyzes the parent's past photography history and suggests the optimal photography method. For example, the reception desk analyzes the time periods of notices previously photographed by the parent and suggests the optimal time for photography. The reception desk can also prioritize suggesting photography methods previously used by the parent (manual, voice commands, etc.). Furthermore, the reception desk can suggest the optimal photography method for specific situations based on the parent's past photography history. In this way, the optimal photography method can be suggested by analyzing past photography history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the parent's past photography history data into a generating AI and have the generating AI execute the process of suggesting the optimal photography method.

[0040] The reception desk filters the photos taken of notices based on the parent's current situation and areas of interest. For example, if the parent is busy, the reception desk suggests prioritizing the photography of only important information. The reception desk can also prioritize photographing relevant notices based on the parent's areas of interest. Furthermore, the reception desk can suggest the optimal timing for photography depending on the parent's current situation (e.g., being out). This allows for prioritizing the photography of important information by filtering based on the parent's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the parent's current situation into a generating AI and have the generating AI execute a process to suggest the optimal timing for photography.

[0041] The reception desk prioritizes photographing relevant notices by considering the parent's geographical location when taking pictures of notices. For example, if the parent is near the school, the reception desk will prioritize photographing school-related notices. The reception desk can also prioritize photographing notices containing information needed at home if the parent is at home. Furthermore, if the parent is out, the reception desk can prioritize photographing notices containing information needed at their destination. This allows the reception desk to prioritize photographing relevant notices by considering the parent's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the parent's geographical location data into a generating AI and have the generating AI execute the process of prioritizing the photography of relevant notices.

[0042] The reception desk analyzes parents' social media activity when taking photos of notices and photographs relevant notices. For example, the reception desk prioritizes photographing relevant notices based on information shared by parents on social media. The reception desk can also photograph relevant notices based on parents' social media interests. Furthermore, the reception desk can analyze parents' social media activity history and suggest the optimal timing for taking photos. This allows for the priority of photographing relevant notices by analyzing parents' social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input parents' social media activity data into a generating AI and have the generating AI execute the task of photographing relevant notices.

[0043] The analysis unit adjusts the level of detail in the analysis based on the importance of each letter. For example, the analysis unit provides detailed analysis results for letters of high importance. The analysis unit can also provide concise analysis results for letters of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of each letter. This allows for detailed analysis of important information by adjusting the level of detail in the analysis based on the importance of each letter. 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 importance data of the letters into a generating AI and have the generating AI perform the actions to adjust the level of detail in the analysis.

[0044] The analysis unit applies different analysis algorithms depending on the category of the message during analysis. For example, for messages about school events, the analysis unit applies an algorithm that analyzes the details of the event. The analysis unit can also apply an analysis algorithm specialized in task management to messages about to-do lists. Furthermore, for general information sharing messages, the analysis unit can apply an analysis algorithm that extracts the key points of the information. By applying different analysis algorithms depending on the category of the message, the optimal analysis results can be provided. 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 message category data into a generating AI and have the generating AI execute the process of applying the optimal analysis algorithm.

[0045] The analysis unit determines the priority of analysis based on the publication date of the newsletters during the analysis process. For example, the analysis unit prioritizes the analysis of recently published newsletters. The analysis unit can also determine the priority of analysis for older newsletters according to their importance. Furthermore, the analysis unit can adjust the timing of the analysis based on the publication date. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the publication date of the newsletters. 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 newsletter publication date data into a generating AI and have the generating AI execute the process of determining the analysis priority.

[0046] The analysis unit adjusts the order of analysis based on the relevance of the letters during the analysis process. For example, the analysis unit prioritizes the analysis of letters with high relevance. The analysis unit can also provide a concise analysis result for letters with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the letters. This allows for the prioritization of analysis of highly relevant information by adjusting the order of analysis based on the relevance of the letters. 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 relevance data of the letters into a generating AI and have the generating AI execute the process of adjusting the order of analysis.

[0047] The management department analyzes the content of the messages during management and selects the optimal management method. For example, the management department provides detailed management methods for high-priority messages. The management department can also provide concise management methods for low-priority messages. Furthermore, the management department can determine management priorities according to the content of the messages. This allows for the provision of the optimal management method according to the content of the messages. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the content data of the messages into a generating AI and have the generating AI perform the task of selecting the optimal management method.

[0048] The management department customizes the means of management based on the parent's current situation during management. For example, if the parent is busy, the management department provides simplified management methods. The management department can also provide detailed management methods if the parent is relaxed. Furthermore, the management department can customize the means of management according to the parent's current situation. This allows for the provision of the optimal management method by customizing the means of management according to the parent's current situation. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input data on the parent's current situation into a generating AI and have the generating AI execute the process of customizing the means of management.

[0049] The management department selects the optimal management method when managing information, taking into account the geographical location of the parents. For example, if a parent is near the school, the management department will prioritize managing school-related notices. The management department can also prioritize managing notices containing information needed at home if the parent is at home. Furthermore, if the parent is out, the management department can prioritize managing notices containing information needed while out. This allows the management department to provide the optimal management method by considering the parents' geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the parents' geographical location data into a generating AI and have the generating AI perform the task of selecting the optimal management method.

[0050] The management department analyzes parents' social media activity during management and proposes management methods. For example, the management department prioritizes managing relevant notices based on information shared by parents on social media. The management department can also manage relevant notices based on parents' social media interests. Furthermore, the management department can analyze parents' social media activity history and propose the most suitable management methods. This allows the management department to propose the most suitable management methods by analyzing parents' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input parents' social media activity data into a generating AI and have the generating AI execute the content of the proposed management methods.

[0051] The proposal unit adjusts the level of detail in its proposals based on the importance of the actions. For example, it provides detailed proposals for high-importance actions. It can also provide concise proposals for low-importance actions. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the actions. This allows for detailed proposals for important actions by adjusting the level of detail based on the importance of the actions. 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 action importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0052] The suggestion unit applies different suggestion algorithms depending on the action category when making a suggestion. For example, for actions related to school events, the suggestion unit applies an algorithm that suggests details of the event. The suggestion unit can also apply a suggestion algorithm specialized in task management for actions related to to-dos. Furthermore, for general information sharing actions, the suggestion unit can apply a suggestion algorithm that extracts the key points of the information. In this way, by applying different suggestion algorithms depending on the action category, the optimal suggestion can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input action category data into a generating AI and have the generating AI execute the process of applying the optimal suggestion algorithm.

