system

Abstract

The system according to this embodiment aims to centrally manage messages from multiple communication tools and to prioritize notifying users of important communications. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a classification unit, and a notification unit. The collection unit collects messages from various communication tools. The analysis unit analyzes the messages collected by the collection unit. The classification unit classifies the messages according to the user's settings based on the messages analyzed by the analysis unit. The notification unit notifies important communications classified by the classification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is room for improvement in efficiently managing messages from multiple communication tools and preventing important communications from being missed.

[0005] The system according to the embodiment aims to centrally manage messages from multiple communication tools and preferentially notify users of important communications.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a classification unit, and a notification unit. The collection unit collects messages from various communication tools. The analysis unit analyzes the messages collected by the collection unit. The classification unit classifies the messages according to the user's settings based on the messages analyzed by the analysis unit. The notification unit notifies the user of important communications classified by the classification unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage messages from multiple communication tools and prioritize notifying users of important communications. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[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 message management system according to an embodiment of the present invention is a system in which messages from various communication tools (email, chat, SNS, etc.) are centralized and managed by a generating AI. This message management system provides an AI assistant that prioritizes notifying the user of important communications. The user can customize the priorities, and the generating AI classifies and notifies the user of messages based on these settings. For example, work-related emails can be set to high priority, and SNS messages to low priority. The generating AI analyzes messages based on the user's settings and prioritizes notifying the user of important communications. First, messages from various communication tools are collected. For example, messages from email, chat, SNS, etc., are collected centrally. These collected messages are analyzed by the generating AI. The generating AI determines the importance of the messages based on the content of the messages and the sender's information. Next, the user can customize the priorities. For example, work-related emails can be set to high priority, and SNS messages to low priority. The generating AI classifies messages based on the user's settings and prioritizes notifying the user of important communications. Furthermore, the generating AI analyzes messages based on the user's settings and prioritizes notifying the user of important communications. For example, if a work-related email arrives, the generating AI will classify it as high priority and notify the user. On the other hand, if a social media message arrives, the generating AI will classify it as low priority and delay the notification. This system allows users to efficiently manage messages without missing important communications. For instance, if an important email arrives during work, the generating AI will notify the user immediately, allowing them to respond quickly. Meanwhile, social media messages can be reviewed later, so they do not disrupt work efficiency. In this way, the message management system allows users to efficiently manage messages without missing important communications.

[0029] The message management system according to the embodiment comprises a collection unit, an analysis unit, a classification unit, and a notification unit. The collection unit collects messages from various communication tools. For example, the collection unit can centrally collect messages from email, chat, SNS, etc. For example, the collection unit can retrieve emails from an email server, retrieve chat messages through a chat application API, and retrieve SNS messages through an SNS API. The analysis unit analyzes the messages collected by the collection unit. For example, the analysis unit uses a generation AI to analyze the content of messages and sender information and determines the importance of the messages. For example, the generation AI can use natural language processing technology to extract keywords from messages and determine importance based on the sender's position and the frequency of past interactions. The classification unit classifies messages according to user settings based on the messages analyzed by the analysis unit. For example, the classification unit can classify messages into high priority, medium priority, and low priority based on the priority set by the user. The notification unit notifies important communications classified by the classification unit. For example, the notification unit can immediately notify the user of high-priority messages, notify them of medium-priority messages after a certain period of time, and notify them of low-priority messages later. As a result, the message management system according to this embodiment can efficiently manage messages without the user missing important communications.

[0030] Furthermore, the message management system includes a settings reception unit that accepts user settings. For example, the settings reception unit can provide an interface for users to customize message priorities. Users can, for instance, set work-related emails to high priority and social media messages to low priority. The settings reception unit can save the user-defined priorities and provide them to the message generation AI. This allows users to customize message priorities to suit their needs.

[0031] The analysis unit can determine the importance of a message based on its content and sender information. For example, the analysis unit can use generative AI to analyze the message content and sender information. For instance, it can extract keywords from a message and determine its importance based on the sender's position and the frequency of past interactions. The analysis unit can also perform sentiment analysis on a message and determine its importance based on the intensity and type of emotion. For example, it can detect positive and negative emotions from the message content and determine its importance according to the intensity of the emotion. This allows the analysis unit to accurately determine the importance of a message.

[0032] The classification unit can categorize messages based on user settings. For example, it can categorize messages based on user-defined priorities. For instance, it can categorize work-related emails as high priority and social media messages as low priority. The classification unit can also categorize messages based on their content and sender information. For example, it can categorize messages based on categories such as business, personal, and urgent. This allows the classification unit to appropriately categorize messages based on user settings.

[0033] The notification unit can prioritize the notification of important communications. For example, it can immediately notify users of messages classified as high priority. For instance, it can notify users of important messages using push notifications or email notifications. It can also notify users of medium-priority messages after a certain period of time and notify them of low-priority messages later. For example, it can adjust the timing of notifications based on a time period set by the user. This allows the notification unit to prioritize the notification of important communications to the user.

[0034] The settings reception unit may include a customization unit for users to customize priorities. For example, the settings reception unit may provide an interface for users to customize message priorities. For instance, it could offer customization options for users to set work-related emails as high priority and social media messages as low priority. The settings reception unit may also offer options for users to customize notification methods and timing. For example, users can select notification methods such as push notifications, email notifications, and voice notifications. This allows the settings reception unit to provide an interface for users to customize priorities.

[0035] The data collection unit can analyze the frequency of use of various communication tools and select the optimal collection method. For example, the data collection unit can use generative AI to analyze the frequency of use of various communication tools. For example, the data collection unit can analyze a user's email usage frequency and prioritize email collection. It can also analyze a user's chat tool usage frequency and prioritize chat message collection. Furthermore, the data collection unit can analyze a user's SNS usage frequency and prioritize SNS message collection. This allows the data collection unit to select the optimal collection method based on the frequency of use of various communication tools. For example, the data collection unit can use generative AI to analyze a user's email usage frequency and prioritize email collection. It can also use generative AI to analyze a user's chat tool usage frequency and prioritize chat message collection. Furthermore, the data collection unit can use generative AI to analyze a user's SNS usage frequency and prioritize SNS message collection. This allows the data collection unit to select the optimal collection method based on the frequency of use of various communication tools.

[0036] The message collection unit can filter messages based on the user's current activity status. For example, the collection unit can analyze the user's current activity status using generative AI. For instance, the collection unit can analyze the user's schedule information to understand their current activity status. It can also analyze the user's location information to understand their current activity status. This allows the collection unit to filter message collection based on the user's current activity status. For example, if the user is in a meeting, the collection unit can collect only important messages and postpone other messages. If the user is exercising, the collection unit can collect only urgent messages and notify the user of other messages later. Furthermore, if the user is taking a break, the collection unit can collect all messages and notify the user in real time. This allows the collection unit to filter message collection according to the user's activity status.

