system

Abstract

The system according to the embodiment aims to efficiently manage the large amount of emails received each day and quickly grasp important information. [Solution] A system according to an embodiment includes an analysis unit, a summarization unit, a registration unit, and a display unit. The analysis unit analyzes the content of an email. The summarization unit generates a summary based on the content analyzed by the analysis unit. The registration unit registers specific keywords. The display unit displays related information based on the keywords registered by the registration 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 technology has had the problem of making it difficult to efficiently manage the large amount of emails received each day and quickly grasp important information.

[0005] The system according to the embodiment aims to efficiently manage the large amount of emails received each day and quickly grasp important information. [Means for solving the problem]

[0006] The system according to this embodiment comprises an analysis unit, a summarization unit, a registration unit, and a display unit. The analysis unit analyzes the content of an email. The summarization unit generates a summary based on the content analyzed by the analysis unit. The registration unit registers specific keywords. The display unit displays related information based on the keywords registered by the registration unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the large amount of emails received each day and quickly grasp important information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The email summarization system according to an embodiment of the present invention is a system that summarizes the text of emails received in a day, thread by thread, and provides a function to summarize what kind of conversations are taking place and what kind of incidents are occurring. This email summarization system is customizable on the site, and if specific keywords are registered, not only will the information related to those keywords be displayed, but the summarized content will also be displayed. Specifically, first, the user registers specific keywords. Next, the system analyzes the emails received in a day, thread by thread, and generates a summary. This summary includes the content of the conversations and the incidents that are occurring. Furthermore, it displays related information based on the registered keywords. This allows the user to quickly grasp important information. For example, if the user registers the keyword "project progress," the system analyzes emails related to that keyword and generates a summary regarding the project progress. This allows the user to easily grasp the project progress. This system uses a generative AI to summarize emails. The generative AI analyzes the content of the emails, extracts important information, and generates a summary. For example, the generative AI extracts important keywords and phrases from the body of the email and creates a summary based on them. This system allows users to efficiently manage the large volume of emails they receive each day and quickly grasp important information. Furthermore, by customizing information based on specific keywords, it can provide information tailored to user needs. For example, if a user registers the keyword "project progress," the system analyzes emails related to that keyword and generates a summary of project progress. This allows users to easily understand project progress. In short, the email summarization system enables users to efficiently manage the large volume of emails they receive each day and quickly grasp important information.

[0029] An email summarization system according to an embodiment includes an analysis unit, a summarization unit, a registration unit, and a display unit. The analysis unit analyzes the content of an email. For example, the analysis unit analyzes the content of the email using natural language processing technology. For example, the analysis unit extracts important keywords and phrases from the body of the email. The analysis unit can also analyze the content of the email using a machine learning algorithm. The summarization unit generates a summary based on the content analyzed by the analysis unit. For example, the summarization unit generates a summary using a generation AI. The generation AI can use models such as GPT-4 (registered trademark) or Gemini. For example, the summarization unit extracts important information from the body of the email and creates a summary based on the extracted information. The summarization unit can also generate a summary based on the length of the sentence or the importance of the information to be summarized. The registration unit has a function for a user to register specific keywords. For example, the registration unit stores keywords entered by the user in a database. For example, the registration unit can automatically extract frequently occurring words and register them as keywords. The display unit displays related information based on the keywords registered by the registration unit. The display unit displays, for example, related documents and links. The display unit can also display, for example, related images. The display unit displays the summary generated by the summarization unit. The display unit displays, for example, the summary in text format. The display unit can also display, for example, the summary as a graph or chart. As a result, the email summarization system according to the embodiment allows the user to efficiently manage the large volume of emails received in a day and quickly grasp important information.

[0030] The analysis unit can analyze the content of the email using the generation AI and extract important information. The generation AI can use models such as GPT-4 and Gemini. The generation AI can extract important keywords and phrases from the body of the email, for example. The generation AI can analyze the content of the email and extract important information, for example. The generation AI can also analyze the content of the email using natural language processing technology, for example. This makes it possible to efficiently analyze the content of the email and extract important information by using the generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the body of the email into the generation AI and have the generation AI extract important information.

