system

Abstract

The system according to this embodiment aims to extract agreements from the content of conversations and emails, and to manage and remind users of them. [Solution] The system according to the embodiment comprises a collection unit, a summarization unit, a presentation unit, an approval unit, a storage unit, and a reminder unit. The collection unit collects the contents of conversations and emails. The summarization unit summarizes the contents collected by the collection unit. The presentation unit presents the contents summarized by the summarization unit to the user. The approval unit accepts approval of the contents presented by the presentation unit. The storage unit stores the agreements approved by the approval unit as a list. The reminder unit reminds the user of the agreements stored by the storage unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to extract and manage agreements from the contents of conversations and emails, and there is a lack of reminders to ensure the fulfillment of agreements.

[0005] The system according to the embodiment aims to extract agreements from the contents of conversations and emails, and perform management and reminders.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a summarization unit, a presentation unit, an approval unit, a storage unit, and a reminder unit. The collection unit collects the content of conversations and emails. The summarization unit summarizes the content collected by the collection unit. The presentation unit presents the content summarized by the summarization unit to the user. The approval unit accepts approval of the content presented by the presentation unit. The storage unit stores the agreements approved by the approval unit as a list. The reminder unit reminds the user of the agreements stored by the storage unit. [Effects of the Invention]

[0007] The system according to this embodiment can extract agreements from conversations and emails, manage them, and send reminders. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] [[ID=1,4]]As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The promise management system according to an embodiment of the present invention is a system that extracts, summarizes, and manages promises from the content of conversations, emails, etc. This promise management system is activated when the user launches the app and sets the operating period. The promise management system records conversations during the operating period and summarizes their content. Furthermore, the promise management system compiles verbal promises into bullet points and, in cooperation with other apps, also compiles promises other than those recorded and summarized. For example, the promise management system displays a digest of promises to the user at predetermined times, allowing the user to approve promises they actually intend to keep and reject those made as a joke. If both parties who made a promise are using the same app and both approve it, the promise management system saves the content as a list and can be linked with a scheduler, etc. The promise management system also provides reminders of saved promises within the app at appropriate times. Furthermore, the promise management system has a function to generate and suggest promises that may be needed in the future, using past promises as training data. As a result, the promise management system can formalize and manage everything from casual verbal promises in daily life to promises in business settings. This allows the appointment management system to efficiently collect, summarize, present, approve, save, and remind users of conversations and emails.

[0029] The appointment management system according to this embodiment comprises a collection unit, a summarization unit, a presentation unit, an approval unit, a storage unit, and a reminder unit. The collection unit collects the content of conversations and emails. The collection unit converts the content of conversations into text data, for example, using speech recognition technology. The collection unit can also analyze the content of emails and extract important information. For example, the collection unit collects the content of various types of conversations and emails, such as business emails and personal conversations. The summarization unit summarizes the content collected by the collection unit. The summarization unit performs summarization based on the length of the text and the importance of the information to be summarized, for example, using natural language processing technology. For example, the summarization unit extracts important points and generates a concise summary. The presentation unit presents the content summarized by the summarization unit to the user. The presentation unit, for example, sends a notification to the user's device and displays the summarized content. The presentation unit can also present the summarized content at an appropriate time based on the user's schedule. The approval unit accepts approval of the content presented by the presentation unit. The approval unit allows the user to approve, for example, by clicking or using speech recognition. The approval unit records the content approved by the user and sends it to the storage unit. The storage unit stores the appointments approved by the approval unit as a list. The storage unit can, for example, save the appointment content in digital format so that the user can access it at any time. The storage unit can also link the saved appointment content with scheduler or task management apps. The reminder unit reminds the user of appointments saved by the storage unit. The reminder unit can, for example, send a notification to the user's device to remind them of the appointment. The reminder unit can also send reminders at appropriate times based on the user's schedule. As a result, the appointment management system according to the embodiment can efficiently collect, summarize, present, approve, save, and remind users of the content of conversations and emails.

[0030] The data collection unit collects the content of conversations and emails. For example, it converts conversation content into text data using speech recognition technology. Specifically, the unit captures conversation audio data in real time and uses an advanced speech recognition algorithm to convert the audio to text. This speech recognition algorithm includes noise cancellation to remove background noise and improve speech clarity. The data collection unit can also analyze email content and extract important information. For example, it uses natural language processing technology to extract important information such as date, time, location, and participants from the email body. This allows the unit to collect content from various types of conversations and emails, including business emails and personal conversations. Furthermore, the data collection unit can integrate information from multiple data sources and manage it centrally. For example, it can integrate audio data, text data, and email data into a single database and store it in a format easily accessible to subsequent processing departments. This allows the data collection unit to collect data efficiently and accurately, improving the overall system performance.

[0031] The summarization unit summarizes the content collected by the data collection unit. The summarization unit uses, for example, natural language processing techniques to perform summaries based on the length of the text and the importance of the information being summarized. Specifically, the summarization unit analyzes the collected text data and extracts important keywords and phrases. This allows the summarization unit to extract key points and generate a concise summary. The summarization unit uses machine learning algorithms to improve the accuracy of the summaries. For example, the summarization unit learns from past summarization data and builds a model to accurately extract important information. This model can understand context and evaluate the relevance of information. Furthermore, the summarization unit can customize the style of the summary according to user preferences. For example, if a user prefers a short summary, the summarization unit extracts only the key points and generates a concise summary. On the other hand, for users who prefer a detailed summary, it provides a summary containing more information. This allows the summarization unit to provide flexible summaries tailored to user needs and support information comprehension.

[0032] The presentation unit presents the content summarized by the summary unit to the user. For example, the presentation unit sends a notification to the user's device and displays the summary. Specifically, the presentation unit sends pop-up notifications or email notifications to devices such as smartphones, tablets, and PCs and displays the summary. The presentation unit can also present the summary at an appropriate time based on the user's schedule. For example, the presentation unit can link with the user's calendar app and notify them of the summary just before a meeting. This allows the user to check important information in a timely manner. Furthermore, the presentation unit can customize the user interface. For example, the presentation unit can change the format and display method of notifications according to the user's preferences. This allows the presentation unit to provide a user-friendly interface and facilitate smooth information verification.

[0033] The approval unit accepts approval of the content presented by the presentation unit. The approval unit allows users to approve using methods such as clicks or voice recognition. Specifically, the approval unit provides an interface that allows users to approve by clicking a button on their device screen. Users can also approve using voice commands via voice recognition technology. For example, approval is completed when the user says "approved." Furthermore, the approval unit records the content approved by the user and sends it to the storage unit. The approval unit saves the approval history to a database for later reference. This allows the approval unit to streamline the user's approval process and maintain an accurate record of approved content.

[0034] The storage unit stores appointments approved by the approval unit as a list. The storage unit, for example, stores appointment details digitally, allowing users to access them at any time. Specifically, the storage unit securely stores appointment details using cloud storage. This allows users to access appointment details from any internet-connected device. The storage unit can also integrate stored appointment details with schedulers and task management apps. For example, it can synchronize with the user's calendar app and automatically add appointment dates and times to the schedule. This allows users to efficiently manage their schedules without forgetting appointments. Furthermore, the storage unit includes a data backup function to ensure security against data loss or corruption. This ensures that the storage unit reliably stores users' important appointment details and allows for quick access when needed.

[0035] The reminder unit reminds users of appointments saved by the storage unit. For example, the reminder unit sends notifications to the user's device to remind them of appointments. Specifically, the reminder unit sends pop-up notifications or email notifications to devices such as smartphones, tablets, and PCs to remind users of appointments. The reminder unit can also send reminders at appropriate times based on the user's schedule. For example, the reminder unit can integrate with the user's calendar app and send a reminder notification just before an appointment. This allows users to act promptly and avoid forgetting important appointments. Furthermore, the reminder unit can customize the frequency and timing of reminders according to the user's preferences. For example, the reminder unit provides an interface that allows users to set the time and frequency at which they receive reminder notifications. This enables the reminder unit to provide flexible reminder functionality tailored to user needs and support appointment management.