[0053] The proposal unit determines the priority of proposals based on the timing of the actions. For example, the proposal unit prioritizes actions that will occur soon. The proposal unit can also determine the priority of actions that will occur far in the future based on their importance. Furthermore, the proposal unit can adjust the timing of proposals based on the timing of the actions. This allows for prioritizing the proposal of the most recent actions by determining the priority of proposals based on the timing of the actions. 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 action timing data into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0054] The proposal unit adjusts the order of proposals based on the relevance of the actions when making a proposal. For example, the proposal unit prioritizes proposing highly relevant actions. The proposal unit can also provide a concise proposal for less relevant actions. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the actions. This allows for prioritizing the proposal of highly relevant actions by adjusting the order of proposals based on the relevance of the actions. 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 action relevance data into a generating AI and have the generating AI perform the task of adjusting the order of proposals.

[0055] The data conversion unit analyzes the content of the letters and selects the optimal data conversion method during the data conversion process. For example, the data conversion unit provides a detailed data conversion method for letters of high importance. The data conversion unit can also provide a concise data conversion method for letters of low importance. Furthermore, the data conversion unit can determine the priority of data conversion according to the content of the letters. This allows the unit to provide the optimal data conversion method according to the content of the letters. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the content data of the letters into a generating AI and have the generating AI perform the task of selecting the optimal data conversion method.

[0056] The data conversion unit adjusts the order of data conversion based on the publication date of the newsletters. For example, the data conversion unit prioritizes the conversion of recently published newsletters. The data conversion unit can also determine the priority of data conversion for older newsletters according to their importance. Furthermore, the data conversion unit can adjust the timing of data conversion based on the publication date. This allows for prioritization of the latest information by adjusting the order of data conversion based on the publication date of the newsletters. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input newsletter publication date data into a generating AI and have the generating AI execute the process of adjusting the order of data conversion.

[0057] The response unit adjusts the level of detail in its response based on the parent's request. For example, it provides a detailed response for high-priority requests. It can also provide a concise response for low-priority requests. Furthermore, the response unit can determine the priority of the response depending on the content of the request. This allows it to provide an optimal response by adjusting the level of detail based on the parent's request. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the parent's request data into a generating AI and have the generating AI perform the action of adjusting the level of detail in the response.

[0058] The response unit provides the optimal response by referring to the parent's past request history when responding. For example, the response unit provides the optimal response based on what the parent has requested in the past. The response unit can also prioritize providing relevant information from the parent's past request history. Furthermore, the response unit can analyze the parent's past request history and provide the most efficient response. This allows the response unit to provide the optimal response by referring to the parent's past request history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the parent's past request history data into a generating AI and have the generating AI execute the content to provide the optimal response.

[0059] The notification unit, when issuing a notification, selects the optimal notification method by referring to the parent's past notification history. For example, the notification unit provides the optimal notification method based on the content of notifications the parent has received in the past. The notification unit can also prioritize notifying relevant information from the parent's past notification history. Furthermore, the notification unit can analyze the parent's past notification history and provide the most efficient notification method. This allows the notification unit to provide the optimal notification method by referring to the parent's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the parent's past notification history data into a generating AI and have the generating AI perform the task of selecting the optimal notification method.

[0060] The notification unit selects the optimal notification method when sending a notification, taking into account the parent's device information. For example, if the parent is using a smartphone, the notification unit provides a notification method that matches the screen size. The notification unit can also provide a notification method optimized for larger screens if the parent is using a tablet. Furthermore, if the parent is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This ensures that the optimal notification method is provided by considering the parent's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the parent's device information data into a generating AI and have the generating AI perform the task of selecting the optimal notification method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The management department can analyze parents' past behavioral history and propose optimal management methods. For example, based on actions parents have taken in the past, it can propose the best management method for similar situations. It can also analyze parents' behavioral patterns and propose management methods at the optimal time. Furthermore, it can propose the best management method for specific situations based on parents' past behavioral history. In this way, by analyzing past behavioral history, it is possible to propose the best management method.

[0063] The reception desk can suggest the optimal timing for taking photos, taking into account the geographical location of the parents. For example, if a parent is near the school, it can suggest prioritizing the taking of school-related notices. If a parent is at home, it can also prioritize taking photos of notices containing information needed at home. Furthermore, if a parent is out, it can prioritize taking photos of notices containing information needed while out. In this way, the system can suggest the optimal timing for taking photos by considering geographical location.

[0064] The analysis unit can determine the priority of analysis based on the content of the messages. For example, it can prioritize the analysis of high-priority messages and provide detailed analysis results. Conversely, it can provide concise analysis results for less important messages. Furthermore, it can adjust the order of analysis according to the content of the messages. In this way, by determining the priority of analysis based on the content of the messages, important information can be analyzed preferentially.

[0065] The proposal department can analyze parents' social media activity and make relevant suggestions. For example, it can prioritize relevant suggestions based on information parents share on social media. It can also make relevant suggestions based on parents' social media interests. Furthermore, it can analyze parents' social media activity history to make optimal suggestions. In this way, relevant suggestions can be made by analyzing social media activity.

