System

Abstract

To provide a system capable of immediately notifying urgent or important matters only and summarizing the rest of the message.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages. The analysis unit analyzes the messages collected by the collection unit. The notification unit notifies urgent or important matters identified by the analysis unit. The summary unit creates a summary of the message not identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of excessive message notifications that can reduce concentration.

[0005] The system according to the embodiment aims to immediately notify only urgent or important matters and summarize the other messages. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages. The analysis unit analyzes the messages collected by the collection unit. The notification unit notifies of urgent or important matters identified by the analysis unit. The summary unit creates summaries of messages not identified by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can immediately notify only urgent or important matters and summarize the other messages. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that uses a generation AI to integrate communication apps such as email and communication tools and analyze the contents of text messages. This system collects messages from multiple communication apps used by the user, and the generation AI analyzes the collected messages and immediately notifies the user of only urgent or important matters. For other messages, the generation AI creates a summary and provides it to the user. This mechanism can solve the current situation where the user is always distracted by notifications and has low concentration. For example, messages are collected from multiple communication apps used by the user. For example, messages are collected from email apps, chat apps, SNS, etc. At this time, the messages can be obtained using the API of each app. Next, the generation AI analyzes the collected messages. The generation AI analyzes the contents of the messages and identifies urgent or important matters. For example, messages containing specific keywords or phrases can be classified as urgent or important matters. This allows the user to immediately grasp important messages. Only urgent or important matters are immediately notified. The user is immediately notified of urgent or important matters identified by the generation AI. For example, the user can be immediately notified using push notifications or email notifications. This allows the user to respond without missing important messages. For other messages, the generation AI creates a summary. The generation AI summarizes the contents of the collected messages and creates a summary. For example, a summary of all messages can be provided to the user at the end of the day. This allows the user to concentrate on their work without being distracted by unimportant messages. This mechanism solves the current situation where users are constantly distracted by notifications and their concentration is reduced. Users can efficiently manage communication by immediately understanding only urgent or important matters and checking summaries for other messages. For example, frequent notifications during work will not break the user's concentration, improving work efficiency. This allows the system to quickly grasp important messages while maintaining the user's concentration.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages from a plurality of communication applications used by a user. The collection unit can collect messages from, for example, an email application, a chat application, an SNS, and the like. The collection unit can acquire messages by using the API of each application. For example, the collection unit acquires email messages by using the API of an email application. The collection unit can also acquire chat messages by using the API of a chat application. Furthermore, the collection unit can also acquire SNS messages by using the API of an SNS. The analysis unit analyzes the messages collected by the collection unit using a generation AI. The analysis unit analyzes the contents of the messages and identifies urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the contents of the messages by using natural language processing technology. For example, the analysis unit analyzes the words of the messages by using morphological analysis. The analysis unit can also analyze the sentence structure of the messages by using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages by using semantic analysis. The notification unit notifies the user of the urgent or important matter identified by the analysis unit. The notification unit immediately notifies the user of the urgent or important matter, for example, by using a push notification or an email notification. The notification unit can immediately inform the user. For example, the notification unit notifies the user of the urgent or important matter by using a push notification. The notification unit can also notify the user of the urgent or important matter by using an email notification. The summary unit creates a summary of the message not identified by the analysis unit. The summary unit uses a generation AI to summarize the contents of the collected messages and create a summary. The summary unit can provide the user with a summary of all messages, for example, at the end of the day. The summary unit creates a summary to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates a summary. As a result, the system according to the embodiment can quickly grasp important messages while maintaining the user's concentration.Some or all of the above-mentioned processing in the summary unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of the collected messages to the generation AI and have the generation AI create a summary.

[0030] The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, email apps, chat apps, SNS, and the like. Specifically, the collection unit acquires messages using the API of each app. For example, when acquiring email messages using the API of an email app, the API is accessed using the user's authentication information to acquire message data from the inbox or outbox. Similarly, in the case of a chat app, messages are acquired from chat rooms or individual conversations through the API. When using the API of an SNS, messages can be collected from the user's timeline or direct messages. These APIs are usually accessed securely using an authentication protocol such as OAuth. The collection unit centrally manages the message data acquired through these APIs and stores it in a database. The database also includes metadata such as the sender, recipient, sending date and time, and message content of the message. This allows the collection unit to efficiently collect messages from multiple communication apps used by the user and manage them in an integrated manner. Furthermore, the collection unit has a scheduling function that periodically calls the API to acquire new messages, thereby realizing real-time message collection. This allows the user to keep track of the latest messages without missing them.

[0031] The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the contents of the messages and identifies urgent or important cases. Specifically, the analysis unit analyzes the contents of the messages using natural language processing technology. For example, the analysis unit uses morphological analysis to analyze the words of the messages and grammatical analysis to analyze the sentence structure of the messages. In addition, the meaning of the messages can be analyzed using semantic analysis. The generation AI combines these analysis techniques to deeply understand the contents of the messages and identify urgent or important cases. For example, messages containing specific keywords or phrases can be classified as urgent or important cases. The analysis unit inputs a prompt to the generation AI to have it analyze the contents of the messages. The prompt includes, for example, instructions such as "Please determine whether this message is urgent or important." The generation AI analyzes the contents of the messages based on the prompt and identifies urgent or important cases. The analysis unit lists urgent or important cases based on the output results of the generation AI and passes them to the notification unit. This allows the analysis unit to quickly and accurately analyze the collected messages and enable the user to respond without missing important messages. Furthermore, the analysis unit can use past message data as learning data to continuously improve the analysis accuracy of the generation AI, so that the analysis unit always uses the latest technology to achieve highly accurate message analysis.