[0036] It includes an integration unit that connects with other applications. The integration unit connects with other applications. For example, it connects with calendar applications and task management applications to automatically synchronize appointment details. It can also connect with the user's email and messaging applications to collect conversation and email content. For example, the integration unit connects with calendar applications to automatically update appointment schedules. It can also connect with task management applications to add appointment details as tasks. It can connect with email applications to collect important email content and send it to the summarization unit. It can also connect with messaging applications to collect conversation content and send it to the summarization unit. This streamlines information collection and management through integration with other applications.

[0037] The system includes a generation unit that uses generative AI to generate appointments that are likely to be needed in the future. The generation unit uses generative AI to generate appointments that are likely to be needed in the future. For example, the generation unit learns from past appointment data and predicts appointments that may be needed in the future. The generation unit can also use generative AI to analyze the user's schedule and task trends and generate future appointments. For example, the generation unit generates the schedule for the next meeting based on past meeting data. The generation unit can also generate appointments for the next task based on the user's task deadlines. The generation unit uses generative AI to analyze the user's past behavior patterns and predict future appointments. This improves user convenience by automatically generating appointments that are likely to be needed in the future.

[0038] The system includes a suggestion unit that suggests generated appointments. The suggestion unit suggests generated appointments. For example, the suggestion unit presents appointments generated by the generation AI to the user and confirms whether the user approves them. The suggestion unit can also suggest appointments at the appropriate time based on the user's schedule and task status. For example, the suggestion unit suggests the next meeting based on the user's calendar. The suggestion unit can also suggest appointments for the next task based on the user's task management app. The suggestion unit notifies the user of appointments generated by the generation AI and confirms whether the user approves them. This improves user convenience by suggesting generated appointments to the user.

[0039] The data collection unit can prioritize collecting high-importance content by referring to the user's past conversation history during collection. For example, the data collection unit prioritizes collecting topics that the user has frequently mentioned in the past. The data collection unit can also select content to collect based on keywords from conversations that the user has considered important in the past. If the user has considered conversations with a particular person important in the past, the data collection unit can also prioritize collecting conversations with that person. This allows for the priority collection of high-importance content by referring to past conversation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0040] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit prioritizes collecting conversations and emails related to projects the user is currently working on. The data collection unit can also collect information related to topics the user is interested in. If the user is interested in a particular industry or field, the data collection unit can also collect conversations and emails related to that field. This allows for the collection of highly relevant information by filtering based on current projects and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI.

[0041] The data collection unit can prioritize the collection of highly relevant content by considering the user's geographical location during the collection process. For example, if the user is in a specific location, the data collection unit will prioritize the collection of conversations and emails related to that location. If the user is traveling, the data collection unit can also collect information related to their travel destination. If the user is participating in a specific event, the data collection unit can also collect information related to that event. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0042] The data collection unit can analyze the user's social media activity and collect relevant content during the collection process. For example, the data collection unit can collect information related to topics mentioned by the user on social media. The data collection unit can also collect posts from accounts that the user follows. The data collection unit can also collect information about groups and communities that the user participates in. This allows relevant information to be collected by analyzing social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0043] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation or email during summary generation. For example, the summarization unit provides a detailed summary for high-importance conversations or emails. For low-importance conversations or emails, the summarization unit can also provide a concise summary. The summarization unit can also adjust the length and content of the summary according to its importance. This allows for the provision of an appropriate summary by adjusting the level of detail based on importance. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI.

[0044] The summarization unit can apply different summarization algorithms depending on the category of the conversation or email when generating a summary. For example, in the case of business-related conversations or emails, the summarization unit will provide a summary that includes technical terms. In the case of private conversations or emails, the summarization unit can also provide a summary using casual language. The summarization unit can also select an appropriate summarization algorithm depending on the category and generate a summary. This ensures that an appropriate summary is provided by applying the appropriate summarization algorithm according to the category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without using AI.

[0045] The summarization unit can prioritize summaries based on when conversations or emails were sent. For example, it can prioritize summarizing recently sent conversations or emails. It can also prioritize summarizing conversations or emails sent immediately before important events. The summarization unit can also adjust the summarization priority based on the user's schedule. This allows important information to be summarized preferentially by prioritizing summaries based on when they were sent. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not.

[0046] The summarization unit can adjust the order of summaries based on the relevance of conversations and emails during summary generation. For example, the summarization unit can prioritize the display of summaries of highly relevant conversations and emails. It can also postpone the display of summaries of less relevant conversations and emails. The summarization unit can also adjust the display order of summaries according to their relevance. This allows important information to be presented preferentially by adjusting the order of summaries based on relevance. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI.

[0047] The presentation unit can select the optimal presentation method by referring to the user's past approval history at the time of presentation. For example, the presentation unit selects the optimal presentation method based on content that the user has previously approved. The presentation unit can also analyze preferred presentation methods from the user's past approval history. The presentation unit can also select the optimal presentation method by referring to presentation methods of content that the user has previously approved. In this way, the optimal presentation method can be selected by referring to past approval history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0048] The presentation unit can customize the content presented based on the user's current situation at the time of presentation. For example, if the user is busy, the presentation unit may present only essential information. If the user is relaxed, the presentation unit may also provide content that includes detailed information. The presentation unit can also customize the content presented according to the user's current situation. This allows the presentation unit to provide appropriate information by customizing the content based on the current situation. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0049] The display unit can select the optimal display method when presenting information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0050] The presentation unit can present information at the optimal time, taking into account the user's schedule. For example, the presentation unit can present important information at the optimal time based on the user's schedule. The presentation unit can also present information while avoiding the user's busy times. The presentation unit can also adjust the timing of presentation according to the user's schedule. This allows information to be provided at the optimal time by considering the schedule. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0051] The approval unit can select the optimal approval method by referring to the user's past approval history during the approval process. For example, the approval unit can select the optimal approval method based on the content the user has previously approved. The approval unit can also analyze preferred approval methods from the user's past approval history. The approval unit can also select the optimal approval method by referring to the approval methods used for content the user has previously approved. In this way, the optimal approval method can be selected by referring to past approval history. Some or all of the above processing in the approval unit may be performed using AI, for example, or without using AI.

[0052] The approval unit can customize the approval content based on the user's current situation during the approval process. For example, if the user is busy, the approval unit will only approve essential information. If the user is relaxed, the approval unit may also provide approval content that includes detailed information. The approval unit can also customize the approval content according to the user's current situation. This allows for the provision of appropriate approval content by customizing it based on the current situation. Some or all of the above processes in the approval unit may be performed using AI, for example, or not using AI.

[0053] The approval unit can select the optimal approval method when approving, taking into account the user's device information. For example, if the user is using a smartphone, the approval unit can provide an approval method that is adapted to the screen size. If the user is using a tablet, the approval unit can also provide an approval method optimized for a larger screen. If the user is using a smartwatch, the approval unit can also provide a concise and highly visible approval method. In this way, the optimal approval method can be provided by taking device information into consideration. Some or all of the above processing in the approval unit may be performed using AI, for example, or without using AI.

[0054] The approval unit can prompt for approval at the optimal time, taking into account the user's schedule. For example, the approval unit will approve important information at the optimal time based on the user's schedule. The approval unit can also prompt for approval while avoiding busy times for the user. The approval unit can also adjust the timing of approval according to the user's schedule. This allows for prompting for approval at the optimal time by considering the schedule. Some or all of the above processes in the approval unit may be performed using AI, for example, or not using AI.

[0055] The storage unit can select the optimal storage method by referring to the user's past saving history when saving. For example, the storage unit selects the optimal storage method based on the content the user has saved in the past. The storage unit can also analyze preferred storage methods from the user's past saving history. The storage unit can also select the optimal storage method by referring to the storage methods used for content the user has saved in the past. In this way, the optimal storage method can be selected by referring to the past saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0056] The storage unit can customize the content saved based on the user's current situation at the time of saving. For example, if the user is busy, the storage unit will save only important information. If the user is relaxed, the storage unit can also provide content that includes detailed information. The storage unit can also customize the content saved according to the user's current situation. This allows the storage unit to provide appropriate content by customizing the content saved based on the current situation. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI.

