system

Abstract

The system according to the embodiment aims to automatically summarize long messages, saving the user time. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives a message. The analysis unit analyzes the message received by the reception unit. The generation unit extracts important information from the message analyzed by the analysis unit and generates a summary. The display unit displays the summary generated by the generation unit.

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 techniques have difficulty summarizing long messages efficiently, which can waste a user's time.

[0005] The system according to the embodiment aims to automatically summarize long messages, saving the user time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives a message. The analysis unit analyzes the message received by the reception unit. The generation unit extracts important information from the message analyzed by the analysis unit and generates a summary. The display unit displays the summary generated by the generation unit. [Effects of the Invention]

[0007] An embodiment of the system can automatically summarize long messages, saving users time. [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 message summarization system according to an embodiment of the present invention receives a message, analyzes it using a generation AI, generates a summary, and displays it to a user. When a message is received, the message is input to the generation AI. The generation AI analyzes the message, extracts important information, and generates a summary. The generated summary is then displayed to the user. For example, the message summarization system targets messages received via messaging or email services. The message content is sent to the generation AI as is. The generation AI then analyzes the input message. The generation AI uses natural language processing technology to understand the message content and extract important information. For example, it identifies particularly important points or keywords in the message and generates a summary based on them. The generated summary is then displayed to the user. For example, the summary is displayed below the received message in the messaging or email service app. This summary allows users to efficiently acquire information without having to read long sentences. This allows the message summarization system to efficiently acquire information without having to read long sentences. For example, the message summarization system allows younger generations, who place importance on typing, to efficiently acquire information without having to read long sentences. For example, busy students and working adults can understand long messages in a short amount of time. Also, by using the summarized information, they can read only the necessary parts in detail, making it easier to select and discard information.

[0029] A message summarization system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives messages. Examples of messages include, but are not limited to, text messages, voice messages, and image messages. The reception unit transmits messages received via, for example, a messaging service or an email service to a generation AI. The analysis unit uses a generation AI to analyze the messages received by the reception unit. The analysis unit understands the content of the messages and extracts important information using, for example, natural language processing technology. For example, the analysis unit may use morphological analysis to break down words in the messages, grammatical analysis to understand the structure of the sentences, and semantic analysis to understand the meaning of the sentences. The generation unit uses the generation AI to extract important information from the messages analyzed by the analysis unit and generate summaries. The generation unit generates summaries based on, for example, the extracted important information. For example, the generation unit may generate summaries based on the frequency of keyword appearances or information related to a specific topic. The display unit displays the summaries generated by the generation unit to a user. For example, the display unit may display the summaries below the received messages within an app for the messaging service or email service. This allows the message summarization system according to the embodiment to efficiently summarize messages and display them to the user.

[0030] The analysis unit can understand the content of the message using natural language processing technology and extract important information. The analysis unit, for example, uses morphological analysis to break down the words of the message. For example, the analysis unit can use morphological analysis to break down the words of the message and identify the part of speech of each word. The analysis unit can also understand the structure of the sentence of the message using grammatical analysis. For example, the analysis unit can use grammatical analysis to identify the structure of the sentence, such as the subject, predicate, and object. The analysis unit can also understand the meaning of the sentence of the message using semantic analysis. For example, the analysis unit can understand the meaning of the sentence using semantic analysis and extract important information. As a result, the analysis unit can accurately understand the content of the message and extract important information by using natural language processing technology.

[0031] The generation unit can generate a summary based on the extracted important information. For example, the generation unit can generate a summary based on the extracted important information. For example, the generation unit can generate a summary based on the frequency of keyword appearances. The generation unit can also generate a summary based on information related to a specific topic. For example, the generation unit can extract keywords or phrases related to a specific topic and generate a summary based on the keywords or phrases. The generation unit can also generate a summary based on the extracted important information using a generation AI. For example, the generation unit inputs the extracted important information into the generation AI, which then generates a summary. In this way, the generation unit can provide a useful summary to the user by generating a summary based on the important information.

[0032] The display unit can display the generated summary to the user. For example, the display unit can display the generated summary to the user. For example, the display unit can display the summary below a received message in an app for a messaging service or an email service. The display unit can also display the generated summary as a pop-up window or a notification. For example, the display unit can display the generated summary as a pop-up window so that the user can immediately check the summary. The display unit can also display the generated summary as a notification to notify the user of the existence of the summary. In this way, the display unit can display the generated summary to the user, allowing the user to efficiently acquire information.

[0033] The generation unit can make adjustments to improve the accuracy of the summary. The generation unit, for example, makes adjustments to improve the accuracy of the summary. For example, the generation unit can adjust parameters of an algorithm to improve the accuracy of the summary. The generation unit can also introduce a feedback loop to improve the accuracy of the summary. For example, the generation unit can collect feedback from users and adjust the summarization algorithm based on the feedback. The generation unit can also make adjustments to improve the accuracy of the summary using a generation AI. For example, the generation unit can cause the generation AI to make adjustments to improve the accuracy of the summary. As a result, the generation unit can improve the accuracy of the summary and provide a more accurate summary.