[0032] The notification unit notifies the user of the urgent or important matter identified by the analysis unit. Specifically, the notification unit immediately notifies the user of the urgent or important matter by using a push notification or an email notification. For example, when a push notification is used to notify the user of the urgent or important matter to the user's smartphone, the notification unit generates the contents of the push notification based on the information of the urgent or important matter received from the analysis unit. The push notification includes information such as the sender of the message, the subject, and the importance. The notification unit can send a push notification to the user's smartphone and immediately notify the user. Furthermore, when an email notification is used to notify the user of the urgent or important matter, the notification unit generates the contents of the email based on the information of the urgent or important matter received from the analysis unit. The email includes the sender of the message, the subject, the importance, a part of the message content, and the like. The notification unit can send an email to the user's email address and immediately notify the user. Furthermore, the notification unit can customize the notification method according to the user's settings. For example, when a user prioritizes push notification, the notification unit sends push notification preferentially and uses email notification complementary to the push notification. In addition, if a user does not want to receive notifications during a specific time period, the notification unit will send notifications during times other than that time period. This allows the notification unit to provide flexible notification methods according to the needs of the user, and ensure that urgent or important matters are conveyed to the user.

[0033] The summary unit creates summaries of messages that were not identified by the analysis unit. Specifically, the summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. For example, the summary unit can provide a summary of all messages to the user at the end of the day. The summary unit inputs the contents of the collected messages into the generation AI and has the generation AI create summaries. The generation AI analyzes the contents of the messages, extracts important information, and generates summaries. For example, the generation AI extracts the subject and important points of each message and combines them to create a concise summary. The summary unit provides the user with the summary created by the generation AI. The summary is provided to the user, for example, via email or a dedicated dashboard. This allows the user to check the summaries of all messages at the end of the day and grasp important information without missing it. Furthermore, the summary unit can continuously improve the accuracy of the summary based on user feedback. For example, if the user provides feedback on the contents of the summary, the summary unit reflects that feedback in the generation AI and uses it to create the next summary. This allows the summarizing section to provide a highly accurate summary that meets the needs of the user, enabling the user to quickly grasp important information while maintaining concentration.

[0034] The collection unit can collect messages by using the API of each application. The collection unit can, for example, acquire email messages by using the API of an email application. The collection unit can also acquire chat messages by using the API of a chat application. The collection unit can also acquire SNS messages by using the API of an SNS. This allows messages to be collected efficiently by using the API of each application. Some or all of the above-mentioned processing in the collection unit can be performed by using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the messages acquired by using the API of each application to an AI and cause the AI ​​to collect the messages.

[0035] The collection unit can collect messages using scraping technology. The collection unit can collect messages from a webpage using, for example, HTML parsing. The collection unit can also analyze the structure of a webpage using DOM analysis and collect messages. The collection unit can collect messages from an app for which an API is not provided using scraping technology. As a result, by using scraping technology, messages can be collected from an app for which an API is not provided. A part or all of the above-mentioned processing in the collection unit can be performed using, for example, AI or can be performed without using AI. For example, the collection unit can input messages collected using scraping technology to AI and cause AI to collect messages.

[0036] The analysis unit can analyze the contents of the message using natural language processing technology and detect specific keywords and phrases. The analysis unit can, for example, analyze the words of the message using morphological analysis. The analysis unit can also analyze the sentence structure of the message using grammatical analysis. The analysis unit can also analyze the meaning of the message using semantic analysis. This allows the content of the message to be accurately analyzed using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the collected messages to the generation AI and have the generation AI analyze the messages.

[0037] The analysis unit can set specific criteria that can be customized by the user. The analysis unit can set keyword-based filtering, for example. The analysis unit can also set rule-based filtering. This allows the user to customize the specific criteria, enabling more flexible analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the specific criteria set by the user to AI and cause it to perform message analysis based on AI.

[0038] The notification unit can immediately notify the user of urgent or important matters by using push notification or email notification. The notification unit, for example, notifies the user of urgent or important matters by using push notification. The notification unit can also notify the user of urgent or important matters by using email notification. In this way, the user can be immediately notified of important matters by using push notification or email notification. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input urgent or important matters into AI and execute notification based on AI.

[0039] The summary unit can summarize the contents of the collected messages and create summaries. The summary unit can, for example, summarize the contents of the collected messages and create summaries. The summary unit can provide a summary of all messages to the user at the end of the day. This allows the user to efficiently grasp information by summarizing the contents of the messages. A part or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input the collected messages to the generation AI and have the generation AI create summaries.

[0040] The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit preferentially collects messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection in a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has determined to be important in the past and preferentially collect similar messages. This allows the optimal collection method to be selected by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

[0041] The collection unit can filter messages based on the user's current project or field of interest when collecting messages. For example, the collection unit preferentially collects messages related to a project currently being carried out by the user. The collection unit can also filter highly relevant messages based on the user's field of interest. The collection unit can also collect specific messages based on keywords set by the user. This makes it possible to preferentially collect highly relevant messages by filtering messages based on the user's project or field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for filtering based on the user's current project or field of interest to AI, and cause filtering to be performed based on AI.

[0042] The collection unit can preferentially collect highly relevant messages in consideration of the geographical location information of the user when collecting messages. For example, when the user is in a specific area, the collection unit preferentially collects messages related to the area. When the user is traveling, the collection unit can also preferentially collect messages related to the travel destination. When the user is at home, the collection unit can also preferentially collect messages related to the home. In this way, highly relevant messages can be preferentially collected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and preferentially collect highly relevant messages based on AI.

