system

Abstract

The system according to the embodiment aims to efficiently extract and organize necessary information from conversations within a messaging app. [Solution] The system according to the embodiment comprises an analysis unit, an extraction unit, an organization unit, a provision unit, and a customization unit. The analysis unit analyzes conversations within the messaging application. The extraction unit extracts important information from the conversations analyzed by the analysis unit. The organization unit organizes the information extracted by the extraction unit. The provision unit provides the information organized by the organization unit to the user. The customization unit customizes the information provided by the provision unit to the user's preferences.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of making it difficult to efficiently extract and organize necessary information from the large amount of conversations within messaging apps.

[0005] The system according to the embodiment aims to efficiently extract and organize necessary information from conversations within a messaging app. [Means for solving the problem]

[0006] The system according to this embodiment comprises an analysis unit, an extraction unit, an organization unit, a provision unit, and a customization unit. The analysis unit analyzes conversations within a messaging application. The extraction unit extracts important information from the conversations analyzed by the analysis unit. The organization unit organizes the information extracted by the extraction unit. The provision unit provides the information organized by the organization unit to the user. The customization unit customizes the information provided by the provision unit to the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently extract and organize necessary information from conversations within a messaging app. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The conversation organization system according to an embodiment of the present invention is designed to solve the problem that, in an era where everyone owns a smartphone, parents, in particular, often have multiple messaging app groups for school PTAs and club activities, and as conversations increase, it becomes difficult to find necessary information among buried conversations. This conversation organization system utilizes AI to organize and tidy up buried conversations on messaging apps. First, the user has the AI ​​analyze conversations within the messaging app. The AI ​​analyzes the content of the conversation and classifies important information from unimportant information. For example, it extracts dates, times, locations, and important announcements from the conversation, and excludes other irrelevant information such as tidbits and stamps. In this way, the user can efficiently obtain the necessary information. Next, the AI ​​organizes the extracted important information and provides it to the user. For example, it can display important announcements in a list or automatically add them to the calendar based on the date and time and location. This enables the user to achieve smooth communication and efficient information acquisition on messaging apps. Furthermore, the AI ​​can learn the user's past conversation history and customize the information according to the user's preferences. For example, it can filter information based on specific keywords or phrases, or prioritize the display of messages from specific members. In this way, the conversation organization system is a system that uses AI to organize and sort through buried conversations on messaging apps, allowing users to efficiently obtain the information they need. This enables users to achieve smoother communication and more efficient information retrieval on messaging apps.

[0029] The conversation organization system according to this embodiment comprises an analysis unit, an extraction unit, an organization unit, a provision unit, and a customization unit. The analysis unit analyzes conversations within a messaging application. The analysis unit analyzes the content of conversations using, for example, natural language processing technology. The analysis unit can also analyze conversation patterns using, for example, machine learning algorithms. The analysis unit can also apply, for example, algorithms that evaluate importance considering the context of the conversation. The extraction unit extracts important information from the conversations analyzed by the analysis unit. The extraction unit extracts, for example, dates and times, locations, important contact information, etc. The extraction unit can also extract important information using, for example, keyword matching technology. The extraction unit can also extract important information using, for example, contextual analysis technology. The organization unit organizes the information extracted by the extraction unit. The organization unit displays the extracted information in a list. The organization unit can also automatically add the extracted information to a calendar. The organization unit can also organize the extracted information by category. The provision unit provides the information organized by the organization unit to the user. The information provider unit provides information to the user, for example, using a notification function. The information provider unit can also provide information to the user, for example, using a dashboard display function. Furthermore, the information provider unit can adjust the level of detail provided based on the importance of the information, for example. The customization unit customizes the information provided by the information provider unit to the user's preferences. The customization unit can, for example, learn the user's past conversation history and filter information based on specific keywords or phrases. The customization unit can also customize information based on the user's current areas of interest, for example. Furthermore, the customization unit can, for example, estimate the user's emotions and adjust the customization method based on the estimated emotions of the user. As a result, the conversation organization system according to the embodiment can efficiently analyze, extract, organize, provide, and customize conversations within a messaging app.

[0030] The analysis unit can analyze conversations within a messaging app and classify important and unimportant information. For example, the analysis unit can analyze the content of conversations using natural language processing techniques. For instance, it can identify important information based on frequently occurring keywords in the conversation. The analysis unit can also analyze conversation patterns using machine learning algorithms. For example, it can analyze the flow of conversation and the transition of topics to identify important information. Furthermore, the analysis unit can apply algorithms that evaluate importance considering the context of the conversation. For example, it can evaluate importance considering the frequency and content of statements made by conversation participants. This allows the analysis unit to efficiently obtain necessary information by classifying important and unimportant information. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the content of conversations into an AI and have the AI ​​classify important and unimportant information.

[0031] The extraction unit can extract information such as the date, time, location, and important announcements from a conversation. For example, the extraction unit can extract important information using keyword matching technology. For instance, it can detect keywords related to the date, time, and location in the conversation and extract important information based on them. The extraction unit can also extract important information using contextual analysis technology. For example, it can analyze the flow of the conversation and the changes in topics to identify important announcements. The extraction unit can also extract important information by considering the frequency and content of statements made by the conversation participants. For example, it can prioritize analyzing messages from frequently speaking participants and extract important information. In this way, the extraction unit can efficiently obtain the necessary information by extracting the date, time, location, and important announcements. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the content of the conversation into AI and have the AI ​​perform the extraction of important information.

[0032] The organization unit can display the extracted information in a list or automatically add it to a calendar. For example, the organization unit can display the extracted information in a list format, such as a table or list. The organization unit can also automatically add the extracted information to a calendar, such as adding it to Google Calendar or Outlook Calendar. The organization unit can also organize the extracted information by category, such as classifying it into school-related information, club activity-related information, private information, etc. This makes it easier to organize information by displaying the extracted information in a list or automatically adding it to a calendar. Some or all of the above processing in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input the extracted information into an AI and have the AI ​​perform the information organization.

[0033] The information provider can provide important information to users. For example, the information provider can provide information to users using a notification function. For example, the information provider can notify users of important information using push notifications on smartphones. The information provider can also provide information to users using a dashboard display function. For example, the information provider can display information on the dashboard of a web application or mobile application. The information provider can also adjust the level of detail provided based on the importance of the information. For example, the information provider can select a delivery method that includes detailed explanations for important information and a concise delivery method for less important information. This allows the information provider to efficiently obtain the necessary information by providing important information to users. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information into AI and have the AI ​​execute the method of providing the information.