[0057] The storage unit can select the optimal storage method when saving, taking into account the user's device information. For example, if the user is using a smartphone, the storage unit can provide a storage method that matches the screen size. If the user is using a tablet, the storage unit can also provide a storage method optimized for a larger screen. If the user is using a smartwatch, the storage unit can also provide a concise and highly visible storage method. In this way, the optimal storage method can be provided by taking device information into consideration. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0058] The storage unit can save data at the optimal time, taking into account the user's schedule. For example, the storage unit can save important information at the optimal time based on the user's schedule. The storage unit can also save information while avoiding the user's busy hours. The storage unit can also adjust the timing of saving according to the user's schedule. This allows information to be saved at the optimal time by considering the schedule. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without using AI.

[0059] The reminder unit can select the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal reminder method based on the content of past reminders the user has received. The reminder unit can also analyze preferred reminder methods from the user's past reminder history. The reminder unit can also select the optimal reminder method by referring to the reminder methods used for content that the user has received past reminders about. In this way, the optimal reminder method can be selected by referring to past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0060] The reminder function can customize the reminder content based on the user's current situation. For example, if the user is busy, the reminder function will only remind them of important information. If the user is relaxed, the reminder function can also provide a reminder with more detailed information. The reminder function can customize the reminder content according to the user's current situation. This allows the system to provide appropriate reminders by customizing them based on the current situation. Some or all of the above processing in the reminder function may be performed using AI, for example, or without AI.

[0061] The reminder unit can select the optimal reminder method by considering the user's device information when sending a reminder. For example, if the user is using a smartphone, the reminder unit can provide a reminder method that is adapted to the screen size. If the user is using a tablet, the reminder unit can also provide a reminder method optimized for a larger screen. If the user is using a smartwatch, the reminder unit can also provide a concise and highly visible reminder method. In this way, the optimal reminder method can be provided by considering device information. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0062] The reminder unit can send reminders at the optimal time, taking into account the user's schedule. For example, the reminder unit can remind users of important information at the optimal time based on their schedule. The reminder unit can also remind users of information while avoiding busy times. The reminder unit can also adjust the timing of reminders according to the user's schedule. This allows for reminders to be sent at the optimal time by considering the schedule. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0063] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit can select the optimal integration method based on the apps the user has previously integrated with. The integration unit can also analyze preferred integration methods from the user's past integration history. The integration unit can also select the optimal integration method by referring to the integration methods of apps the user has previously integrated with. In this way, the optimal integration method can be selected by referring to past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0064] The integration unit can select the optimal integration method by considering the user's device information during integration. For example, if the user is using a smartphone, the integration unit provides an integration method that matches the screen size. If the user is using a tablet, the integration unit can also provide an integration method optimized for a larger screen. If the user is using a smartwatch, the integration unit can also provide a concise and highly visible integration method. In this way, the optimal integration method can be provided by considering device information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0065] The generation unit can generate the optimal promise content by referring to the user's past promise history during generation. For example, the generation unit generates the optimal promise content based on promises the user has made in the past. The generation unit can also analyze preferred promise content from the user's past promise history. The generation unit can also generate the optimal promise content by referring to the content of promises the user has made in the past. In this way, the optimal promise content can be generated by referring to the past promise history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0066] The generation unit can customize the promise content based on the user's current situation during generation. For example, if the user is busy, the generation unit can generate a promise content that includes only essential information. If the user is relaxed, the generation unit can also generate a promise content that includes detailed information. The generation unit can also customize the promise content according to the user's current situation. This allows for the generation of appropriate promise content by customizing it based on the current situation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0067] The generation unit can generate optimal promise content by considering the user's device information during generation. For example, if the user is using a smartphone, the generation unit will generate promise content that matches the screen size. If the user is using a tablet, the generation unit can also generate promise content optimized for a larger screen. If the user is using a smartwatch, the generation unit can also generate concise and highly visible promise content. In this way, optimal promise content can be generated by considering device information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0068] The generation unit can generate appointments at the optimal time, taking into account the user's schedule. For example, the generation unit can generate important information at the optimal time based on the user's schedule. The generation unit can also generate appointments while avoiding the user's busy times. The generation unit can also adjust the timing of generation according to the user's schedule. This allows for the generation of appointments at the optimal time by considering the schedule. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0069] The suggestion unit can select the optimal suggestion method by referring to the user's past suggestion history when making suggestions. For example, the suggestion unit can select the optimal suggestion method based on the content previously suggested to the user. The suggestion unit can also analyze preferred suggestion methods from the user's past suggestion history. The suggestion unit can also select the optimal suggestion method by referring to the suggestion methods of the content previously suggested to the user. In this way, the optimal suggestion method can be selected by referring to the past suggestion history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI.

[0070] The suggestion function can customize the suggested content based on the user's current situation at the time of suggestion. For example, if the user is busy, the suggestion function will only suggest important information. If the user is relaxed, the suggestion function can also provide suggestions that include detailed information. The suggestion function can also customize the suggested content according to the user's current situation. This allows the suggestion function to provide appropriate suggestions by customizing the suggested content based on the current situation. Some or all of the above processing in the suggestion function may be performed using, for example, generative AI, or without using generative AI.

[0071] The suggestion unit can select the optimal suggestion method by considering the user's device information when making suggestions. For example, if the user is using a smartphone, the suggestion unit can provide a suggestion method that matches the screen size. If the user is using a tablet, the suggestion unit can also provide a suggestion method optimized for a larger screen. If the user is using a smartwatch, the suggestion unit can also provide a concise and highly visible suggestion method. In this way, the optimal suggestion method can be provided by considering device information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI.

[0072] The suggestion unit can make suggestions at the optimal time, taking into account the user's schedule. For example, the suggestion unit can suggest important information at the optimal time based on the user's schedule. The suggestion unit can also suggest information while avoiding the user's busy times. The suggestion unit can also adjust the timing of suggestions according to the user's schedule. This allows for suggestions to be made at the optimal time by considering the schedule. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI.

[0073] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0074] The data collection unit can prioritize collecting high-importance content by referring to the user's past conversation history during collection. For example, the data collection unit prioritizes collecting topics that the user has frequently mentioned in the past. The data collection unit can also select content to collect based on keywords from conversations that the user has considered important in the past. If the user has considered conversations with a particular person important in the past, the data collection unit can also prioritize collecting conversations with that person. This allows for the priority collection of high-importance content by referring to past conversation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0075] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit prioritizes collecting conversations and emails related to projects the user is currently working on. The data collection unit can also collect information related to topics the user is interested in. If the user is interested in a particular industry or field, the data collection unit can also collect conversations and emails related to that field. This allows for the collection of highly relevant information by filtering based on current projects and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI.

[0076] The data collection unit can prioritize the collection of highly relevant content by considering the user's geographical location during the collection process. For example, if the user is in a specific location, the data collection unit will prioritize the collection of conversations and emails related to that location. If the user is traveling, the data collection unit can also collect information related to their travel destination. If the user is participating in a specific event, the data collection unit can also collect information related to that event. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0077] The data collection unit can analyze the user's social media activity and collect relevant content during the collection process. For example, the data collection unit can collect information related to topics mentioned by the user on social media. The data collection unit can also collect posts from accounts that the user follows. The data collection unit can also collect information about groups and communities that the user participates in. This allows relevant information to be collected by analyzing social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0078] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation or email during summary generation. For example, the summarization unit provides a detailed summary for high-importance conversations or emails. For low-importance conversations or emails, the summarization unit can also provide a concise summary. The summarization unit can also adjust the length and content of the summary according to its importance. This allows for the provision of an appropriate summary by adjusting the level of detail based on importance. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI.