[0066] The notification unit can select the optimal notification method by considering the parent's device information. For example, if the parent is using a smartphone, it can provide a notification method that is optimized for the screen size. If the parent is using a tablet, it can provide a notification method optimized for the larger screen. Furthermore, if the parent is using a smartwatch, it can provide a concise and highly visible notification method. In this way, the system can provide the most suitable notification method by considering the device information.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk takes a picture of the letter and imports it. Parents can take a picture of the letter using their smartphone camera and import the image data. They can also directly import letters that are provided in digital format. Step 2: The analysis unit analyzes the content of the letters received by the reception unit. It converts the image data of the letters into text data using OCR technology and analyzes the content using NLP technology. It can also analyze the content of the letters using image analysis technology. Step 3: The management department creates summaries, manages to-do lists, and sends reminders based on the analysis performed by the analysis department. It summarizes the analyzed content of the communications, extracts important information, and creates a summary. It can create to-do lists and remind parents of necessary actions. Furthermore, it includes a function to send reminder notifications. Step 4: The proposal team suggests actions based on standard school events and local events. They refer to the school's annual event schedule and local event information to suggest necessary actions for parents. They can also suggest actions at the optimal time based on parents' schedules.

[0069] (Example of form 2) The communication management system according to an embodiment of the present invention is a system in which an AI concierge manages "communications" issued by schools and daycare centers. When a parent takes a picture of a communication with their smartphone camera and uploads it, the AI ​​concierge analyzes the content and performs tasks such as summarizing, to-do list management, and reminders. Furthermore, it refers not only to the content of the communication but also to standard school events and current events, and proactively suggests necessary actions to the parent. This significantly reduces the burden of managing school-related information on parents. For example, if a parent requests, "What's for lunch tomorrow?", the AI ​​concierge will reply, "It's curry!" It also sends a reminder notification such as, "Swimming starts next week!" to encourage the preparation of swimsuits. It also sends notifications such as, "Make your hair and makeup appointment for the graduation ceremony early!" This communication management system uses OCR and NLP to digitize the content of communication, and the AI ​​manages the information. The notification interface is intended to use a general messaging app. As a result, the communication management system can significantly reduce the burden of managing school-related information on parents.

[0070] The communication management system according to this embodiment comprises a reception unit, an analysis unit, a management unit, and a proposal unit. The reception unit photographs and captures communication. For example, the reception unit can capture image data of communication taken by a parent using the camera of a smartphone. The reception unit can also directly capture communication provided in digital format. The analysis unit analyzes the content of communication captured by the reception unit. For example, the analysis unit converts the image data of communication into text data using OCR technology and analyzes its content using NLP technology. The analysis unit can also analyze the content of communication using image analysis technology. The management unit performs summaries, to-do management, and reminders based on the content analyzed by the analysis unit. For example, the management unit summarizes the content of the analyzed communication, extracts important information, and creates a summary. The management unit can also create a to-do list and remind parents of necessary actions. The management unit further includes a function to send reminder notifications. The proposal department suggests actions by referring to standard school events and general events. For example, the proposal department refers to the school's annual event schedule and local event information to suggest necessary actions for parents. The proposal department can also suggest actions at the optimal time based on parents' schedules. As a result, the communication management system according to the embodiment can streamline communication management and reduce the information management burden on parents.

[0071] The reception desk can take photos of notices and import them. For example, parents can take photos of notices using their smartphone cameras, and the reception desk can import the image data. Specifically, parents launch a dedicated app on their smartphone and use the camera function to take a picture of the notice. The captured image is automatically cropped within the app, so that only the necessary parts are cut out. Furthermore, distortion and tilt of the image are automatically corrected, making it easy to read. The reception desk can also directly import notices provided in digital format. For example, PDF files sent by email from the school or notices downloaded from the online portal can be uploaded to the app and imported as digital data. This makes it possible to centrally manage not only paper notices but also digital notices. The reception desk sends the imported image data and digital data to a cloud server, making it accessible to other departments. This allows parents to easily import notices and efficiently share data across the entire system.

[0072] The analysis unit analyzes the content of letters received by the reception unit. For example, the analysis unit uses OCR technology to convert image data of letters into text data and then uses NLP technology to analyze its content. Specifically, it uses OCR technology to recognize characters in images and extracts them as text data. The extracted text data is then subjected to grammatical and semantic analysis using NLP technology to identify important information and keywords. For example, the date, time, and location of events, and lists of items to bring are extracted and classified. The analysis unit can also analyze the content of letters using image analysis technology. For example, it recognizes diagrams and illustrations in images and analyzes what they represent. This enables comprehensive analysis that includes not only textual information but also visual information. Furthermore, the analysis unit has a function to learn past data and patterns and automatically classify similar letters. This allows for efficient processing even when letters with similar content are sent multiple times. The analysis unit saves the analysis results to a database, making it accessible to the management unit and the proposal unit. This allows the analysis unit to quickly and accurately analyze the content of the collected messages, supporting the overall information processing of the system.

[0073] The management department creates summaries, manages to-do lists, and sends reminders based on the data analyzed by the analysis department. For example, the management department summarizes the content of analyzed notices, extracts important information, and creates summaries. Specifically, it extracts important information such as the date, time, and location of events, and lists of items to bring, and provides these to parents as concise summaries. The management department can also create to-do lists and remind parents of necessary actions. For example, it can send reminder notifications the day before an event to encourage preparation of belongings, or notify parents of departure times on the day of the event, providing timely reminders. The management department also has a function to send reminder notifications. Reminder notifications are sent through multiple means, such as smartphone push notifications, email, and integration with calendar apps. This allows parents to take action at the appropriate time without missing important information. The management department can also save past reminder history and analyze parents' behavior patterns to provide more effective reminders. This allows the management department to reduce the information management burden on parents and support efficient information management.

[0074] The suggestion department proposes actions by referring to standard school events and general events. For example, it refers to the school's annual event schedule and local event information to suggest necessary actions for parents. Specifically, it registers the school's annual event schedule in a database and notifies parents of necessary preparations and points to note before events. For example, before a sports day, it notifies parents of a list of things to bring and the day's schedule to encourage necessary preparations. It can also refer to local event information and suggest activities to encourage parents to participate. The suggestion department can also suggest actions at the optimal time based on parents' schedules. For example, it can link with parents' calendar apps and send notifications prompting them to prepare during their free time. This allows parents to efficiently prepare even in their busy daily lives. Furthermore, the suggestion department can analyze past suggestion history and provide personalized suggestions based on parents' preferences and behavioral patterns. This allows the suggestion department to propose the most suitable actions for parents and support the efficiency of information management.