[0043] The collection unit can analyze the user's social media activity and collect related messages when collecting messages. For example, the collection unit collects related messages based on the content frequently posted by the user on a specific social media. The collection unit can also collect related messages based on the content posted by an account the user follows. The collection unit can also collect related messages based on the activities of a group or community in which the user participates. In this way, highly relevant messages can be collected by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and collect related messages based on AI.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, the analysis unit performs a detailed analysis on messages with high importance. The analysis unit can also perform a concise analysis on messages with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

[0045] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit applies a business analysis algorithm to a business-related message. The analysis unit can also apply a private analysis algorithm to a private message. The analysis unit can also apply a social media analysis algorithm to a social media message. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the message category. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input message category data to the generation AI and apply the analysis algorithm based on the generation AI.

[0046] During analysis, the analysis unit can determine the priority of analysis based on the time when the message was sent. For example, the analysis unit preferentially analyzes a message sent recently. The analysis unit can also preferentially analyze a message sent during an important time period. The analysis unit can also preferentially analyze a message received by a user during a specific time period. In this way, important messages can be preferentially analyzed by determining the priority of analysis based on the time when the message was sent. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the message was sent into AI and determine the priority of analysis based on AI.

[0047] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit prioritizes analysis of highly relevant messages. The analysis unit can also postpone analysis of less relevant messages. The analysis unit can also dynamically adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of messages. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message relevance data to AI and adjust the order of analysis based on AI.

[0048] The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, the notification unit provides detailed notification for messages with high importance. The notification unit can also provide concise notification for messages with low importance. The notification unit can also determine the priority of the notification according to the importance. This allows for efficient notification by adjusting the level of detail of the notification according to the importance of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI or may be performed without using AI. For example, the notification unit can input the importance data of the message to AI and adjust the level of detail of the notification based on AI.

[0049] The notification unit can apply different notification algorithms depending on the category of the message at the time of notification. For example, the notification unit applies a business notification algorithm to a business-related message. The notification unit can also apply a private notification algorithm to a private message. The notification unit can also apply a social media notification algorithm to a social media message. This improves notification accuracy by applying an appropriate notification algorithm depending on the message category. Some or all of the above-mentioned processing in the notification unit may be performed using AI or may be performed without using AI. For example, the notification unit can input message category data to AI and apply a notification algorithm based on AI.

[0050] The notification unit can determine the priority of notifications by considering attribute information of the sender of the message at the time of notification. For example, the notification unit gives priority to notifying messages from important senders. The notification unit can also determine the priority of notifications based on the job title and relationship of the sender. The notification unit can also analyze the sender's past message history and determine the importance. In this way, important messages can be given priority by considering the attribute information of the sender of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the sender's attribute information into AI and determine the priority of notifications based on AI.

[0051] The notification unit can adjust the order of notifications based on the relevance of messages at the time of notification. For example, the notification unit prioritizes notifications of highly relevant messages. The notification unit can also postpone notifications of less relevant messages. The notification unit can also dynamically adjust the order of notifications according to the relevance. This enables efficient notifications by adjusting the order of notifications based on the relevance of messages. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message relevance data to AI and adjust the order of notifications based on AI.

[0052] The summary unit can adjust the level of detail of the summary based on the importance of the message when creating the summary. For example, the summary unit provides a detailed summary for a message with high importance. For a message with low importance, the summary unit can also provide a concise summary. The summary unit can also determine the priority of the summary according to the importance. This allows for efficient summary creation by adjusting the level of detail of the summary according to the importance of the message. Some or all of the above-mentioned processing in the summary unit may be performed using AI or may be performed without using AI. For example, the summary unit can input message importance data into AI and adjust the level of detail of the summary based on AI.

[0053] The summary unit can apply different summary algorithms depending on the category of the message when creating the summary. For example, the summary unit applies a business summary algorithm to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

[0054] The summary unit can determine the priority of the summary based on the time when the message was sent when creating the summary. For example, the summary unit can prioritize the inclusion of a recently sent message in the summary. The summary unit can also prioritize the inclusion of a message sent during an important time period in the summary. The summary unit can also prioritize the inclusion of a message received by the user during a specific time period in the summary. In this way, important messages can be prioritized for inclusion in the summary by determining the priority of the summary based on the time when the message was sent. A part or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message sending time data to the generation AI and determine the priority of the summary based on the generation AI.

[0055] The summary unit can adjust the order of summaries based on the relevance of messages when creating summaries. For example, the summary unit prioritizes including highly relevant messages in the summary. The summary unit can also postpone less relevant messages. The summary unit can also dynamically adjust the order of summaries according to the relevance. This allows for efficient summary creation by adjusting the order of summaries based on the relevance of messages. A part or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message relevance data to the generation AI and adjust the order of summaries based on the generation AI.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit prioritizes collection of messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection in a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has determined to be important in the past and prioritize collection of similar messages. In this way, the optimal collection method can be selected by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, a detailed analysis is performed for a message with high importance. The analysis unit can also perform a concise analysis for a message with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

[0059] The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, for a message of high importance, a detailed notification is provided. For a message of low importance, the notification unit can also provide a concise notification. The notification unit can also determine the priority of the notification according to the importance. This allows for efficient notification by adjusting the level of detail of the notification according to the importance of the message. A part or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the importance data of the message to AI and adjust the level of detail of the notification based on AI.