[0031] The summarization unit can use a generation AI to generate a summary based on the information extracted by the analysis unit. The generation AI can use models such as GPT-4 or Gemini. The generation AI generates a summary based on the information extracted by the analysis unit. The generation AI can extract important information from the body of an email, for example, and create a summary based on that information. The generation AI can also generate a summary based on the length of the sentence or the importance of the information to be summarized, for example. This allows the generation AI to efficiently generate a summary based on the extracted information. Some or all of the above-mentioned processing in the summarization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the summarization unit can input the information extracted by the analysis unit into the generation AI and have the generation AI generate a summary.

[0032] The registration unit may have a function that allows users to register specific keywords. For example, the registration unit can save keywords entered by the user to a database. The registration unit can also automatically extract frequently occurring words and register them as keywords. The registration unit can also register keywords entered by the user in real time. This allows users to efficiently display related information by registering specific keywords. Some or all of the above-described processes in the registration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the registration unit can input keywords entered by the user into a generative AI and have the generative AI perform the keyword registration.

[0033] The display unit can display relevant information based on keywords registered by the registration unit. For example, the display unit can display related documents or links. The display unit can also display related images. For example, the display unit can display related videos. For example, the display unit can display related news articles. This allows users to quickly grasp important information by displaying relevant information based on registered keywords. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input keywords registered by the registration unit into a generating AI and cause the generating AI to display the relevant information.

[0034] The display unit can display the summary generated by the summarization unit. The display unit can, for example, display the summary in text format. The display unit can also display the summary as a graph or chart, for example. The display unit can, for example, read the summary aloud. The display unit can, for example, display the summary in video format. In this way, by displaying the generated summary, the user can quickly understand the content of the conversation or the incident that is occurring. Some or all of the above-mentioned processing in the display unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the display unit can input the summary generated by the summarization unit to the generation AI and cause the generation AI to display the summary.

[0035] The analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the sender of the email. For example, if the sender is a boss, the analysis unit sets the importance to a high level. For example, if the sender is a customer, the analysis unit can also perform a detailed analysis. For example, if the sender is a colleague, the analysis unit can also perform a normal analysis. In this way, the accuracy of the analysis is improved by taking into account the attribute information of the sender. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the attribute information of the sender into the generation AI and have the generation AI improve the accuracy of the analysis.

[0036] The analysis unit can prioritize analyzing information related to a specific topic based on the content of the email. For example, the analysis unit prioritizes analyzing emails related to projects. For example, the analysis unit can also prioritize analyzing emails related to urgent matters. For example, the analysis unit can also prioritize analyzing emails related to meeting minutes. In this way, by analyzing information related to a specific topic prioritized, important information can be quickly grasped. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the content of the email into the generation AI and have the generation AI analyze information related to a specific topic.

[0037] The analysis unit can determine the analysis priority based on the reception time of the email. For example, the analysis unit prioritizes analysis of the most recently received email. For example, the analysis unit can also prioritize analysis of emails received during an important time period. For example, the analysis unit can also prioritize analysis of emails received during a time period specified by the user. In this way, by determining the analysis priority based on the reception time of the email, important emails can be analyzed quickly. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input email reception time data into the generation AI and cause the generation AI to execute processing to determine the analysis priority.

[0038] The analysis unit can analyze the contents of email attachments and extract relevant information. The analysis unit can, for example, analyze the text content of the attachments and extract important information. The analysis unit can also, for example, analyze the image content of the attachments and extract relevant information. The analysis unit can, for example, analyze the PDF content of the attachments and extract important information. This allows for efficient extraction of relevant information by analyzing the contents of email attachments. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the data of the attachments into the generation AI and have the generation AI extract relevant information.