[0037] The message collection unit can prioritize collecting highly relevant messages by considering the user's geographical location information during message collection. For example, the collection unit can analyze the user's geographical location information using generative AI. For instance, the collection unit can analyze the user's GPS data to determine their current location. It can also analyze the user's IP address to determine their location. This allows the collection unit to prioritize collecting highly relevant messages based on the user's geographical location information. For example, if the user is in a specific location, it can prioritize collecting messages related to that location. If the user is traveling, it can prioritize collecting messages related to their travel destination. Furthermore, if the user is at home, it can prioritize collecting messages related to their home. This allows the collection unit to prioritize collecting highly relevant messages based on the user's geographical location information.

[0038] The collection unit can analyze a user's social media activity and collect relevant messages when collecting messages. For example, the collection unit can analyze a user's social media activity using generative AI. For instance, it can analyze a user's posts, the number of likes, the number of followers, etc., to understand their social media activity. This allows the collection unit to collect relevant messages based on the user's social media activity. For example, if a user is active on a specific social media platform, the collection unit can prioritize collecting messages related to that platform. Similarly, if a user uses a specific hashtag, the collection unit can prioritize collecting messages related to that hashtag. Furthermore, if a user belongs to a specific group, the collection unit can prioritize collecting messages related to that group. This allows the collection unit to collect relevant messages based on the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the content of the message. For example, the analysis unit analyzes the content of the message using a generative AI. For instance, in the case of an important message, the analysis unit can perform a detailed analysis and provide all the information. For general messages, the analysis unit can perform a concise analysis and extract only the important points. Furthermore, in the case of an urgent message, the analysis unit can perform a rapid analysis and provide immediate notification. In this way, the analysis unit can adjust the level of detail of the analysis according to the content of the message.

[0040] The analysis unit can apply different analysis algorithms depending on the message category. For example, the analysis unit can analyze the message category using a generation AI. For instance, the analysis unit can apply a business-oriented analysis algorithm to work-related messages, a social media-oriented analysis algorithm to SNS messages, and a rapid analysis algorithm to urgent messages. This allows the analysis unit to apply the appropriate analysis algorithm according to the message category.

[0041] The analysis unit can adjust the order of analysis based on the message transmission date. For example, the analysis unit can use a generation AI to analyze the message transmission date. For instance, the analysis unit can prioritize analyzing recently sent messages. It can also prioritize analyzing messages sent during important time periods. Furthermore, it can prioritize analyzing messages sent during user-specified time periods. This allows the analysis unit to adjust the order of analysis based on the message transmission date.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the messages. For example, the analysis unit can analyze the relevance of messages using a generative AI. For instance, the analysis unit can prioritize the analysis of important messages. It can also prioritize the analysis of highly relevant messages based on user settings. Furthermore, it can prioritize the analysis of urgent messages. This allows the analysis unit to adjust the order of analysis based on the relevance of the messages.

[0043] The classification unit can improve the accuracy of classification by considering the interrelationships between messages. For example, the classification unit can analyze the interrelationships between messages using generative AI. For example, the classification unit can group related messages to improve classification accuracy. Furthermore, the classification unit can perform classification considering the relationship between the message sender and receiver. In addition, the classification unit can perform classification considering the content of the message and related topics. This allows the classification unit to improve the accuracy of classification by considering the interrelationships between messages. For example, the classification unit can group related messages using generative AI to improve classification accuracy. Furthermore, the classification unit can perform classification considering the relationship between the message sender and receiver using generative AI. In addition, the classification unit can perform classification considering the content of the message and related topics using generative AI. This allows the classification unit to improve the accuracy of classification by considering the interrelationships between messages.

[0044] The classification unit can classify messages by considering the sender's attribute information. For example, the classification unit can analyze the sender's attribute information using generative AI. For instance, the classification unit can classify messages by considering the sender's occupation and position. Furthermore, the classification unit can classify messages by considering the sender's past message history. Additionally, the classification unit can classify messages by considering the sender's relationship to the sender (friend, colleague, boss, etc.). This allows the classification unit to perform more appropriate classifications by classifying messages based on the sender's attribute information. For example, the classification unit can classify messages by considering the sender's occupation and position using generative AI. Furthermore, the classification unit can classify messages by considering the sender's past message history using generative AI. Additionally, the classification unit can classify messages by considering the sender's relationship to the sender (friend, colleague, boss, etc.) using generative AI. This allows the classification unit to perform more appropriate classifications by classifying messages based on the sender's attribute information.

[0045] The classification unit can perform classification while considering the geographical distribution of messages. For example, the classification unit can analyze the geographical distribution of messages using generative AI. For instance, the classification unit can prioritize the classification of messages related to the user's current location. Furthermore, the classification unit can group and classify messages related to specific regions. Additionally, the classification unit can classify messages while considering the user's movement history. This allows the classification unit to perform more appropriate classification by classifying messages based on their geographical distribution. For example, the classification unit can prioritize the classification of messages related to the user's current location using generative AI. Furthermore, the classification unit can group and classify messages related to specific regions using generative AI. Additionally, the classification unit can classify messages while considering the user's movement history using generative AI. This allows the classification unit to perform more appropriate classification by classifying messages based on their geographical distribution.

[0046] The classification unit can improve the accuracy of its classification by referring to relevant literature for messages. For example, the classification unit can analyze relevant literature for messages using generative AI. For instance, the classification unit can classify messages by referring to relevant academic papers. Furthermore, the classification unit can classify messages by referring to relevant news articles. In addition, the classification unit can classify messages by referring to relevant technical documents. This allows the classification unit to improve the accuracy of its classification by referring to relevant literature for messages. For example, the classification unit can classify messages by referring to relevant academic papers using generative AI. Furthermore, the classification unit can classify messages by referring to relevant news articles using generative AI. In addition, the classification unit can classify messages by referring to relevant technical documents using generative AI. This allows the classification unit to improve the accuracy of its classification by referring to relevant literature for messages.

[0047] The notification unit can adjust the level of detail of notifications based on the importance of the message. For example, the notification unit can analyze the importance of a message using generative AI. For instance, the notification unit can provide detailed notifications for important messages, while providing concise notifications for general messages. Furthermore, for urgent messages, the notification unit can provide immediate notifications. This allows the notification unit to adjust the level of detail of notifications based on the importance of the message. For example, the notification unit can provide detailed notifications for important messages using generative AI, while providing concise notifications for general messages using generative AI. Furthermore, the notification unit can provide immediate notifications for urgent messages using generative AI. This allows the notification unit to adjust the level of detail of notifications based on the importance of the message.