[0060] The summary unit can apply different summary algorithms depending on the category of the message when creating the summary. For example, a business summary algorithm is applied to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

[0061] When collecting messages, the collection unit can preferentially collect highly relevant messages by taking into account the geographical location information of the user. For example, when the user is in a specific area, messages related to the area are preferentially collected. When the user is traveling, the collection unit can also preferentially collect messages related to the travel destination. When the user is at home, the collection unit can also preferentially collect messages related to the home. In this way, highly relevant messages can be preferentially collected by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and preferentially collect highly relevant messages based on AI.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, an email app, a chat app, an SNS, etc. The collection unit can acquire messages using the API of each app. For example, the collection unit can acquire email messages using the API of an email app. The collection unit can also acquire chat messages using the API of a chat app. Furthermore, the collection unit can acquire SNS messages using the API of an SNS. Step 2: The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the content of the messages to identify urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the content of the messages using natural language processing techniques. For example, the analysis unit can analyze the words of the messages using morphological analysis. The analysis unit can also analyze the sentence structure of the messages using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages using semantic analysis. Step 3: The notification unit notifies the user of the urgent or important matter identified by the analysis unit. The notification unit immediately notifies the user of the urgent or important matter, for example, by using a push notification or an email notification. The notification unit can immediately notify the user. For example, the notification unit notifies the user of the urgent or important matter by using a push notification. The notification unit can also notify the user of the urgent or important matter by using an email notification. Step 4: The summary unit creates summaries of messages that were not identified by the analysis unit. The summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. The summary unit can provide the user with a summary of all messages at the end of a day, for example. The summary unit creates summaries to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates summaries. This allows the system according to the embodiment to quickly grasp important messages while maintaining the user's concentration. Some or all of the above-mentioned processing in the summary unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of the collected messages into the generation AI and have the generation AI create summaries.

[0064] (Example 2) The system according to the embodiment of the present invention is a system that uses a generation AI to integrate communication apps such as email and communication tools and analyze the contents of text messages. This system collects messages from multiple communication apps used by the user, and the generation AI analyzes the collected messages and immediately notifies the user of only urgent or important matters. For other messages, the generation AI creates a summary and provides it to the user. This mechanism can solve the current situation where the user is always distracted by notifications and has low concentration. For example, messages are collected from multiple communication apps used by the user. For example, messages are collected from email apps, chat apps, SNS, etc. At this time, the messages can be obtained using the API of each app. Next, the generation AI analyzes the collected messages. The generation AI analyzes the contents of the messages and identifies urgent or important matters. For example, messages containing specific keywords or phrases can be classified as urgent or important matters. This allows the user to immediately grasp important messages. Only urgent or important matters are immediately notified. The user is immediately notified of urgent or important matters identified by the generation AI. For example, the user can be immediately notified using push notifications or email notifications. This allows the user to respond without missing important messages. For other messages, the generation AI creates a summary. The generation AI summarizes the contents of the collected messages and creates a summary. For example, a summary of all messages can be provided to the user at the end of the day. This allows the user to concentrate on their work without being distracted by unimportant messages. This mechanism solves the current situation where users are constantly distracted by notifications and their concentration is reduced. Users can efficiently manage communication by immediately understanding only urgent or important matters and checking summaries for other messages. For example, frequent notifications during work will not break the user's concentration, improving work efficiency. This allows the system to quickly grasp important messages while maintaining the user's concentration.

[0065] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages from a plurality of communication applications used by a user. The collection unit can collect messages from, for example, an email application, a chat application, an SNS, and the like. The collection unit can acquire messages by using the API of each application. For example, the collection unit acquires email messages by using the API of an email application. The collection unit can also acquire chat messages by using the API of a chat application. Furthermore, the collection unit can also acquire SNS messages by using the API of an SNS. The analysis unit analyzes the messages collected by the collection unit using a generation AI. The analysis unit analyzes the contents of the messages and identifies urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the contents of the messages by using natural language processing technology. For example, the analysis unit analyzes the words of the messages by using morphological analysis. The analysis unit can also analyze the sentence structure of the messages by using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages by using semantic analysis. The notification unit notifies the user of the urgent or important matter identified by the analysis unit. The notification unit immediately notifies the user of the urgent or important matter, for example, by using a push notification or an email notification. The notification unit can immediately inform the user. For example, the notification unit notifies the user of the urgent or important matter by using a push notification. The notification unit can also notify the user of the urgent or important matter by using an email notification. The summary unit creates a summary of the message not identified by the analysis unit. The summary unit uses a generation AI to summarize the contents of the collected messages and create a summary. The summary unit can provide the user with a summary of all messages, for example, at the end of the day. The summary unit creates a summary to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates a summary. As a result, the system according to the embodiment can quickly grasp important messages while maintaining the user's concentration.Some or all of the above-mentioned processing in the summary unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of the collected messages to the generation AI and have the generation AI create a summary.

[0066] The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, email apps, chat apps, SNS, and the like. Specifically, the collection unit acquires messages using the API of each app. For example, when acquiring email messages using the API of an email app, the API is accessed using the user's authentication information to acquire message data from the inbox or outbox. Similarly, in the case of a chat app, messages are acquired from chat rooms or individual conversations through the API. When using the API of an SNS, messages can be collected from the user's timeline or direct messages. These APIs are usually accessed securely using an authentication protocol such as OAuth. The collection unit centrally manages the message data acquired through these APIs and stores it in a database. The database also includes metadata such as the sender, recipient, sending date and time, and message content of the message. This allows the collection unit to efficiently collect messages from multiple communication apps used by the user and manage them in an integrated manner. Furthermore, the collection unit has a scheduling function that periodically calls the API to acquire new messages, thereby realizing real-time message collection. This allows the user to keep track of the latest messages without missing them.

