System

Abstract

An object of a system according to an embodiment is to make it possible to easily grasp the frequency and content of communication in a message application.SOLUTION: A system includes a collection part, an analysis part, a summarization part, and a provision part. The collection unit collects conversation data from a message application. The analysis part analyzes the data collected by the collection part and totalizes the frequency or contents of communication with the other party. The summarization unit generates a summary of the communication on the basis of the aggregation result obtained by the analysis unit. The providing unit provides the user with the summary generated by the summarizing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 the drawback of making it difficult to easily grasp the frequency and content of communication via messaging apps.

[0005] The system according to the embodiment aims to make it possible to easily grasp the frequency and content of communication via a messaging app. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects conversation data from a messaging app. The analysis unit analyzes the data collected by the collection unit and tally up the frequency or content of communication with the other party. The summarization unit generates a summary of the communication based on the tallying result obtained by the analysis unit. The provision unit provides the summary generated by the summarization unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can easily grasp the frequency and content of communication via 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) A communication summarization system according to an embodiment of the present invention collects conversation data from a messaging app, analyzes it using a generation AI, tallying the frequency and content of communications with other parties, and generates and provides summaries. The communication summarization system collects conversation data from a messaging app used by a user, analyzes it using a generation AI, and tallying the frequency and content of communications with other parties. For example, the system can analyze the number of messages sent and received with a specific party and the topics of the conversations. Next, the communication summarization system generates summaries of communication this year and over the years based on the tallying results. The generation AI summarizes the status of communication with other parties based on the analysis results. For example, the system can summarize the number of messages sent and received this year and the main topics of conversations over the years. This summary is provided to the user, allowing the user to understand the status of communication with other parties. This allows the communication summarization system to efficiently collect, analyze, summarize, and provide conversation data from the user's messaging app. For example, the system can determine whether the user's communication with a specific party is increasing or decreasing, and what topics are being discussed. This mechanism allows the user to easily understand the status of communication with other parties, which can be useful for improving and maintaining communication.

[0029] A communication summarization system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects conversation data from a messaging app. The conversation data includes, but is not limited to, text messages, voice messages, and image messages. The collection unit collects, for example, conversation history, message history, and chat history from the messaging app. The analysis unit analyzes the data collected by the collection unit and compiles the frequency and content of communication with other parties. A generation AI is used for the analysis. The generation AI analyzes the collected conversation data and analyzes the number of messages sent and received with a specific party and the topics of the conversation. For example, the generation AI compiles the number of messages sent and received with a specific party. The generation AI can also compile the topics of the conversation. The summarization unit generates a summary of the communication based on the compilation results obtained by the analysis unit. The summary summarizes, for example, the number of messages sent and received this year and the main topics of conversations to date. The generation AI summarizes the status of communication with the other party based on the analysis results. The provision unit provides the summary generated by the summarization unit to a user. The providing unit, for example, displays the summary on the user's device. The providing unit can also send the summary as an email or a notification. This allows the communication summarization system according to the embodiment to efficiently collect, analyze, summarize, and provide conversation data from the user's messaging app. For example, the system can find out whether the user's communication with a specific person is increasing or decreasing, what topics the conversations are about, and so on. This mechanism allows the user to easily understand the status of communication with the other person, which can be useful for improving and maintaining communication.

[0030] The collection unit can collect conversation data from multiple messaging apps. The collection unit collects conversation data from multiple messaging apps, such as LINE (registered trademark), WhatsApp (registered trademark), and Facebook (registered trademark) Messenger. The collection unit can acquire data using the API of each messaging app. The collection unit can also collect conversation history stored on a user's device. For example, the collection unit collects message history stored on a user's smartphone. By collecting data from multiple messaging apps, more comprehensive communication analysis becomes possible. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input conversation data acquired from multiple messaging apps into a generation AI and cause the generation AI to collect data.

[0031] The analysis unit can analyze the collected conversation data and tally the number of messages sent and received with specific parties. The analysis unit, for example, analyzes the collected conversation data and tally the number of messages sent and received with specific parties. The specific parties are identified, for example, based on a contact list or past message history. The analysis unit can tally the number of messages sent and received with specific parties by day, week, or month, for example. The analysis unit can also visualize the number of messages sent and received with specific parties as a graph or chart. This allows the frequency of communication to be understood by tallying the number of messages sent and received with specific parties. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected conversation data into a generation AI and have the generation AI tally the number of messages sent and received.