[0079] The summarization unit can apply different summarization algorithms depending on the category of the conversation or email when generating a summary. For example, in the case of business-related conversations or emails, the summarization unit will provide a summary that includes technical terms. In the case of private conversations or emails, the summarization unit can also provide a summary using casual language. The summarization unit can also select an appropriate summarization algorithm depending on the category and generate a summary. This ensures that an appropriate summary is provided by applying the appropriate summarization algorithm according to the category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without using AI.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The collection unit collects the content of conversations and emails. The collection unit uses speech recognition technology to convert the content of conversations into text data and analyzes the content of emails to extract important information. For example, it collects the content of various types of conversations and emails, such as business emails and personal conversations. Step 2: The summarization unit summarizes the content collected by the data collection unit. The summarization unit uses natural language processing techniques to perform summarization based on the length of the text and the importance of the information being summarized. For example, it extracts key points and generates a concise summary. Step 3: The presentation unit presents the content summarized by the summary unit to the user. The presentation unit sends a notification to the user's device and displays the summary. It can also present the summary at an appropriate time based on the user's schedule. Step 4: The approval unit accepts approval of the content presented by the presentation unit. The approval unit allows users to approve using clicks or voice recognition, records the approved content, and sends it to the storage unit. Step 5: The storage unit saves the agreements approved by the approval unit as a list. The storage unit saves the agreement details in digital format, making them accessible to the user at any time. The saved agreement details can also be integrated with scheduler and task management apps. Step 6: The reminder unit reminds users of appointments saved by the storage unit. The reminder unit sends a notification to the user's device to remind them of the appointment. It can also send reminders at appropriate times based on the user's schedule.

[0082] (Example of form 2) The promise management system according to an embodiment of the present invention is a system that extracts, summarizes, and manages promises from the content of conversations, emails, etc. This promise management system is activated when the user launches the app and sets the operating period. The promise management system records conversations during the operating period and summarizes their content. Furthermore, the promise management system compiles verbal promises into bullet points and, in cooperation with other apps, also compiles promises other than those recorded and summarized. For example, the promise management system displays a digest of promises to the user at predetermined times, allowing the user to approve promises they actually intend to keep and reject those made as a joke. If both parties who made a promise are using the same app and both approve it, the promise management system saves the content as a list and can be linked with a scheduler, etc. The promise management system also provides reminders of saved promises within the app at appropriate times. Furthermore, the promise management system has a function to generate and suggest promises that may be needed in the future, using past promises as training data. As a result, the promise management system can formalize and manage everything from casual verbal promises in daily life to promises in business settings. This allows the appointment management system to efficiently collect, summarize, present, approve, save, and remind users of conversations and emails.

[0083] The appointment management system according to this embodiment comprises a collection unit, a summarization unit, a presentation unit, an approval unit, a storage unit, and a reminder unit. The collection unit collects the content of conversations and emails. The collection unit converts the content of conversations into text data, for example, using speech recognition technology. The collection unit can also analyze the content of emails and extract important information. For example, the collection unit collects the content of various types of conversations and emails, such as business emails and personal conversations. The summarization unit summarizes the content collected by the collection unit. The summarization unit performs summarization based on the length of the text and the importance of the information to be summarized, for example, using natural language processing technology. For example, the summarization unit extracts important points and generates a concise summary. The presentation unit presents the content summarized by the summarization unit to the user. The presentation unit, for example, sends a notification to the user's device and displays the summarized content. The presentation unit can also present the summarized content at an appropriate time based on the user's schedule. The approval unit accepts approval of the content presented by the presentation unit. The approval unit allows the user to approve, for example, by clicking or using speech recognition. The approval unit records the content approved by the user and sends it to the storage unit. The storage unit stores the appointments approved by the approval unit as a list. The storage unit can, for example, save the appointment content in digital format so that the user can access it at any time. The storage unit can also link the saved appointment content with scheduler or task management apps. The reminder unit reminds the user of appointments saved by the storage unit. The reminder unit can, for example, send a notification to the user's device to remind them of the appointment. The reminder unit can also send reminders at appropriate times based on the user's schedule. As a result, the appointment management system according to the embodiment can efficiently collect, summarize, present, approve, save, and remind users of the content of conversations and emails.

[0084] The data collection unit collects the content of conversations and emails. For example, it converts conversation content into text data using speech recognition technology. Specifically, the unit captures conversation audio data in real time and uses an advanced speech recognition algorithm to convert the audio to text. This speech recognition algorithm includes noise cancellation to remove background noise and improve speech clarity. The data collection unit can also analyze email content and extract important information. For example, it uses natural language processing technology to extract important information such as date, time, location, and participants from the email body. This allows the unit to collect content from various types of conversations and emails, including business emails and personal conversations. Furthermore, the data collection unit can integrate information from multiple data sources and manage it centrally. For example, it can integrate audio data, text data, and email data into a single database and store it in a format easily accessible to subsequent processing departments. This allows the data collection unit to collect data efficiently and accurately, improving the overall system performance.

[0085] The summarization unit summarizes the content collected by the data collection unit. The summarization unit uses, for example, natural language processing techniques to perform summaries based on the length of the text and the importance of the information being summarized. Specifically, the summarization unit analyzes the collected text data and extracts important keywords and phrases. This allows the summarization unit to extract key points and generate a concise summary. The summarization unit uses machine learning algorithms to improve the accuracy of the summaries. For example, the summarization unit learns from past summarization data and builds a model to accurately extract important information. This model can understand context and evaluate the relevance of information. Furthermore, the summarization unit can customize the style of the summary according to user preferences. For example, if a user prefers a short summary, the summarization unit extracts only the key points and generates a concise summary. On the other hand, for users who prefer a detailed summary, it provides a summary containing more information. This allows the summarization unit to provide flexible summaries tailored to user needs and support information comprehension.

[0086] The presentation unit presents the content summarized by the summary unit to the user. For example, the presentation unit sends a notification to the user's device and displays the summary. Specifically, the presentation unit sends pop-up notifications or email notifications to devices such as smartphones, tablets, and PCs and displays the summary. The presentation unit can also present the summary at an appropriate time based on the user's schedule. For example, the presentation unit can link with the user's calendar app and notify them of the summary just before a meeting. This allows the user to check important information in a timely manner. Furthermore, the presentation unit can customize the user interface. For example, the presentation unit can change the format and display method of notifications according to the user's preferences. This allows the presentation unit to provide a user-friendly interface and facilitate smooth information verification.

[0087] The approval unit accepts approval of the content presented by the presentation unit. The approval unit allows users to approve using methods such as clicks or voice recognition. Specifically, the approval unit provides an interface that allows users to approve by clicking a button on their device screen. Users can also approve using voice commands via voice recognition technology. For example, approval is completed when the user says "approved." Furthermore, the approval unit records the content approved by the user and sends it to the storage unit. The approval unit saves the approval history to a database for later reference. This allows the approval unit to streamline the user's approval process and maintain an accurate record of approved content.

[0088] The storage unit stores appointments approved by the approval unit as a list. The storage unit, for example, stores appointment details digitally, allowing users to access them at any time. Specifically, the storage unit securely stores appointment details using cloud storage. This allows users to access appointment details from any internet-connected device. The storage unit can also integrate stored appointment details with schedulers and task management apps. For example, it can synchronize with the user's calendar app and automatically add appointment dates and times to the schedule. This allows users to efficiently manage their schedules without forgetting appointments. Furthermore, the storage unit includes a data backup function to ensure security against data loss or corruption. This ensures that the storage unit reliably stores users' important appointment details and allows for quick access when needed.

[0089] The reminder unit reminds users of appointments saved by the storage unit. For example, the reminder unit sends notifications to the user's device to remind them of appointments. Specifically, the reminder unit sends pop-up notifications or email notifications to devices such as smartphones, tablets, and PCs to remind users of appointments. The reminder unit can also send reminders at appropriate times based on the user's schedule. For example, the reminder unit can integrate with the user's calendar app and send a reminder notification just before an appointment. This allows users to act promptly and avoid forgetting important appointments. Furthermore, the reminder unit can customize the frequency and timing of reminders according to the user's preferences. For example, the reminder unit provides an interface that allows users to set the time and frequency at which they receive reminder notifications. This enables the reminder unit to provide flexible reminder functionality tailored to user needs and support appointment management.

[0090] It includes an integration unit that connects with other applications. The integration unit connects with other applications. For example, it connects with calendar applications and task management applications to automatically synchronize appointment details. It can also connect with the user's email and messaging applications to collect conversation and email content. For example, the integration unit connects with calendar applications to automatically update appointment schedules. It can also connect with task management applications to add appointment details as tasks. It can connect with email applications to collect important email content and send it to the summarization unit. It can also connect with messaging applications to collect conversation content and send it to the summarization unit. This streamlines information collection and management through integration with other applications.