[0034] The customization unit can learn the user's past conversation history and filter information based on specific keywords and phrases. For example, the customization unit learns the user's past conversation history. For example, the customization unit analyzes past conversation history using text mining techniques. The customization unit can also filter information based on specific keywords and phrases. For example, the customization unit filters information based on keywords and phrases that the user has frequently used in the past. The customization unit can also customize information based on the user's current areas of interest. For example, the customization unit identifies the user's current areas of interest from survey results or past search history and customizes information based on that. This allows the customization unit to provide information tailored to the user's preferences by learning the user's past conversation history and filtering information based on specific keywords and phrases. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input past conversation history into AI and have the AI ​​perform the information filtering.

[0035] The analysis unit can apply algorithms to evaluate importance while considering the context of the conversation. For example, the analysis unit can evaluate importance based on keywords that frequently appear in the conversation. For example, the analysis unit can analyze the flow of the conversation and the changes in topics to identify important information. The analysis unit can also evaluate importance by considering the frequency and content of what each participant says in the conversation. For example, the analysis unit can prioritize analyzing messages from frequently speaking participants to identify important information. In this way, the analysis unit can provide more accurate information by evaluating importance while considering the context of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the conversation into AI and have the AI ​​perform the importance evaluation.

[0036] The analysis unit can apply different analysis methods to each conversation topic during analysis. For example, for school-related topics, the analysis unit prioritizes analyzing information related to date, time, and location. For example, for club activity-related topics, the analysis unit prioritizes analyzing information related to participants and activity details. The analysis unit can also prioritize analyzing personal communications for private topics. For example, the analysis unit applies different analysis methods to each conversation topic to identify important information. In this way, the analysis unit can provide more appropriate information by applying different analysis methods to each conversation topic. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation topics into AI and have the AI ​​apply an analysis method for each topic.

[0037] The analysis unit can perform the analysis while taking into account the attribute information of the participants in the conversation. For example, if the participants in the conversation are parents, the analysis unit prioritizes analyzing information about their children. For example, if the participants in the conversation are teachers, the analysis unit prioritizes analyzing information about their education. Furthermore, if the participants in the conversation are members of a club activity, the analysis unit can also prioritize analyzing information about their activities. For example, the analysis unit performs the analysis while taking into account the attribute information of the participants in the conversation and identifies important information. In this way, the analysis unit can provide more appropriate information by performing the analysis while taking into account the attribute information of the participants in the conversation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the attribute information of the participants in the conversation into AI and have the AI ​​perform the analysis.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis based on the frequency and length of the conversation. For example, the analysis unit prioritizes analysis of frequently occurring conversations. For example, the analysis unit identifies important information in long conversations and analyzes it efficiently. The analysis unit can also improve the accuracy of analysis when important information is contained in short conversations. For example, the analysis unit improves the accuracy of the analysis based on the frequency and length of the conversation. In this way, the analysis unit can provide more accurate information by improving the accuracy of the analysis based on the frequency and length of the conversation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency and length of the conversation into AI and have the AI ​​improve the accuracy of the analysis.

[0039] During extraction, the extraction unit can apply an algorithm that identifies important information by taking into account the context of the conversation. The extraction unit, for example, identifies important information based on keywords that frequently appear in the conversation. For example, the extraction unit can analyze the flow of the conversation and topic transitions to identify important information. The extraction unit can also identify important information by taking into account the frequency and content of comments made by participants in the conversation. For example, the extraction unit can prioritize analyzing messages from participants who frequently speak and identify important information. This allows the extraction unit to identify important information by taking into account the context of the conversation, thereby providing more accurate information. Some or all of the above-described processing in the extraction unit may be performed using, or without, AI. For example, the extraction unit can input the content of the conversation into AI and have the AI ​​identify the important information.

[0040] The extraction unit can apply different extraction methods to each conversation topic during the extraction process. For example, for school-related topics, the extraction unit prioritizes extracting information about the date, time, and location. For example, for club activity-related topics, the extraction unit prioritizes extracting information about participants and activities. The extraction unit can also prioritize extracting personal contact information for private topics. For example, the extraction unit applies different extraction methods to each conversation topic to identify important information. This allows the extraction unit to provide more relevant information by applying different extraction methods to each conversation topic. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the conversation topics into the AI ​​and have the AI ​​perform the application of topic-specific extraction methods.

[0041] The extraction unit can perform extraction while considering the attribute information of the conversation participants. For example, if a conversation participant is a parent, the extraction unit will prioritize extracting information about children. For example, if a conversation participant is a teacher, the extraction unit will prioritize extracting information about education. The extraction unit can also prioritize extracting information about club activities if a conversation participant is a member of a club activity. For example, the extraction unit will perform extraction while considering the attribute information of the conversation participants and identify important information. In this way, the extraction unit can provide more appropriate information by performing extraction while considering the attribute information of the conversation participants. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input the attribute information of the conversation participants into AI and have the AI ​​perform the extraction.

[0042] During extraction, the extraction unit can improve the accuracy of extraction based on the frequency and length of the conversation. For example, the extraction unit prioritizes extraction of frequently occurring conversations. For example, the extraction unit identifies important information in long conversations and efficiently extracts it. The extraction unit can also improve the accuracy of extraction when important information is contained in short conversations. For example, the extraction unit improves the accuracy of extraction based on the frequency and length of the conversation. In this way, the extraction unit can provide more accurate information by improving the accuracy of extraction based on the frequency and length of the conversation. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input data on the frequency and length of conversations into AI and have the AI ​​improve the accuracy of extraction.

[0043] The organizing unit can improve the accuracy of the organizing by taking into account the relevance of the extracted information when organizing. The organizing unit, for example, groups related information and organizes it efficiently. For example, the organizing unit analyzes the relevance of information and organizes important information preferentially. The organizing unit can also select an optimal organizing method based on the relevance of information. For example, the organizing unit evaluates the relevance of information using co-occurrence network analysis and improves the accuracy of the organizing. In this way, the organizing unit can improve the accuracy of the organizing by taking into account the relevance of the extracted information, thereby providing more accurate information. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the extracted information into AI and have the AI ​​evaluate the relevance of the information and organize it.