[0039] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the email. For example, the summarization unit provides a detailed summary for emails of high importance. For example, the summarization unit can also provide a concise summary for emails of low importance. For example, the summarization unit can also provide a summary with appropriate detail for emails of medium importance. In this way, by adjusting the level of detail of the summary based on the importance of the email, important information can be grasped in detail. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input email importance data into the generation AI and cause the generation AI to perform processing to adjust the level of detail of the summary.

[0040] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the email. For example, the summarization unit can apply a project-specific summarization algorithm to project-related emails. For example, the summarization unit can also apply an emergency-specific summarization algorithm to emergency-related emails. For example, the summarization unit can also apply a meeting-specific summarization algorithm to meeting-related emails. In this way, by applying different summarization algorithms depending on the category of the email, a more appropriate summary can be generated. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input email category data into the generation AI and have the generation AI apply the summarization algorithm.

[0041] When generating a summary, the summarization unit can generate a summary taking into account the attribute information of the sender of the email. For example, if the sender is a boss, the summarization unit generates a summary with a high importance setting. For example, if the sender is a customer, the summarization unit can also generate a detailed summary. For example, if the sender is a colleague, the summarization unit can also generate a normal summary. In this way, by taking into account the attribute information of the sender of the email, a more appropriate summary is generated. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input the attribute information of the sender into the generation AI and have the generation AI generate the summary.

[0042] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to literature related to the email. For example, the summarization unit can improve the accuracy of the summary by referring to literature related to the content of the email. For example, the summarization unit can also improve the accuracy of the summary by referring to past emails related to the content of the email. For example, the summarization unit can also improve the accuracy of the summary by referring to websites related to the content of the email. In this way, the accuracy of the summary is improved by referring to related literature. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the summary.

[0043] When registering a keyword, the registration unit can suggest optimal keywords by referring to the user's past keyword registration history. For example, the registration unit can automatically display keywords that the user has frequently registered in the past as candidates. For example, the registration unit can also preferentially suggest keywords that the user has used in the past. For example, the registration unit can predict and suggest keywords that will be used in a specific time period based on the user's past keyword registration history. This makes it possible to suggest optimal keywords by referring to the past keyword registration history. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the user's past keyword registration history into the generation AI and cause the generation AI to suggest optimal keywords.

[0044] The registration unit can suggest highly relevant keywords by taking into account the user's geographical location information when registering keywords. For example, if the user is in a specific area, the registration unit can suggest keywords related to that area. For example, if the user is traveling, the registration unit can also suggest keywords related to the travel destination. For example, if the user is at home, the registration unit can also suggest keywords related to the home. In this way, highly relevant keywords can be suggested by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest highly relevant keywords.

[0045] When displaying information, the display unit can select the optimal display method by referring to the user's past browsing history. For example, the display unit prioritizes displaying information that the user has frequently viewed in the past. For example, the display unit can predict and suggest information to be displayed during a specific time period based on the user's past browsing history. For example, the display unit can analyze the user's past browsing history and suggest the most efficient display method. In this way, the optimal display method can be selected by referring to the past browsing history. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's past browsing history into the generation AI and have the generation AI select the optimal display method.

[0046] The display unit can select the optimal display method by taking into account the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method to be provided by taking into account the device information. Some or all of the above-mentioned processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit can improve the accuracy of its analysis by referring to the sender's past email history when analyzing the content of an email. For example, by analyzing the content of emails the sender has sent in the past and extracting specific patterns and keywords, it can improve the accuracy of the analysis of the current email. Furthermore, by considering the tone and style of emails the sender has sent in the past, more accurate analysis becomes possible. In this way, by referring to the sender's past email history, the accuracy of the analysis is improved, and important information can be extracted more precisely.

[0049] The summarization unit, using generation AI, can customize the content of summaries based on information extracted by the analysis unit by referencing the user's past browsing history. For example, it can prioritize including information related to topics the user has frequently viewed in the past. Furthermore, by considering keywords the user has shown interest in in the past when generating summaries, it can provide more relevant information to the user. In this way, by referencing the user's past browsing history, the content of the summaries can be better tailored to the user's needs.