[0075] The management unit includes a data conversion unit that uses OCR and NLP to convert the contents of the letters into data. The data conversion unit, for example, uses OCR technology to convert the image data of the letters into text data. The data conversion unit can also use NLP technology to analyze the content of the text data and extract important information. Furthermore, the data conversion unit can also use image analysis technology to convert the contents of the letters into data. This makes management easier by converting the contents of the letters into data. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input text data acquired using OCR technology into a generating AI and have the generating AI perform analysis of the text data.

[0076] The management unit includes a response unit that provides information in response to requests from parents. For example, if a parent requests, "What's for lunch tomorrow?", the response unit will respond, "It's curry!" based on the analyzed contents of the school newsletter. The response unit can also respond, if a parent requests, "What events are happening next week?", it can respond, "There's a sports day!" based on the school's annual event schedule. Furthermore, if a parent requests, "What about graduation preparations?", the response unit can provide information regarding graduation preparations. This enables the provision of information in response to parents' requests. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input a request from a parent into a generating AI and have the generating AI execute the optimal response to the request.

[0077] The management unit includes a notification unit that sends reminder notifications. The notification unit can, for example, send a reminder notification saying, "Swimming starts next week!" to prompt students to prepare their swimsuits. The notification unit can also send notifications such as, "Make your hair and makeup appointment for graduation early!" Furthermore, the notification unit can respond to requests such as, "What do I need to bring tomorrow?" with, "Gym clothes!" This ensures that parents do not miss important information by sending reminder notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the content of the reminder notification into a generating AI and have the generating AI execute the most appropriate notification content.

[0078] The suggestion department proposes actions by referring to standard school events and societal events. For example, it refers to the school's annual event schedule and local event information to propose necessary actions for parents. The suggestion department can also propose actions at the optimal time based on the parents' schedules. Furthermore, the suggestion department can propose relevant actions based on the parents' areas of interest. This allows parents to proactively take necessary actions by proposing actions by referring to standard school events and societal events. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input school event and event information into a generating AI and have the generating AI execute the content of the optimal action proposal.

[0079] The notification unit sends notifications using a common messaging app. The notification unit can also notify parents of important information using a messaging app they are familiar with. Furthermore, the notification unit can provide information requested by parents through the messaging app. This allows parents to receive notifications using an app they are familiar with. Some or all of the above processes in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input the notification content to be sent via the messaging app into a generating AI and have the generating AI execute the optimal notification method.

[0080] The reception desk estimates the parent's emotions and adjusts the timing of taking the message based on the estimated emotions. For example, if the parent is feeling busy, the reception desk will have the AI ​​suggest the optimal time to take the message and send a notification. The reception desk can also have the AI ​​send a reminder to encourage the parent to take the message if they are relaxed. Furthermore, if the parent is feeling stressed, the reception desk can have the AI ​​provide guidance to simplify the photo shoot. This allows for taking the message at the optimal time by adjusting the timing according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input the parent's emotion data into the generative AI and have the generative AI suggest the optimal time to take the message.

[0081] The reception desk analyzes the parent's past photography history and suggests the optimal photography method. For example, the reception desk analyzes the time periods of notices previously photographed by the parent and suggests the optimal time for photography. The reception desk can also prioritize suggesting photography methods previously used by the parent (manual, voice commands, etc.). Furthermore, the reception desk can suggest the optimal photography method for specific situations based on the parent's past photography history. In this way, the optimal photography method can be suggested by analyzing past photography history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the parent's past photography history data into a generating AI and have the generating AI execute the process of suggesting the optimal photography method.

[0082] The reception desk filters the photos taken of notices based on the parent's current situation and areas of interest. For example, if the parent is busy, the reception desk suggests prioritizing the photography of only important information. The reception desk can also prioritize photographing relevant notices based on the parent's areas of interest. Furthermore, the reception desk can suggest the optimal timing for photography depending on the parent's current situation (e.g., being out). This allows for prioritizing the photography of important information by filtering based on the parent's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the parent's current situation into a generating AI and have the generating AI execute a process to suggest the optimal timing for photography.

[0083] The reception desk estimates the parent's emotions and determines the priority of which notices to photograph based on the estimated emotions. For example, if the parent is stressed, the reception desk will prioritize photographing the most important notices. The reception desk may also suggest photographing all notices if the parent is relaxed. Furthermore, if the parent is in a hurry, the reception desk may prioritize photographing notices containing the most important information. This ensures that important information is photographed preferentially by prioritizing the notices to be photographed according to the parent'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 reception desk may be performed using AI or not. For example, the reception desk can input parent emotion data into a generative AI and have the generative AI perform the task of determining the priority of the notices to photograph.

[0084] The reception desk prioritizes photographing relevant notices by considering the parent's geographical location when taking pictures of notices. For example, if the parent is near the school, the reception desk will prioritize photographing school-related notices. The reception desk can also prioritize photographing notices containing information needed at home if the parent is at home. Furthermore, if the parent is out, the reception desk can prioritize photographing notices containing information needed at their destination. This allows the reception desk to prioritize photographing relevant notices by considering the parent's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the parent's geographical location data into a generating AI and have the generating AI execute the process of prioritizing the photography of relevant notices.

[0085] The reception desk analyzes parents' social media activity when taking photos of notices and photographs relevant notices. For example, the reception desk prioritizes photographing relevant notices based on information shared by parents on social media. The reception desk can also photograph relevant notices based on parents' social media interests. Furthermore, the reception desk can analyze parents' social media activity history and suggest the optimal timing for taking photos. This allows for the priority of photographing relevant notices by analyzing parents' social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input parents' social media activity data into a generating AI and have the generating AI execute the task of photographing relevant notices.

[0086] The analysis unit estimates the parent's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the parent is tense, the analysis unit provides a simple and easy-to-understand analysis result. The analysis unit can also provide a detailed analysis result if the parent is relaxed. Furthermore, if the parent is in a hurry, the analysis unit can provide a concise analysis result. This allows for the provision of optimal analysis results by adjusting the presentation of the analysis according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 parent's emotion data into the generative AI and have the generative AI perform the adjustment of the presentation of the analysis.