[0044] The organizing unit can apply different organizing methods to each information category when organizing. For example, the organizing unit organizes school-related information based on date, time, and location. For example, the organizing unit organizes club activity-related information based on participants and activity content. The organizing unit can also organize private information based on personal contacts. For example, the organizing unit applies different organizing methods to each information category to organize efficiently. In this way, the organizing unit can provide more appropriate information by applying different organizing methods to each information category. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input information categories into AI and have the AI ​​apply the organizing method for each category.

[0045] When organizing, the organizing unit can determine the priority of organizing based on the time of submission of information. For example, the organizing unit prioritizes organizing the latest information. For example, the organizing unit postpones older information. The organizing unit can also prioritize organizing important information based on the time of submission. For example, the organizing unit determines the priority of information based on the submission date and time or deadline. In this way, the organizing unit can provide more appropriate information by determining the priority of organizing based on the time of submission of information. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, AI, for example. For example, the organizing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of organizing.

[0046] The organizing unit can adjust the order of organizing based on the relevance of the information when organizing. For example, the organizing unit groups related information and organizes it efficiently. For example, the organizing unit analyzes the relevance of the information and organizes important information preferentially. The organizing unit can also select an optimal organizing method based on the relevance of the information. For example, the organizing unit evaluates the relevance of the information using co-occurrence network analysis and adjusts the order of organizing. In this way, the organizing unit can provide more appropriate information by adjusting the order of organizing based on the relevance of the information. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the extracted information into AI and have the AI ​​evaluate the relevance of the information and organize it.

[0047] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit selects a providing method that includes detailed explanations for important information. For example, the providing unit selects a concise providing method for information with low importance. The providing unit can also select an optimal providing method based on the importance of the information. For example, the providing unit evaluates the importance of the information using an importance score and adjusts the level of detail of the information provided. In this way, the providing unit can provide more appropriate information by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the importance of the information into AI and have the AI ​​adjust the level of detail of the information provided.

[0048] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit provides school-related information based on date, time, and location. For example, the providing unit provides club activity-related information based on participants and activity content. The providing unit can also provide private information based on personal contacts. For example, the providing unit applies different provision algorithms depending on the category of information to provide the information efficiently. In this way, the providing unit can provide more appropriate information by applying different provision algorithms depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information into AI and cause the AI ​​to apply a provision algorithm for each category.

[0049] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides the latest information preferentially. For example, the providing unit postpones old information. The providing unit can also provide important information preferentially based on the time of submission. For example, the providing unit determines the priority of information based on the submission date and time or deadline. In this way, the providing unit can provide more appropriate information by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of provision.

[0050] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The providing unit, for example, groups related information and provides it efficiently. For example, the providing unit analyzes the relevance of the information and provides important information preferentially. The providing unit can also select an optimal provision method based on the relevance of the information. For example, the providing unit evaluates the relevance of the information using co-occurrence network analysis and adjusts the order of provision. In this way, the providing unit can provide more appropriate information by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the extracted information to AI and have the AI ​​evaluate the relevance of the information and provide it.

[0051] During customization, the customization unit can analyze the user's past conversation history and select the optimal customization method. The customization unit, for example, analyzes the user's past conversation history. For example, the customization unit may analyze the past conversation history using text mining technology. The customization unit may also preferentially suggest customization options that the user has previously preferred. For example, the customization unit may suggest a customization method based on specific keywords or phrases from the user's past conversation history. The customization unit may also analyze the user's past conversation history and select the most efficient customization method. For example, the customization unit may evaluate the user's past conversation history using frequency analysis and select the optimal customization method. This allows the customization unit to provide more appropriate information by analyzing the user's past conversation history and selecting the optimal customization method. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit may input the past conversation history into AI and have the AI ​​select the customization method.

[0052] The customization unit can customize the means of customization based on the user's current areas of interest during the customization process. For example, the customization unit can identify the user's current areas of interest. For example, the customization unit can identify the user's areas of interest using survey results or past search history. The customization unit can also suggest customization methods based on topics the user is currently interested in. For example, the customization unit can prioritize customizing information relevant to the user's current areas of interest. The customization unit can also analyze the user's current areas of interest and select the optimal customization method. For example, the customization unit can input the user's current areas of interest into an AI and have the AI ​​select the customization method. This allows the customization unit to provide more appropriate information by customizing the means of customization based on the user's current areas of interest. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without using AI.

[0053] The customization unit can select the optimal customization method while considering the user's geographical location information. For example, the customization unit can consider the user's geographical location information. For example, the customization unit can identify the user's geographical location information using GPS data or location services. The customization unit can also prioritize customizing information related to a specific region if the user is in that region. For example, the customization unit can propose the optimal customization method based on the user's geographical location information. The customization unit can also analyze the user's geographical location information and select the most efficient customization method. For example, the customization unit can input the user's geographical location information into an AI and have the AI ​​select the customization method. This allows the customization unit to provide more appropriate information by selecting the optimal customization method while considering the user's geographical location information. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without using AI.

[0054] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit, for example, analyzes the user's social media activity. For example, the customization unit analyzes the user's post content, the number of likes, the number of followers, etc. The customization unit can also suggest customization methods based on topics of interest from the user's social media activity. For example, the customization unit analyzes the user's social media activity and prioritizes customization of related information. The customization unit can also select the optimal customization method based on the user's social media activity. For example, the customization unit can input the user's social media activity into an AI and have the AI ​​suggest customization methods. In this way, the customization unit can provide more appropriate information by analyzing the user's social media activity and suggesting customization methods. Some or all of the above-described processing in the customization unit may be performed, for example, using AI or without AI.

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

[0056] The analysis unit can learn the user's past behavioral patterns and improve the accuracy of the analysis. For example, the analysis unit can learn what information the user considered important in the past and evaluate the importance of the current conversation based on that information. The analysis unit can also learn what topics the user was interested in in the past and prioritize analysis of information related to those topics. Furthermore, the analysis unit can predict the importance of future conversations based on the user's past behavioral patterns and adjust the analysis priority. In this way, the analysis unit can provide more accurate information by learning the user's past behavioral patterns.