[0032] The analysis unit can analyze the collected conversation data and tally the topics of the conversation content. The analysis unit, for example, analyzes the collected conversation data and tally the topics of the conversation content. Topics are extracted using techniques such as keyword extraction and topic modeling. The analysis unit can, for example, tally the topics of the conversation content by day, week, or month. The analysis unit can also visualize the topics of the conversation content as graphs or charts. By tallying the topics of the conversation content, it is possible to understand which topics are most common. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected conversation data into a generation AI and have the generation AI tally the topics of the conversation content.

[0033] The summarization unit can summarize the number of messages sent and received this year based on the aggregation results. The summarization unit, for example, summarizes the number of messages sent and received this year based on the aggregation results. The number of messages sent and received this year is calculated based on, for example, a calendar year or a fiscal year. The summarization unit can, for example, visualize the number of messages sent and received this year as a graph or chart. The summarization unit can also compare the number of messages sent and received this year with other years. In this way, by summarizing the number of messages sent and received this year, the communication situation for the year can be understood. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the aggregation results into a generation AI and cause the generation AI to summarize the number of messages sent and received this year.

[0034] The summarization unit can summarize the main topics of the conversation so far based on the aggregation results. The summarization unit, for example, summarizes the main topics of the conversation so far based on the aggregation results. The main topics are extracted based on criteria such as topics with high frequency or topics with high importance. The summarization unit, for example, can visualize the main topics of the conversation so far as a graph or chart. The summarization unit can also compare the main topics of the conversation so far with other topics. In this way, by summarizing the main topics of the conversation so far, it is possible to understand long-term communication trends. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit may input the aggregation results into a generation AI and have the generation AI perform a summary of the main topics of the conversation so far.

[0035] The collection unit can analyze the user's past conversation history and select the optimal collection method. For example, the collection unit prioritizes collecting data from messaging apps that the user uses frequently. Furthermore, if the user sends and receives many messages during a specific time period, the collection unit can also collect data during that time period. Furthermore, the collection unit can prioritize collecting conversation data with specific people from the user's past conversation history. This allows the optimal collection method to be selected by analyzing the past conversation history. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past conversation history into the generation AI and have the generation AI select the optimal collection method.

[0036] When collecting conversation data, the collection unit can filter the conversation data based on the user's current areas of interest. For example, the collection unit prioritizes collection of conversation data related to topics that the user has recently been interested in. Furthermore, if the user has many conversations containing a specific keyword, the collection unit can also collect data related to that keyword. Furthermore, the collection unit can analyze the user's social media activity and collect conversation data related to the user's areas of interest. This allows highly relevant data to be collected by filtering the data based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's areas of interest into the generation AI and have the generation AI perform the filtering.

[0037] When collecting conversation data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user frequently uses voice messages, the collection unit can prioritize collecting voice data. Also, if the user frequently uses text messages, the collection unit can prioritize collecting text data. Also, if the user frequently sends images, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting conversation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting conversation data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting conversation data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting conversation data related to the home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] When collecting conversation data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects conversation data related to content frequently posted by the user on social media. The collection unit can also collect conversation data related to the user's interactions with friends on social media. The collection unit can also collect conversation data related to the user's check-in information on social media. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0040] When collecting conversation data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular collection method, the collection unit can preferentially use that method. The collection unit can also analyze the user's past feedback and suggest an optimal collection method. In this way, the optimal collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past feedback into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the conversation. For example, the analysis unit performs a detailed analysis of important conversations. The analysis unit can also perform a simplified analysis of general conversations. The analysis unit can also perform a detailed analysis for each topic of conversations related to specific topics. In this way, by adjusting the level of detail of the analysis based on the importance of the conversation, detailed analysis can be performed for important conversations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of the conversation to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the conversation. For example, the analysis unit can apply a business analysis algorithm to a business-related conversation. The analysis unit can also apply a private analysis algorithm to a private conversation. The analysis unit can also apply an analysis algorithm optimal for a specific topic to a conversation related to that topic. In this way, by applying different analysis algorithms depending on the category of the conversation, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the conversation into the generation AI and have the generation AI apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of transmission and reception of the conversation. For example, the analysis unit prioritizes analysis of recent conversations. Furthermore, for conversations that were concentrated in a specific period, the analysis unit can also analyze based on that period. Furthermore, if a user sends and receives many messages during a specific time period, the analysis unit can prioritize analysis of conversations during that time period. Thus, by determining the analysis priority based on the time of transmission and reception of the conversation, conversations during important periods can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the time of transmission and reception of the conversation into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the conversations. For example, the analysis unit prioritizes analysis of highly relevant conversations. Furthermore, the analysis unit can also analyze conversations related to a specific topic based on that topic. Furthermore, the analysis unit can also prioritize analysis of conversations related to topics in which the user is interested. In this way, by adjusting the order of analysis based on the relevance of the conversations, highly relevant conversations can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of the conversations to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms in the analysis.