[0091] The system includes a generation unit that uses generative AI to generate appointments that are likely to be needed in the future. The generation unit uses generative AI to generate appointments that are likely to be needed in the future. For example, the generation unit learns from past appointment data and predicts appointments that may be needed in the future. The generation unit can also use generative AI to analyze the user's schedule and task trends and generate future appointments. For example, the generation unit generates the schedule for the next meeting based on past meeting data. The generation unit can also generate appointments for the next task based on the user's task deadlines. The generation unit uses generative AI to analyze the user's past behavior patterns and predict future appointments. This improves user convenience by automatically generating appointments that are likely to be needed in the future.

[0092] The system includes a suggestion unit that suggests generated appointments. The suggestion unit suggests generated appointments. For example, the suggestion unit presents appointments generated by the generation AI to the user and confirms whether the user approves them. The suggestion unit can also suggest appointments at the appropriate time based on the user's schedule and task status. For example, the suggestion unit suggests the next meeting based on the user's calendar. The suggestion unit can also suggest appointments for the next task based on the user's task management app. The suggestion unit notifies the user of appointments generated by the generation AI and confirms whether the user approves them. This improves user convenience by suggesting generated appointments to the user.

[0093] The data collection unit can estimate the user's emotions and select the content of conversations and emails to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority conversations and emails. If the user is relaxed, the data collection unit can also collect a wide range of conversations and emails. If the user is in a hurry, the data collection unit can also collect conversations and emails containing important information in a short amount of time. In this way, important information can be prioritized by selecting the content to collect according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The data collection unit can prioritize collecting high-importance content by referring to the user's past conversation history during collection. For example, the data collection unit prioritizes collecting topics that the user has frequently mentioned in the past. The data collection unit can also select content to collect based on keywords from conversations that the user has considered important in the past. If the user has considered conversations with a particular person important in the past, the data collection unit can also prioritize collecting conversations with that person. This allows for the priority collection of high-importance content by referring to past conversation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0095] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit prioritizes collecting conversations and emails related to projects the user is currently working on. The data collection unit can also collect information related to topics the user is interested in. If the user is interested in a particular industry or field, the data collection unit can also collect conversations and emails related to that field. This allows for the collection of highly relevant information by filtering based on current projects and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI.

[0096] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of collection and collect only important information. If the user is relaxed, the data collection unit can increase the frequency of collection and collect a wider range of information. If the user is in a hurry, the data collection unit can adjust the timing of collection to collect important information in a short amount of time. In this way, by adjusting the timing of collection according to the user's emotions, information can be collected at the appropriate time. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The data collection unit can prioritize the collection of highly relevant content by considering the user's geographical location during the collection process. For example, if the user is in a specific location, the data collection unit will prioritize the collection of conversations and emails related to that location. If the user is traveling, the data collection unit can also collect information related to their travel destination. If the user is participating in a specific event, the data collection unit can also collect information related to that event. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0098] The data collection unit can analyze the user's social media activity and collect relevant content during the collection process. For example, the data collection unit can collect information related to topics mentioned by the user on social media. The data collection unit can also collect posts from accounts that the user follows. The data collection unit can also collect information about groups and communities that the user participates in. This allows relevant information to be collected by analyzing social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0099] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is stressed, the summarization unit will provide a simple and easy-to-understand summary. If the user is relaxed, the summarization unit may also provide a summary that includes detailed information. If the user is in a hurry, the summarization unit may also provide a summary that highlights only the important points. In this way, an appropriate summary can be provided by adjusting the way the summary is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation or email during summary generation. For example, the summarization unit provides a detailed summary for high-importance conversations or emails. For low-importance conversations or emails, the summarization unit can also provide a concise summary. The summarization unit can also adjust the length and content of the summary according to its importance. This allows for the provision of an appropriate summary by adjusting the level of detail based on importance. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI.

[0101] The summarization unit can apply different summarization algorithms depending on the category of the conversation or email when generating a summary. For example, in the case of business-related conversations or emails, the summarization unit will provide a summary that includes technical terms. In the case of private conversations or emails, the summarization unit can also provide a summary using casual language. The summarization unit can also select an appropriate summarization algorithm depending on the category and generate a summary. This ensures that an appropriate summary is provided by applying the appropriate summarization algorithm according to the category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without using AI.

[0102] 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 stressed, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can also provide a longer summary with more detailed information. If the user is in a hurry, the summarization unit can provide a short summary that highlights only the important points. This allows for the provision of an appropriate summary by adjusting the length of the summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The summarization unit can prioritize summaries based on when conversations or emails were sent. For example, it can prioritize summarizing recently sent conversations or emails. It can also prioritize summarizing conversations or emails sent immediately before important events. The summarization unit can also adjust the summarization priority based on the user's schedule. This allows important information to be summarized preferentially by prioritizing summaries based on when they were sent. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not.

[0104] The summarization unit can adjust the order of summaries based on the relevance of conversations and emails during summary generation. For example, the summarization unit can prioritize the display of summaries of highly relevant conversations and emails. It can also postpone the display of summaries of less relevant conversations and emails. The summarization unit can also adjust the display order of summaries according to their relevance. This allows important information to be presented preferentially by adjusting the order of summaries based on relevance. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI.

[0105] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is stressed, the presentation unit can provide a simple and highly visible presentation method. If the user is relaxed, the presentation unit can also provide a presentation method that includes detailed information. If the user is in a hurry, the presentation unit can also provide a presentation method that gets straight to the point. In this way, appropriate information can be provided by adjusting the presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The presentation unit can select the optimal presentation method by referring to the user's past approval history at the time of presentation. For example, the presentation unit selects the optimal presentation method based on content that the user has previously approved. The presentation unit can also analyze preferred presentation methods from the user's past approval history. The presentation unit can also select the optimal presentation method by referring to presentation methods of content that the user has previously approved. In this way, the optimal presentation method can be selected by referring to past approval history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0107] The presentation unit can customize the content presented based on the user's current situation at the time of presentation. For example, if the user is busy, the presentation unit may present only essential information. If the user is relaxed, the presentation unit may also provide content that includes detailed information. The presentation unit can also customize the content presented according to the user's current situation. This allows the presentation unit to provide appropriate information by customizing the content based on the current situation. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0108] The presentation unit can estimate the user's emotions and adjust the timing of presentations based on the estimated emotions. For example, if the user is stressed, the presentation unit can reduce the frequency of presentations and present only important information. If the user is relaxed, the presentation unit can increase the frequency of presentations and present a wider range of information. If the user is in a hurry, the presentation unit can adjust the timing of presentations and present important information in a short amount of time. In this way, by adjusting the timing of presentations according to the user's emotions, information can be provided at the appropriate time. 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.

[0109] The display unit can select the optimal display method when presenting information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0110] The presentation unit can present information at the optimal time, taking into account the user's schedule. For example, the presentation unit can present important information at the optimal time based on the user's schedule. The presentation unit can also present information while avoiding the user's busy times. The presentation unit can also adjust the timing of presentation according to the user's schedule. This allows information to be provided at the optimal time by considering the schedule. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0111] The approval unit can estimate the user's emotions and adjust the approval method based on the estimated emotions. For example, if the user is stressed, the approval unit can provide a simple and quick approval method. If the user is relaxed, the approval unit can also provide an approval method that includes detailed information. If the user is in a hurry, the approval unit can also provide an approval method that highlights only the important points. In this way, by adjusting the approval method according to the user's emotions, an appropriate approval method can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The approval unit can select the optimal approval method by referring to the user's past approval history during the approval process. For example, the approval unit can select the optimal approval method based on the content the user has previously approved. The approval unit can also analyze preferred approval methods from the user's past approval history. The approval unit can also select the optimal approval method by referring to the approval methods used for content the user has previously approved. In this way, the optimal approval method can be selected by referring to past approval history. Some or all of the above processing in the approval unit may be performed using AI, for example, or without using AI.

[0113] The approval unit can customize the approval content based on the user's current situation during the approval process. For example, if the user is busy, the approval unit will only approve essential information. If the user is relaxed, the approval unit may also provide approval content that includes detailed information. The approval unit can also customize the approval content according to the user's current situation. This allows for the provision of appropriate approval content by customizing it based on the current situation. Some or all of the above processes in the approval unit may be performed using AI, for example, or not using AI.