[0057] The extraction unit can adjust the information to be extracted based on the user's current situation. For example, the extraction unit can consider the user's current location and prioritize extracting information related to that location. The extraction unit can also consider the device the user is currently using and extract information in a format optimal for that device. Furthermore, the extraction unit can consider the user's current activity status (e.g., whether the user is at work or on vacation) and extract information according to that status. In this way, the extraction unit can provide more appropriate information by adjusting the information based on the user's current situation.

[0058] The organizing unit can learn the user's past organizing patterns and improve the accuracy of organization. For example, the organizing unit can learn how the user organized information in the past and organize current information based on that method. The organizing unit can also learn what categories the user classified information into in the past and organize information based on those categories. Furthermore, the organizing unit can predict how the user will organize information in the future based on the user's past organizing patterns and improve the efficiency of organization. In this way, the organizing unit can achieve more efficient information organization by learning the user's past organizing patterns.

[0059] The providing unit can learn the user's past information acquisition patterns and improve the accuracy of the information provided. For example, the providing unit can learn how the user acquired information in the past and provide current information based on that method. The providing unit can also learn in what format the user received information in the past and provide information based on that format. Furthermore, the providing unit can predict future information provision methods based on the user's past information acquisition patterns and improve the efficiency of information provision. In this way, the providing unit can achieve more efficient information provision by learning the user's past information acquisition patterns.

[0060] The customization unit can learn the user's past customization patterns and improve the accuracy of customization. For example, the customization unit can learn how the user customized information in the past and customize the current information based on that method. The customization unit can also learn what options the user selected in the past and customize information based on those options. Furthermore, the customization unit can predict future customization methods based on the user's past customization patterns and improve the efficiency of customization. In this way, the customization unit can achieve more efficient information customization by learning the user's past customization patterns.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The analysis unit analyzes conversations within the messaging app. The analysis unit can use natural language processing techniques and machine learning algorithms to analyze the content and patterns of conversations, and can apply algorithms that evaluate importance while considering the context of the conversation. Step 2: The extraction unit extracts important information from the conversation analyzed by the analysis unit. The extraction unit can extract important information such as the date and time, location, and important contact details, and can use keyword matching and contextual analysis techniques to extract key information. Step 3: The organization unit organizes the information extracted by the extraction unit. The organization unit can display the extracted information in a list, automatically add it to a calendar, or organize it by category. Step 4: The delivery unit provides the information organized by the organization unit to the user. The delivery unit can provide information using notification functions and dashboard display functions, and can adjust the level of detail provided based on the importance of the information. Step 5: The customization unit customizes the information provided by the provision unit to suit the user's preferences. The customization unit can learn the user's past conversation history and filter information based on specific keywords or phrases, or customize information based on the user's current interests or emotions.

[0063] (Example 2) The conversation organization system according to an embodiment of the present invention is designed to solve the problem that, in an era where everyone owns a smartphone, parents, in particular, often have multiple messaging app groups for school PTAs and club activities, and as conversations increase, it becomes difficult to find necessary information among buried conversations. This conversation organization system utilizes AI to organize and tidy up buried conversations on messaging apps. First, the user has the AI ​​analyze conversations within the messaging app. The AI ​​analyzes the content of the conversation and classifies important information from unimportant information. For example, it extracts dates, times, locations, and important announcements from the conversation, and excludes other irrelevant information such as tidbits and stamps. In this way, the user can efficiently obtain the necessary information. Next, the AI ​​organizes the extracted important information and provides it to the user. For example, it can display important announcements in a list or automatically add them to the calendar based on the date and time and location. This enables the user to achieve smooth communication and efficient information acquisition on messaging apps. Furthermore, the AI ​​can learn the user's past conversation history and customize the information according to the user's preferences. For example, it can filter information based on specific keywords or phrases, or prioritize the display of messages from specific members. In this way, the conversation organization system is a system that uses AI to organize and sort through buried conversations on messaging apps, allowing users to efficiently obtain the information they need. This enables users to achieve smoother communication and more efficient information retrieval on messaging apps.

[0064] The conversation organization system according to this embodiment comprises an analysis unit, an extraction unit, an organization unit, a provision unit, and a customization unit. The analysis unit analyzes conversations within a messaging application. The analysis unit analyzes the content of conversations using, for example, natural language processing technology. The analysis unit can also analyze conversation patterns using, for example, machine learning algorithms. The analysis unit can also apply, for example, algorithms that evaluate importance considering the context of the conversation. The extraction unit extracts important information from the conversations analyzed by the analysis unit. The extraction unit extracts, for example, dates and times, locations, important contact information, etc. The extraction unit can also extract important information using, for example, keyword matching technology. The extraction unit can also extract important information using, for example, contextual analysis technology. The organization unit organizes the information extracted by the extraction unit. The organization unit displays the extracted information in a list. The organization unit can also automatically add the extracted information to a calendar. The organization unit can also organize the extracted information by category. The provision unit provides the information organized by the organization unit to the user. The information provider unit provides information to the user, for example, using a notification function. The information provider unit can also provide information to the user, for example, using a dashboard display function. Furthermore, the information provider unit can adjust the level of detail provided based on the importance of the information, for example. The customization unit customizes the information provided by the information provider unit to the user's preferences. The customization unit can, for example, learn the user's past conversation history and filter information based on specific keywords or phrases. The customization unit can also customize information based on the user's current areas of interest, for example. Furthermore, the customization unit can, for example, estimate the user's emotions and adjust the customization method based on the estimated emotions of the user. As a result, the conversation organization system according to the embodiment can efficiently analyze, extract, organize, provide, and customize conversations within a messaging app.

[0065] The analysis unit can analyze conversations within a messaging app and classify important and unimportant information. For example, the analysis unit can analyze the content of conversations using natural language processing techniques. For instance, it can identify important information based on frequently occurring keywords in the conversation. The analysis unit can also analyze conversation patterns using machine learning algorithms. For example, it can analyze the flow of conversation and the transition of topics to identify important information. Furthermore, the analysis unit can apply algorithms that evaluate importance considering the context of the conversation. For example, it can evaluate importance considering the frequency and content of statements made by conversation participants. This allows the analysis unit to efficiently obtain necessary information by classifying important and unimportant information. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the content of conversations into an AI and have the AI ​​classify important and unimportant information.