[0050] The registration section can suggest the most suitable keywords when a user registers a specific keyword, by referring to the user's past search history. For example, it can automatically display keywords that the user has frequently searched for in the past as suggestions. Furthermore, by prioritizing the suggestion of keywords that the user has used in the past, the efficiency of keyword registration can be improved. In this way, by referring to the user's past search history, the most suitable keywords can be suggested, reducing the effort required for keyword registration.

[0051] When displaying related information based on the keywords registered by the registration unit, the display unit can provide an optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Furthermore, if the user is using a tablet, a display method optimized for a large screen can be provided. This allows the optimal display method to be provided by taking into account the device information, thereby improving user convenience.

[0052] When analyzing the contents of an email, the analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the email sender. For example, if the sender is a superior, the importance level is set high for analysis. Furthermore, if the sender is a customer, a more detailed analysis can be performed. By taking into account the attribute information of the sender, the accuracy of the analysis can be improved and important information can be extracted more accurately.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The analysis unit analyzes the content of the email. The analysis unit uses natural language processing technology and machine learning algorithms to extract important keywords and phrases from the email body. Step 2: The summarization unit generates a summary based on the content analyzed by the analysis unit. The summarization unit uses a generation AI (e.g., GPT-4 or Gemini) to extract important information from the email body and create a summary based on that information. Step 3: The registration section includes a function for users to register specific keywords. In addition to saving keywords entered by the user to the database, it can also automatically extract frequently occurring words and register them as keywords. Step 4: The display unit displays relevant information based on the keywords registered by the registration unit. In addition to displaying related documents, links, and images, it also displays summaries generated by the summarization unit in text format, graphs, and charts.

[0055] (Example 2) The email summarization system according to an embodiment of the present invention is a system that summarizes the text of emails received in a day, thread by thread, and provides a function to summarize what kind of conversations are taking place and what kind of incidents are occurring. This email summarization system is customizable on the site, and if specific keywords are registered, not only will the information related to those keywords be displayed, but the summarized content will also be displayed. Specifically, first, the user registers specific keywords. Next, the system analyzes the emails received in a day, thread by thread, and generates a summary. This summary includes the content of the conversations and the incidents that are occurring. Furthermore, it displays related information based on the registered keywords. This allows the user to quickly grasp important information. For example, if the user registers the keyword "project progress," the system analyzes emails related to that keyword and generates a summary regarding the project progress. This allows the user to easily grasp the project progress. This system uses a generative AI to summarize emails. The generative AI analyzes the content of the emails, extracts important information, and generates a summary. For example, the generative AI extracts important keywords and phrases from the body of the email and creates a summary based on them. This system allows users to efficiently manage the large volume of emails they receive each day and quickly grasp important information. Furthermore, by customizing information based on specific keywords, it can provide information tailored to user needs. For example, if a user registers the keyword "project progress," the system analyzes emails related to that keyword and generates a summary of project progress. This allows users to easily understand project progress. In short, the email summarization system enables users to efficiently manage the large volume of emails they receive each day and quickly grasp important information.

[0056] The email summarization system according to this embodiment comprises an analysis unit, a summarization unit, a registration unit, and a display unit. The analysis unit analyzes the content of an email. The analysis unit analyzes the content of an email using, for example, natural language processing technology. The analysis unit extracts important keywords and phrases from the body of the email. The analysis unit can also analyze the content of an email using, for example, a machine learning algorithm. The summarization unit generates a summary based on the content analyzed by the analysis unit. The summarization unit generates a summary using, for example, a generative AI. The generative AI can use, for example, models such as GPT-4 or Gemini. The summarization unit extracts important information from the body of the email and creates a summary based on it. The summarization unit can also generate a summary based on, for example, the length of the text or the importance of the information to be summarized. The registration unit has a function for users to register specific keywords. The registration unit saves the keywords entered by the user to a database. The registration unit can also automatically extract frequently occurring words and register them as keywords. The display unit displays relevant information based on the keywords registered by the registration unit. The display unit displays, for example, related documents and links. The display unit can also display, for example, related images. The display unit displays the summary generated by the summarization unit. The display unit displays, for example, the summary in text format. The display unit can also display, for example, the summary as a graph or chart. As a result, the email summarization system according to the embodiment allows the user to efficiently manage the large volume of emails received in a day and quickly grasp important information.