[0047] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the conversation. For example, the summarization unit provides a detailed summary for an important conversation. The summarization unit can also provide a simplified summary for a general conversation. The summarization unit can also provide a detailed summary for each topic for a conversation related to a specific topic. In this way, by adjusting the level of detail of the summary based on the importance of the conversation, a detailed summary can be provided for an important conversation. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input the importance of the conversation to the generation AI and have the generation AI adjust the level of detail of the summary.

[0048] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the conversation. For example, the summarization unit can apply a business summarization algorithm to a business-related conversation. The summarization unit can also apply a private summarization algorithm to a private conversation. The summarization unit can also apply a summarization algorithm optimal for a specific topic to a conversation related to that topic. In this way, by applying different summarization algorithms depending on the category of the conversation, more appropriate summaries can be provided. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI, for example. For example, the summarization unit can input the category of the conversation into the generation AI and have the generation AI apply the summarization algorithm.

[0049] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit, for example, adjusts the summarization algorithm based on the user's past summarization results. The summarization unit can also improve the accuracy of the summary based on feedback provided by the user in the past. The summarization unit can also analyze the user's past summarization results and suggest an optimal summarization method. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the user's past summarization results into the generation AI and have the generation AI improve the accuracy of the summary.

[0050] When generating summaries, the summarization unit can determine the priority of summaries based on the time of transmission and reception of the conversation. For example, the summarization unit prioritizes summarization of recent conversations. Furthermore, for conversations that occurred concentratedly during a specific period, the summarization unit can also summarize based on that period. Furthermore, if a user sends and receives many messages during a specific time period, the summarization unit can prioritize summarizing conversations from that time period. Thus, by determining the priority of summaries based on the time of transmission and reception of the conversation, conversations from important periods can be prioritized for summarization. Some or all of the above-described processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the time of transmission and reception of the conversation into the generation AI and have the generation AI determine the priority of summaries.

[0051] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the conversations. For example, the summarization unit prioritizes summarization of highly relevant conversations. Furthermore, for conversations related to a specific topic, the summarization unit can also prioritize summarization based on that topic. Furthermore, the summarization unit can prioritize summarization of conversations related to topics in which the user is interested. In this way, by adjusting the order of summaries based on the relevance of the conversations, highly relevant conversations can be prioritized for summarization. Some or all of the above-described processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input the relevance of the conversations into the generation AI and have the generation AI adjust the order of summaries.

[0052] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the summarization unit can provide a summary in simple language. The summarization unit can also adjust the way the summary is expressed according to the user's level of expertise. This allows the use of technical terms in the summary to be adjusted according to the user's level of expertise, thereby providing a summary that is easy for the user to understand. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terms in the summary.

[0053] When providing a summary, the providing unit can select the optimal delivery method by referring to the user's past feedback. The providing unit, for example, adjusts the delivery method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular delivery method, the providing unit can preferentially use that method. Furthermore, the providing unit can analyze the user's past feedback and suggest the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback into the generation AI and have the generation AI select the delivery method.

[0054] When providing a summary, the providing unit can customize the content to be provided according to the user's current task. For example, when the user is at work, the providing unit can prioritize providing a work-related summary. Furthermore, when the user is on vacation, the providing unit can provide a summary of relaxing content. Furthermore, when the user is performing a specific task, the providing unit can provide a summary related to that task. In this way, by customizing the content to be provided according to the user's current task, a more appropriate summary can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current task into the generation AI and cause the generation AI to customize the content to be provided.

[0055] When providing a summary, the providing unit can select the optimal means of provision by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a summary that fits the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a summary that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a concise summary with high visibility. This allows the optimal means of provision to be selected by taking into account the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the means of provision.

[0056] When providing a summary, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit can provide a summary related to that location. Furthermore, if the user is traveling, the providing unit can provide a summary related to the travel destination. Furthermore, if the user is at home, the providing unit can provide a summary related to the home. In this way, the optimal delivery method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the delivery method.

[0057] When providing a summary, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the summary based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide a summary in a specific language when the user selects that language. This makes it possible to provide a summary that is easy for the user to understand by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's language setting into the generation AI and cause the generation AI to perform multilingual support for the provided content.