[0066] The extraction unit can extract information such as the date, time, location, and important announcements from a conversation. For example, the extraction unit can extract important information using keyword matching technology. For instance, it can detect keywords related to the date, time, and location in the conversation and extract important information based on them. The extraction unit can also extract important information using contextual analysis technology. For example, it can analyze the flow of the conversation and the changes in topics to identify important announcements. The extraction unit can also extract important information by considering the frequency and content of statements made by the conversation participants. For example, it can prioritize analyzing messages from frequently speaking participants and extract important information. In this way, the extraction unit can efficiently obtain the necessary information by extracting the date, time, location, and important announcements. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the content of the conversation into AI and have the AI ​​perform the extraction of important information.

[0067] The organizing unit can display the extracted information in a list or automatically add it to a calendar. The organizing unit, for example, displays the extracted information in a list. For example, the organizing unit can display the information in a table format or a list format. The organizing unit can also automatically add the extracted information to a calendar. For example, the organizing unit can add the information to a Google calendar or an Outlook calendar. The organizing unit can also organize the extracted information by category. For example, the organizing unit can classify and organize the information into school-related information, club activity-related information, private information, etc. This makes it easy to organize the information by displaying the extracted information in a list or automatically adding it to a calendar. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the extracted information into AI and have the AI ​​organize the information.

[0068] The information provider can provide important information to users. For example, the information provider can provide information to users using a notification function. For example, the information provider can notify users of important information using push notifications on smartphones. The information provider can also provide information to users using a dashboard display function. For example, the information provider can display information on the dashboard of a web application or mobile application. The information provider can also adjust the level of detail provided based on the importance of the information. For example, the information provider can select a delivery method that includes detailed explanations for important information and a concise delivery method for less important information. This allows the information provider to efficiently obtain the necessary information by providing important information to users. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information into AI and have the AI ​​execute the method of providing the information.

[0069] The customization unit can learn the user's past conversation history and filter information based on specific keywords and phrases. For example, the customization unit learns the user's past conversation history. For example, the customization unit analyzes past conversation history using text mining techniques. The customization unit can also filter information based on specific keywords and phrases. For example, the customization unit filters information based on keywords and phrases that the user has frequently used in the past. The customization unit can also customize information based on the user's current areas of interest. For example, the customization unit identifies the user's current areas of interest from survey results or past search history and customizes information based on that. This allows the customization unit to provide information tailored to the user's preferences by learning the user's past conversation history and filtering information based on specific keywords and phrases. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input past conversation history into AI and have the AI ​​perform the information filtering.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the analysis unit to adjust the analysis priority based on the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the analysis priorities.

[0071] The analysis unit can apply algorithms to evaluate importance while considering the context of the conversation. For example, the analysis unit can evaluate importance based on keywords that frequently appear in the conversation. For example, the analysis unit can analyze the flow of the conversation and the changes in topics to identify important information. The analysis unit can also evaluate importance by considering the frequency and content of what each participant says in the conversation. For example, the analysis unit can prioritize analyzing messages from frequently speaking participants to identify important information. In this way, the analysis unit can provide more accurate information by evaluating importance while considering the context of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the conversation into AI and have the AI ​​perform the importance evaluation.

[0072] The analysis unit can apply different analysis methods to each conversation topic during analysis. For example, for school-related topics, the analysis unit prioritizes analyzing information related to date, time, and location. For example, for club activity-related topics, the analysis unit prioritizes analyzing information related to participants and activity details. The analysis unit can also prioritize analyzing personal communications for private topics. For example, the analysis unit applies different analysis methods to each conversation topic to identify important information. In this way, the analysis unit can provide more appropriate information by applying different analysis methods to each conversation topic. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation topics into AI and have the AI ​​apply an analysis method for each topic.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the display method of the analysis results.

[0074] The analysis unit can perform the analysis while taking into account the attribute information of the participants in the conversation. For example, if the participants in the conversation are parents, the analysis unit prioritizes analyzing information about their children. For example, if the participants in the conversation are teachers, the analysis unit prioritizes analyzing information about their education. Furthermore, if the participants in the conversation are members of a club activity, the analysis unit can also prioritize analyzing information about their activities. For example, the analysis unit performs the analysis while taking into account the attribute information of the participants in the conversation and identifies important information. In this way, the analysis unit can provide more appropriate information by performing the analysis while taking into account the attribute information of the participants in the conversation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the attribute information of the participants in the conversation into AI and have the AI ​​perform the analysis.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis based on the frequency and length of the conversation. For example, the analysis unit prioritizes analysis of frequently occurring conversations. For example, the analysis unit identifies important information in long conversations and analyzes it efficiently. The analysis unit can also improve the accuracy of analysis when important information is contained in short conversations. For example, the analysis unit improves the accuracy of the analysis based on the frequency and length of the conversation. In this way, the analysis unit can provide more accurate information by improving the accuracy of the analysis based on the frequency and length of the conversation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency and length of the conversation into AI and have the AI ​​improve the accuracy of the analysis.

[0076] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. The extraction unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the extraction unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The extraction unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the extraction unit can analyze the tone and speed of the voice and calculate an emotion score. The extraction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the extraction unit can calculate an emotion score based on heart rate fluctuations. This allows the extraction unit to determine the priority of information to be extracted based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user emotion data into AI and have AI determine the priority of the information to be extracted.

[0077] During extraction, the extraction unit can apply an algorithm that identifies important information by taking into account the context of the conversation. The extraction unit, for example, identifies important information based on keywords that frequently appear in the conversation. For example, the extraction unit can analyze the flow of the conversation and topic transitions to identify important information. The extraction unit can also identify important information by taking into account the frequency and content of comments made by participants in the conversation. For example, the extraction unit can prioritize analyzing messages from participants who frequently speak and identify important information. This allows the extraction unit to identify important information by taking into account the context of the conversation, thereby providing more accurate information. Some or all of the above-described processing in the extraction unit may be performed using, or without, AI. For example, the extraction unit can input the content of the conversation into AI and have the AI ​​identify the important information.

[0078] The extraction unit can apply different extraction methods to each conversation topic during the extraction process. For example, for school-related topics, the extraction unit prioritizes extracting information about the date, time, and location. For example, for club activity-related topics, the extraction unit prioritizes extracting information about participants and activities. The extraction unit can also prioritize extracting personal contact information for private topics. For example, the extraction unit applies different extraction methods to each conversation topic to identify important information. This allows the extraction unit to provide more relevant information by applying different extraction methods to each conversation topic. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the conversation topics into the AI ​​and have the AI ​​perform the application of topic-specific extraction methods.