[0114] The approval unit can estimate the user's emotions and adjust the timing of approvals based on those emotions. For example, if the user is stressed, the approval unit can reduce the frequency of approvals and approve only important information. If the user is relaxed, the approval unit can increase the frequency of approvals and approve a wider range of information. If the user is in a hurry, the approval unit can adjust the timing of approvals to approve important information quickly. This allows for timely approvals by adjusting the timing of approvals according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The approval unit can select the optimal approval method when approving, taking into account the user's device information. For example, if the user is using a smartphone, the approval unit can provide an approval method that is adapted to the screen size. If the user is using a tablet, the approval unit can also provide an approval method optimized for a larger screen. If the user is using a smartwatch, the approval unit can also provide a concise and highly visible approval method. In this way, the optimal approval method can be provided by taking device information into consideration. Some or all of the above processing in the approval unit may be performed using AI, for example, or without using AI.

[0116] The approval unit can prompt for approval at the optimal time, taking into account the user's schedule. For example, the approval unit will approve important information at the optimal time based on the user's schedule. The approval unit can also prompt for approval while avoiding busy times for the user. The approval unit can also adjust the timing of approval according to the user's schedule. This allows for prompting for approval at the optimal time by considering the schedule. Some or all of the above processes in the approval unit may be performed using AI, for example, or not using AI.

[0117] The storage unit can estimate the user's emotions and determine the priority of content to save based on the estimated emotions. For example, if the user is stressed, the storage unit will prioritize saving information of high importance. If the user is relaxed, the storage unit can also save a wide range of information. If the user is in a hurry, the storage unit can also save important information quickly. This allows for the priority of saving important information by determining the priority of content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The storage unit can select the optimal storage method by referring to the user's past saving history when saving. For example, the storage unit selects the optimal storage method based on the content the user has saved in the past. The storage unit can also analyze preferred storage methods from the user's past saving history. The storage unit can also select the optimal storage method by referring to the storage methods used for content the user has saved in the past. In this way, the optimal storage method can be selected by referring to the past saving history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0119] The storage unit can customize the content saved based on the user's current situation at the time of saving. For example, if the user is busy, the storage unit will save only important information. If the user is relaxed, the storage unit can also provide content that includes detailed information. The storage unit can also customize the content saved according to the user's current situation. This allows the storage unit to provide appropriate content by customizing the content saved based on the current situation. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI.

[0120] The storage unit can estimate the user's emotions and adjust the timing of saving based on the estimated emotions. For example, if the user is stressed, the storage unit can reduce the frequency of saving and save only important information. If the user is relaxed, the storage unit can increase the frequency of saving and save a wider range of information. If the user is in a hurry, the storage unit can adjust the timing of saving and save important information in a short amount of time. In this way, by adjusting the timing of saving according to the user's emotions, information can be saved at the appropriate time. 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.

[0121] The storage unit can select the optimal storage method when saving, taking into account the user's device information. For example, if the user is using a smartphone, the storage unit can provide a storage method that matches the screen size. If the user is using a tablet, the storage unit can also provide a storage method optimized for a larger screen. If the user is using a smartwatch, the storage unit can also provide a concise and highly visible storage method. In this way, the optimal storage method can be provided by taking device information into consideration. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0122] The storage unit can save data at the optimal time, taking into account the user's schedule. For example, the storage unit can save important information at the optimal time based on the user's schedule. The storage unit can also save information while avoiding the user's busy hours. The storage unit can also adjust the timing of saving according to the user's schedule. This allows information to be saved at the optimal time by considering the schedule. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without using AI.

[0123] The reminder function can estimate the user's emotions and adjust the reminder method based on the estimated emotions. For example, if the user is stressed, the reminder function can provide a simple and highly visible reminder method. If the user is relaxed, the reminder function can also provide a reminder method that includes detailed information. If the user is in a hurry, the reminder function can also provide a concise reminder method. In this way, by adjusting the reminder method according to the user's emotions, an appropriate reminder method 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.

[0124] The reminder unit can select the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal reminder method based on the content of past reminders the user has received. The reminder unit can also analyze preferred reminder methods from the user's past reminder history. The reminder unit can also select the optimal reminder method by referring to the reminder methods used for content that the user has received past reminders about. In this way, the optimal reminder method can be selected by referring to past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0125] The reminder function can customize the reminder content based on the user's current situation. For example, if the user is busy, the reminder function will only remind them of important information. If the user is relaxed, the reminder function can also provide a reminder with more detailed information. The reminder function can customize the reminder content according to the user's current situation. This allows the system to provide appropriate reminders by customizing them based on the current situation. Some or all of the above processing in the reminder function may be performed using AI, for example, or without AI.

[0126] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is stressed, the reminder unit can reduce the frequency of reminders and only remind users of important information. If the user is relaxed, the reminder unit can increase the frequency of reminders and remind users of a wider range of information. If the user is in a hurry, the reminder unit can adjust the timing of reminders and remind users of important information in a short amount of time. This allows for timely reminders by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The reminder unit can select the optimal reminder method by considering the user's device information when sending a reminder. For example, if the user is using a smartphone, the reminder unit can provide a reminder method that is adapted to the screen size. If the user is using a tablet, the reminder unit can also provide a reminder method optimized for a larger screen. If the user is using a smartwatch, the reminder unit can also provide a concise and highly visible reminder method. In this way, the optimal reminder method can be provided by considering device information. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0128] The reminder unit can send reminders at the optimal time, taking into account the user's schedule. For example, the reminder unit can remind users of important information at the optimal time based on their schedule. The reminder unit can also remind users of information while avoiding busy times. The reminder unit can also adjust the timing of reminders according to the user's schedule. This allows for reminders to be sent at the optimal time by considering the schedule. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0129] The integration unit can estimate the user's emotions and select apps to integrate with based on those estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating with simple and easy-to-use apps. If the user is relaxed, the integration unit can also integrate with apps that have a wide range of functions. If the user is in a hurry, the integration unit can select apps that can integrate quickly. This allows for integration with appropriate apps by selecting apps according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0130] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit can select the optimal integration method based on the apps the user has previously integrated with. The integration unit can also analyze preferred integration methods from the user's past integration history. The integration unit can also select the optimal integration method by referring to the integration methods of apps the user has previously integrated with. In this way, the optimal integration method can be selected by referring to past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0131] The communication unit can estimate the user's emotions and adjust the timing of communication based on the estimated emotions. For example, if the user is stressed, the communication unit can reduce the frequency of communication and communicate only important information. If the user is relaxed, the communication unit can increase the frequency of communication and communicate a wider range of information. If the user is in a hurry, the communication unit can adjust the timing of communication and communicate important information in a short amount of time. In this way, by adjusting the timing of communication according to the user's emotions, communication can be performed at the appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0132] The integration unit can select the optimal integration method by considering the user's device information during integration. For example, if the user is using a smartphone, the integration unit provides an integration method that matches the screen size. If the user is using a tablet, the integration unit can also provide an integration method optimized for a larger screen. If the user is using a smartwatch, the integration unit can also provide a concise and highly visible integration method. In this way, the optimal integration method can be provided by considering device information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0133] The generation unit can estimate the user's emotions and adjust the content of the promise it generates based on those emotions. For example, if the user is stressed, the generation unit will generate a simple and easy-to-understand promise. If the user is relaxed, the generation unit can also generate a promise that includes detailed information. If the user is in a hurry, the generation unit can also generate a promise that highlights only the important points. In this way, by adjusting the content of the promise generated according to the user's emotions, it is possible to generate an appropriate promise. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0134] The generation unit can generate the optimal promise content by referring to the user's past promise history during generation. For example, the generation unit generates the optimal promise content based on promises the user has made in the past. The generation unit can also analyze preferred promise content from the user's past promise history. The generation unit can also generate the optimal promise content by referring to the content of promises the user has made in the past. In this way, the optimal promise content can be generated by referring to the past promise history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0135] The generation unit can customize the promise content based on the user's current situation during generation. For example, if the user is busy, the generation unit can generate a promise content that includes only essential information. If the user is relaxed, the generation unit can also generate a promise content that includes detailed information. The generation unit can also customize the promise content according to the user's current situation. This allows for the generation of appropriate promise content by customizing it based on the current situation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0136] The generation unit can estimate the user's emotions and adjust the timing of generation based on the estimated emotions. For example, if the user is stressed, the generation unit can reduce the frequency of generation and generate only important information. If the user is relaxed, the generation unit can increase the frequency of generation and generate a wide range of information. If the user is in a hurry, the generation unit can adjust the timing of generation and generate important information in a short amount of time. This allows for the generation of promises at the appropriate time by adjusting the timing of generation according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0137] The generation unit can generate optimal promise content by considering the user's device information during generation. For example, if the user is using a smartphone, the generation unit will generate promise content that matches the screen size. If the user is using a tablet, the generation unit can also generate promise content optimized for a larger screen. If the user is using a smartwatch, the generation unit can also generate concise and highly visible promise content. In this way, optimal promise content can be generated by considering device information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0138] The generation unit can generate appointments at the optimal time, taking into account the user's schedule. For example, the generation unit can generate important information at the optimal time based on the user's schedule. The generation unit can also generate appointments while avoiding the user's busy times. The generation unit can also adjust the timing of generation according to the user's schedule. This allows for the generation of appointments at the optimal time by considering the schedule. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0139] The suggestion function can estimate the user's emotions and adjust its suggestion method based on those emotions. For example, if the user is stressed, the suggestion function can provide simple and highly visible suggestions. If the user is relaxed, it can also provide suggestions that include more detailed information. If the user is in a hurry, it can provide concise suggestions. By adjusting the suggestion method according to the user's emotions, it can provide appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0140] The suggestion unit can select the optimal suggestion method by referring to the user's past suggestion history when making suggestions. For example, the suggestion unit can select the optimal suggestion method based on the content previously suggested to the user. The suggestion unit can also analyze preferred suggestion methods from the user's past suggestion history. The suggestion unit can also select the optimal suggestion method by referring to the suggestion methods of the content previously suggested to the user. In this way, the optimal suggestion method can be selected by referring to the past suggestion history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI.