[0058] When providing a summary, the providing unit can analyze the user's social media activity and provide a relevant summary. For example, the providing unit can provide a summary of places the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide a relevant summary. The providing unit can also provide a relevant summary by referring to the activities of the user's friends on social media. In this way, a relevant summary can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to provide a relevant summary.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze the user's past conversation history and select the optimal collection method. For example, it can prioritize data collection from messaging apps that the user uses frequently. Also, if the user sends and receives many messages during a specific time period, it can collect data during that time period. Furthermore, it can prioritize collection of conversation data with specific people from the user's past conversation history. This allows the optimal collection method to be selected by analyzing the past conversation history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's past conversation history into the generation AI and have the generation AI select the optimal collection method.

[0061] When collecting conversation data, the collection unit can filter the conversation data based on the user's current areas of interest. For example, the collection unit can prioritize collection of conversation data related to topics that the user has recently been interested in. Also, if the user has many conversations containing a specific keyword, data related to that keyword can be collected. Furthermore, the collection unit can analyze the user's social media activity and collect conversation data related to the user's areas of interest. This allows highly relevant data to be collected by filtering the data based on the user's areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's areas of interest into the generation AI and have the generation AI perform the filtering.

[0062] When collecting conversation data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user frequently uses voice messages, it can prioritize collecting voice data. Also, if the user frequently uses text messages, it can prioritize collecting text data. Furthermore, if the user frequently sends images, it can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0063] When collecting conversation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific location, conversation data related to that location can be prioritized. Also, if the user is traveling, conversation data related to the travel destination can be prioritized. Furthermore, if the user is at home, conversation data related to the home can be prioritized. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0064] When collecting conversation data, the collection unit can analyze the user's social media activity and collect related data. For example, conversation data related to content frequently posted by the user on social media can be collected. Conversation data related to the user's interactions with friends on social media can also be collected. Furthermore, conversation data related to the user's check-in information on social media can also be collected. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI, for example. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects conversation data from a messaging app. The conversation data includes text messages, voice messages, image messages, etc. The collection unit collects conversation history, message history, chat history, etc. from the messaging app. Step 2: The analysis unit analyzes the data collected by the collection unit and compiles the frequency and content of communication with the other party. The analysis uses a generative AI to analyze the number of messages sent and received with a specific party, the topics of conversation, etc. Step 3: The summarization unit generates a summary of the communication based on the results of the analysis unit. The summary may include, for example, the number of messages sent and received this year, the main topics of conversations, etc. Step 4: The providing unit provides the summary generated by the summarizing unit to the user. The providing unit can display the summary on the user's device, or can send it as an email or a notification.

[0067] (Example 2) A communication summarization system according to an embodiment of the present invention collects conversation data from a messaging app, analyzes it using a generation AI, tallying the frequency and content of communications with other parties, and generates and provides summaries. The communication summarization system collects conversation data from a messaging app used by a user, analyzes it using a generation AI, and tallying the frequency and content of communications with other parties. For example, the system can analyze the number of messages sent and received with a specific party and the topics of the conversations. Next, the communication summarization system generates summaries of communication this year and over the years based on the tallying results. The generation AI summarizes the status of communication with other parties based on the analysis results. For example, the system can summarize the number of messages sent and received this year and the main topics of conversations over the years. This summary is provided to the user, allowing the user to understand the status of communication with other parties. This allows the communication summarization system to efficiently collect, analyze, summarize, and provide conversation data from the user's messaging app. For example, the system can determine whether the user's communication with a specific party is increasing or decreasing, and what topics are being discussed. This mechanism allows the user to easily understand the status of communication with other parties, which can be useful for improving and maintaining communication.

[0068] A communication summarization system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects conversation data from a messaging app. The conversation data includes, but is not limited to, text messages, voice messages, and image messages. The collection unit collects, for example, conversation history, message history, and chat history from the messaging app. The analysis unit analyzes the data collected by the collection unit and compiles the frequency and content of communication with other parties. A generation AI is used for the analysis. The generation AI analyzes the collected conversation data and analyzes the number of messages sent and received with a specific party and the topics of the conversation. For example, the generation AI compiles the number of messages sent and received with a specific party. The generation AI can also compile the topics of the conversation. The summarization unit generates a summary of the communication based on the compilation results obtained by the analysis unit. The summary summarizes, for example, the number of messages sent and received this year and the main topics of conversations to date. The generation AI summarizes the status of communication with the other party based on the analysis results. The provision unit provides the summary generated by the summarization unit to a user. The providing unit, for example, displays the summary on the user's device. The providing unit can also send the summary as an email or a notification. This allows the communication summarization system according to the embodiment to efficiently collect, analyze, summarize, and provide conversation data from the user's messaging app. For example, the system can find out whether the user's communication with a specific person is increasing or decreasing, what topics the conversations are about, and so on. This mechanism allows the user to easily understand the status of communication with the other person, which can be useful for improving and maintaining communication.