[0034] The reception unit can analyze the user's past message reception history and select the optimal reception method. The reception unit, for example, analyzes the user's past message reception history and selects the optimal reception method. For example, the reception unit can adjust the timing of message reception based on the time period during which the user frequently received messages in the past. The reception unit can also prioritize the reception method (voice notification, vibration, etc.) that the user has used in the past. The reception unit can also prioritize receiving messages from specific senders based on the user's past message reception history. In this way, the reception unit can provide the optimal reception method for the user by analyzing the past message reception history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past message reception history data into a generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit can filter messages based on the user's current activity status and areas of interest when receiving messages. For example, when receiving messages, the reception unit can filter messages based on the user's current activity status and areas of interest. For example, when the user is at work, the reception unit can prioritize receiving only work-related messages. Furthermore, when the user is spending time on a hobby, the reception unit can prioritize receiving hobby-related messages. Furthermore, when the user is on a break, the reception unit can receive all messages but change the notification method depending on the importance of the messages. In this way, the reception unit can prioritize receiving messages that are highly relevant to the user by filtering messages based on the user's activity status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's activity status data into a generation AI and have the generation AI perform message filtering.

[0036] The reception unit can select the optimal receiving means according to the user's input method when receiving a message. For example, the reception unit selects the optimal receiving means according to the user's input method when receiving a message. For example, if the user prefers voice input, the reception unit can prioritize voice notification. Also, if the user prefers text input, the reception unit can prioritize text notification. Also, if the user prefers image input, the reception unit can prioritize image notification. In this way, the reception unit can select the optimal receiving means according to the user's input method, thereby providing a receiving method that is easy for the user to use. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal receiving means.

[0037] When receiving messages, the reception unit can prioritize receiving highly relevant messages by taking into account the user's geographical location information. For example, when receiving messages, the reception unit prioritizes receiving highly relevant messages by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving messages related to that location. Furthermore, when the user is traveling, the reception unit can prioritize receiving messages related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving messages related to the home. In this way, the reception unit can prioritize receiving highly relevant messages by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant messages.

[0038] The reception unit can analyze the user's social media activity upon receiving a message and receive related messages. For example, the reception unit can analyze the user's social media activity upon receiving a message and receive related messages. For example, the reception unit can prioritize receiving messages related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related messages. The reception unit can also refer to the activities of the user's friends on social media to prioritize receiving related messages. In this way, the reception unit can analyze the user's social media activity and prioritize receiving related messages. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related messages.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a message. For example, the reception unit can customize the reception method by reflecting the user's past feedback when receiving a message. For example, the reception unit can suggest an optimal reception method based on the user's past preferred reception methods. The reception unit can also prioritize receiving messages from specific senders based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest an optimal notification method. In this way, the reception unit can provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the message. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the message. For example, the analysis unit can analyze highly important messages in detail and analyze less important messages briefly. The analysis unit can also determine the priority of the analysis based on the importance of the message. The analysis unit can also analyze highly important messages in detail by applying multiple analysis algorithms. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the message, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business analysis algorithm to business-related messages. The analysis unit can also apply a private analysis algorithm to private messages. The analysis unit can also apply an advertising analysis algorithm to advertising or promotional messages. In this way, the analysis unit can apply an appropriate analysis algorithm depending on the message category, thereby improving the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input message category data to the generation AI and cause the generation AI to select an analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and provide feedback to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis 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, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to perform adjustments to improve the accuracy of the analysis.

[0043] The analysis unit can determine the analysis priority based on the time of message submission during analysis. The analysis unit can, for example, determine the analysis priority based on the time of message submission during analysis. For example, the analysis unit can prioritize analyzing recently received messages. The analysis unit can also prioritize analyzing messages received during a specific time period. The analysis unit can also prioritize analyzing messages received during a time period specified by the user. This enables efficient analysis by the analysis unit determining the analysis priority based on the time of message submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message submission time data into the generation AI and have the generation AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit can prioritize analyzing highly relevant messages. The analysis unit can also postpone analyzing less relevant messages. The analysis unit can also dynamically adjust the order of analysis according to the relevance of messages. In this way, the analysis unit can prioritize analyzing highly relevant messages by adjusting the order of analysis based on the relevance of messages. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input message relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. 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 provide concise and easy-to-understand analysis results. The analysis unit can also adjust the level of detail in the analysis results according to the user's level of expertise. This allows the analysis unit to provide 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, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms in the analysis.