[0048] The notification unit can apply different notification algorithms depending on the message category when a notification is sent. For example, the notification unit can analyze the message category using generative AI. For instance, it can apply a business-oriented notification algorithm to work-related messages. It can also apply a social media-oriented notification algorithm to SNS messages. Furthermore, it can apply a rapid notification algorithm to urgent messages. This allows the notification unit to apply the appropriate notification algorithm according to the message category. For example, the notification unit can use generative AI to apply a business-oriented notification algorithm to work-related messages. It can also use generative AI to apply a social media-oriented notification algorithm to SNS messages. Furthermore, it can use generative AI to apply a rapid notification algorithm to urgent messages. This allows the notification unit to apply the appropriate notification algorithm according to the message category.

[0049] The notification unit can determine notification priorities based on when the message was sent. For example, the notification unit can analyze the message sending time using generative AI. For instance, the notification unit can prioritize notifications for recently sent messages. It can also prioritize notifications for messages sent during important time periods. Furthermore, it can prioritize notifications for messages sent during time periods specified by the user. This allows the notification unit to determine notification priorities based on when the message was sent. For example, the notification unit can prioritize notifications for recently sent messages using generative AI. It can also prioritize notifications for messages sent during important time periods using generative AI. Furthermore, it can prioritize notifications for messages sent during time periods specified by the user using generative AI. This allows the notification unit to determine notification priorities based on when the message was sent.

[0050] The notification unit can adjust the order of notifications based on the relevance of the messages when a notification is sent. For example, the notification unit can analyze the relevance of messages using generative AI. For example, the notification unit can prioritize important messages. It can also prioritize highly relevant messages based on user settings. Furthermore, it can prioritize urgent messages. This allows the notification unit to adjust the order of notifications based on the relevance of the messages. For example, the notification unit can prioritize important messages using generative AI. It can also prioritize highly relevant messages based on user settings using generative AI. Furthermore, the notification unit can prioritize urgent messages using generative AI. This allows the notification unit to adjust the order of notifications based on the relevance of the messages.

[0051] The settings reception unit can suggest the optimal settings method by referring to the user's past settings history when a settings request is received. For example, the settings reception unit can analyze the user's past settings history using generative AI. For example, the settings reception unit can automatically display settings that the user has frequently used in the past as candidates. Furthermore, the settings reception unit can prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past. In addition, the settings reception unit can predict and suggest settings to be used during specific time periods based on the user's past settings history. This allows the settings reception unit to suggest the optimal settings method based on the user's past settings history. For example, the settings reception unit can automatically display settings that the user has frequently used in the past as candidates using generative AI. Furthermore, the settings reception unit can prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past using generative AI. Furthermore, the settings reception unit can predict and suggest settings to be used during specific time periods based on the user's past settings history using generative AI. This allows the settings reception unit to suggest the optimal settings method based on the user's past settings history.

[0052] The settings reception unit can propose the optimal settings method when a user requests settings, taking into account the user's device information. For example, the settings reception unit can analyze the user's device information using generative AI. For instance, if the user is using a smartphone, the settings reception unit can provide settings tailored to the screen size. Furthermore, if the user is using a tablet, the settings reception unit can provide settings optimized for larger screens. Additionally, if the user is using a smartwatch, the settings reception unit can provide concise and highly visible settings. This allows the settings reception unit to propose the optimal settings method based on the user's device information. For example, using generative AI, the settings reception unit can provide settings tailored to the screen size if the user is using a smartphone. Furthermore, using generative AI, the settings reception unit can provide settings optimized for larger screens if the user is using a tablet. Additionally, using generative AI, the settings reception unit can provide concise and highly visible settings if the user is using a smartwatch. This allows the settings reception unit to propose the optimal settings method based on the user's device information.

[0053] The customization unit can suggest the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit can analyze the user's past customization history using generative AI. For instance, the customization unit can automatically display customizations the user has frequently used in the past as candidates. Furthermore, the customization unit can prioritize suggesting customization methods (voice, text, etc.) the user has used in the past. In addition, the customization unit can predict and suggest customizations to be used during specific time periods based on the user's past customization history. This allows the customization unit to suggest the optimal customization method based on the user's past customization history. For example, the customization unit can automatically display customizations the user has frequently used in the past as candidates using generative AI. Furthermore, the customization unit can prioritize suggesting customization methods (voice, text, etc.) the user has used in the past using generative AI. Furthermore, the customization unit can predict and suggest customizations to be used during specific time periods based on the user's past customization history using generative AI. This allows the customization unit to suggest the optimal customization method based on the user's past customization history.

[0054] The customization unit can propose the optimal customization method when customizing, taking into account the user's geographical location information. For example, the customization unit can analyze the user's geographical location information using generative AI. For instance, if the user is in a specific location, the customization unit can propose customizations related to that location. Furthermore, if the user is traveling, the customization unit can propose customizations related to their travel destination. Additionally, if the user is at home, the customization unit can propose customizations related to their home. This allows the customization unit to propose the optimal customization method based on the user's geographical location information. For example, the customization unit can use generative AI to propose customizations related to a specific location if the user is in a specific location. Furthermore, if the user is traveling, the customization unit can use generative AI to propose customizations related to their travel destination. Additionally, if the user is at home, the customization unit can use generative AI to propose customizations related to their home. This allows the customization unit to propose the optimal customization method based on the user's geographical location information.

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

[0056] The analysis unit can adjust the level of detail in its analysis based on the message content. For example, in the case of an important message, the analysis unit can perform a detailed analysis and provide all the information. In the case of a general message, the analysis unit can perform a concise analysis and extract only the important points. Furthermore, in the case of an urgent message, the analysis unit can perform a rapid analysis and provide immediate notification. In this way, the analysis unit can adjust the level of detail in its analysis according to the content of the message.

[0057] The classification unit can improve the accuracy of its classification by considering the interrelationships between messages. For example, it can improve classification accuracy by grouping related messages. It can also perform classification by considering the relationship between the sender and receiver of a message. Furthermore, it can perform classification by considering the content of the message and related topics. In this way, the classification unit can improve the accuracy of its classification by considering the interrelationships between messages.

[0058] The notification unit can adjust the level of detail in notifications based on the importance of the message. For example, it can provide detailed notifications for important messages, concise notifications for general messages, and immediate notifications for urgent messages. This allows the notification unit to adjust the level of detail in notifications based on the importance of the message.