[0141] The suggestion function can customize the suggested content based on the user's current situation at the time of suggestion. For example, if the user is busy, the suggestion function will only suggest important information. If the user is relaxed, the suggestion function can also provide suggestions that include detailed information. The suggestion function can also customize the suggested content according to the user's current situation. This allows the suggestion function to provide appropriate suggestions by customizing the suggested content based on the current situation. Some or all of the above processing in the suggestion function may be performed using, for example, generative AI, or without using generative AI.

[0142] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function can reduce the frequency of suggestions and suggest only important information. If the user is relaxed, the suggestion function can increase the frequency of suggestions and suggest a wider range of information. If the user is in a hurry, the suggestion function can adjust the timing of suggestions to suggest important information quickly. This allows for suggestions to be made at the appropriate time by adjusting the timing of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0143] The suggestion unit can select the optimal suggestion method by considering the user's device information when making suggestions. For example, if the user is using a smartphone, the suggestion unit can provide a suggestion method that matches the screen size. If the user is using a tablet, the suggestion unit can also provide a suggestion method optimized for a larger screen. If the user is using a smartwatch, the suggestion unit can also provide a concise and highly visible suggestion method. In this way, the optimal suggestion method can be provided by considering device information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI.

[0144] The suggestion unit can make suggestions at the optimal time, taking into account the user's schedule. For example, the suggestion unit can suggest important information at the optimal time based on the user's schedule. The suggestion unit can also suggest information while avoiding the user's busy times. The suggestion unit can also adjust the timing of suggestions according to the user's schedule. This allows for suggestions to be made at the optimal time by considering the schedule. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI.

[0145] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0146] The data collection unit can estimate the user's emotions and select the content of conversations and emails to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority conversations and emails. If the user is relaxed, the data collection unit can also collect a wide range of conversations and emails. If the user is in a hurry, the data collection unit can also collect conversations and emails containing important information in a short amount of time. In this way, important information can be prioritized by selecting the content to collect according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0147] The data collection unit can prioritize collecting high-importance content by referring to the user's past conversation history during collection. For example, the data collection unit prioritizes collecting topics that the user has frequently mentioned in the past. The data collection unit can also select content to collect based on keywords from conversations that the user has considered important in the past. If the user has considered conversations with a particular person important in the past, the data collection unit can also prioritize collecting conversations with that person. This allows for the priority collection of high-importance content by referring to past conversation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0148] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit prioritizes collecting conversations and emails related to projects the user is currently working on. The data collection unit can also collect information related to topics the user is interested in. If the user is interested in a particular industry or field, the data collection unit can also collect conversations and emails related to that field. This allows for the collection of highly relevant information by filtering based on current projects and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI.

[0149] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of collection and collect only important information. If the user is relaxed, the data collection unit can increase the frequency of collection and collect a wider range of information. If the user is in a hurry, the data collection unit can adjust the timing of collection to collect important information in a short amount of time. In this way, by adjusting the timing of collection according to the user's emotions, information can be collected at the appropriate time. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0150] The data collection unit can prioritize the collection of highly relevant content by considering the user's geographical location during the collection process. For example, if the user is in a specific location, the data collection unit will prioritize the collection of conversations and emails related to that location. If the user is traveling, the data collection unit can also collect information related to their travel destination. If the user is participating in a specific event, the data collection unit can also collect information related to that event. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0151] The data collection unit can analyze the user's social media activity and collect relevant content during the collection process. For example, the data collection unit can collect information related to topics mentioned by the user on social media. The data collection unit can also collect posts from accounts that the user follows. The data collection unit can also collect information about groups and communities that the user participates in. This allows relevant information to be collected by analyzing social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0152] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is stressed, the summarization unit will provide a simple and easy-to-understand summary. If the user is relaxed, the summarization unit may also provide a summary that includes detailed information. If the user is in a hurry, the summarization unit may also provide a summary that highlights only the important points. In this way, an appropriate summary can be provided by adjusting the way the summary is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0153] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation or email during summary generation. For example, the summarization unit provides a detailed summary for high-importance conversations or emails. For low-importance conversations or emails, the summarization unit can also provide a concise summary. The summarization unit can also adjust the length and content of the summary according to its importance. This allows for the provision of an appropriate summary by adjusting the level of detail based on importance. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI.

[0154] The summarization unit can apply different summarization algorithms depending on the category of the conversation or email when generating a summary. For example, in the case of business-related conversations or emails, the summarization unit will provide a summary that includes technical terms. In the case of private conversations or emails, the summarization unit can also provide a summary using casual language. The summarization unit can also select an appropriate summarization algorithm depending on the category and generate a summary. This ensures that an appropriate summary is provided by applying the appropriate summarization algorithm according to the category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without using AI.

[0155] 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 stressed, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can also provide a longer summary with more detailed information. If the user is in a hurry, the summarization unit can provide a short summary that highlights only the important points. This allows for the provision of an appropriate summary by adjusting the length of the summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0156] The following briefly describes the processing flow for example form 2.

[0157] Step 1: The collection unit collects the content of conversations and emails. The collection unit uses speech recognition technology to convert the content of conversations into text data and analyzes the content of emails to extract important information. For example, it collects the content of various types of conversations and emails, such as business emails and personal conversations. Step 2: The summarization unit summarizes the content collected by the data collection unit. The summarization unit uses natural language processing techniques to perform summarization based on the length of the text and the importance of the information being summarized. For example, it extracts key points and generates a concise summary. Step 3: The presentation unit presents the content summarized by the summary unit to the user. The presentation unit sends a notification to the user's device and displays the summary content. It can also present the summary content at an appropriate time based on the user's schedule. Step 4: The approval unit accepts approval of the content presented by the presentation unit. The approval unit allows the user to approve using clicks or voice recognition, records the approved content, and sends it to the storage unit. Step 5: The storage unit saves the agreements approved by the approval unit as a list. The storage unit saves the agreement details in digital format, making them accessible to the user at any time. The saved agreement details can also be integrated with scheduler and task management apps. Step 6: The reminder unit reminds users of appointments saved by the storage unit. The reminder unit sends a notification to the user's device to remind them of the appointment. It can also send reminders at appropriate times based on the user's schedule.