[0046] The generation unit can adjust the level of detail of the summary based on the importance of the message when generating the summary. For example, the generation unit can generate a detailed summary for a message of high importance. The generation unit can also generate a concise summary for a message of low importance. The generation unit can also dynamically adjust the level of detail of the summary according to the importance of the message. This allows the generation unit to adjust the level of detail of the summary based on the importance of the message, thereby enabling efficient summarization. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0047] The generation unit can apply different summarization algorithms depending on the message category when generating a summary. For example, the generation unit can apply different summarization algorithms depending on the message category when generating a summary. For example, the generation unit can apply a business summarization algorithm to business-related messages. The generation unit can also apply a private summarization algorithm to private messages. The generation unit can also apply an advertising summarization algorithm to advertising or promotional messages. In this way, the generation unit can apply an appropriate summarization algorithm depending on the message category, thereby improving the accuracy of the summary. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input message category data to the generation AI and have the generation AI select a summarization algorithm.

[0048] The generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit can adjust the summarization algorithm based on the user's past summarization results. The generation unit can also extract specific patterns from the user's past summarization results to improve the accuracy of the summary. The generation unit can also analyze the user's past summarization results and provide feedback to improve the accuracy of the summary. In this way, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past summarization result data into the generation AI and cause the generation AI to perform adjustments to improve the accuracy of the summary.

[0049] The generation unit can determine the priority of summaries based on the submission time of messages when generating summaries. The generation unit, for example, can determine the priority of summaries based on the submission time of messages when generating summaries. For example, the generation unit can prioritize summarizing recently received messages. The generation unit can also prioritize summarizing messages received within a specific time period. The generation unit can also prioritize summarizing messages received within a time period specified by the user. This enables efficient summarization by the generation unit determining the priority of summaries based on the submission time of messages. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message submission time data into the generation AI and have the generation AI determine the priority of summaries.

[0050] The generation unit can adjust the order of summaries based on the relevance of messages when generating summaries. The generation unit, for example, adjusts the order of summaries based on the relevance of messages when generating summaries. For example, the generation unit can prioritize summarizing highly relevant messages. The generation unit can also postpone less relevant messages. The generation unit can also dynamically adjust the order of summaries according to the relevance of messages. In this way, the generation unit can prioritize summarizing highly relevant messages by adjusting the order of summaries based on the relevance of messages. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input message relevance data to the generation AI and cause the generation AI to adjust the order of summaries.

[0051] The generation unit can adjust the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, the generation unit can adjust the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, if the user has technical expertise, the generation unit can generate a summary that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate a concise and easy-to-understand summary. The generation unit can also adjust the level of detail in the summary according to the user's level of expertise. This allows the generation unit to provide a summary that is easy for the user to understand by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to control the use of technical terms in the summary.

[0052] The display unit can select the optimal display method by referring to the user's past operation history when displaying a summary. For example, the display unit can select the optimal display method by referring to the user's past operation history when displaying a summary. For example, the display unit can suggest the optimal display method based on the user's preferred display methods in the past. The display unit can also preferentially provide a specific display method based on the user's past operation history. The display unit can also analyze the user's past operation history and suggest the optimal display method. In this way, the display unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and cause the generation AI to select the optimal display method.

[0053] The display unit can customize the display content according to the user's current task when displaying a summary. For example, when displaying a summary, the display unit customizes the display content according to the user's current task. For example, when the user is working, the display unit can prioritize displaying summaries related to work. Furthermore, when the user is spending time on a hobby, the display unit can prioritize displaying summaries related to the hobby. Furthermore, when the user is taking a break, the display unit can display all summaries, but change the display method according to the importance of the summaries. This allows the display unit to provide highly relevant information to the user by customizing the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the user's current task data into a generation AI and have the generation AI customize the display content.

[0054] The display unit can select the optimal display method by taking into account the user's device information when displaying a summary. For example, the display unit selects the optimal display method by taking into account the user's device information when displaying a summary. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a display method that is concise and highly visible. In this way, the display unit can provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0055] The display unit can select the optimal display method by taking into account the user's device information when displaying a summary. For example, the display unit selects the optimal display method by taking into account the user's device information when displaying a summary. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a display method that is concise and highly visible. In this way, the display unit can provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0056] The display unit can make the display content multilingual in accordance with the user's language setting when displaying a summary. For example, the display unit can automatically set the language of the summary based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also display the summary in a specific language when the user selects that language. This allows the display unit to provide multilingual display content in accordance with the user's language setting, thereby enabling a display that is easy for the user to understand. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the user's language setting data into a generation AI and cause the generation AI to set the display content to be multilingual.

[0057] The display unit can analyze the user's social media activity and provide related information when displaying a summary. For example, the display unit can analyze the user's social media activity and provide related information when displaying a summary. For example, the display unit can provide information about places the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, the display unit can provide related information by analyzing the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's past message reception history and select the optimal reception method. For example, the reception unit can adjust the timing of message reception based on the time period during which the user frequently received messages in the past. The reception unit can also prioritize the reception method (voice notification, vibration, etc.) that the user has used in the past. The reception unit can also prioritize receiving messages from specific senders based on the user's past message reception history. In this way, the reception unit can provide the optimal reception method for the user by analyzing the past message reception history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past message reception history data into the generation AI and have the generation AI select the optimal reception method.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the message. For example, the analysis unit can analyze messages of high importance in detail and messages of low importance briefly. The analysis unit can also determine the priority of the analysis based on the importance of the message. The analysis unit can also analyze messages of high importance in detail by applying multiple analysis algorithms. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the message, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0061] When generating a summary, the generation unit can apply different summarization algorithms depending on the message category. For example, the generation unit can apply a business summarization algorithm to business-related messages. The generation unit can also apply a private summarization algorithm to private messages. The generation unit can also apply an advertising summarization algorithm to advertising or promotional messages. In this way, the generation unit can apply an appropriate summarization algorithm depending on the message category, thereby improving the accuracy of the summary. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message category data to the generation AI and have the generation AI select a summarization algorithm.