[0087] The analysis unit adjusts the level of detail in the analysis based on the importance of each letter. For example, the analysis unit provides detailed analysis results for letters of high importance. The analysis unit can also provide concise analysis results for letters of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of each letter. This allows for detailed analysis of important information by adjusting the level of detail in the analysis based on the importance of each letter. 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 importance data of the letters into a generating AI and have the generating AI perform the actions to adjust the level of detail in the analysis.

[0088] The analysis unit applies different analysis algorithms depending on the category of the message during analysis. For example, for messages about school events, the analysis unit applies an algorithm that analyzes the details of the event. The analysis unit can also apply an analysis algorithm specialized in task management to messages about to-do lists. Furthermore, for general information sharing messages, the analysis unit can apply an analysis algorithm that extracts the key points of the information. By applying different analysis algorithms depending on the category of the message, the optimal analysis results can be provided. 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 message category data into a generating AI and have the generating AI execute the process of applying the optimal analysis algorithm.

[0089] The analysis unit estimates the parent's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the parent is in a hurry, the analysis unit provides a short, concise analysis. The analysis unit can also provide a detailed analysis if the parent is relaxed. Furthermore, if the parent is excited, the analysis unit can provide a visually stimulating analysis. This allows for the provision of optimal analysis results by adjusting the length of the analysis according to the parent'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 processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the parent's emotion data into the generative AI and have the generative AI perform the actions of adjusting the length of the analysis.

[0090] The analysis unit determines the priority of analysis based on the publication date of the newsletters during the analysis process. For example, the analysis unit prioritizes the analysis of recently published newsletters. The analysis unit can also determine the priority of analysis for older newsletters according to their importance. Furthermore, the analysis unit can adjust the timing of the analysis based on the publication date. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the publication date of the newsletters. 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 newsletter publication date data into a generating AI and have the generating AI execute the process of determining the analysis priority.

[0091] The analysis unit adjusts the order of analysis based on the relevance of the letters during the analysis process. For example, the analysis unit prioritizes the analysis of letters with high relevance. The analysis unit can also provide a concise analysis result for letters with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the letters. This allows for the prioritization of analysis of highly relevant information by adjusting the order of analysis based on the relevance of the letters. 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 relevance data of the letters into a generating AI and have the generating AI execute the process of adjusting the order of analysis.

[0092] The management unit estimates the parent's emotions and adjusts the management method based on the estimated emotions. For example, if the parent is stressed, the management unit provides a simple management method. The management unit can also provide a more detailed management method if the parent is relaxed. Furthermore, if the parent is in a hurry, the management unit can provide a quick management method. This allows for the provision of the optimal management method by adjusting the management method according to the parent'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 management unit may be performed using AI or not. For example, the management unit can input parent emotion data into a generative AI and have the generative AI perform the actions to adjust the management method.

[0093] The management department analyzes the content of the messages during management and selects the optimal management method. For example, the management department provides detailed management methods for high-priority messages. The management department can also provide concise management methods for low-priority messages. Furthermore, the management department can determine management priorities according to the content of the messages. This allows for the provision of the optimal management method according to the content of the messages. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the content data of the messages into a generating AI and have the generating AI perform the task of selecting the optimal management method.

[0094] The management department customizes the means of management based on the parent's current situation during management. For example, if the parent is busy, the management department provides simplified management methods. The management department can also provide detailed management methods if the parent is relaxed. Furthermore, the management department can customize the means of management according to the parent's current situation. This allows for the provision of the optimal management method by customizing the means of management according to the parent's current situation. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input data on the parent's current situation into a generating AI and have the generating AI execute the process of customizing the means of management.

[0095] The management department estimates the emotions of parents and determines management priorities based on the estimated emotions. For example, if a parent is stressed, the management department will prioritize managing high-priority notices. The management department may also manage all notices if the parent is relaxed. Furthermore, if the parent is in a hurry, the management department may prioritize managing notices containing the most important information. This ensures that important information is prioritized by determining management priorities according to the parent'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 management department may be performed using AI or not. For example, the management department can input parent emotion data into a generative AI and have the generative AI perform the task of determining management priorities.

[0096] The management department selects the optimal management method when managing information, taking into account the geographical location of the parents. For example, if a parent is near the school, the management department will prioritize managing school-related notices. The management department can also prioritize managing notices containing information needed at home if the parent is at home. Furthermore, if the parent is out, the management department can prioritize managing notices containing information needed while out. This allows the management department to provide the optimal management method by considering the parents' geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the parents' geographical location data into a generating AI and have the generating AI perform the task of selecting the optimal management method.

[0097] The management department analyzes parents' social media activity during management and proposes management methods. For example, the management department prioritizes managing relevant notices based on information shared by parents on social media. The management department can also manage relevant notices based on parents' social media interests. Furthermore, the management department can analyze parents' social media activity history and propose the most suitable management methods. This allows the management department to propose the most suitable management methods by analyzing parents' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input parents' social media activity data into a generating AI and have the generating AI execute the content of the proposed management methods.

[0098] The suggestion unit estimates the parent's emotions and adjusts the way the suggestion is presented based on the estimated emotions. For example, if the parent is stressed, the suggestion unit provides a simple and easily understandable suggestion. If the parent is relaxed, the suggestion unit can also provide a more detailed suggestion. Furthermore, if the parent is in a hurry, the suggestion unit can provide a concise suggestion. This allows the system to provide the most suitable suggestion by adjusting the way the suggestion is presented according to the parent'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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input parent emotion data into the generative AI and have the generative AI perform the adjustment of the suggestion's presentation.