[0057] The analysis unit can analyze the content of the email using the generation AI and extract important information. The generation AI can use models such as GPT-4 and Gemini. The generation AI can extract important keywords and phrases from the body of the email, for example. The generation AI can analyze the content of the email and extract important information, for example. The generation AI can also analyze the content of the email using natural language processing technology, for example. This makes it possible to efficiently analyze the content of the email and extract important information by using the generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the body of the email into the generation AI and have the generation AI extract important information.

[0058] The summarization unit can use a generation AI to generate a summary based on the information extracted by the analysis unit. The generation AI can use models such as GPT-4 or Gemini. The generation AI generates a summary based on the information extracted by the analysis unit. The generation AI can extract important information from the body of an email, for example, and create a summary based on that information. The generation AI can also generate a summary based on the length of the sentence or the importance of the information to be summarized, for example. This allows the generation AI to efficiently generate a summary based on the extracted information. Some or all of the above-mentioned processing in the summarization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the summarization unit can input the information extracted by the analysis unit into the generation AI and have the generation AI generate a summary.

[0059] The registration unit may have a function that allows users to register specific keywords. For example, the registration unit can save keywords entered by the user to a database. The registration unit can also automatically extract frequently occurring words and register them as keywords. The registration unit can also register keywords entered by the user in real time. This allows users to efficiently display related information by registering specific keywords. Some or all of the above-described processes in the registration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the registration unit can input keywords entered by the user into a generative AI and have the generative AI perform the keyword registration.

[0060] The display unit can display relevant information based on keywords registered by the registration unit. For example, the display unit can display related documents or links. The display unit can also display related images. For example, the display unit can display related videos. For example, the display unit can display related news articles. This allows users to quickly grasp important information by displaying relevant information based on registered keywords. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input keywords registered by the registration unit into a generating AI and cause the generating AI to display the relevant information.

[0061] The display unit can display the summary generated by the summarization unit. The display unit can, for example, display the summary in text format. The display unit can also display the summary as a graph or chart, for example. The display unit can, for example, read the summary aloud. The display unit can, for example, display the summary in video format. In this way, by displaying the generated summary, the user can quickly understand the content of the conversation or the incident that is occurring. Some or all of the above-mentioned processing in the display unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the display unit can input the summary generated by the summarization unit to the generation AI and cause the generation AI to display the summary.

[0062] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing high-priority emails. If the user is relaxed, the analysis unit may also analyze all emails equally. If the user is in a hurry, the analysis unit may also prioritize analyzing emails that can be analyzed quickly. By adjusting the analysis priority according to the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0063] The analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the sender of the email. For example, if the sender is a boss, the analysis unit sets the importance to a high level. For example, if the sender is a customer, the analysis unit can also perform a detailed analysis. For example, if the sender is a colleague, the analysis unit can also perform a normal analysis. In this way, the accuracy of the analysis is improved by taking into account the attribute information of the sender. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the attribute information of the sender into the generation AI and have the generation AI improve the accuracy of the analysis.

[0064] The analysis unit can prioritize analyzing information related to a specific topic based on the content of the email. For example, the analysis unit prioritizes analyzing emails related to projects. For example, the analysis unit can also prioritize analyzing emails related to urgent matters. For example, the analysis unit can also prioritize analyzing emails related to meeting minutes. In this way, by analyzing information related to a specific topic prioritized, important information can be quickly grasped. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the content of the email into the generation AI and have the generation AI analyze information related to a specific topic.