[0059] The message collection unit can filter messages based on the user's current activity status. For example, if a user is in a meeting, the unit can collect only important messages and postpone others. If a user is exercising, the unit can collect only urgent messages and notify the user of others later. Furthermore, if a user is taking a break, the unit can collect all messages and notify the user in real time. This allows the unit to filter message collection according to the user's activity status.

[0060] The settings reception unit can suggest the optimal settings method by referring to the user's past settings history when a settings request is made. For example, it can automatically display settings that the user has frequently used in the past as candidates. It can also prioritize suggesting settings methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest settings that the user will use during specific time periods based on their past settings history. In this way, the settings reception unit can suggest the optimal settings method based on the user's past settings history.

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

[0062] Step 1: The collection unit collects messages from various communication tools. For example, the collection unit can centrally collect messages from email, chat, and social media. The collection unit can retrieve emails from email servers, chat messages through chat application APIs, and social media messages through social media APIs. Step 2: The analysis unit analyzes the messages collected by the collection unit. For example, the analysis unit uses a generation AI to analyze the content of the messages and sender information, and determines the importance of the messages. The generation AI can use natural language processing technology to extract keywords from the messages and determine importance based on the sender's job title and the frequency of past interactions. Step 3: The classification unit classifies messages according to user settings based on the messages analyzed by the analysis unit. For example, the classification unit can classify messages into high priority, medium priority, and low priority based on the priority set by the user. Step 4: The notification unit notifies important communications classified by the classification unit. For example, the notification unit can immediately notify the user of high-priority messages, notify them of medium-priority messages after a certain period of time, and notify them of low-priority messages later.

[0063] (Example of form 2) The message management system according to an embodiment of the present invention is a system in which messages from various communication tools (email, chat, SNS, etc.) are centralized and managed by a generating AI. This message management system provides an AI assistant that prioritizes notifying the user of important communications. The user can customize the priorities, and the generating AI classifies and notifies the user of messages based on these settings. For example, work-related emails can be set to high priority, and SNS messages to low priority. The generating AI analyzes messages based on the user's settings and prioritizes notifying the user of important communications. First, messages from various communication tools are collected. For example, messages from email, chat, SNS, etc., are collected centrally. These collected messages are analyzed by the generating AI. The generating AI determines the importance of the messages based on the content of the messages and the sender's information. Next, the user can customize the priorities. For example, work-related emails can be set to high priority, and SNS messages to low priority. The generating AI classifies messages based on the user's settings and prioritizes notifying the user of important communications. Furthermore, the generating AI analyzes messages based on the user's settings and prioritizes notifying the user of important communications. For example, if a work-related email arrives, the generating AI will classify it as high priority and notify the user. On the other hand, if a social media message arrives, the generating AI will classify it as low priority and delay the notification. This system allows users to efficiently manage messages without missing important communications. For instance, if an important email arrives during work, the generating AI will notify the user immediately, allowing them to respond quickly. Meanwhile, social media messages can be reviewed later, so they do not disrupt work efficiency. In this way, the message management system allows users to efficiently manage messages without missing important communications.

[0064] The message management system according to the embodiment comprises a collection unit, an analysis unit, a classification unit, and a notification unit. The collection unit collects messages from various communication tools. For example, the collection unit can centrally collect messages from email, chat, SNS, etc. For example, the collection unit can retrieve emails from an email server, retrieve chat messages through a chat application API, and retrieve SNS messages through an SNS API. The analysis unit analyzes the messages collected by the collection unit. For example, the analysis unit uses a generation AI to analyze the content of messages and sender information and determines the importance of the messages. For example, the generation AI can use natural language processing technology to extract keywords from messages and determine importance based on the sender's position and the frequency of past interactions. The classification unit classifies messages according to user settings based on the messages analyzed by the analysis unit. For example, the classification unit can classify messages into high priority, medium priority, and low priority based on the priority set by the user. The notification unit notifies important communications classified by the classification unit. For example, the notification unit can immediately notify the user of high-priority messages, notify them of medium-priority messages after a certain period of time, and notify them of low-priority messages later. As a result, the message management system according to this embodiment can efficiently manage messages without the user missing important communications.

[0065] Furthermore, the message management system includes a settings reception unit that accepts user settings. For example, the settings reception unit can provide an interface for users to customize message priorities. Users can, for instance, set work-related emails to high priority and social media messages to low priority. The settings reception unit can save the user-defined priorities and provide them to the message generation AI. This allows users to customize message priorities to suit their needs.

[0066] The analysis unit can determine the importance of a message based on its content and sender information. For example, the analysis unit can use generative AI to analyze the message content and sender information. For instance, it can extract keywords from a message and determine its importance based on the sender's position and the frequency of past interactions. The analysis unit can also perform sentiment analysis on a message and determine its importance based on the intensity and type of emotion. For example, it can detect positive and negative emotions from the message content and determine its importance according to the intensity of the emotion. This allows the analysis unit to accurately determine the importance of a message.

[0067] The classification unit can categorize messages based on user settings. For example, it can categorize messages based on user-defined priorities. For instance, it can categorize work-related emails as high priority and social media messages as low priority. The classification unit can also categorize messages based on their content and sender information. For example, it can categorize messages based on categories such as business, personal, and urgent. This allows the classification unit to appropriately categorize messages based on user settings.

[0068] The notification unit can prioritize the notification of important communications. For example, it can immediately notify users of messages classified as high priority. For instance, it can notify users of important messages using push notifications or email notifications. It can also notify users of medium-priority messages after a certain period of time and notify them of low-priority messages later. For example, it can adjust the timing of notifications based on a time period set by the user. This allows the notification unit to prioritize the notification of important communications to the user.

[0069] The settings reception unit may include a customization unit for users to customize priorities. For example, the settings reception unit may provide an interface for users to customize message priorities. For instance, it could offer customization options for users to set work-related emails as high priority and social media messages as low priority. The settings reception unit may also offer options for users to customize notification methods and timing. For example, users can select notification methods such as push notifications, email notifications, and voice notifications. This allows the settings reception unit to provide an interface for users to customize priorities.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of message collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using generative AI. For example, it can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. For example, it can estimate emotions by analyzing the tone and speed of the user's voice. Furthermore, it can estimate emotions using text analysis technology. For example, it can estimate emotions by analyzing the content of the user's messages. This allows the data collection unit to adjust the timing of message collection according to the user's emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of message collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of message collection to provide real-time information. Furthermore, if the user is focused, the data collection unit can collect only important messages, postponing others. This allows the data collection unit to adjust the timing of message collection according to the user's emotions.