[0062] When displaying a summary, the display unit can select the optimal display method by referring to the user's past operation history. For example, the display unit can suggest the optimal display method based on the user's preferred display method in the past. The display unit can also preferentially provide a specific display method based on the user's past operation history. The display unit can also analyze the user's past operation history and suggest the optimal display method. In this way, the display unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and cause the generation AI to select the optimal display method.

[0063] When receiving messages, the reception unit can filter messages based on the user's current activity status and areas of interest. For example, when the user is at work, the reception unit can prioritize receiving only work-related messages. Also, when the user is spending time on a hobby, the reception unit can prioritize receiving hobby-related messages. Also, when the user is on a break, the reception unit can receive all messages, but change the notification method depending on the importance of the messages. In this way, the reception unit can prioritize receiving messages that are highly relevant to the user by filtering messages based on the user's activity status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's activity status data into a generation AI and have the generation AI perform message filtering.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives a message. The message can be a text message, a voice message, an image message, or the like. The reception unit sends the message received via a messaging service or email service as is to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the message received by the reception unit. The analysis unit uses natural language processing technology to understand the content of the message and extract important information. For example, it uses morphological analysis to break down the words in the message, grammatical analysis to understand the structure of the sentence, and semantic analysis to understand the meaning of the sentence. Step 3: The generator uses AI to extract key information from the messages analyzed by the analyzer and generate a summary. The generator generates a summary based on the extracted key information, for example, based on the frequency of keyword occurrences or information related to a specific topic. Step 4: The display unit displays the summary generated by the generation unit to the user. The display unit can display the summary below the received message in an app for a messaging service or email service.

[0066] (Example 2) A message summarization system according to an embodiment of the present invention receives a message, analyzes it using a generation AI, generates a summary, and displays it to a user. When a message is received, the message is input to the generation AI. The generation AI analyzes the message, extracts important information, and generates a summary. The generated summary is then displayed to the user. For example, the message summarization system targets messages received via messaging or email services. The message content is sent to the generation AI as is. The generation AI then analyzes the input message. The generation AI uses natural language processing technology to understand the message content and extract important information. For example, it identifies particularly important points or keywords in the message and generates a summary based on them. The generated summary is then displayed to the user. For example, the summary is displayed below the received message in the messaging or email service app. This summary allows users to efficiently acquire information without having to read long sentences. This allows the message summarization system to efficiently acquire information without having to read long sentences. For example, the message summarization system allows younger generations, who place importance on typing, to efficiently acquire information without having to read long sentences. For example, busy students and working adults can understand long messages in a short amount of time. Also, by using the summarized information, they can read only the necessary parts in detail, making it easier to select and discard information.

[0067] A message summarization system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives messages. Examples of messages include, but are not limited to, text messages, voice messages, and image messages. The reception unit transmits messages received via, for example, a messaging service or an email service to a generation AI. The analysis unit uses a generation AI to analyze the messages received by the reception unit. The analysis unit understands the content of the messages and extracts important information using, for example, natural language processing technology. For example, the analysis unit may use morphological analysis to break down words in the messages, grammatical analysis to understand the structure of the sentences, and semantic analysis to understand the meaning of the sentences. The generation unit uses the generation AI to extract important information from the messages analyzed by the analysis unit and generate summaries. The generation unit generates summaries based on, for example, the extracted important information. For example, the generation unit may generate summaries based on the frequency of keyword appearances or information related to a specific topic. The display unit displays the summaries generated by the generation unit to a user. For example, the display unit may display the summaries below the received messages within an app for the messaging service or email service. This allows the message summarization system according to the embodiment to efficiently summarize messages and display them to the user.

[0068] The analysis unit can understand the content of the message using natural language processing technology and extract important information. The analysis unit, for example, uses morphological analysis to break down the words of the message. For example, the analysis unit can use morphological analysis to break down the words of the message and identify the part of speech of each word. The analysis unit can also understand the structure of the sentence of the message using grammatical analysis. For example, the analysis unit can use grammatical analysis to identify the structure of the sentence, such as the subject, predicate, and object. The analysis unit can also understand the meaning of the sentence of the message using semantic analysis. For example, the analysis unit can understand the meaning of the sentence using semantic analysis and extract important information. As a result, the analysis unit can accurately understand the content of the message and extract important information by using natural language processing technology.