[0065] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0066] The analysis unit can determine the analysis priority based on the reception time of the email. For example, the analysis unit prioritizes analysis of the most recently received email. For example, the analysis unit can also prioritize analysis of emails received during an important time period. For example, the analysis unit can also prioritize analysis of emails received during a time period specified by the user. In this way, by determining the analysis priority based on the reception time of the email, important emails can be analyzed quickly. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input email reception time data into the generation AI and cause the generation AI to execute processing to determine the analysis priority.

[0067] The analysis unit can analyze the contents of email attachments and extract relevant information. The analysis unit can, for example, analyze the text content of the attachments and extract important information. The analysis unit can also, for example, analyze the image content of the attachments and extract relevant information. The analysis unit can, for example, analyze the PDF content of the attachments and extract important information. This allows for efficient extraction of relevant information by analyzing the contents of email attachments. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the data of the attachments into the generation AI and have the generation AI extract relevant information.

[0068] The summarization unit can estimate the user's emotions and adjust the summary presentation style based on the estimated user emotions. For example, when the user is stressed, the summarization unit can provide a concise and to-the-point summary. For example, when the user is relaxed, the summarization unit can provide a detailed summary. For example, when the user is in a hurry, the summarization unit can provide a summary that can be understood in a short time. This allows for a more appropriate summary to be provided by adjusting the summary presentation style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the summarization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the summarization unit can input user emotion data into the generation AI and cause the generation AI to perform a process of adjusting the summary presentation style.

[0069] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the email. For example, the summarization unit provides a detailed summary for emails of high importance. For example, the summarization unit can also provide a concise summary for emails of low importance. For example, the summarization unit can also provide a summary with appropriate detail for emails of medium importance. In this way, by adjusting the level of detail of the summary based on the importance of the email, important information can be grasped in detail. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input email importance data into the generation AI and cause the generation AI to perform processing to adjust the level of detail of the summary.

[0070] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the email. For example, the summarization unit can apply a project-specific summarization algorithm to project-related emails. For example, the summarization unit can also apply an emergency-specific summarization algorithm to emergency-related emails. For example, the summarization unit can also apply a meeting-specific summarization algorithm to meeting-related emails. In this way, by applying different summarization algorithms depending on the category of the email, a more appropriate summary can be generated. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input email category data into the generation AI and have the generation AI apply the summarization algorithm.

[0071] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit can provide a short summary. For example, if the user is relaxed, the summarization unit can provide a longer summary. For example, if the user is excited, the summarization unit can provide a visually stimulating summary. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using a generative AI, or not using a generative AI. For example, the summarization unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the length of the summary.

[0072] When generating a summary, the summarization unit can generate a summary taking into account the attribute information of the sender of the email. For example, if the sender is a boss, the summarization unit generates a summary with a high importance setting. For example, if the sender is a customer, the summarization unit can also generate a detailed summary. For example, if the sender is a colleague, the summarization unit can also generate a normal summary. In this way, by taking into account the attribute information of the sender of the email, a more appropriate summary is generated. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input the attribute information of the sender into the generation AI and have the generation AI generate the summary.

[0073] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to literature related to the email. For example, the summarization unit can improve the accuracy of the summary by referring to literature related to the content of the email. For example, the summarization unit can also improve the accuracy of the summary by referring to past emails related to the content of the email. For example, the summarization unit can also improve the accuracy of the summary by referring to websites related to the content of the email. In this way, the accuracy of the summary is improved by referring to related literature. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, a generation AI. For example, the summarization unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the summary.

[0074] The registration unit can estimate the user's emotions and adjust the keyword registration method based on the estimated user emotions. For example, when the user is stressed, the registration unit can provide a simple interface and minimize the keyword registration procedure. For example, when the user is relaxed, the registration unit can provide detailed input options and suggest a customizable keyword registration method. For example, when the user is in a hurry, the registration unit can prioritize voice input and enable quick keyword registration. This enables more appropriate keyword registration by adjusting the keyword registration method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the registration unit can be performed using, for example, the generation AI. For example, the registration unit can input the user's emotion data into the generation AI and cause the generation AI to execute a process of adjusting the keyword registration method.