[0071] The data collection unit can analyze the frequency of use of various communication tools and select the optimal collection method. For example, the data collection unit can use generative AI to analyze the frequency of use of various communication tools. For example, the data collection unit can analyze a user's email usage frequency and prioritize email collection. It can also analyze a user's chat tool usage frequency and prioritize chat message collection. Furthermore, the data collection unit can analyze a user's SNS usage frequency and prioritize SNS message collection. This allows the data collection unit to select the optimal collection method based on the frequency of use of various communication tools. For example, the data collection unit can use generative AI to analyze a user's email usage frequency and prioritize email collection. It can also use generative AI to analyze a user's chat tool usage frequency and prioritize chat message collection. Furthermore, the data collection unit can use generative AI to analyze a user's SNS usage frequency and prioritize SNS message collection. This allows the data collection unit to select the optimal collection method based on the frequency of use of various communication tools.

[0072] The message collection unit can filter messages based on the user's current activity status. For example, the collection unit can analyze the user's current activity status using generative AI. For instance, the collection unit can analyze the user's schedule information to understand their current activity status. It can also analyze the user's location information to understand their current activity status. This allows the collection unit to filter message collection based on the user's current activity status. For example, if the user is in a meeting, the collection unit can collect only important messages and postpone other messages. If the user is exercising, the collection unit can collect only urgent messages and notify the user of other messages later. Furthermore, if the user is taking a break, the collection unit can collect all messages and notify the user in real time. This allows the collection unit to filter message collection according to the user's activity status.

[0073] The data collection unit can estimate the user's emotions and determine the priority of messages to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using generative AI. For example, the data collection unit can estimate the user's emotions using facial recognition technology. The data collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the data collection unit can estimate the user's emotions using text analysis technology. This allows the data collection unit to determine the priority of messages to collect according to the user's emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important messages. If the user is relaxed, the data collection unit can collect all messages equally. Furthermore, if the user is focused, the data collection unit can prioritize collecting work-related messages. This allows the data collection unit to determine the priority of messages to collect according to the user's emotions.

[0074] The message collection unit can prioritize collecting highly relevant messages by considering the user's geographical location information during message collection. For example, the collection unit can analyze the user's geographical location information using generative AI. For instance, the collection unit can analyze the user's GPS data to determine their current location. It can also analyze the user's IP address to determine their location. This allows the collection unit to prioritize collecting highly relevant messages based on the user's geographical location information. For example, if the user is in a specific location, it can prioritize collecting messages related to that location. If the user is traveling, it can prioritize collecting messages related to their travel destination. Furthermore, if the user is at home, it can prioritize collecting messages related to their home. This allows the collection unit to prioritize collecting highly relevant messages based on the user's geographical location information.

[0075] The collection unit can analyze a user's social media activity and collect relevant messages when collecting messages. For example, the collection unit can analyze a user's social media activity using generative AI. For instance, it can analyze a user's posts, the number of likes, the number of followers, etc., to understand their social media activity. This allows the collection unit to collect relevant messages based on the user's social media activity. For example, if a user is active on a specific social media platform, the collection unit can prioritize collecting messages related to that platform. Similarly, if a user uses a specific hashtag, the collection unit can prioritize collecting messages related to that hashtag. Furthermore, if a user belongs to a specific group, the collection unit can prioritize collecting messages related to that group. This allows the collection unit to collect relevant messages based on the user's social media activity.

[0076] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using generative AI. For example, the analysis unit can estimate the user's emotions using user facial recognition technology. The analysis unit can also estimate the user's emotions using user voice analysis technology. Furthermore, the analysis unit can estimate the user's emotions using user text analysis technology. This allows the analysis unit to adjust the message analysis method according to the user's emotions. For example, if the user is stressed, the analysis unit can perform a concise analysis and extract only the important points. If the user is relaxed, the analysis unit can perform a detailed analysis and provide all the information. Furthermore, if the user is focused, the analysis unit can prioritize analyzing work-related messages. This allows the analysis unit to adjust the message analysis method according to the user's emotions.

[0077] The analysis unit can adjust the level of detail of the analysis based on the content of the message. For example, the analysis unit analyzes the content of the message using a generative AI. For instance, in the case of an important message, the analysis unit can perform a detailed analysis and provide all the information. For general messages, the analysis unit can perform a concise analysis and extract only the important points. Furthermore, in the case of an urgent message, the analysis unit can perform a rapid analysis and provide immediate notification. In this way, the analysis unit can adjust the level of detail of the analysis according to the content of the message.

[0078] The analysis unit can apply different analysis algorithms depending on the message category. For example, the analysis unit can analyze the message category using a generation AI. For instance, the analysis unit can apply a business-oriented analysis algorithm to work-related messages, a social media-oriented analysis algorithm to SNS messages, and a rapid analysis algorithm to urgent messages. This allows the analysis unit to apply the appropriate analysis algorithm according to the message category.

[0079] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using generative AI. For example, the analysis unit can estimate the user's emotions using user facial recognition technology. The analysis unit can also estimate the user's emotions using user voice analysis technology. Furthermore, the analysis unit can estimate the user's emotions using user text analysis technology. This allows the analysis unit to determine the priority of analysis according to the user's emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing important messages. If the user is relaxed, the analysis unit can analyze all messages equally. Furthermore, if the user is focused, the analysis unit can prioritize analyzing work-related messages. This allows the analysis unit to determine the priority of analysis according to the user's emotions.

[0080] The analysis unit can adjust the order of analysis based on the message transmission date. For example, the analysis unit can use a generation AI to analyze the message transmission date. For instance, the analysis unit can prioritize analyzing recently sent messages. It can also prioritize analyzing messages sent during important time periods. Furthermore, it can prioritize analyzing messages sent during user-specified time periods. This allows the analysis unit to adjust the order of analysis based on the message transmission date.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the messages. For example, the analysis unit can analyze the relevance of messages using a generative AI. For instance, the analysis unit can prioritize the analysis of important messages. It can also prioritize the analysis of highly relevant messages based on user settings. Furthermore, it can prioritize the analysis of urgent messages. This allows the analysis unit to adjust the order of analysis based on the relevance of the messages.

[0082] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated emotions. For example, the classification unit can estimate the user's emotions using generative AI. For example, the classification unit can estimate the user's emotions using facial recognition technology. The classification unit can also estimate the user's emotions using voice analysis technology. Furthermore, the classification unit can estimate the user's emotions using text analysis technology. This allows the classification unit to adjust the classification criteria according to the user's emotions. For example, if the user is stressed, the classification unit can prioritize classifying only important messages. If the user is relaxed, the classification unit can classify all messages equally. Furthermore, if the user is focused, the classification unit can prioritize classifying work-related messages. This allows the classification unit to adjust the classification criteria according to the user's emotions.