[0079] The extraction unit can estimate the user's emotions and adjust the display method of the extraction results based on the estimated user emotions. For example, the extraction unit estimates the user's emotions using an emotion analysis algorithm. For example, the extraction unit captures the user's facial expressions with a camera and estimates emotions using facial recognition technology. The extraction unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the extraction unit analyzes the tone and speed of the voice and calculates an emotion score. The extraction unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the extraction unit calculates an emotion score based on fluctuations in heart rate. As a result, the extraction unit can provide more appropriate information by adjusting the display method of the extraction results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user emotion data into the AI ​​and have the AI ​​adjust how the extraction results are displayed.

[0080] The extraction unit can perform extraction while considering the attribute information of the conversation participants. For example, if a conversation participant is a parent, the extraction unit will prioritize extracting information about children. For example, if a conversation participant is a teacher, the extraction unit will prioritize extracting information about education. The extraction unit can also prioritize extracting information about club activities if a conversation participant is a member of a club activity. For example, the extraction unit will perform extraction while considering the attribute information of the conversation participants and identify important information. In this way, the extraction unit can provide more appropriate information by performing extraction while considering the attribute information of the conversation participants. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input the attribute information of the conversation participants into AI and have the AI ​​perform the extraction.

[0081] During extraction, the extraction unit can improve the accuracy of extraction based on the frequency and length of the conversation. For example, the extraction unit prioritizes extraction of frequently occurring conversations. For example, the extraction unit identifies important information in long conversations and efficiently extracts it. The extraction unit can also improve the accuracy of extraction when important information is contained in short conversations. For example, the extraction unit improves the accuracy of extraction based on the frequency and length of the conversation. In this way, the extraction unit can provide more accurate information by improving the accuracy of extraction based on the frequency and length of the conversation. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input data on the frequency and length of conversations into AI and have the AI ​​improve the accuracy of extraction.

[0082] The data processing unit can estimate the user's emotions and adjust the processing method based on the estimated emotions. For example, the processing unit can estimate the user's emotions using an emotion analysis algorithm. For example, the processing unit can capture the user's facial expressions with a camera and estimate emotions using facial recognition technology. The processing unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the processing unit can analyze the tone and speed of the voice and calculate an emotion score. The processing unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the processing unit can calculate an emotion score based on fluctuations in heart rate. This allows the processing unit to provide more appropriate information by adjusting the processing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit may input user emotion data into AI and have the AI ​​adjust the organizing method.

[0083] The organizing unit can improve the accuracy of the organizing by taking into account the relevance of the extracted information when organizing. The organizing unit, for example, groups related information and organizes it efficiently. For example, the organizing unit analyzes the relevance of information and organizes important information preferentially. The organizing unit can also select an optimal organizing method based on the relevance of information. For example, the organizing unit evaluates the relevance of information using co-occurrence network analysis and improves the accuracy of the organizing. In this way, the organizing unit can improve the accuracy of the organizing by taking into account the relevance of the extracted information, thereby providing more accurate information. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the extracted information into AI and have the AI ​​evaluate the relevance of the information and organize it.

[0084] The organizing unit can apply different organizing methods to each information category when organizing. For example, the organizing unit organizes school-related information based on date, time, and location. For example, the organizing unit organizes club activity-related information based on participants and activity content. The organizing unit can also organize private information based on personal contacts. For example, the organizing unit applies different organizing methods to each information category to organize efficiently. In this way, the organizing unit can provide more appropriate information by applying different organizing methods to each information category. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input information categories into AI and have the AI ​​apply the organizing method for each category.

[0085] The organizing unit can estimate the user's emotions and adjust the display method of the organized results based on the estimated user emotions. The organizing unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the organizing unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The organizing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the organizing unit analyzes the tone and speed of the voice and calculates an emotion score. The organizing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the organizing unit calculates an emotion score based on heart rate fluctuations. This allows the organizing unit to adjust the display method of the organized results based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit may input user emotion data into AI and have the AI ​​adjust the display method of the organized results.

[0086] When organizing, the organizing unit can determine the priority of organizing based on the time of submission of information. For example, the organizing unit prioritizes organizing the latest information. For example, the organizing unit postpones older information. The organizing unit can also prioritize organizing important information based on the time of submission. For example, the organizing unit determines the priority of information based on the submission date and time or deadline. In this way, the organizing unit can provide more appropriate information by determining the priority of organizing based on the time of submission of information. Some or all of the above-mentioned processing in the organizing unit may be performed using, or without, AI, for example. For example, the organizing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of organizing.

[0087] The organizing unit can adjust the order of organizing based on the relevance of the information when organizing. For example, the organizing unit groups related information and organizes it efficiently. For example, the organizing unit analyzes the relevance of the information and organizes important information preferentially. The organizing unit can also select an optimal organizing method based on the relevance of the information. For example, the organizing unit evaluates the relevance of the information using co-occurrence network analysis and adjusts the order of organizing. In this way, the organizing unit can provide more appropriate information by adjusting the order of organizing based on the relevance of the information. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the extracted information into AI and have the AI ​​evaluate the relevance of the information and organize it.

[0088] The providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using an emotion analysis algorithm. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice and calculate an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on heart rate fluctuations. This allows the providing unit to adjust the presentation method of the information to be provided based on the user's emotion, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into AI and have the AI ​​adjust the way information is expressed.

[0089] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit selects a providing method that includes detailed explanations for important information. For example, the providing unit selects a concise providing method for information with low importance. The providing unit can also select an optimal providing method based on the importance of the information. For example, the providing unit evaluates the importance of the information using an importance score and adjusts the level of detail of the information provided. In this way, the providing unit can provide more appropriate information by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the importance of the information into AI and have the AI ​​adjust the level of detail of the information provided.

[0090] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit provides school-related information based on date, time, and location. For example, the providing unit provides club activity-related information based on participants and activity content. The providing unit can also provide private information based on personal contacts. For example, the providing unit applies different provision algorithms depending on the category of information to provide the information efficiently. In this way, the providing unit can provide more appropriate information by applying different provision algorithms depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information into AI and cause the AI ​​to apply a provision algorithm for each category.