[0067] The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the contents of the messages and identifies urgent or important cases. Specifically, the analysis unit analyzes the contents of the messages using natural language processing technology. For example, the analysis unit uses morphological analysis to analyze the words of the messages and grammatical analysis to analyze the sentence structure of the messages. In addition, the meaning of the messages can be analyzed using semantic analysis. The generation AI combines these analysis techniques to deeply understand the contents of the messages and identify urgent or important cases. For example, messages containing specific keywords or phrases can be classified as urgent or important cases. The analysis unit inputs a prompt to the generation AI to have it analyze the contents of the messages. The prompt includes, for example, instructions such as "Please determine whether this message is urgent or important." The generation AI analyzes the contents of the messages based on the prompt and identifies urgent or important cases. The analysis unit lists urgent or important cases based on the output results of the generation AI and passes them to the notification unit. This allows the analysis unit to quickly and accurately analyze the collected messages and enable the user to respond without missing important messages. Furthermore, the analysis unit can use past message data as learning data to continuously improve the analysis accuracy of the generation AI, so that the analysis unit always uses the latest technology to achieve highly accurate message analysis.

[0068] The notification unit notifies the user of the urgent or important matter identified by the analysis unit. Specifically, the notification unit immediately notifies the user of the urgent or important matter by using a push notification or an email notification. For example, when a push notification is used to notify the user of the urgent or important matter to the user's smartphone, the notification unit generates the contents of the push notification based on the information of the urgent or important matter received from the analysis unit. The push notification includes information such as the sender of the message, the subject, and the importance. The notification unit can send a push notification to the user's smartphone and immediately notify the user. Furthermore, when an email notification is used to notify the user of the urgent or important matter, the notification unit generates the contents of the email based on the information of the urgent or important matter received from the analysis unit. The email includes the sender of the message, the subject, the importance, a part of the message content, and the like. The notification unit can send an email to the user's email address and immediately notify the user. Furthermore, the notification unit can customize the notification method according to the user's settings. For example, when a user prioritizes push notification, the notification unit sends push notification preferentially and uses email notification complementary to the push notification. In addition, if a user does not want to receive notifications during a specific time period, the notification unit will send notifications during times other than that time period. This allows the notification unit to provide flexible notification methods according to the needs of the user, and ensure that urgent or important matters are conveyed to the user.

[0069] The summary unit creates summaries of messages that were not identified by the analysis unit. Specifically, the summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. For example, the summary unit can provide a summary of all messages to the user at the end of the day. The summary unit inputs the contents of the collected messages into the generation AI and has the generation AI create summaries. The generation AI analyzes the contents of the messages, extracts important information, and generates summaries. For example, the generation AI extracts the subject and important points of each message and combines them to create a concise summary. The summary unit provides the user with the summary created by the generation AI. The summary is provided to the user, for example, via email or a dedicated dashboard. This allows the user to check the summaries of all messages at the end of the day and grasp important information without missing it. Furthermore, the summary unit can continuously improve the accuracy of the summary based on user feedback. For example, if the user provides feedback on the contents of the summary, the summary unit reflects that feedback in the generation AI and uses it to create the next summary. This allows the summarizing section to provide a highly accurate summary that meets the needs of the user, enabling the user to quickly grasp important information while maintaining concentration.

[0070] The collection unit can collect messages by using the API of each application. The collection unit can, for example, acquire email messages by using the API of an email application. The collection unit can also acquire chat messages by using the API of a chat application. The collection unit can also acquire SNS messages by using the API of an SNS. This allows messages to be collected efficiently by using the API of each application. Some or all of the above-mentioned processing in the collection unit can be performed by using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the messages acquired by using the API of each application to an AI and cause the AI ​​to collect the messages.

[0071] The collection unit can collect messages using scraping technology. The collection unit can collect messages from a webpage using, for example, HTML parsing. The collection unit can also analyze the structure of a webpage using DOM analysis and collect messages. The collection unit can collect messages from an app for which an API is not provided using scraping technology. As a result, by using scraping technology, messages can be collected from an app for which an API is not provided. A part or all of the above-mentioned processing in the collection unit can be performed using, for example, AI or can be performed without using AI. For example, the collection unit can input messages collected using scraping technology to AI and cause AI to collect messages.

[0072] The analysis unit can analyze the contents of the message using natural language processing technology and detect specific keywords and phrases. The analysis unit can, for example, analyze the words of the message using morphological analysis. The analysis unit can also analyze the sentence structure of the message using grammatical analysis. The analysis unit can also analyze the meaning of the message using semantic analysis. This allows the content of the message to be accurately analyzed using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the collected messages to the generation AI and have the generation AI analyze the messages.

[0073] The analysis unit can set specific criteria that can be customized by the user. The analysis unit can set keyword-based filtering, for example. The analysis unit can also set rule-based filtering. This allows the user to customize the specific criteria, enabling more flexible analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the specific criteria set by the user to AI and cause it to perform message analysis based on AI.

[0074] The notification unit can immediately notify the user of urgent or important matters by using push notification or email notification. The notification unit, for example, notifies the user of urgent or important matters by using push notification. The notification unit can also notify the user of urgent or important matters by using email notification. In this way, the user can be immediately notified of important matters by using push notification or email notification. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input urgent or important matters into AI and execute notification based on AI.

[0075] The summary unit can summarize the contents of the collected messages and create summaries. The summary unit can, for example, summarize the contents of the collected messages and create summaries. The summary unit can provide a summary of all messages to the user at the end of the day. This allows the user to efficiently grasp information by summarizing the contents of the messages. A part or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input the collected messages to the generation AI and have the generation AI create summaries.