[0069] The collection unit can collect conversation data from multiple messaging apps. The collection unit collects conversation data from multiple messaging apps, such as LINE, WhatsApp, and Facebook Messenger. The collection unit can acquire data using the API of each messaging app. The collection unit can also collect conversation history stored on a user's device. For example, the collection unit collects message history stored on a user's smartphone. By collecting data from multiple messaging apps, more comprehensive communication analysis becomes possible. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input conversation data acquired from multiple messaging apps into a generation AI and cause the generation AI to collect data.

[0070] The analysis unit can analyze the collected conversation data and tally the number of messages sent and received with specific parties. The analysis unit, for example, analyzes the collected conversation data and tally the number of messages sent and received with specific parties. The specific parties are identified, for example, based on a contact list or past message history. The analysis unit can tally the number of messages sent and received with specific parties by day, week, or month, for example. The analysis unit can also visualize the number of messages sent and received with specific parties as a graph or chart. This allows the frequency of communication to be understood by tallying the number of messages sent and received with specific parties. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected conversation data into a generation AI and have the generation AI tally the number of messages sent and received.

[0071] The analysis unit can analyze the collected conversation data and tally the topics of the conversation content. The analysis unit, for example, analyzes the collected conversation data and tally the topics of the conversation content. Topics are extracted using techniques such as keyword extraction and topic modeling. The analysis unit can, for example, tally the topics of the conversation content by day, week, or month. The analysis unit can also visualize the topics of the conversation content as graphs or charts. By tallying the topics of the conversation content, it is possible to understand which topics are most common. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected conversation data into a generation AI and have the generation AI tally the topics of the conversation content.

[0072] The summarization unit can summarize the number of messages sent and received this year based on the aggregation results. The summarization unit, for example, summarizes the number of messages sent and received this year based on the aggregation results. The number of messages sent and received this year is calculated based on, for example, a calendar year or a fiscal year. The summarization unit can, for example, visualize the number of messages sent and received this year as a graph or chart. The summarization unit can also compare the number of messages sent and received this year with other years. In this way, by summarizing the number of messages sent and received this year, the communication situation for the year can be understood. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the aggregation results into a generation AI and cause the generation AI to summarize the number of messages sent and received this year.

[0073] The summarization unit can summarize the main topics of the conversation so far based on the aggregation results. The summarization unit, for example, summarizes the main topics of the conversation so far based on the aggregation results. The main topics are extracted based on criteria such as topics with high frequency or topics with high importance. The summarization unit, for example, can visualize the main topics of the conversation so far as a graph or chart. The summarization unit can also compare the main topics of the conversation so far with other topics. In this way, by summarizing the main topics of the conversation so far, it is possible to understand long-term communication trends. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit may input the aggregation results into a generation AI and have the generation AI perform a summary of the main topics of the conversation so far.

[0074] The collection unit can estimate the user's emotions and adjust the timing of conversation data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect data and perform real-time analysis. Furthermore, if the user is in a hurry, the collection unit can shorten the collection timing and collect data quickly. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0075] The collection unit can analyze the user's past conversation history and select the optimal collection method. For example, the collection unit prioritizes collecting data from messaging apps that the user uses frequently. Furthermore, if the user sends and receives many messages during a specific time period, the collection unit can also collect data during that time period. Furthermore, the collection unit can prioritize collecting conversation data with specific people from the user's past conversation history. This allows the optimal collection method to be selected by analyzing the past conversation history. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past conversation history into the generation AI and have the generation AI select the optimal collection method.

[0076] When collecting conversation data, the collection unit can filter the conversation data based on the user's current areas of interest. For example, the collection unit prioritizes collection of conversation data related to topics that the user has recently been interested in. Furthermore, if the user has many conversations containing a specific keyword, the collection unit can also collect data related to that keyword. Furthermore, the collection unit can analyze the user's social media activity and collect conversation data related to the user's areas of interest. This allows highly relevant data to be collected by filtering the data based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's areas of interest into the generation AI and have the generation AI perform the filtering.