[0069] The generation unit can generate a summary based on the extracted important information. For example, the generation unit can generate a summary based on the extracted important information. For example, the generation unit can generate a summary based on the frequency of keyword appearances. The generation unit can also generate a summary based on information related to a specific topic. For example, the generation unit can extract keywords or phrases related to a specific topic and generate a summary based on the keywords or phrases. The generation unit can also generate a summary based on the extracted important information using a generation AI. For example, the generation unit inputs the extracted important information into the generation AI, which then generates a summary. In this way, the generation unit can provide a useful summary to the user by generating a summary based on the important information.

[0070] The display unit can display the generated summary to the user. For example, the display unit can display the generated summary to the user. For example, the display unit can display the summary below a received message in an app for a messaging service or an email service. The display unit can also display the generated summary as a pop-up window or a notification. For example, the display unit can display the generated summary as a pop-up window so that the user can immediately check the summary. The display unit can also display the generated summary as a notification to notify the user of the existence of the summary. In this way, the display unit can display the generated summary to the user, allowing the user to efficiently acquire information.

[0071] The generation unit can make adjustments to improve the accuracy of the summary. The generation unit, for example, makes adjustments to improve the accuracy of the summary. For example, the generation unit can adjust parameters of an algorithm to improve the accuracy of the summary. The generation unit can also introduce a feedback loop to improve the accuracy of the summary. For example, the generation unit can collect feedback from users and adjust the summarization algorithm based on the feedback. The generation unit can also make adjustments to improve the accuracy of the summary using a generation AI. For example, the generation unit can cause the generation AI to make adjustments to improve the accuracy of the summary. As a result, the generation unit can improve the accuracy of the summary and provide a more accurate summary.

[0072] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of message reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay message reception so that the user can receive the message when they are relaxed. The reception unit can also allow the user to receive messages immediately if the user is relaxed. The reception unit can also prioritize important messages if the user is in a hurry. In this way, the reception unit can adjust the timing of message reception according to the user's emotions, allowing the user to receive messages at the optimal timing for the user. 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-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The reception unit can analyze the user's past message reception history and select the optimal reception method. The reception unit, for example, analyzes the user's past message reception history and selects the optimal reception method. For example, the reception unit can adjust the timing of message reception based on the time period during which the user frequently received messages in the past. The reception unit can also prioritize the reception method (voice notification, vibration, etc.) that the user has used in the past. The reception unit can also prioritize receiving messages from specific senders based on the user's past message reception history. In this way, the reception unit can provide the optimal reception method for the user by analyzing the past message reception history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past message reception history data into a generation AI and have the generation AI select the optimal reception method.

[0074] The reception unit can filter messages based on the user's current activity status and areas of interest when receiving messages. For example, when receiving messages, the reception unit can filter messages based on the user's current activity status and areas of interest. For example, when the user is at work, the reception unit can prioritize receiving only work-related messages. Furthermore, when the user is spending time on a hobby, the reception unit can prioritize receiving hobby-related messages. Furthermore, when the user is on a break, the reception unit can receive all messages but change the notification method depending on the importance of the messages. In this way, the reception unit can prioritize receiving messages that are highly relevant to the user by filtering messages based on the user's activity status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's activity status data into a generation AI and have the generation AI perform message filtering.

[0075] The reception unit can select the optimal receiving means according to the user's input method when receiving a message. For example, the reception unit selects the optimal receiving means according to the user's input method when receiving a message. For example, if the user prefers voice input, the reception unit can prioritize voice notification. Also, if the user prefers text input, the reception unit can prioritize text notification. Also, if the user prefers image input, the reception unit can prioritize image notification. In this way, the reception unit can select the optimal receiving means according to the user's input method, thereby providing a receiving method that is easy for the user to use. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal receiving means.

[0076] The reception unit can estimate the user's emotions and determine the priority of messages to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of messages to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize receiving only important messages. Furthermore, when the user is relaxed, the reception unit can receive all messages. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving messages with high urgency. In this way, the reception unit can prioritize messages according to the user's emotions and receive important messages preferentially. 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 reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] When receiving messages, the reception unit can prioritize receiving highly relevant messages by taking into account the user's geographical location information. For example, when receiving messages, the reception unit prioritizes receiving highly relevant messages by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving messages related to that location. Furthermore, when the user is traveling, the reception unit can prioritize receiving messages related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving messages related to the home. In this way, the reception unit can prioritize receiving highly relevant messages by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant messages.