[0076] The collection unit can estimate the user's emotion and adjust the timing of message collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit delays the collection timing to reduce the burden on the user. When the user is relaxed, the collection unit can also collect messages in real time to prompt an immediate response. When the user is concentrating, the collection unit can also adjust the collection timing so as not to disturb the work. In this way, the burden on the user can be reduced by adjusting the collection timing according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and adjust the collection timing based on the generation AI.

[0077] The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit preferentially collects messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection in a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has determined to be important in the past and preferentially collect similar messages. This allows the optimal collection method to be selected by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

[0078] The collection unit can filter messages based on the user's current project or field of interest when collecting messages. For example, the collection unit preferentially collects messages related to a project currently being carried out by the user. The collection unit can also filter highly relevant messages based on the user's field of interest. The collection unit can also collect specific messages based on keywords set by the user. This makes it possible to preferentially collect highly relevant messages by filtering messages based on the user's project or field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for filtering based on the user's current project or field of interest to AI, and cause filtering to be performed based on AI.

[0079] The collection unit can estimate the user's emotion and determine the priority of messages to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit postpones messages of low importance. When the user is relaxed, the collection unit can also collect all messages evenly. When the user is concentrating, the collection unit can also collect messages of high importance preferentially. In this way, important messages can be preferentially collected by determining the priority of messages according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and determine the priority of messages based on the generation AI.

[0080] The collection unit can preferentially collect highly relevant messages in consideration of the geographical location information of the user when collecting messages. For example, when the user is in a specific area, the collection unit preferentially collects messages related to the area. When the user is traveling, the collection unit can also preferentially collect messages related to the travel destination. When the user is at home, the collection unit can also preferentially collect messages related to the home. In this way, highly relevant messages can be preferentially collected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and preferentially collect highly relevant messages based on AI.

[0081] The collection unit can analyze the user's social media activity and collect related messages when collecting messages. For example, the collection unit collects related messages based on the content frequently posted by the user on a specific social media. The collection unit can also collect related messages based on the content posted by an account the user follows. The collection unit can also collect related messages based on the activities of a group or community in which the user participates. In this way, highly relevant messages can be collected by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and collect related messages based on AI.

[0082] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. When the user is relaxed, the analysis unit can also provide a detailed analysis result. When the user is in a hurry, the analysis unit can also provide a concise analysis result with the main points. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and adjust the expression method of the analysis based on the generation AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, the analysis unit performs a detailed analysis on messages with high importance. The analysis unit can also perform a concise analysis on messages with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

[0084] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit applies a business analysis algorithm to a business-related message. The analysis unit can also apply a private analysis algorithm to a private message. The analysis unit can also apply a social media analysis algorithm to a social media message. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the message category. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input message category data to the generation AI and apply the analysis algorithm based on the generation AI.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. When the user is relaxed, the analysis unit can also provide a detailed analysis result. When the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. This allows the analysis result to be optimal for the user by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the length of the analysis based on the generation AI.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time when the message was sent. For example, the analysis unit preferentially analyzes a message sent recently. The analysis unit can also preferentially analyze a message sent during an important time period. The analysis unit can also preferentially analyze a message received by a user during a specific time period. In this way, important messages can be preferentially analyzed by determining the priority of analysis based on the time when the message was sent. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the message was sent into AI and determine the priority of analysis based on AI.

[0087] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit prioritizes analysis of highly relevant messages. The analysis unit can also postpone analysis of less relevant messages. The analysis unit can also dynamically adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of messages. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message relevance data to AI and adjust the order of analysis based on AI.

[0088] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, the notification unit uses a quiet notification sound when the user is nervous. The notification unit can also use a bright notification sound when the user is relaxed. The notification unit can also use a quick and concise notification method when the user is in a hurry. This allows the optimal notification for the user by adjusting the notification method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using an AI or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and adjust the notification method based on the generation AI.

[0089] The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, the notification unit provides detailed notification for messages with high importance. The notification unit can also provide concise notification for messages with low importance. The notification unit can also determine the priority of the notification according to the importance. This allows for efficient notification by adjusting the level of detail of the notification according to the importance of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI or may be performed without using AI. For example, the notification unit can input the importance data of the message to AI and adjust the level of detail of the notification based on AI.

[0090] The notification unit can apply different notification algorithms depending on the category of the message at the time of notification. For example, the notification unit applies a business notification algorithm to a business-related message. The notification unit can also apply a private notification algorithm to a private message. The notification unit can also apply a social media notification algorithm to a social media message. This improves notification accuracy by applying an appropriate notification algorithm depending on the message category. Some or all of the above-mentioned processing in the notification unit may be performed using AI or may be performed without using AI. For example, the notification unit can input message category data to AI and apply a notification algorithm based on AI.

[0091] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, when the user is nervous, the notification unit delays the notification to reduce the burden on the user. When the user is relaxed, the notification unit can also perform the notification in real time. When the user is concentrating, the notification unit can also adjust the notification timing so as not to disturb the work. This allows the notification to be performed at the optimal timing for the user by adjusting the notification timing according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and adjust the timing of the notification based on the generation AI.

[0092] The notification unit can determine the priority of notifications by considering attribute information of the sender of the message at the time of notification. For example, the notification unit gives priority to notifying messages from important senders. The notification unit can also determine the priority of notifications based on the job title and relationship of the sender. The notification unit can also analyze the sender's past message history and determine the importance. In this way, important messages can be given priority by considering the attribute information of the sender of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the sender's attribute information into AI and determine the priority of notifications based on AI.