[0075] When registering a keyword, the registration unit can suggest optimal keywords by referring to the user's past keyword registration history. For example, the registration unit can automatically display keywords that the user has frequently registered in the past as candidates. For example, the registration unit can also preferentially suggest keywords that the user has used in the past. For example, the registration unit can predict and suggest keywords that will be used in a specific time period based on the user's past keyword registration history. This makes it possible to suggest optimal keywords by referring to the past keyword registration history. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the user's past keyword registration history into the generation AI and cause the generation AI to suggest optimal keywords.

[0076] The registration unit can suggest highly relevant keywords by taking into account the user's geographical location information when registering keywords. For example, if the user is in a specific area, the registration unit can suggest keywords related to that area. For example, if the user is traveling, the registration unit can also suggest keywords related to the travel destination. For example, if the user is at home, the registration unit can also suggest keywords related to the home. In this way, highly relevant keywords can be suggested by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest highly relevant keywords.

[0077] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is nervous, the display unit can provide an interface with subdued colors to reduce visual stress. For example, if the user is having fun, the display unit can provide an interface with bright colors to make display work more enjoyable. For example, if the user is tired, the display unit can provide a simple, highly visible interface to make display work easier. This enables more appropriate display by adjusting the display method according to the user's emotions. 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-mentioned processing in the display unit can be performed using, for example, the generation AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0078] When displaying information, the display unit can select the optimal display method by referring to the user's past browsing history. For example, the display unit prioritizes displaying information that the user has frequently viewed in the past. For example, the display unit can predict and suggest information to be displayed during a specific time period based on the user's past browsing history. For example, the display unit can analyze the user's past browsing history and suggest the most efficient display method. In this way, the optimal display method can be selected by referring to the past browsing history. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's past browsing history into the generation AI and have the generation AI select the optimal display method.

[0079] The display unit can estimate the user's emotions and determine the priority of display content based on the estimated user emotions. For example, when the user is feeling stressed, the display unit can prioritize displaying information of high importance. For example, when the user is relaxed, the display unit can also display all information evenly. For example, when the user is in a hurry, the display unit can also prioritize displaying information that can be understood in a short time. This allows important information to be displayed preferentially by determining the priority of display content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the display unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the display unit can input user emotion data to the generation AI and cause the generation AI to execute a process of determining the priority of display content.

[0080] The display unit can select the optimal display method by taking into account the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method to be provided by taking into account the device information. Some or all of the above-mentioned processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, summarization unit, registration unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and analyzes the content of the email. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates a summary based on the analyzed content. The registration unit is implemented by the control unit 46A of the smart device 14, and has the function of allowing the user to register specific keywords. The display unit is implemented by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and displays relevant information based on the registered keywords. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, summarization unit, registration unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12, and analyzes the content of the email. The summarization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a summary based on the analyzed content. The registration unit is implemented, for example, by the control unit 46A of the smart glasses 214, and has the function of allowing the user to register specific keywords. The display unit is implemented, for example, by the display of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12, and displays relevant information based on the registered keywords. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, summarization unit, registration 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 analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and analyzes the content of the email. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a summary based on the analyzed content. The registration unit is realized, for example, by the control unit 46A of the headset type terminal 314 and has a function that allows the user to register specific keywords. The display unit is realized, for example, by the display 343 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and displays related information based on the registered keywords. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, summarization unit, registration 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 analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12 and analyzes the content of the email. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a summary based on the analyzed content. The registration unit is realized, for example, by the control unit 46A of the robot 414 and has a function that allows the user to register specific keywords. The display unit is realized, for example, by the display of the robot 414 or the specific processing unit 290 of the data processing device 12 and displays related information based on the registered keywords.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The analysis unit can improve the accuracy of its analysis by referring to the sender's past email history when analyzing the content of an email. For example, by analyzing the content of emails the sender has sent in the past and extracting specific patterns and keywords, it can improve the accuracy of the analysis of the current email. Furthermore, by considering the tone and style of emails the sender has sent in the past, more accurate analysis becomes possible. In this way, by referring to the sender's past email history, the accuracy of the analysis is improved, and important information can be extracted more precisely.