[0083] The classification unit can improve the accuracy of classification by considering the interrelationships between messages. For example, the classification unit can analyze the interrelationships between messages using generative AI. For example, the classification unit can group related messages to improve classification accuracy. Furthermore, the classification unit can perform classification considering the relationship between the message sender and receiver. In addition, the classification unit can perform classification considering the content of the message and related topics. This allows the classification unit to improve the accuracy of classification by considering the interrelationships between messages. For example, the classification unit can group related messages using generative AI to improve classification accuracy. Furthermore, the classification unit can perform classification considering the relationship between the message sender and receiver using generative AI. In addition, the classification unit can perform classification considering the content of the message and related topics using generative AI. This allows the classification unit to improve the accuracy of classification by considering the interrelationships between messages.

[0084] The classification unit can classify messages by considering the sender's attribute information. For example, the classification unit can analyze the sender's attribute information using generative AI. For instance, the classification unit can classify messages by considering the sender's occupation and position. Furthermore, the classification unit can classify messages by considering the sender's past message history. Additionally, the classification unit can classify messages by considering the sender's relationship to the sender (friend, colleague, boss, etc.). This allows the classification unit to perform more appropriate classifications by classifying messages based on the sender's attribute information. For example, the classification unit can classify messages by considering the sender's occupation and position using generative AI. Furthermore, the classification unit can classify messages by considering the sender's past message history using generative AI. Additionally, the classification unit can classify messages by considering the sender's relationship to the sender (friend, colleague, boss, etc.) using generative AI. This allows the classification unit to perform more appropriate classifications by classifying messages based on the sender's attribute information.

[0085] The classification unit can estimate the user's emotions and adjust the order in which it displays classification results based on the estimated emotions. For example, the classification unit can estimate the user's emotions using generative AI. For example, the classification unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using text analysis technology. This allows the classification unit to adjust the order in which it displays classification results according to the user's emotions. For example, if the user is stressed, the classification unit can prioritize displaying important messages. If the user is relaxed, the classification unit can display all messages equally. Furthermore, if the user is focused, the classification unit can prioritize displaying work-related messages. This allows the classification unit to adjust the order in which it displays classification results according to the user's emotions.

[0086] The classification unit can perform classification while considering the geographical distribution of messages. For example, the classification unit can analyze the geographical distribution of messages using generative AI. For instance, the classification unit can prioritize the classification of messages related to the user's current location. Furthermore, the classification unit can group and classify messages related to specific regions. Additionally, the classification unit can classify messages while considering the user's movement history. This allows the classification unit to perform more appropriate classification by classifying messages based on their geographical distribution. For example, the classification unit can prioritize the classification of messages related to the user's current location using generative AI. Furthermore, the classification unit can group and classify messages related to specific regions using generative AI. Additionally, the classification unit can classify messages while considering the user's movement history using generative AI. This allows the classification unit to perform more appropriate classification by classifying messages based on their geographical distribution.

[0087] The classification unit can improve the accuracy of its classification by referring to relevant literature for messages. For example, the classification unit can analyze relevant literature for messages using generative AI. For instance, the classification unit can classify messages by referring to relevant academic papers. Furthermore, the classification unit can classify messages by referring to relevant news articles. In addition, the classification unit can classify messages by referring to relevant technical documents. This allows the classification unit to improve the accuracy of its classification by referring to relevant literature for messages. For example, the classification unit can classify messages by referring to relevant academic papers using generative AI. Furthermore, the classification unit can classify messages by referring to relevant news articles using generative AI. In addition, the classification unit can classify messages by referring to relevant technical documents using generative AI. This allows the classification unit to improve the accuracy of its classification by referring to relevant literature for messages.

[0088] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, the notification unit can estimate the user's emotions using generative AI. For example, the notification unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using text analysis technology. This allows the notification unit to adjust the notification method according to the user's emotions. For example, if the user is stressed, the notification unit can choose a quiet notification method. If the user is relaxed, the notification unit can choose a visually appealing notification method. Furthermore, if the user is focused, the notification unit can only notify important messages. This allows the notification unit to adjust the notification method according to the user's emotions.

[0089] The notification unit can adjust the level of detail of notifications based on the importance of the message. For example, the notification unit can analyze the importance of a message using generative AI. For instance, the notification unit can provide detailed notifications for important messages, while providing concise notifications for general messages. Furthermore, for urgent messages, the notification unit can provide immediate notifications. This allows the notification unit to adjust the level of detail of notifications based on the importance of the message. For example, the notification unit can provide detailed notifications for important messages using generative AI, while providing concise notifications for general messages using generative AI. Furthermore, the notification unit can provide immediate notifications for urgent messages using generative AI. This allows the notification unit to adjust the level of detail of notifications based on the importance of the message.

[0090] The notification unit can apply different notification algorithms depending on the message category when a notification is sent. For example, the notification unit can analyze the message category using generative AI. For instance, it can apply a business-oriented notification algorithm to work-related messages. It can also apply a social media-oriented notification algorithm to SNS messages. Furthermore, it can apply a rapid notification algorithm to urgent messages. This allows the notification unit to apply the appropriate notification algorithm according to the message category. For example, the notification unit can use generative AI to apply a business-oriented notification algorithm to work-related messages. It can also use generative AI to apply a social media-oriented notification algorithm to SNS messages. Furthermore, it can use generative AI to apply a rapid notification algorithm to urgent messages. This allows the notification unit to apply the appropriate notification algorithm according to the message category.

[0091] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, the notification unit can estimate the user's emotions using generative AI. For example, the notification unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using text analysis technology. This allows the notification unit to adjust the timing of notifications according to the user's emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications. Conversely, if the user is relaxed, the notification unit can increase the frequency of notifications. Furthermore, if the user is focused, the notification unit can only notify important messages. This allows the notification unit to adjust the timing of notifications according to the user's emotions.

[0092] The notification unit can determine notification priorities based on when the message was sent. For example, the notification unit can analyze the message sending time using generative AI. For instance, the notification unit can prioritize notifications for recently sent messages. It can also prioritize notifications for messages sent during important time periods. Furthermore, it can prioritize notifications for messages sent during time periods specified by the user. This allows the notification unit to determine notification priorities based on when the message was sent. For example, the notification unit can prioritize notifications for recently sent messages using generative AI. It can also prioritize notifications for messages sent during important time periods using generative AI. Furthermore, it can prioritize notifications for messages sent during time periods specified by the user using generative AI. This allows the notification unit to determine notification priorities based on when the message was sent.