[0093] The notification unit can adjust the order of notifications based on the relevance of messages at the time of notification. For example, the notification unit prioritizes notifications of highly relevant messages. The notification unit can also postpone notifications of less relevant messages. The notification unit can also dynamically adjust the order of notifications according to the relevance. This enables efficient notifications by adjusting the order of notifications based on the relevance of messages. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message relevance data to AI and adjust the order of notifications based on AI.

[0094] The summary unit can estimate the user's emotion and adjust the way the summary is expressed based on the estimated user's emotion. For example, when the user is nervous, the summary unit provides a simple and highly visible summary. When the user is relaxed, the summary unit can also provide a detailed summary. When the user is in a hurry, the summary unit can also provide a concise summary that covers the main points. This allows the summary to be provided in an easy-to-understand manner by adjusting the way the summary is expressed according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the user's emotion data into the generation AI and adjust the way the summary is expressed based on the generation AI.

[0095] The summary unit can adjust the level of detail of the summary based on the importance of the message when creating the summary. For example, the summary unit provides a detailed summary for a message with high importance. For a message with low importance, the summary unit can also provide a concise summary. The summary unit can also determine the priority of the summary according to the importance. This allows for efficient summary creation by adjusting the level of detail of the summary according to the importance of the message. Some or all of the above-mentioned processing in the summary unit may be performed using AI or may be performed without using AI. For example, the summary unit can input message importance data into AI and adjust the level of detail of the summary based on AI.

[0096] The summary unit can apply different summary algorithms depending on the category of the message when creating the summary. For example, the summary unit applies a business summary algorithm to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

[0097] The summary unit can estimate the user's emotion and adjust the length of the summary based on the estimated user's emotion. For example, when the user is in a hurry, the summary unit provides a short summary that covers the main points. When the user is relaxed, the summary unit can also provide a detailed summary. When the user is excited, the summary unit can also provide a summary with a visually stimulating effect. This allows the summary to be optimal for the user by adjusting the length of the summary according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the user's emotion data into the generation AI and adjust the length of the summary based on the generation AI.

[0098] The summary unit can determine the priority of the summary based on the time when the message was sent when creating the summary. For example, the summary unit can prioritize the inclusion of a recently sent message in the summary. The summary unit can also prioritize the inclusion of a message sent during an important time period in the summary. The summary unit can also prioritize the inclusion of a message received by the user during a specific time period in the summary. In this way, important messages can be prioritized for inclusion in the summary by determining the priority of the summary based on the time when the message was sent. A part or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message sending time data to the generation AI and determine the priority of the summary based on the generation AI.

[0099] The summary unit can adjust the order of summaries based on the relevance of messages when creating summaries. For example, the summary unit prioritizes including highly relevant messages in the summary. The summary unit can also postpone less relevant messages. The summary unit can also dynamically adjust the order of summaries according to the relevance. This allows for efficient summary creation by adjusting the order of summaries based on the relevance of messages. A part or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message relevance data to the generation AI and adjust the order of summaries based on the generation AI.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. For example, when the user is feeling stressed, messages with high importance are analyzed with priority. When the user is relaxed, all messages can be analyzed evenly. When the user is concentrating, messages with low importance can be postponed. In this way, important messages can be analyzed with priority by determining the analysis priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and determine the analysis priority based on the generation AI.

[0102] The notification unit can estimate the user's emotion and adjust the content of the notification based on the estimated user's emotion. For example, if the user is nervous, a simple and to the point notification is provided. If the user is relaxed, a detailed notification can be provided. If the user is in a hurry, a quick and simple notification can be provided. This allows the optimal notification for the user to be provided by adjusting the content of the notification according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit can input the user's emotion data into the generation AI and adjust the content of the notification based on the generation AI.

[0103] The summary unit can estimate the user's emotion and adjust the format of the summary based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible summary can be provided. If the user is relaxed, a detailed summary can be provided. If the user is in a hurry, a concise summary with the main points can be provided. This allows the summary to be easily understood by the user by adjusting the format of the summary according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the user's emotion data into the generation AI and adjust the format of the summary based on the generation AI.

[0104] The collection unit can estimate the user's emotion and determine the type of message to be collected based on the estimated user's emotion. For example, if the user is stressed, messages with low importance are not collected. If the user is relaxed, all messages can be collected. If the user is focused, messages with high importance can be preferentially collected. This can reduce the burden on the user by determining the type of message to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and determine the type of message to be collected based on the generation AI.

[0105] The analysis unit can estimate the user's emotion and adjust the level of detail of the analysis based on the estimated user's emotion. For example, if the user is nervous, a concise and to the point analysis result is provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a quick and concise analysis result can be provided. This allows the analysis result that is optimal for the user to be provided by adjusting the level of detail of the analysis according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and adjust the level of detail of the analysis based on the generation AI.

[0106] The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit prioritizes collection of messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection in a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has determined to be important in the past and prioritize collection of similar messages. In this way, the optimal collection method can be selected by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, a detailed analysis is performed for a message with high importance. The analysis unit can also perform a concise analysis for a message with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

[0108] The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, for a message of high importance, a detailed notification is provided. For a message of low importance, the notification unit can also provide a concise notification. The notification unit can also determine the priority of the notification according to the importance. This allows for efficient notification by adjusting the level of detail of the notification according to the importance of the message. A part or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the importance data of the message to AI and adjust the level of detail of the notification based on AI.