[0078] The reception unit can analyze the user's social media activity upon receiving a message and receive related messages. For example, the reception unit can analyze the user's social media activity upon receiving a message and receive related messages. For example, the reception unit can prioritize receiving messages related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related messages. The reception unit can also refer to the activities of the user's friends on social media to prioritize receiving related messages. In this way, the reception unit can analyze the user's social media activity and prioritize receiving related messages. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related messages.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a message. For example, the reception unit can customize the reception method by reflecting the user's past feedback when receiving a message. For example, the reception unit can suggest an optimal reception method based on the user's past preferred reception methods. The reception unit can also prioritize receiving messages from specific senders based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest an optimal notification method. In this way, the reception unit can provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0080] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated user emotions. For example, the analysis unit can provide a concise analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also extract only important points and provide an analysis result when the user is in a hurry. This allows the analysis unit to adjust the analysis method according to the user's emotions and provide a more appropriate analysis result. Emotion estimation is achieved 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 AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the message. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the message. For example, the analysis unit can analyze highly important messages in detail and analyze less important messages briefly. The analysis unit can also determine the priority of the analysis based on the importance of the message. The analysis unit can also analyze highly important messages in detail by applying multiple analysis algorithms. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the message, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business analysis algorithm to business-related messages. The analysis unit can also apply a private analysis algorithm to private messages. The analysis unit can also apply an advertising analysis algorithm to advertising or promotional messages. In this way, the analysis unit can apply an appropriate analysis algorithm depending on the message category, thereby improving the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input message category data to the generation AI and cause the generation AI to select an analysis algorithm.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and provide feedback to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis 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, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to perform adjustments to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priority based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing important messages. Also, if the user is relaxed, the analysis unit can analyze all messages equally. Also, if the user is in a hurry, the analysis unit can prioritize analyzing messages with high urgency. Thus, the analysis unit can prioritize analyzing important messages by determining the analysis priority based on 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 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 may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The analysis unit can determine the analysis priority based on the time of message submission during analysis. The analysis unit can, for example, determine the analysis priority based on the time of message submission during analysis. For example, the analysis unit can prioritize analyzing recently received messages. The analysis unit can also prioritize analyzing messages received during a specific time period. The analysis unit can also prioritize analyzing messages received during a time period specified by the user. This enables efficient analysis by the analysis unit determining the analysis priority based on the time of message submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message submission time data into the generation AI and have the generation AI determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit can prioritize analyzing highly relevant messages. The analysis unit can also postpone analyzing less relevant messages. The analysis unit can also dynamically adjust the order of analysis according to the relevance of messages. In this way, the analysis unit can prioritize analyzing highly relevant messages by adjusting the order of analysis based on the relevance of messages. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input message relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. 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 provide concise and easy-to-understand analysis results. The analysis unit can also adjust the level of detail in the analysis results according to the user's level of expertise. This allows the analysis unit to provide 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, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms in the analysis.

[0088] The generation unit can estimate the user's emotion and adjust the summary presentation style based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the summary presentation style based on the estimated user emotion. For example, if the user is stressed, the generation unit can generate a concise summary that focuses on the main points. If the user is relaxed, the generation unit can generate a detailed summary. If the user is in a hurry, the generation unit can generate a summary that extracts only the important points. This allows the generation unit to adjust the summary presentation style according to the user's emotion, thereby providing an optimal summary for the user. Emotion estimation is achieved 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 these examples. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The generation unit can adjust the level of detail of the summary based on the importance of the message when generating the summary. For example, the generation unit can generate a detailed summary for a message of high importance. The generation unit can also generate a concise summary for a message of low importance. The generation unit can also dynamically adjust the level of detail of the summary according to the importance of the message. This allows the generation unit to adjust the level of detail of the summary based on the importance of the message, thereby enabling efficient summarization. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0090] The generation unit can apply different summarization algorithms depending on the message category when generating a summary. For example, the generation unit can apply different summarization algorithms depending on the message category when generating a summary. For example, the generation unit can apply a business summarization algorithm to business-related messages. The generation unit can also apply a private summarization algorithm to private messages. The generation unit can also apply an advertising summarization algorithm to advertising or promotional messages. In this way, the generation unit can apply an appropriate summarization algorithm depending on the message category, thereby improving the accuracy of the summary. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input message category data to the generation AI and have the generation AI select a summarization algorithm.

[0091] The generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit can adjust the summarization algorithm based on the user's past summarization results. The generation unit can also extract specific patterns from the user's past summarization results to improve the accuracy of the summary. The generation unit can also analyze the user's past summarization results and provide feedback to improve the accuracy of the summary. In this way, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past summarization result data into the generation AI and cause the generation AI to perform adjustments to improve the accuracy of the summary.

[0092] The generation unit can estimate the user's emotion and adjust the length of the summary based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the summary based on the estimated user emotion. For example, if the user is stressed, the generation unit can generate a short, concise summary. If the user is relaxed, the generation unit can generate a detailed summary. If the user is in a hurry, the generation unit can generate a short summary that extracts only the important points. This allows the generation unit to adjust the length of the summary according to the user's emotion, thereby providing an optimal summary 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, 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 generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The generation unit can determine the priority of summaries based on the submission time of messages when generating summaries. The generation unit, for example, can determine the priority of summaries based on the submission time of messages when generating summaries. For example, the generation unit can prioritize summarizing recently received messages. The generation unit can also prioritize summarizing messages received within a specific time period. The generation unit can also prioritize summarizing messages received within a time period specified by the user. This enables efficient summarization by the generation unit determining the priority of summaries based on the submission time of messages. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message submission time data into the generation AI and have the generation AI determine the priority of summaries.