[0158] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0159] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0160] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0161] Each of the multiple elements described above, including the collection unit, summarization unit, presentation unit, approval unit, storage unit, reminder unit, collaboration unit, generation unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the content of conversations and emails using the camera 42 and microphone 38B of the smart device 14 and converts it into text data using the control unit 46A. The summarization unit summarizes the collected content using the identification processing unit 290 of the data processing unit 12. The presentation unit presents the summarized content to the user using the display 40A of the smart device 14. The approval unit accepts user approval using the touch panel 38A of the smart device 14. The storage unit stores approved promises in the database 24 of the data processing unit 12. The reminder unit reminds the user of the promise using the speaker 40B of the smart device 14. The collaboration unit collaborates with other applications using the communication I / F 26 of the data processing unit 12. The generation unit generates promises that are likely to be needed in the future using generation AI, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the generated promises to the user, for example, by using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0163] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, summarization unit, presentation unit, approval unit, storage unit, reminder unit, collaboration unit, generation unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the content of conversations and emails using the camera 42 and microphone 238 of the smart glasses 214 and converts it into text data using the control unit 46A. The summarization unit summarizes the collected content using the identification processing unit 290 of the data processing unit 12. The presentation unit presents the summarized content to the user using the display of the smart glasses 214. The approval unit accepts user approval using the touch panel of the smart glasses 214. The storage unit stores approved appointments in the database 24 of the data processing unit 12. The reminder unit reminds the user of appointments using the speaker 240 of the smart glasses 214. The collaboration unit collaborates with other applications using the communication I / F 26 of the data processing unit 12. The generation unit generates promises that are likely to be needed in the future using generation AI, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the generated promises to the user, for example, by using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0179] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0180] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0181] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0182] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0184] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0185] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0186] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0187] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0188] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0189] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0190] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0191] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0192] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0193] Each of the multiple elements described above, including the collection unit, summarization unit, presentation unit, approval unit, storage unit, reminder unit, collaboration unit, generation unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the content of conversations and emails using the camera 42 and microphone 238 of the headset terminal 314 and converts it into text data using the control unit 46A. The summarization unit summarizes the collected content using the identification processing unit 290 of the data processing unit 12. The presentation unit presents the summarized content to the user using the display 343 of the headset terminal 314. The approval unit accepts user approval using the touch panel of the headset terminal 314. The storage unit stores approved appointments in the database 24 of the data processing unit 12. The reminder unit reminds the user of appointments using the speaker 240 of the headset terminal 314. The collaboration unit collaborates with other applications using the communication I / F 26 of the data processing unit 12. The generation unit generates promises that are likely to be needed in the future using generation AI, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the generated promises to the user, for example, by using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0194] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0195] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0196] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0197] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0198] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0199] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0200] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0201] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0202] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0203] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0204] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0205] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0206] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0207] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0208] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0209] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0210] Each of the multiple elements described above, including the collection unit, summarization unit, presentation unit, approval unit, storage unit, reminder unit, collaboration unit, generation unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the content of conversations and emails using the camera 42 and microphone 238 of the robot 414 and converts it into text data by the control unit 46A. The summarization unit summarizes the collected content using the identification processing unit 290 of the data processing unit 12. The presentation unit presents the summarized content to the user using the display of the robot 414. The approval unit accepts user approval using the touch panel of the robot 414. The storage unit stores approved promises in the database 24 of the data processing unit 12. The reminder unit reminds the user of the promise using the speaker 240 of the robot 414. The collaboration unit collaborates with other applications using the communication I / F 26 of the data processing unit 12. The generation unit generates promises that are likely to be needed in the future using generation AI, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the generated promises to the user, for example, by using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0211] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0212] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0213] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0214] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0215] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0216] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0217] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0218] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0219] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0220] 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.

[0221] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0222] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0223] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0224] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0225] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0226] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0227] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0228] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0229] (Note 1) The collection department collects the contents of conversations and emails, A summarization unit that summarizes the contents collected by the collection unit, A presentation unit that presents the content summarized by the summarization unit to the user, An approval unit that accepts approval of the content presented by the aforementioned presentation unit, A storage unit that stores the promises approved by the aforementioned approval unit as a list, The storage unit includes a reminder unit that reminds the user of appointments saved by the storage unit. A system characterized by the following features. (Note 2) It includes a connectivity section for linking with other applications. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a generation unit that uses generative AI to generate promises that are likely to be needed in the future. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a suggestion section that suggests generated promises. The system described in Appendix 3, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and selects the content of conversations and emails to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the system prioritizes collecting high-priority content by referencing the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary section above is, When generating summaries, adjust the level of detail in the summary based on the importance of the conversation or email. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the conversation or email. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, When generating summaries, prioritize summaries based on when the conversation or email was sent. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the conversations and emails. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is, It estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, When presenting information, the system will refer to the user's past approval history to select the most appropriate presentation method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, When presenting information, customize the content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, It estimates the user's emotions and adjusts the timing of presentations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting the information, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When presenting the information, we will consider the user's schedule and present it at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned approval unit, It estimates the user's emotions and adjusts the approval method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned approval unit, During the approval process, the system will refer to the user's past approval history to select the most appropriate approval method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned approval unit, During the approval process, customize the approval criteria based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned approval unit, It estimates the user's emotions and adjusts the timing of approval based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned approval unit, During the approval process, the optimal approval method is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned approval unit, When approving, prompt the user for approval at the optimal time, taking their schedule into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned storage unit is It estimates the user's emotions and determines the priority of content to save based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned storage unit is When saving, the system will refer to the user's past save history to select the optimal saving method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned storage unit is When saving, customize the saved content based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned storage unit is It estimates the user's emotions and adjusts the timing of saving based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned storage unit is When saving, the system selects the optimal saving method, taking into account the user's device information. The system according to Appendix 1, characterized in that... (Appendix 34) The storage unit stores at an optimal timing considering the user's schedule during storage The system according to Appendix 1, characterized in that... (Appendix 35) The reminder unit estimates the user's emotion and adjusts the reminder method based on the estimated user emotion The system according to Appendix 1, characterized in that... (Appendix 36) The reminder unit [[ID=​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​It estimates the user's emotions and selects apps to collaborate with based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 42) The aforementioned linkage unit is, During integration, the system selects the optimal integration method by referring to the user's past integration history. The system described in Appendix 2, characterized by the features described herein. (Note 43) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the timing of collaboration based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 44) The aforementioned linkage unit is, During integration, the optimal integration method is selected by considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 45) The generating unit is It estimates the user's emotions and adjusts the content of the promises generated based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 46) The generating unit is During generation, the system references the user's past promise history to generate the most suitable promise content. The system described in Appendix 3, characterized by the features described herein. (Note 47) The generating unit is During generation, the promise details are customized based on the user's current situation. The system described in Appendix 3, characterized by the features described herein. (Note 48) The generating unit is It estimates the user's emotions and adjusts the timing of generation based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 49) The generating unit is During generation, the system takes into account the user's device information to generate the most appropriate agreement. The system according to Appendix 3, characterized in that... (Appendix 50) The generation unit generates an appointment at an optimal timing considering the user's schedule during generation The system according to Appendix 3, characterized in that... (Appendix 51) The suggestion unit estimates the user's emotion and adjusts the suggestion method based on the estimated user's emotion <​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects the contents of conversations and emails, A summarization unit that summarizes the contents collected by the collection unit, A presentation unit that presents the content summarized by the summarization unit to the user, An approval unit that accepts approval of the content presented by the aforementioned presentation unit, A storage unit that stores the promises approved by the aforementioned approval unit as a list, The storage unit includes a reminder unit that reminds the user of appointments saved by the storage unit. A system characterized by the following features.

2. It includes a connectivity section for linking with other applications. The system according to feature 1.

3. It includes a generation unit that uses generative AI to generate promises that are likely to be needed in the future. The system according to feature 1.

4. It includes a suggestion section that suggests generated promises. The system according to claim 3.

5. The aforementioned collection unit is It estimates the user's emotions and selects the content of conversations and emails to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the system prioritizes collecting high-priority content by referencing the user's past conversation history. The system according to feature 1.

7. The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant content by considering the user's geographical location. The system according to feature 1.

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