[0109] The summary unit can apply different summary algorithms depending on the category of the message when creating the summary. For example, a business summary algorithm is applied to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using a generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

[0110] When collecting messages, the collection unit can preferentially collect highly relevant messages by taking into account the geographical location information of the user. For example, when the user is in a specific area, messages related to the area are preferentially collected. When the user is traveling, the collection unit can also preferentially collect messages related to the travel destination. When the user is at home, the collection unit can also preferentially collect messages related to the home. In this way, highly relevant messages can be preferentially collected by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and preferentially collect highly relevant messages based on AI.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, an email app, a chat app, an SNS, etc. The collection unit can acquire messages using the API of each app. For example, the collection unit can acquire email messages using the API of an email app. The collection unit can also acquire chat messages using the API of a chat app. Furthermore, the collection unit can acquire SNS messages using the API of an SNS. Step 2: The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the content of the messages to identify urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the content of the messages using natural language processing techniques. For example, the analysis unit can analyze the words of the messages using morphological analysis. The analysis unit can also analyze the sentence structure of the messages using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages using semantic analysis. Step 3: The notification unit notifies the user of the urgent or important matter identified by the analysis unit. The notification unit immediately notifies the user of the urgent or important matter, for example, by using a push notification or an email notification. The notification unit can immediately notify the user. For example, the notification unit notifies the user of the urgent or important matter by using a push notification. The notification unit can also notify the user of the urgent or important matter by using an email notification. Step 4: The summary unit creates summaries of messages that were not identified by the analysis unit. The summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. The summary unit can provide the user with a summary of all messages at the end of a day, for example. The summary unit creates summaries to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates summaries. This allows the system according to the embodiment to quickly grasp important messages while maintaining the user's concentration. Some or all of the above-mentioned processing in the summary unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of the collected messages into the generation AI and have the generation AI create summaries.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and summary unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using the generation AI. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and immediately notifies of urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using the generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and summary unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using a generation AI. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and immediately notifies urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using a generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and summary unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using the generation AI. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and immediately notifies of urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using the generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, and summary unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using the generation AI. The notification unit is realized, for example, by the control unit 46A of the robot 414, and immediately notifies of urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using the generation AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit that collects messages; an analysis unit that analyzes the messages collected by the collection unit; a notification unit that notifies of an urgent or important matter identified by the analysis unit; A summary unit that creates a summary of messages that have not been identified by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect messages using each app's API 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect messages using scraping technology 2. The system of claim 1, (Appendix 4) The analysis unit is Uses natural language processing techniques to analyze the content of messages and detect specific keywords and phrases 2. The system of claim 1, (Appendix 5) The analysis unit is Set specific criteria that users can customize 2. The system of claim 1, (Appendix 6) The notification unit is Get notified immediately of urgent or important matters using push or email notifications 2. The system of claim 1, (Appendix 7) The summary section includes: Summarize the contents of collected messages and create a summary 2. The system of claim 1, (Appendix 8) The collecting unit includes: To estimate a user's emotion and adjust a timing of message collection based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze the user's past message collection history and select the optimal collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: Filter messages as they are collected based on the user's current projects and interests 2. The system of claim 1, (Appendix 11) The collecting unit includes: To estimate a user's emotion and determine a priority of messages to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting messages, the system takes into account the user's geographic location information to prioritize collection of relevant messages. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting messages, analyze the user's social media activity and collect related messages. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the verbosity of the analysis based on the severity of the message 2. The system of claim 1, (Appendix 16) The analysis unit is During parsing, different parsing algorithms are applied depending on the category of the message 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During parsing, prioritize parsing based on when the message was sent 2. The system of claim 1, (Appendix 19) The analysis unit is During parsing, adjust the order of parsing based on message relevance 2. The system of claim 1, (Appendix 20) The notification unit is Infer user emotions and adjust notification methods based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The notification unit is When you receive a notification, adjust the notification detail based on the importance of the message. 2. The system of claim 1, (Appendix 22) The notification unit is During notification, apply different notification algorithms depending on the message category 2. The system of claim 1, (Appendix 23) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 24) The notification unit is When sending a notification, the priority of the notification is determined based on the attribute information of the message sender. 2. The system of claim 1, (Appendix 25) The notification unit is At notification time, adjust the order of notifications based on message relevance 2. The system of claim 1, (Appendix 26) The summary section includes: Infer user emotions and adjust the way the summary is presented based on the estimated user emotions 2. The system of claim 1, (Appendix 27) The summary section includes: When creating a summary, adjust the summary detail based on the importance of the message 2. The system of claim 1, (Appendix 28) The summary section includes: When summarizing, apply different summarization algorithms depending on the message category 2. The system of claim 1, (Appendix 29) The summary section includes: Infer user sentiment and adjust the length of the summary based on the estimated user sentiment 2. The system of claim 1, (Appendix 30) The summary section includes: When summaries are created, they are prioritized based on when the messages were sent 2. The system of claim 1, (Appendix 31) The summary section includes: When creating summaries, adjust the order of summaries based on message relevance 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects messages; an analysis unit that analyzes the messages collected by the collection unit; a notification unit that notifies of an urgent or important matter identified by the analysis unit; A summary unit that creates a summary of messages that have not been identified by the analysis unit. A system characterized by:

2. The collecting unit includes: Collect messages using each app's API 2. The system of claim 1 .

3. The collecting unit includes: Collect messages using scraping technology 2. The system of claim 1 .

4. The analysis unit is Uses natural language processing techniques to analyze the content of messages and detect specific keywords and phrases 2. The system of claim 1 .

5. The analysis unit is Set specific criteria that users can customize 2. The system of claim 1 .

6. The notification unit is Get notified immediately of urgent or important matters using push or email notifications 2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotion and adjust a timing of message collection based on the estimated user's emotion.

2. The system of claim 1 .

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