[0094] The generation unit can adjust the order of summaries based on the relevance of messages when generating summaries. The generation unit, for example, adjusts the order of summaries based on the relevance of messages when generating summaries. For example, the generation unit can prioritize summarizing highly relevant messages. The generation unit can also postpone less relevant messages. The generation unit can also dynamically adjust the order of summaries according to the relevance of messages. In this way, the generation unit can prioritize summarizing highly relevant messages by adjusting the order of summaries based on the relevance of messages. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input message relevance data to the generation AI and cause the generation AI to adjust the order of summaries.

[0095] The generation unit can adjust the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, the generation unit can adjust the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, if the user has technical expertise, the generation unit can generate a summary that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate a concise and easy-to-understand summary. The generation unit can also adjust the level of detail in the summary according to the user's level of expertise. This allows the generation unit to provide a summary that is easy for the user to understand by adjusting the use of technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to control the use of technical terms in the summary.

[0096] The display unit can estimate the user's emotion and adjust the summary display method based on the estimated user emotion. For example, the display unit can estimate the user's emotion and adjust the summary display method based on the estimated user emotion. For example, if the user is feeling stressed, the display unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display method that focuses on the main points. Thus, the display unit can adjust the summary display method according to the user's emotion, thereby providing an optimal display method 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, 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 display unit may be performed using, for example, an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0097] The display unit can select the optimal display method by referring to the user's past operation history when displaying a summary. For example, the display unit can select the optimal display method by referring to the user's past operation history when displaying a summary. For example, the display unit can suggest the optimal display method based on the user's preferred display methods in the past. The display unit can also preferentially provide a specific display method based on the user's past operation history. The display unit can also analyze the user's past operation history and suggest the optimal display method. In this way, the display unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and cause the generation AI to select the optimal display method.

[0098] The display unit can customize the display content according to the user's current task when displaying a summary. For example, when displaying a summary, the display unit customizes the display content according to the user's current task. For example, when the user is working, the display unit can prioritize displaying summaries related to work. Furthermore, when the user is spending time on a hobby, the display unit can prioritize displaying summaries related to the hobby. Furthermore, when the user is taking a break, the display unit can display all summaries, but change the display method according to the importance of the summaries. This allows the display unit to provide highly relevant information to the user by customizing the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the user's current task data into a generation AI and have the generation AI customize the display content.

[0099] The display unit can select the optimal display method by taking into account the user's device information when displaying a summary. For example, the display unit selects the optimal display method by taking into account the user's device information when displaying a summary. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a display method that is concise and highly visible. In this way, the display unit can provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0100] The display unit can estimate the user's emotion and adjust the display order of summaries based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the display order of summaries based on the estimated user's emotion. For example, when the user is stressed, the display unit can prioritize displaying important summaries. Furthermore, when the user is relaxed, the display unit can equally display all summaries. Furthermore, when the user is in a hurry, the display unit can prioritize displaying summaries with high urgency. Thus, the display unit can prioritize displaying important summaries by adjusting the display order of summaries according to the user's emotion. 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 display unit may be performed using, for example, an AI. For example, the display unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0101] The display unit can select the optimal display method by taking into account the user's device information when displaying a summary. For example, the display unit selects the optimal display method by taking into account the user's device information when displaying a summary. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a display method that is concise and highly visible. In this way, the display unit can provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0102] The display unit can make the display content multilingual in accordance with the user's language setting when displaying a summary. For example, the display unit can automatically set the language of the summary based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also display the summary in a specific language when the user selects that language. This allows the display unit to provide multilingual display content in accordance with the user's language setting, thereby enabling a display that is easy for the user to understand. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the user's language setting data into a generation AI and cause the generation AI to set the display content to be multilingual.

[0103] The display unit can analyze the user's social media activity and provide related information when displaying a summary. For example, the display unit can analyze the user's social media activity and provide related information when displaying a summary. For example, the display unit can provide information about places the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The display unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, the display unit can provide related information by analyzing the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the smart device 14 and receives a message. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the message using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information to generate a summary. The display unit is realized by the output device 40 of the smart device 14 and displays the generated summary to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the smart glasses 214 and receives a message. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the message using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information to generate a summary. The display unit is realized by the speaker 240 of the smart glasses 214 and displays the generated summary to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the headset type terminal 314 and receives a message. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the message using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information to generate a summary. The display unit is realized by the display 343 of the headset type terminal 314 and displays the generated summary to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the robot 414 and receives a message. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the message using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information to generate a summary. The display unit is realized by the speaker 240 of the robot 414 and displays the generated summary to the user.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can analyze the user's past message reception history and select the optimal reception method. For example, the reception unit can adjust the timing of message reception based on the time period during which the user frequently received messages in the past. The reception unit can also prioritize the reception method (voice notification, vibration, etc.) that the user has used in the past. The reception unit can also prioritize receiving messages from specific senders based on the user's past message reception history. In this way, the reception unit can provide the optimal reception method for the user by analyzing the past message reception history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past message reception history data into the generation AI and have the generation AI select the optimal reception method.