[0091] The service provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion analysis algorithm. For example, the service provider can capture the user's facial expressions with a camera and estimate emotions using facial recognition technology. The service provider can also record the user's voice and estimate emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows the service provider to provide more appropriate information by adjusting the length of the information provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user emotion data into AI and have AI adjust the length of the information.

[0092] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides the latest information preferentially. For example, the providing unit postpones old information. The providing unit can also provide important information preferentially based on the time of submission. For example, the providing unit determines the priority of information based on the submission date and time or deadline. In this way, the providing unit can provide more appropriate information by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of provision.

[0093] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The providing unit, for example, groups related information and provides it efficiently. For example, the providing unit analyzes the relevance of the information and provides important information preferentially. The providing unit can also select an optimal provision method based on the relevance of the information. For example, the providing unit evaluates the relevance of the information using co-occurrence network analysis and adjusts the order of provision. In this way, the providing unit can provide more appropriate information by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the extracted information to AI and have the AI ​​evaluate the relevance of the information and provide it.

[0094] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user's emotions. The customization unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The customization unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the voice and calculates an emotion score. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the customization unit calculates an emotion score based on heart rate fluctuations. This allows the customization unit to adjust the customization method based on the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit may input user emotion data into AI and have the AI ​​adjust the customization method.

[0095] During customization, the customization unit can analyze the user's past conversation history and select the optimal customization method. The customization unit, for example, analyzes the user's past conversation history. For example, the customization unit may analyze the past conversation history using text mining technology. The customization unit may also preferentially suggest customization options that the user has previously preferred. For example, the customization unit may suggest a customization method based on specific keywords or phrases from the user's past conversation history. The customization unit may also analyze the user's past conversation history and select the most efficient customization method. For example, the customization unit may evaluate the user's past conversation history using frequency analysis and select the optimal customization method. This allows the customization unit to provide more appropriate information by analyzing the user's past conversation history and selecting the optimal customization method. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without AI. For example, the customization unit may input the past conversation history into AI and have the AI ​​select the customization method.

[0096] The customization unit can customize the means of customization based on the user's current areas of interest during the customization process. For example, the customization unit can identify the user's current areas of interest. For example, the customization unit can identify the user's areas of interest using survey results or past search history. The customization unit can also suggest customization methods based on topics the user is currently interested in. For example, the customization unit can prioritize customizing information relevant to the user's current areas of interest. The customization unit can also analyze the user's current areas of interest and select the optimal customization method. For example, the customization unit can input the user's current areas of interest into an AI and have the AI ​​select the customization method. This allows the customization unit to provide more appropriate information by customizing the means of customization based on the user's current areas of interest. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without using AI.

[0097] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated user emotions. The customization unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using facial expression recognition technology. The customization unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the voice to calculate an emotion score. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the customization unit calculates an emotion score based on heart rate fluctuations. This allows the customization unit to determine customization priorities based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the customization section may be performed using AI, for example, or without AI. For example, the customization section can input user emotion data into the AI ​​and have the AI ​​determine the priority of the customization.

[0098] The customization unit can select the optimal customization method while considering the user's geographical location information. For example, the customization unit can consider the user's geographical location information. For example, the customization unit can identify the user's geographical location information using GPS data or location services. The customization unit can also prioritize customizing information related to a specific region if the user is in that region. For example, the customization unit can propose the optimal customization method based on the user's geographical location information. The customization unit can also analyze the user's geographical location information and select the most efficient customization method. For example, the customization unit can input the user's geographical location information into an AI and have the AI ​​select the customization method. This allows the customization unit to provide more appropriate information by selecting the optimal customization method while considering the user's geographical location information. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without using AI.

[0099] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit, for example, analyzes the user's social media activity. For example, the customization unit analyzes the user's post content, the number of likes, the number of followers, etc. The customization unit can also suggest customization methods based on topics of interest from the user's social media activity. For example, the customization unit analyzes the user's social media activity and prioritizes customization of related information. The customization unit can also select the optimal customization method based on the user's social media activity. For example, the customization unit can input the user's social media activity into an AI and have the AI ​​suggest customization methods. In this way, the customization unit can provide more appropriate information by analyzing the user's social media activity and suggesting customization methods. Some or all of the above-described processing in the customization unit may be performed, for example, using AI or without AI. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, extraction unit, organization unit, provision unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes conversations within the messaging application. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts important information from the analyzed conversation. The organization unit is implemented by the control unit 46A of the smart device 14 and organizes the extracted information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the organized information to the user. The customization unit is implemented by the identification processing unit 290 of the data processing unit 12 and customizes the information according to the user's preferences. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, organization unit, provision unit, and customization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes conversations in a message app. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important information from the analyzed conversation. The organization unit is realized, for example, by the control unit 46A of the smart glasses 214 and organizes the extracted information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the organized information to the user. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and customizes information to suit the user's preferences. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, organization unit, provision unit, and customization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes conversations in a message app. The extraction unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and extracts important information from the analyzed conversation. The organization unit is realized by, for example, the control unit 46A of the headset type terminal 314 and organizes the extracted information. The provision unit is realized by, for example, the display 343 of the headset type terminal 314 and provides the organized information to the user. The customization unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and customizes information to suit the user's preferences. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, extraction unit, organization unit, provision unit, and customization unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes conversations within the messaging application. The extraction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and extracts important information from the analyzed conversation. The organization unit is implemented, for example, by the control unit 46A of the robot 414 and organizes the extracted information. The provision unit is implemented, for example, by the speaker 240 of the robot 414 and provides the organized information to the user. The customization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and customizes the information according to the user's preferences.

[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 learn the user's past behavioral patterns and improve the accuracy of the analysis. For example, the analysis unit can learn what information the user considered important in the past and evaluate the importance of the current conversation based on that information. The analysis unit can also learn what topics the user was interested in in the past and prioritize analysis of information related to those topics. Furthermore, the analysis unit can predict the importance of future conversations based on the user's past behavioral patterns and adjust the analysis priority. In this way, the analysis unit can provide more accurate information by learning the user's past behavioral patterns.