[0083] The summarization unit, using generation AI, can customize the content of summaries based on information extracted by the analysis unit by referencing the user's past browsing history. For example, it can prioritize including information related to topics the user has frequently viewed in the past. Furthermore, by considering keywords the user has shown interest in in the past when generating summaries, it can provide more relevant information to the user. In this way, by referencing the user's past browsing history, the content of the summaries can be better tailored to the user's needs.

[0084] The registration section can suggest the most suitable keywords when a user registers a specific keyword, by referring to the user's past search history. For example, it can automatically display keywords that the user has frequently searched for in the past as suggestions. Furthermore, by prioritizing the suggestion of keywords that the user has used in the past, the efficiency of keyword registration can be improved. In this way, by referring to the user's past search history, the most suitable keywords can be suggested, reducing the effort required for keyword registration.

[0085] When displaying related information based on the keywords registered by the registration unit, the display unit can provide an optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Furthermore, if the user is using a tablet, a display method optimized for a large screen can be provided. This allows the optimal display method to be provided by taking into account the device information, thereby improving user convenience.

[0086] When analyzing the contents of an email, the analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the email sender. For example, if the sender is a superior, the importance level is set high for analysis. Furthermore, if the sender is a customer, a more detailed analysis can be performed. By taking into account the attribute information of the sender, the accuracy of the analysis can be improved and important information can be extracted more accurately.

[0087] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analyzing emails of high importance. Furthermore, if the user is relaxed, it can analyze all emails equally. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions.

[0088] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a concise summary that focuses on the main points can be provided. Furthermore, if the user is feeling relaxed, a detailed summary can be provided. In this way, by adjusting the way the summary is presented depending on the user's emotions, a more appropriate summary can be provided.

[0089] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is nervous, it can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, it can provide an interface with bright colors to make the display task more enjoyable. This allows the display method to be adjusted according to the user's emotions, enabling more appropriate display.

[0090] The display unit can estimate the user's emotions and prioritize the display content based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize displaying information of high importance. Furthermore, if the user is relaxed, it can display all information equally. In this way, by prioritizing the display content according to the user's emotions, it is possible to display important information with priority.

[0091] The registration unit can estimate the user's emotions and adjust the keyword registration method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the keyword registration procedure. Furthermore, if the user is relaxed, detailed input options can be provided to suggest a customizable keyword registration method. This allows for more appropriate keyword registration by adjusting the keyword registration method according to the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The analysis unit analyzes the content of the email. The analysis unit uses natural language processing technology and machine learning algorithms to extract important keywords and phrases from the email body. Step 2: The summarization unit generates a summary based on the content analyzed by the analysis unit. The summarization unit uses a generation AI (e.g., GPT-4 or Gemini) to extract important information from the email body and create a summary based on that information. Step 3: The registration section includes a function for users to register specific keywords. In addition to saving keywords entered by the user to the database, it can also automatically extract frequently occurring words and register them as keywords. Step 4: The display unit displays relevant information based on the keywords registered by the registration unit. In addition to displaying related documents, links, and images, it also displays summaries generated by the summarization unit in text format, graphs, and charts.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of the email; a summarization unit that generates a summary based on the content analyzed by the analysis unit; a registration unit for registering specific keywords; a display unit that displays related information based on the keywords registered by the registration unit; Equipped with A system characterized by:

2. The analysis unit Analyze email content using generative AI to extract important information 2. The system of claim 1.

3. The summary section Generate a summary based on the information extracted by the analysis unit using generation AI.

2. The system of claim 1.

4. The registration unit Equipped with a function that allows users to register specific keywords 2. The system of claim 1.

5. The display unit Displaying related information based on the keywords registered by the registration unit 2. The system of claim 1.

6. The display unit displaying the summary generated by the summarization unit 2. The system of claim 1.

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

8. The analysis unit Improve analysis accuracy by taking into account the attribute information of email senders 2. The system of claim 1.

Images