[0099] The proposal unit adjusts the level of detail in its proposals based on the importance of the actions. For example, it provides detailed proposals for high-importance actions. It can also provide concise proposals for low-importance actions. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the actions. This allows for detailed proposals for important actions by adjusting the level of detail based on the importance of the actions. 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 action importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0100] The suggestion unit applies different suggestion algorithms depending on the action category when making a suggestion. For example, for actions related to school events, the suggestion unit applies an algorithm that suggests details of the event. The suggestion unit can also apply a suggestion algorithm specialized in task management for actions related to to-dos. Furthermore, for general information sharing actions, the suggestion unit can apply a suggestion algorithm that extracts the key points of the information. In this way, by applying different suggestion algorithms depending on the action category, the optimal suggestion can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input action category data into a generating AI and have the generating AI execute the process of applying the optimal suggestion algorithm.

[0101] The suggestion unit estimates the parent's emotions and adjusts the length of the suggestion based on the estimated emotions. For example, if the parent is in a hurry, the suggestion unit provides a short, concise suggestion. If the parent is relaxed, the suggestion unit can also provide a detailed suggestion. Furthermore, if the parent is excited, the suggestion unit can provide a visually stimulating suggestion. This allows for the provision of the most suitable suggestion by adjusting the length of the suggestion according to the parent'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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input parent emotion data into the generative AI and have the generative AI perform the task of adjusting the length of the suggestion.

[0102] The proposal unit determines the priority of proposals based on the timing of the actions. For example, the proposal unit prioritizes actions that will occur soon. The proposal unit can also determine the priority of actions that will occur far in the future based on their importance. Furthermore, the proposal unit can adjust the timing of proposals based on the timing of the actions. This allows for prioritizing the proposal of the most recent actions by determining the priority of proposals based on the timing of the actions. 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 action timing data into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0103] The proposal unit adjusts the order of proposals based on the relevance of the actions when making a proposal. For example, the proposal unit prioritizes proposing highly relevant actions. The proposal unit can also provide a concise proposal for less relevant actions. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the actions. This allows for prioritizing the proposal of highly relevant actions by adjusting the order of proposals based on the relevance of the actions. 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 action relevance data into a generating AI and have the generating AI perform the task of adjusting the order of proposals.

[0104] The data processing unit estimates the parent's emotions and adjusts the data processing method based on the estimated emotions. For example, if the parent is stressed, the data processing unit provides a simple data processing method. The data processing unit can also provide a detailed data processing method if the parent is relaxed. Furthermore, if the parent is in a hurry, the data processing unit can provide a method that allows for rapid data processing. This allows for the provision of the optimal data processing method by adjusting the data processing method according to the parent'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 processing in the data processing unit may be performed using AI or not using AI. For example, the data processing unit can input the parent's emotion data into the generative AI and have the generative AI perform the adjustment of the data processing method.

[0105] The data conversion unit analyzes the content of the letters and selects the optimal data conversion method during the data conversion process. For example, the data conversion unit provides a detailed data conversion method for letters of high importance. The data conversion unit can also provide a concise data conversion method for letters of low importance. Furthermore, the data conversion unit can determine the priority of data conversion according to the content of the letters. This allows the unit to provide the optimal data conversion method according to the content of the letters. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the content data of the letters into a generating AI and have the generating AI perform the task of selecting the optimal data conversion method.

[0106] The data processing unit estimates the parent's emotions and determines the priority of data processing based on the estimated emotions. For example, if the parent is stressed, the data processing unit will prioritize processing high-priority notices. The data processing unit can also process all notices if the parent is relaxed. Furthermore, if the parent is in a hurry, the data processing unit can prioritize processing notices containing the most important information. This ensures that important information is prioritized for processing by determining the priority of data processing according to the parent'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 processing described above in the data processing unit may be performed using AI or not using AI. For example, the data processing unit can input parent emotion data into a generative AI and have the generative AI perform the task of determining the priority of data processing.

[0107] The data conversion unit adjusts the order of data conversion based on the publication date of the newsletters. For example, the data conversion unit prioritizes the conversion of recently published newsletters. The data conversion unit can also determine the priority of data conversion for older newsletters according to their importance. Furthermore, the data conversion unit can adjust the timing of data conversion based on the publication date. This allows for prioritization of the latest information by adjusting the order of data conversion based on the publication date of the newsletters. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input newsletter publication date data into a generating AI and have the generating AI execute the process of adjusting the order of data conversion.

[0108] The response unit estimates the parent's emotions and adjusts the way it expresses its response based on the estimated emotions. For example, if the parent is tense, the response unit provides a simple and easily understandable response. The response unit can also provide a detailed response if the parent is relaxed. Furthermore, if the parent is in a hurry, the response unit can provide a concise response. This allows for the provision of an optimal response by adjusting the way it expresses the response according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the response unit may be performed using AI or not. For example, the response unit can input the parent's emotion data into the generative AI and have the generative AI perform the actions of adjusting the way it expresses the response.

[0109] The response unit adjusts the level of detail in its response based on the parent's request. For example, it provides a detailed response for high-priority requests. It can also provide a concise response for low-priority requests. Furthermore, the response unit can determine the priority of the response depending on the content of the request. This allows it to provide an optimal response by adjusting the level of detail based on the parent's request. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the parent's request data into a generating AI and have the generating AI perform the action of adjusting the level of detail in the response.

[0110] The response unit estimates the parent's emotions and determines the priority of responses based on the estimated emotions. For example, if the parent is stressed, the response unit will prioritize responding to high-priority requests. The response unit can also respond to all requests if the parent is relaxed. Furthermore, if the parent is in a hurry, the response unit can prioritize responding to requests containing the most important information. This ensures that important requests are prioritized by determining the priority of responses according to the parent'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 processing described above in the response unit may be performed using AI or not using AI. For example, the response unit can input the parent's emotion data into a generative AI and have the generative AI perform the task of determining the priority of responses.

[0111] The response unit provides the optimal response by referring to the parent's past request history when responding. For example, the response unit provides the optimal response based on what the parent has requested in the past. The response unit can also prioritize providing relevant information from the parent's past request history. Furthermore, the response unit can analyze the parent's past request history and provide the most efficient response. This allows the response unit to provide the optimal response by referring to the parent's past request history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the parent's past request history data into a generating AI and have the generating AI execute the content to provide the optimal response.