[0106] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the message. For example, the analysis unit can analyze messages of high importance in detail and messages of low importance briefly. The analysis unit can also determine the priority of the analysis based on the importance of the message. The analysis unit can also analyze messages of high importance in detail by applying multiple analysis algorithms. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the message, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0107] When generating a summary, the generation unit can apply different summarization algorithms depending on the message category. For example, the generation unit can apply a business summarization algorithm to business-related messages. The generation unit can also apply a private summarization algorithm to private messages. The generation unit can also apply an advertising summarization algorithm to advertising or promotional messages. In this way, the generation unit can apply an appropriate summarization algorithm depending on the message category, thereby improving the accuracy of the summary. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message category data to the generation AI and have the generation AI select a summarization algorithm.

[0108] When displaying a summary, the display unit can select the optimal display method by referring to the user's past operation history. For example, the display unit can suggest the optimal display method based on the user's preferred display method in the past. The display unit can also preferentially provide a specific display method based on the user's past operation history. The display unit can also analyze the user's past operation history and suggest the optimal display method. In this way, the display unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past operation history data into a generation AI and cause the generation AI to select the optimal display method.

[0109] When receiving messages, the reception unit can filter messages based on the user's current activity status and areas of interest. For example, when the user is at work, the reception unit can prioritize receiving only work-related messages. Also, when the user is spending time on a hobby, the reception unit can prioritize receiving hobby-related messages. Also, when the user is on a break, the reception unit can receive all messages, but change the notification method depending on the importance of the messages. In this way, the reception unit can prioritize receiving messages that are highly relevant to the user by filtering messages based on the user's activity status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's activity status data into a generation AI and have the generation AI perform message filtering.

[0110] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay message reception and allow the user to receive the message when they are relaxed. Furthermore, if the user is relaxed, the reception unit can allow the user to receive the message immediately. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving important messages. Thus, the reception unit can adjust the message reception timing according to the user's emotions, allowing the user to receive the message at the optimal timing. 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 reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0111] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can extract only important points and provide an analysis result. This allows the analysis unit to adjust the analysis method according to the user's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as 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, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0112] The generation 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 generation unit can generate a concise summary that focuses on the main points. If the user is relaxed, the generation unit can generate a detailed summary. If the user is in a hurry, the generation unit can generate a summary that extracts only the important points. This allows the generation unit to provide an optimal summary for the user by adjusting the summary presentation style according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0113] The display unit can estimate the user's emotion and adjust the summary display method based on the estimated user emotion. For example, if the user is feeling stressed, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can provide a display method including detailed information. If the user is in a hurry, the display unit can provide a display method that focuses on the main points. Thus, the display unit can adjust the summary display method according to the user's emotion, thereby providing an optimal display method 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, 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 display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0114] The display unit can estimate the user's emotion and adjust the display order of summaries based on the estimated user's emotion. For example, when the user is stressed, the display unit can prioritize displaying important summaries. Furthermore, when the user is relaxed, the display unit can equally display all summaries. Furthermore, when the user is in a hurry, the display unit can prioritize displaying summaries with high urgency. Thus, the display unit can prioritize displaying important summaries by adjusting the display order of summaries according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives a message. The message can be a text message, a voice message, an image message, or the like. The reception unit sends the message received via a messaging service or email service as is to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the message received by the reception unit. The analysis unit uses natural language processing technology to understand the content of the message and extract important information. For example, it uses morphological analysis to break down the words in the message, grammatical analysis to understand the structure of the sentence, and semantic analysis to understand the meaning of the sentence. Step 3: The generator uses AI to extract key information from the messages analyzed by the analyzer and generate a summary. The generator generates a summary based on the extracted key information, for example, based on the frequency of keyword occurrences or information related to a specific topic. Step 4: The display unit displays the summary generated by the generation unit to the user. The display unit can display the summary below the received message in an app for a messaging service or email service.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 reception unit for receiving a message; an analysis unit that analyzes the message received by the reception unit; a generation unit that extracts important information from the message analyzed by the analysis unit and generates a summary; a display unit that displays the summary generated by the generation unit; Equipped with A system characterized by:

2. The analysis unit Use natural language processing technology to understand the content of messages and extract important information 2. The system of claim 1.

3. The generation unit Generate a summary based on the extracted key information 2. The system of claim 1.

4. The display unit Display the generated summary to the user 2. The system of claim 1.

5. The generation unit Make adjustments to improve the accuracy of the summary 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of receiving messages based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past message reception history and select the optimal reception method 2. The system of claim 1.

8. The reception unit Filter incoming messages based on your current activity and interests 2. The system of claim 1.

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