[0102] The extraction unit can adjust the information to be extracted based on the user's current situation. For example, the extraction unit can consider the user's current location and prioritize extracting information related to that location. The extraction unit can also consider the device the user is currently using and extract information in a format optimal for that device. Furthermore, the extraction unit can consider the user's current activity status (e.g., whether the user is at work or on vacation) and extract information according to that status. In this way, the extraction unit can provide more appropriate information by adjusting the information based on the user's current situation.

[0103] The organizing unit can learn the user's past organizing patterns and improve the accuracy of organization. For example, the organizing unit can learn how the user organized information in the past and organize current information based on that method. The organizing unit can also learn what categories the user classified information into in the past and organize information based on those categories. Furthermore, the organizing unit can predict how the user will organize information in the future based on the user's past organizing patterns and improve the efficiency of organization. In this way, the organizing unit can achieve more efficient information organization by learning the user's past organizing patterns.

[0104] The providing unit can learn the user's past information acquisition patterns and improve the accuracy of the information provided. For example, the providing unit can learn how the user acquired information in the past and provide current information based on that method. The providing unit can also learn in what format the user received information in the past and provide information based on that format. Furthermore, the providing unit can predict future information provision methods based on the user's past information acquisition patterns and improve the efficiency of information provision. In this way, the providing unit can achieve more efficient information provision by learning the user's past information acquisition patterns.

[0105] The customization unit can learn the user's past customization patterns and improve the accuracy of customization. For example, the customization unit can learn how the user customized information in the past and customize the current information based on that method. The customization unit can also learn what options the user selected in the past and customize information based on those options. Furthermore, the customization unit can predict future customization methods based on the user's past customization patterns and improve the efficiency of customization. In this way, the customization unit can achieve more efficient information customization by learning the user's past customization patterns.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can apply a simplified analysis method to reduce the analysis load. Alternatively, if the user is relaxed, the analysis unit can apply a detailed analysis method. Furthermore, the analysis unit can adjust the analysis priority based on the user's emotions and prioritize analysis of information in which the user is most interested. This allows the analysis unit to provide more appropriate information by adjusting the analysis method based on the user's emotions.

[0107] The extraction unit can estimate the user's emotions and adjust the format of the information to be extracted based on the estimated user's emotions. For example, if the user is tired, the extraction unit can extract information in a concise and easy-to-understand format. Alternatively, if the user is excited, the extraction unit can extract information in a format including detailed information. Furthermore, the extraction unit can adjust the priority of the information to be extracted based on the user's emotions and preferentially extract the information that the user most needs. In this way, the extraction unit can provide more appropriate information by adjusting the format of the information based on the user's emotions.

[0108] The organizing unit can estimate the user's emotions and adjust the organizing method based on the estimated user's emotions. For example, when the user is in a hurry, the organizing unit can apply a simplified organizing method to quickly organize information. When the user is calm, the organizing unit can also apply a detailed organizing method. Furthermore, the organizing unit can adjust the priorities of the organizing based on the user's emotions and prioritize organizing the information the user needs most. In this way, the organizing unit can provide more appropriate information by adjusting the organizing method based on the user's emotions.

[0109] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, when the user is busy, the providing unit can provide important information later. Also, when the user is relaxed, the providing unit can provide detailed information immediately. Furthermore, the providing unit can adjust the frequency of providing information based on the user's emotions and provide the information the user needs most at an appropriate timing. In this way, the providing unit can provide more appropriate information by adjusting the timing of information based on the user's emotions.

[0110] The customization unit can estimate the user's emotions and suggest customization options based on the estimated user's emotions. For example, if the user is feeling stressed, the customization unit can suggest customization options that will help the user relax. Also, if the user is excited, the customization unit can suggest customization options that include detailed information. Furthermore, the customization unit can adjust the priority of customization based on the user's emotions and prioritize customization of information that the user needs most. In this way, the customization unit can provide more appropriate information by suggesting customization options based on the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The analysis unit analyzes conversations within the messaging app. The analysis unit can use natural language processing techniques and machine learning algorithms to analyze the content and patterns of conversations, and can apply algorithms that evaluate importance while considering the context of the conversation. Step 2: The extraction unit extracts important information from the conversation analyzed by the analysis unit. The extraction unit can extract important information such as the date and time, location, and important contact details, and can use keyword matching and contextual analysis techniques to extract key information. Step 3: The organization unit organizes the information extracted by the extraction unit. The organization unit can display the extracted information in a list, automatically add it to a calendar, or organize it by category. Step 4: The delivery unit provides the information organized by the organization unit to the user. The delivery unit can provide information using notification functions and dashboard display functions, and can adjust the level of detail provided based on the importance of the information. Step 5: The customization unit customizes the information provided by the provision unit to suit the user's preferences. The customization unit can learn the user's past conversation history and filter information based on specific keywords or phrases, or customize information based on the user's current interests or emotions.

[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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications 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, RAM 30, and 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 audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical 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 carried out in a secure state.

[0124] Fig. 4 shows an example of the 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications 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 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, RAM 30, and 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 audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical 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 carried out 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications 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, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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, RAM 30, and 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 audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to 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 carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) 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 voice indicating a user input regarding 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0167] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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 in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," 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 USB (Universal Serial Bus) 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] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[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] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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 system configured with a single 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 system that uses a processor that realizes the functions of an 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, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

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

[0182] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0184] [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. An analysis unit that analyzes conversations within the messaging app; an extraction unit that extracts important information from the conversation analyzed by the analysis unit; a sorting unit that sorts the information extracted by the extraction unit; a providing unit that provides the information organized by the organizing unit to a user; a customization unit that customizes the information provided by the providing unit to suit the preferences of the user. A system characterized by:

2. The analysis unit Analyze conversations in messaging apps and classify what is important and what is not 2. The system of claim 1.

3. The extraction unit Extract the date, time, location, important information, etc. from the conversation 2. The system of claim 1.

4. The organizing unit View extracted information in a list or automatically add it to your calendar 2. The system of claim 1.

5. The providing unit Providing important information to users 2. The system of claim 1.

6. The customization unit Learn about your past conversation history and filter information based on specific keywords and phrases 2. The system of claim 1.

7. The analysis unit Estimate user emotions and adjust analysis priorities based on the estimated user emotions 2. The system of claim 1.

8. The analysis unit During analysis, we apply an algorithm that takes into account the context of the conversation to assess importance.

2. The system of claim 1.

Images