[0077] When collecting conversation data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user frequently uses voice messages, the collection unit can prioritize collecting voice data. Also, if the user frequently uses text messages, the collection unit can prioritize collecting text data. Also, if the user frequently sends images, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0078] The collection unit can estimate the user's emotions and determine the priority of conversation data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting conversation data for stress reduction. Furthermore, if the user is relaxed, the collection unit can prioritize collecting conversation data in a relaxed state. Furthermore, if the user is excited, the collection unit can prioritize collecting conversation data in an excited state. Thus, by determining the priority of data based on the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the conversation data to be collected.

[0079] When collecting conversation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting conversation data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting conversation data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting conversation data related to the home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0080] When collecting conversation data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects conversation data related to content frequently posted by the user on social media. The collection unit can also collect conversation data related to the user's interactions with friends on social media. The collection unit can also collect conversation data related to the user's check-in information on social media. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0081] When collecting conversation data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular collection method, the collection unit can preferentially use that method. The collection unit can also analyze the user's past feedback and suggest an optimal collection method. In this way, the optimal collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past feedback into the generation AI and have the generation AI customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the conversation. For example, the analysis unit performs a detailed analysis of important conversations. The analysis unit can also perform a simplified analysis of general conversations. The analysis unit can also perform a detailed analysis for each topic of conversations related to specific topics. In this way, by adjusting the level of detail of the analysis based on the importance of the conversation, detailed analysis can be performed for important conversations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of the conversation to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the conversation. For example, the analysis unit can apply a business analysis algorithm to a business-related conversation. The analysis unit can also apply a private analysis algorithm to a private conversation. The analysis unit can also apply an analysis algorithm optimal for a specific topic to a conversation related to that topic. In this way, by applying different analysis algorithms depending on the category of the conversation, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the conversation into the generation AI and have the generation AI apply the analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time of transmission and reception of the conversation. For example, the analysis unit prioritizes analysis of recent conversations. Furthermore, for conversations that were concentrated in a specific period, the analysis unit can also analyze based on that period. Furthermore, if a user sends and receives many messages during a specific time period, the analysis unit can prioritize analysis of conversations during that time period. Thus, by determining the analysis priority based on the time of transmission and reception of the conversation, conversations during important periods can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the time of transmission and reception of the conversation into the generation AI and have the generation AI determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the conversations. For example, the analysis unit prioritizes analysis of highly relevant conversations. Furthermore, the analysis unit can also analyze conversations related to a specific topic based on that topic. Furthermore, the analysis unit can also prioritize analysis of conversations related to topics in which the user is interested. In this way, by adjusting the order of analysis based on the relevance of the conversations, highly relevant conversations can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of the conversations to the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms in the analysis.

[0090] The summarization unit can estimate the user's emotions and adjust the summary presentation style based on the estimated user emotions. For example, if the user is feeling stressed, the summarization unit can provide a simple, highly visible summary. The summarization unit can also provide a detailed summary if the user is relaxed. The summarization unit can also provide a summary that focuses on the main points if the user is in a hurry. This allows the summary presentation style to be adjusted according to the user's emotions, thereby providing a more appropriate summary. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 summarization unit can be performed using, for example, the generation AI, or without the generation AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI adjust the summary presentation style.

[0091] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the conversation. For example, the summarization unit provides a detailed summary for an important conversation. The summarization unit can also provide a simplified summary for a general conversation. The summarization unit can also provide a detailed summary for each topic for a conversation related to a specific topic. In this way, by adjusting the level of detail of the summary based on the importance of the conversation, a detailed summary can be provided for an important conversation. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input the importance of the conversation to the generation AI and have the generation AI adjust the level of detail of the summary.

[0092] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the conversation. For example, the summarization unit can apply a business summarization algorithm to a business-related conversation. The summarization unit can also apply a private summarization algorithm to a private conversation. The summarization unit can also apply a summarization algorithm optimal for a specific topic to a conversation related to that topic. In this way, by applying different summarization algorithms depending on the category of the conversation, more appropriate summaries can be provided. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI, for example. For example, the summarization unit can input the category of the conversation into the generation AI and have the generation AI apply the summarization algorithm.

[0093] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit, for example, adjusts the summarization algorithm based on the user's past summarization results. The summarization unit can also improve the accuracy of the summary based on feedback provided by the user in the past. The summarization unit can also analyze the user's past summarization results and suggest an optimal summarization method. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the user's past summarization results into the generation AI and have the generation AI improve the accuracy of the summary.

[0094] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a detailed summary. If the user is excited, the summarization unit can provide a summary with a visually stimulating effect. By adjusting the length of the summary based on the user's emotions, the summary can be optimally provided for the user. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 summarization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the summary.