[0093] The notification unit can adjust the order of notifications based on the relevance of the messages when a notification is sent. For example, the notification unit can analyze the relevance of messages using generative AI. For example, the notification unit can prioritize important messages. It can also prioritize highly relevant messages based on user settings. Furthermore, it can prioritize urgent messages. This allows the notification unit to adjust the order of notifications based on the relevance of the messages. For example, the notification unit can prioritize important messages using generative AI. It can also prioritize highly relevant messages based on user settings using generative AI. Furthermore, the notification unit can prioritize urgent messages using generative AI. This allows the notification unit to adjust the order of notifications based on the relevance of the messages.

[0094] The settings reception unit can estimate the user's emotions and adjust the settings reception method based on the estimated emotions. For example, the settings reception unit can estimate the user's emotions using generative AI. For example, the settings reception unit can estimate the user's emotions using facial expression recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using text analysis technology. This allows the settings reception unit to adjust the settings reception method according to the user's emotions. For example, if the user is stressed, the settings reception unit can provide a simple interface and minimize the setup procedure. If the user is relaxed, the settings reception unit can provide detailed settings options and suggest a customizable setup method. Furthermore, if the user is in a hurry, the settings reception unit can prioritize voice input to allow for quick setup. This allows the settings reception unit to adjust the settings reception method according to the user's emotions.

[0095] The settings reception unit can suggest the optimal settings method by referring to the user's past settings history when a settings request is received. For example, the settings reception unit can analyze the user's past settings history using generative AI. For example, the settings reception unit can automatically display settings that the user has frequently used in the past as candidates. Furthermore, the settings reception unit can prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past. In addition, the settings reception unit can predict and suggest settings to be used during specific time periods based on the user's past settings history. This allows the settings reception unit to suggest the optimal settings method based on the user's past settings history. For example, the settings reception unit can automatically display settings that the user has frequently used in the past as candidates using generative AI. Furthermore, the settings reception unit can prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past using generative AI. Furthermore, the settings reception unit can predict and suggest settings to be used during specific time periods based on the user's past settings history using generative AI. This allows the settings reception unit to suggest the optimal settings method based on the user's past settings history.

[0096] The settings reception unit can estimate the user's emotions and determine the priority of settings based on those estimated emotions. For example, the settings reception unit can estimate the user's emotions using generative AI. For example, the settings reception unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using text analysis technology. This allows the settings reception unit to determine the priority of settings according to the user's emotions. For example, if the user is stressed, the settings reception unit can prioritize important settings. If the user is relaxed, the settings reception unit can accept all settings equally. Furthermore, if the user is focused, the settings reception unit can prioritize work-related settings. This allows the settings reception unit to determine the priority of settings according to the user's emotions.

[0097] The settings reception unit can propose the optimal settings method when a user requests settings, taking into account the user's device information. For example, the settings reception unit can analyze the user's device information using generative AI. For instance, if the user is using a smartphone, the settings reception unit can provide settings tailored to the screen size. Furthermore, if the user is using a tablet, the settings reception unit can provide settings optimized for larger screens. Additionally, if the user is using a smartwatch, the settings reception unit can provide concise and highly visible settings. This allows the settings reception unit to propose the optimal settings method based on the user's device information. For example, using generative AI, the settings reception unit can provide settings tailored to the screen size if the user is using a smartphone. Furthermore, using generative AI, the settings reception unit can provide settings optimized for larger screens if the user is using a tablet. Additionally, using generative AI, the settings reception unit can provide concise and highly visible settings if the user is using a smartwatch. This allows the settings reception unit to propose the optimal settings method based on the user's device information.

[0098] The customization unit can estimate the user's emotions and adjust the customization method based on those estimated emotions. For example, the customization unit can estimate the user's emotions using generative AI. Alternatively, it can estimate the user's emotions using facial recognition technology. Furthermore, it can estimate the user's emotions using voice analysis technology. In addition, it can estimate the user's emotions using text analysis technology. This allows the customization unit to adjust the customization method according to the user's emotions. For example, if the user is stressed, the customization unit can offer simple customization options and minimize the steps. If the user is relaxed, the customization unit can offer detailed customization options and suggest a highly flexible customization method. Furthermore, if the user is in a hurry, the customization unit can prioritize voice input to enable rapid customization. This allows the customization unit to adjust the customization method according to the user's emotions.

[0099] The customization unit can suggest the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit can analyze the user's past customization history using generative AI. For instance, the customization unit can automatically display customizations the user has frequently used in the past as candidates. Furthermore, the customization unit can prioritize suggesting customization methods (voice, text, etc.) the user has used in the past. In addition, the customization unit can predict and suggest customizations to be used during specific time periods based on the user's past customization history. This allows the customization unit to suggest the optimal customization method based on the user's past customization history. For example, the customization unit can automatically display customizations the user has frequently used in the past as candidates using generative AI. Furthermore, the customization unit can prioritize suggesting customization methods (voice, text, etc.) the user has used in the past using generative AI. Furthermore, the customization unit can predict and suggest customizations to be used during specific time periods based on the user's past customization history using generative AI. This allows the customization unit to suggest the optimal customization method based on the user's past customization history.

[0100] The customization unit can estimate the user's emotions and determine the priority of customizations based on those estimated emotions. For example, the customization unit can estimate the user's emotions using generative AI. Alternatively, it can estimate the user's emotions using facial recognition technology. Furthermore, it can estimate the user's emotions using voice analysis technology. In addition, it can estimate the user's emotions using text analysis technology. This allows the customization unit to determine the priority of customizations according to the user's emotions. For example, if the user is stressed, the customization unit can prioritize important customizations. If the user is relaxed, the customization unit can distribute all customizations evenly. Furthermore, if the user is focused, the customization unit can prioritize work-related customizations. This allows the customization unit to determine the priority of customizations according to the user's emotions.