[0112] The notification unit estimates the parent's emotions and adjusts the way notifications are presented based on the estimated emotions. For example, if the parent is stressed, the notification unit provides a simple and easily visible notification. It can also provide a detailed notification if the parent is relaxed. Furthermore, if the parent is in a hurry, the notification unit can provide a concise notification. This allows for the provision of optimal notifications by adjusting the way notifications are presented according to the parent'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 processing described above in the notification unit may be performed using AI or not. For example, the notification unit can input parent emotion data into the generative AI and have the generative AI perform the actions to adjust the way notifications are presented.

[0113] The notification unit, when issuing a notification, selects the optimal notification method by referring to the parent's past notification history. For example, the notification unit provides the optimal notification method based on the content of notifications the parent has received in the past. The notification unit can also prioritize notifying relevant information from the parent's past notification history. Furthermore, the notification unit can analyze the parent's past notification history and provide the most efficient notification method. This allows the notification unit to provide the optimal notification method by referring to the parent's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the parent's past notification history data into a generating AI and have the generating AI perform the task of selecting the optimal notification method.

[0114] The notification unit estimates the parent's emotions and determines the priority of notifications based on the estimated emotions. For example, if the parent is stressed, the notification unit will prioritize sending high-priority notifications. The notification unit can also send all notifications if the parent is relaxed. Furthermore, if the parent is in a hurry, the notification unit can prioritize sending notifications containing the most important information. This ensures that important notifications are sent preferentially by prioritizing notifications according to the parent'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 processing described above in the notification unit may be performed using AI or not. For example, the notification unit can input parent emotion data into a generative AI and have the generative AI perform the task of determining notification priorities.

[0115] The notification unit selects the optimal notification method when sending a notification, taking into account the parent's device information. For example, if the parent is using a smartphone, the notification unit provides a notification method that matches the screen size. The notification unit can also provide a notification method optimized for larger screens if the parent is using a tablet. Furthermore, if the parent is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This ensures that the optimal notification method is provided by considering the parent's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the parent's device information data into a generating AI and have the generating AI perform the task of selecting the optimal notification method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The suggestion system can also estimate the parent's emotions and adjust the timing of suggestions based on those estimates. For example, if the parent is stressed, the suggestion system can postpone important suggestions and present them when the parent is relaxed. If the parent is busy, the suggestion system can make suggestions concise and easy to understand. Furthermore, if the parent is agitated, the suggestion system can present visually appealing suggestions to capture their attention. This allows the system to adjust the timing of suggestions according to the parent's emotions, ensuring that suggestions are delivered at the optimal time.

[0118] The management team can also estimate the parents' emotions and adjust the frequency of reminders based on those estimates. For example, if a parent is stressed, the frequency of reminders can be reduced, and only important reminders can be sent. Conversely, if a parent is relaxed, the frequency of reminders can be increased, and more detailed information can be provided. Furthermore, if a parent is in a hurry, the content of the reminder can be summarized concisely for quick understanding. This allows for the provision of optimal reminders by adjusting the frequency of reminders according to the parents' emotions.

[0119] The reception desk can also estimate the parent's emotions and suggest a method for taking photos of the letter based on those estimates. For example, if the parent is stressed, a simpler method can be suggested to reduce the effort involved in taking the photo. If the parent is relaxed, a more detailed method can be suggested to obtain more accurate information. Furthermore, if the parent is in a hurry, a method that allows for quick photography can be suggested. In this way, by suggesting a method of photography according to the parent's emotions, the optimal method of photography can be provided.

[0120] The analysis unit can estimate the parent's emotions and adjust the display method of the analysis results based on those estimated emotions. For example, if the parent is stressed, it can provide simple and easy-to-understand analysis results. If the parent is relaxed, it can provide detailed analysis results. Furthermore, if the parent is in a hurry, it can provide concise analysis results. In this way, by adjusting the display method of the analysis results according to the parent's emotions, the system can provide the most optimal analysis results.

[0121] The proposal team can also estimate the parent's emotions and adjust the content of the proposal based on those estimates. For example, if the parent is stressed, only the essential suggestions can be made, and unnecessary information can be omitted. If the parent is relaxed, a detailed suggestion can be made to ensure they fully understand it. Furthermore, if the parent is in a hurry, a concise and to-the-point suggestion can be made. In this way, by adjusting the content of the suggestion according to the parent's emotions, the optimal suggestion can be provided.

[0122] The management department can analyze parents' past behavioral history and propose optimal management methods. For example, based on actions parents have taken in the past, it can propose the best management method for similar situations. It can also analyze parents' behavioral patterns and propose management methods at the optimal time. Furthermore, it can propose the best management method for specific situations based on parents' past behavioral history. In this way, by analyzing past behavioral history, it is possible to propose the best management method.

[0123] The reception desk can suggest the optimal timing for taking photos, taking into account the geographical location of the parents. For example, if a parent is near the school, it can suggest prioritizing the taking of school-related notices. If a parent is at home, it can also prioritize taking photos of notices containing information needed at home. Furthermore, if a parent is out, it can prioritize taking photos of notices containing information needed while out. In this way, the system can suggest the optimal timing for taking photos by considering geographical location.

[0124] The analysis unit can determine the priority of analysis based on the content of the messages. For example, it can prioritize the analysis of high-priority messages and provide detailed analysis results. Conversely, it can provide concise analysis results for less important messages. Furthermore, it can adjust the order of analysis according to the content of the messages. In this way, by determining the priority of analysis based on the content of the messages, important information can be analyzed preferentially.

[0125] The proposal department can analyze parents' social media activity and make relevant suggestions. For example, it can prioritize relevant suggestions based on information parents share on social media. It can also make relevant suggestions based on parents' social media interests. Furthermore, it can analyze parents' social media activity history to make optimal suggestions. In this way, relevant suggestions can be made by analyzing social media activity.