[0095] When generating summaries, the summarization unit can determine the priority of summaries based on the time of transmission and reception of the conversation. For example, the summarization unit prioritizes summarization of recent conversations. Furthermore, for conversations that occurred concentratedly during a specific period, the summarization unit can also summarize based on that period. Furthermore, if a user sends and receives many messages during a specific time period, the summarization unit can prioritize summarizing conversations from that time period. Thus, by determining the priority of summaries based on the time of transmission and reception of the conversation, conversations from important periods can be prioritized for summarization. Some or all of the above-described processing in the summarization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the time of transmission and reception of the conversation into the generation AI and have the generation AI determine the priority of summaries.

[0096] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the conversations. For example, the summarization unit prioritizes summarization of highly relevant conversations. Furthermore, for conversations related to a specific topic, the summarization unit can also prioritize summarization based on that topic. Furthermore, the summarization unit can prioritize summarization of conversations related to topics in which the user is interested. In this way, by adjusting the order of summaries based on the relevance of the conversations, highly relevant conversations can be prioritized for summarization. Some or all of the above-described processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input the relevance of the conversations into the generation AI and have the generation AI adjust the order of summaries.

[0097] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the summarization unit can provide a summary in simple language. The summarization unit can also adjust the way the summary is expressed according to the user's level of expertise. This allows the use of technical terms in the summary to be adjusted according to the user's level of expertise, thereby providing a summary that is easy for the user to understand. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the summarization unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terms in the summary.

[0098] The providing unit can estimate the user's emotions and adjust the summary provision method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible summary. Furthermore, if the user is relaxed, the providing unit can provide a detailed summary. Furthermore, if the user is in a hurry, the providing unit can provide a summary that focuses on the main points. This allows for adjusting the summary provision method according to the user's emotions, thereby providing a more appropriate summary. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the summary provision method.

[0099] When providing a summary, the providing unit can select the optimal delivery method by referring to the user's past feedback. The providing unit, for example, adjusts the delivery method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular delivery method, the providing unit can preferentially use that method. Furthermore, the providing unit can analyze the user's past feedback and suggest the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback into the generation AI and have the generation AI select the delivery method.

[0100] When providing a summary, the providing unit can customize the content to be provided according to the user's current task. For example, when the user is at work, the providing unit can prioritize providing a work-related summary. Furthermore, when the user is on vacation, the providing unit can provide a summary of relaxing content. Furthermore, when the user is performing a specific task, the providing unit can provide a summary related to that task. In this way, by customizing the content to be provided according to the user's current task, a more appropriate summary can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current task into the generation AI and cause the generation AI to customize the content to be provided.

[0101] When providing a summary, the providing unit can select the optimal means of provision by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a summary that fits the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a summary that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a concise summary with high visibility. This allows the optimal means of provision to be selected by taking into account the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the means of provision.

[0102] The providing unit can estimate the user's emotions and adjust the timing of providing the summary based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide the summary when the user is relaxed. Furthermore, if the user is relaxed, the providing unit can also provide the summary immediately. Furthermore, if the user is in a hurry, the providing unit can also provide the summary quickly. By adjusting the timing of providing the summary based on the user's emotions, the summary can be provided at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of providing the summary.

[0103] When providing a summary, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit can provide a summary related to that location. Furthermore, if the user is traveling, the providing unit can provide a summary related to the travel destination. Furthermore, if the user is at home, the providing unit can provide a summary related to the home. In this way, the optimal delivery method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the delivery method.

[0104] When providing a summary, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the summary based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide a summary in a specific language when the user selects that language. This makes it possible to provide a summary that is easy for the user to understand by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's language setting into the generation AI and cause the generation AI to perform multilingual support for the provided content.

[0105] When providing a summary, the providing unit can analyze the user's social media activity and provide a relevant summary. For example, the providing unit can provide a summary of places the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide a relevant summary. The providing unit can also provide a relevant summary by referring to the activities of the user's friends on social media. In this way, a relevant summary can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to provide a relevant summary. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, summarization unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes conversation data using a generation AI. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a summary of the communication based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and displays the summary on the user's device. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, summarization unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes conversation data using a generation AI. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a summary of the communication based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays the summary on the user's device. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, summarization unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes conversation data using a generation AI. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a summary of the communication based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and displays the summary on the user's device. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, summarization unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes conversation data using a generation AI. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a summary of the communication based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and displays the summary on the user's device.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The providing unit can estimate the user's emotions and adjust the summary provision method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible summary can be provided. If the user is relaxed, a detailed summary can be provided. Furthermore, if the user is in a hurry, a summary that focuses on the main points can be provided. By adjusting the summary provision method according to the user's emotions, a more appropriate summary can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the provision method.