[0101] The customization unit can propose the optimal customization method when customizing, taking into account the user's geographical location information. For example, the customization unit can analyze the user's geographical location information using generative AI. For instance, if the user is in a specific location, the customization unit can propose customizations related to that location. Furthermore, if the user is traveling, the customization unit can propose customizations related to their travel destination. Additionally, if the user is at home, the customization unit can propose customizations related to their home. This allows the customization unit to propose the optimal customization method based on the user's geographical location information. For example, the customization unit can use generative AI to propose customizations related to a specific location if the user is in a specific location. Furthermore, if the user is traveling, the customization unit can use generative AI to propose customizations related to their travel destination. Additionally, if the user is at home, the customization unit can use generative AI to propose customizations related to their home. This allows the customization unit to propose the optimal customization method based on the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, classification unit, notification unit, and setting reception unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects messages from various communication tools via the communication I / F 44 of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the content of messages and sender information using a generation AI. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and classifies messages based on user settings. The notification unit is implemented, for example, by the control unit 46A of the smart device 14, and notifies the user of important classified communications. The setting reception unit receives user settings via the reception device 38 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, classification unit, notification unit, and setting reception unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects messages from various communication tools via the communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the content of messages and sender information using generating AI. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and classifies messages based on user settings. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214, and notifies the user of important classified communications. The setting reception unit accepts user settings via the microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, classification unit, notification unit, and setting reception unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects messages from various communication tools via the communication I / F 44 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the content of messages and sender information using a generation AI. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and classifies messages based on user settings. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314, and notifies the user of important classified communications. The setting reception unit accepts user settings via the microphone 238 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, classification unit, notification unit, and setting reception unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects messages from various communication tools via the communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the content of messages and sender information using a generation AI. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and classifies messages based on user settings. The notification unit is implemented, for example, by the control unit 46A of the robot 414, and notifies the user of important classified communications. The setting reception unit accepts user settings via, for example, the microphone 238 of the robot 414.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the user's emotions based on the message content and re-evaluate the importance of messages according to the estimated emotions. For example, if the user is stressed, the analysis unit can re-evaluate work-related messages as high priority, and if the user is relaxed, it can re-evaluate personal messages as high priority. Also, if the user is emotionally unstable, the analysis unit can re-evaluate messages with negative content as low priority. In this way, the analysis unit can dynamically adjust the importance of messages according to the user's emotions.

[0104] The notification unit can estimate the user's emotions and adjust the notification method based on those estimates. For example, if the user is stressed, the notification unit can choose a quiet notification method, while if they are relaxed, it can choose a visually appealing notification method. Furthermore, if the user is focused, the notification unit can only notify them of important messages. In this way, the notification unit can adjust its notification method according to the user's emotions.

[0105] The data collection unit can estimate the user's emotions and adjust the timing of message collection based on those estimates. For example, if the user is stressed, the unit can reduce the frequency of message collection, and if they are relaxed, it can increase the frequency. Also, if the user is focused, the unit can collect only important messages, postponing others. In this way, the data collection unit can adjust the timing of message collection according to the user's emotions.

[0106] The classification unit can estimate the user's emotions and adjust the message classification criteria based on those emotions. For example, if the user is stressed, the classification unit can prioritize classifying only important messages, while if the user is relaxed, it can classify all messages equally. Similarly, if the user is focused, the classification unit can prioritize classifying work-related messages. In this way, the classification unit can adjust the message classification criteria according to the user's emotions.

[0107] The settings reception unit can estimate the user's emotions and adjust the settings reception method based on those estimates. For example, if the user is stressed, the settings reception unit can provide a simple interface, while if they are relaxed, it can provide detailed settings options. Also, if the user is in a hurry, the settings reception unit can prioritize voice input to allow for quick settings completion. In this way, the settings reception unit can adjust the settings reception method according to the user's emotions.

[0108] The analysis unit can adjust the level of detail in its analysis based on the message content. For example, in the case of an important message, the analysis unit can perform a detailed analysis and provide all the information. In the case of a general message, the analysis unit can perform a concise analysis and extract only the important points. Furthermore, in the case of an urgent message, the analysis unit can perform a rapid analysis and provide immediate notification. In this way, the analysis unit can adjust the level of detail in its analysis according to the content of the message.

[0109] The classification unit can improve the accuracy of its classification by considering the interrelationships between messages. For example, it can improve classification accuracy by grouping related messages. It can also perform classification by considering the relationship between the sender and receiver of a message. Furthermore, it can perform classification by considering the content of the message and related topics. In this way, the classification unit can improve the accuracy of its classification by considering the interrelationships between messages.

[0110] The notification unit can adjust the level of detail in notifications based on the importance of the message. For example, it can provide detailed notifications for important messages, concise notifications for general messages, and immediate notifications for urgent messages. This allows the notification unit to adjust the level of detail in notifications based on the importance of the message.

[0111] The message collection unit can filter messages based on the user's current activity status. For example, if a user is in a meeting, the unit can collect only important messages and postpone others. If a user is exercising, the unit can collect only urgent messages and notify the user of others later. Furthermore, if a user is taking a break, the unit can collect all messages and notify the user in real time. This allows the unit to filter message collection according to the user's activity status.

[0112] The settings reception unit can suggest the optimal settings method by referring to the user's past settings history when a settings request is made. For example, it can automatically display settings that the user has frequently used in the past as candidates. It can also prioritize suggesting settings methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest settings that the user will use during specific time periods based on their past settings history. In this way, the settings reception unit can suggest the optimal settings method based on the user's past settings history.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit collects messages from various communication tools. For example, the collection unit can centrally collect messages from email, chat, and social media. The collection unit can retrieve emails from email servers, chat messages through chat application APIs, and social media messages through social media APIs. Step 2: The analysis unit analyzes the messages collected by the collection unit. For example, the analysis unit uses a generation AI to analyze the content of the messages and sender information, and determines the importance of the messages. The generation AI can use natural language processing technology to extract keywords from the messages and determine importance based on the sender's job title and the frequency of past interactions. Step 3: The classification unit classifies messages according to user settings based on the messages analyzed by the analysis unit. For example, the classification unit can classify messages into high priority, medium priority, and low priority based on the priority set by the user. Step 4: The notification unit notifies important communications classified by the classification unit. For example, the notification unit can immediately notify the user of high-priority messages, notify them of medium-priority messages after a certain period of time, and notify them of low-priority messages later.

[0115] 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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.

[0134] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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.

[0150] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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.

[0167] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 collection unit collects messages from various communication tools, An analysis unit analyzes the messages collected by the aforementioned collection unit, A classification unit classifies messages according to user settings based on the messages analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies important communications classified by the classification unit. A system characterized by the following features.

2. It includes a settings reception unit that accepts user settings. The system according to feature 1.

3. The aforementioned analysis unit, The importance of a message is determined based on its content and sender information. The system according to feature 1.

4. The aforementioned classification unit is Classify messages based on user settings. The system according to feature 1.

5. The aforementioned notification unit, Prioritize notifications for important communications. The system according to feature 1.

6. The aforementioned setting reception unit, It includes a customization section for users to customize their priorities. The system according to feature 2.

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

8. The aforementioned collection unit is Analyze the frequency of use of various communication tools and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting messages, filter them based on the user's current activity level. The system according to feature 1.

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