[0126] The notification unit can select the optimal notification method by considering the parent's device information. For example, if the parent is using a smartphone, it can provide a notification method that is optimized for the screen size. If the parent is using a tablet, it can provide a notification method optimized for the larger screen. Furthermore, if the parent is using a smartwatch, it can provide a concise and highly visible notification method. In this way, the system can provide the most suitable notification method by considering the device information.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk takes a picture of the letter and imports it. Parents can take a picture of the letter using their smartphone camera and import the image data. They can also directly import letters that are provided in digital format. Step 2: The analysis unit analyzes the content of the letters received by the reception unit. It converts the image data of the letters into text data using OCR technology and analyzes the content using NLP technology. It can also analyze the content of the letters using image analysis technology. Step 3: The management department creates summaries, manages to-do lists, and sends reminders based on the analysis performed by the analysis department. It summarizes the analyzed content of the communications, extracts important information, and creates a summary. It can create to-do lists and remind parents of necessary actions. Furthermore, it includes a function to send reminder notifications. Step 4: The proposal team suggests actions based on standard school events and local events. They refer to the school's annual event schedule and local event information to suggest necessary actions for parents. They can also suggest actions at the optimal time based on parents' schedules.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, management unit, and proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit uses the camera 42 of the smart device 14 to photograph the letter and capture the image data. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to convert the image data into text data using OCR technology and analyzes the content using NLP technology. The management unit performs summaries, to-do list management, and reminders based on the content analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to propose actions by referring to standard school events and real-world events. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, management unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit uses the camera 42 of the smart glasses 214 to photograph the letter and capture the image data. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to convert the image data into text data using OCR technology and analyzes the content using NLP technology. The management unit performs summaries, to-do list management, and reminders based on the content analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to propose actions by referring to standard school events and real-world events. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, management unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit uses the camera 42 of the headset terminal 314 to photograph the letter and capture the image data. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to convert the image data into text data using OCR technology and analyzes the content using NLP technology. The management unit performs summaries, to-do list management, and reminders based on the content analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to propose actions by referring to standard school events and real-world events. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the reception unit, analysis unit, management unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit uses the camera 42 of the robot 414 to photograph the letter and capture the image data. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to convert the image data into text data using OCR technology and analyzes the content using NLP technology. The management unit performs summaries, to-do list management, and reminders based on the content analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit uses the specific processing unit 290 of the data processing unit 12 to propose actions by referring to standard school events and real-world events. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The reception area takes photos of the letters and uploads them, An analysis unit analyzes the contents of the letters received by the reception unit, Based on the analysis performed by the aforementioned analysis unit, the management unit performs summaries, to-do list management, and reminders. The proposal team suggests actions based on standard school events and current events, Equipped with A system characterized by the following features. (Note 2) The aforementioned management department, It includes a data conversion unit that uses OCR and NLP to convert the content of letters into data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, It is equipped with a response unit that provides information in response to requests from parents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, It includes a notification unit that sends reminder notifications. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose actions based on standard school events and societal events. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Send notifications using a common messaging app. The system described in Appendix 4, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the parents' emotions and adjusts the timing of the photo shoot for the newsletter based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the parent's past photography history and suggest the optimal photography method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When taking photos of letters, filters are applied based on the parents' current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We estimate the parents' emotions and determine the priority of which letters to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When photographing letters, the system prioritizes photographing letters that are highly relevant, taking into account the geographical location information of the parents. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When photographing newsletters, we analyze parents' social media activity and photograph relevant newsletters. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the parents' emotions and adjust the representation of the analysis based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of each message. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the message. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the parents' emotions and adjusts the length of the analysis based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the publication date of the newsletter. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, Estimate the emotions of parents and adjust management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the content of the letters is analyzed to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, customize the management methods based on the parent's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, Estimate the emotions of parents and determine management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, the optimal management method will be selected considering the geographical location information of the guardians. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During management, we analyze parents' social media activity and propose management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We estimate the parents' emotions and adjust the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the action. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the action. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, Estimate the parents' feelings and adjust the length of the suggestion based on the estimated parents' feelings. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the action will occur. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the actions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned data conversion unit, We estimate the emotions of parents and adjust the data processing method based on the estimated emotions of the parents. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned data conversion unit, When digitizing the data, we analyze the content of the letters to select the most suitable digitization method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned data conversion unit, We estimate the emotions of parents and determine the priority of data collection based on the estimated emotions of parents. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned data conversion unit, When digitizing the data, the order of digitization is adjusted based on the publication date of the letters. The system described in Appendix 2, characterized by the features described herein. (Note 35) The response unit is The system estimates the parent's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The response unit is When responding, adjust the level of detail in the response based on the parent's request. The system described in Appendix 3, characterized by the features described herein. (Note 37) The response unit is The system estimates the parent's emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The response unit is When responding, we refer to the parent's past request history to provide the most appropriate response. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned notification unit, We estimate the parents' emotions and adjust the wording of notifications based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned notification unit, When sending a notification, the system will refer to the parent's past notification history to select the most suitable notification method. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned notification unit, The system estimates the parents' emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned notification unit, When sending notifications, the system will select the most appropriate notification method, taking into account the parent's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 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. The reception area takes photos of the letters and uploads them, An analysis unit analyzes the contents of the letters received by the reception unit, Based on the analysis performed by the aforementioned analysis unit, the management unit performs summaries, to-do list management, and reminders. The proposal team suggests actions based on standard school events and current events, Equipped with A system characterized by the following features.

2. The aforementioned management department, It includes a data conversion unit that uses OCR and NLP to convert the content of letters into data. The system according to feature 1.

3. The aforementioned management department, It is equipped with a response unit that provides information in response to requests from parents. The system according to feature 1.

4. The aforementioned management department, It includes a notification unit that sends reminder notifications. The system according to feature 1.

5. The aforementioned proposal section is, We propose actions based on standard school events and societal events. The system according to feature 1.

6. The aforementioned notification unit, Send notifications using a common messaging app. The system according to feature 4.

7. The aforementioned reception unit is The system estimates the parents' emotions and adjusts the timing of the photo shoot for the newsletter based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the parent's past photography history and suggest the optimal photography method. The system according to feature 1.

9. The aforementioned reception unit is When taking photos of letters, filters are applied based on the parents' current situation and areas of interest. The system according to feature 1.

Images