[0108] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is stressed, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. By adjusting the presentation method of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0109] The collection unit can estimate the user's emotions and adjust the timing of conversation data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect data when the user is relaxed. Alternatively, if the user is relaxed, data can be collected immediately and analyzed in real time. Furthermore, if the user is in a hurry, the collection timing can be shortened to collect data quickly. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0110] The summarization unit can estimate the user's emotions and adjust the summary presentation style based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible summary can be provided. If the user is relaxed, a detailed summary can be provided. Furthermore, if the user is in a hurry, a summary that focuses on the main points can be provided. This allows for a more appropriate summary to be provided by adjusting the summary presentation style according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 summarization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI adjust the summary presentation style.

[0111] The providing unit can estimate the user's emotions and adjust the timing of providing the summary based on the estimated user's emotions. For example, if the user is feeling stressed, the summary can be provided when the user is relaxed. Also, if the user is relaxed, the summary can be provided immediately. Furthermore, if the user is in a hurry, the summary can be provided quickly. By adjusting the timing of providing the summary based on the user's emotions, the summary can be provided at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of providing the summary.

[0112] The collection unit can analyze the user's past conversation history and select the optimal collection method. For example, it can prioritize data collection from messaging apps that the user uses frequently. Also, if the user sends and receives many messages during a specific time period, it can collect data during that time period. Furthermore, it can prioritize collection of conversation data with specific people from the user's past conversation history. This allows the optimal collection method to be selected by analyzing the past conversation history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's past conversation history into the generation AI and have the generation AI select the optimal collection method.

[0113] When collecting conversation data, the collection unit can filter the conversation data based on the user's current areas of interest. For example, the collection unit can prioritize collection of conversation data related to topics that the user has recently been interested in. Also, if the user has many conversations containing a specific keyword, data related to that keyword can be collected. Furthermore, the collection unit can analyze the user's social media activity and collect conversation data related to the user's areas of interest. This allows highly relevant data to be collected by filtering the data based on the user's areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's areas of interest into the generation AI and have the generation AI perform the filtering.

[0114] When collecting conversation data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user frequently uses voice messages, it can prioritize collecting voice data. Also, if the user frequently uses text messages, it can prioritize collecting text data. Furthermore, if the user frequently sends images, it can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0115] When collecting conversation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific location, conversation data related to that location can be prioritized. Also, if the user is traveling, conversation data related to the travel destination can be prioritized. Furthermore, if the user is at home, conversation data related to the home can be prioritized. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0116] When collecting conversation data, the collection unit can analyze the user's social media activity and collect related data. For example, conversation data related to content frequently posted by the user on social media can be collected. Conversation data related to the user's interactions with friends on social media can also be collected. Furthermore, conversation data related to the user's check-in information on social media can also be collected. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI, for example. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects conversation data from a messaging app. The conversation data includes text messages, voice messages, image messages, etc. The collection unit collects conversation history, message history, chat history, etc. from the messaging app. Step 2: The analysis unit analyzes the data collected by the collection unit and compiles the frequency and content of communication with the other party. The analysis uses a generative AI to analyze the number of messages sent and received with a specific party, the topics of conversation, etc. Step 3: The summarization unit generates a summary of the communication based on the results of the analysis unit. The summary may include, for example, the number of messages sent and received this year, the main topics of conversations, etc. Step 4: The providing unit provides the summary generated by the summarizing unit to the user. The providing unit can display the summary on the user's device, or can send it as an email or a notification.

[0119] 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.

[0120] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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, in order to avoid confusion and to 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects conversation data from a messaging app; an analysis unit that analyzes the data collected by the collection unit and tallying up the frequency or content of communications with the other party; a summarization unit that generates a summary of the communication based on the aggregation result obtained by the analysis unit; a providing unit that provides the summary generated by the summarizing unit to a user. A system characterized by:

2. The collecting unit Collect conversation data from multiple messaging apps 2. The system of claim 1.

3. The analysis unit Analyze the collected conversation data and count the number of messages sent and received with specific people.

2. The system of claim 1.

4. The analysis unit Analyze the collected conversation data and compile the topics of the conversations 2. The system of claim 1.

5. The summary section Based on the results, summarize the number of messages sent and received this year.

2. The system of claim 1.

6. The summary section Based on the results, summarize the main topics of the conversation so far 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past conversation history and select the optimal collection method 2. The system of claim 1.

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