system

Abstract

The system according to this embodiment aims to provide integrated management of a user's daily tasks and schedules, emails and messages, and location-based information. [Solution] The system according to the embodiment comprises a management unit, a provision unit, and a sensing unit. The management unit automatically manages and notifies the user of their daily tasks or schedule. The provision unit provides email or message summarization, translation, and speech-to-text functionality. The sensing unit senses the user's location or situation and provides necessary information or services.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there is a problem that the management of a user's daily tasks, schedule, summarization / translation of mails and messages, and information provision based on location information are performed individually and not integrated.

[0005] The system according to the embodiment aims to integrally manage a user's daily tasks, schedule, mails and messages, and information provision based on location information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a management unit, a provision unit, and a sensing unit. The management unit automatically manages and notifies the user of their daily tasks or schedule. The provision unit provides email or message summarization, translation, and speech-to-text functionality. The sensing unit senses the user's location or situation and provides necessary information or services. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively manage a user's daily tasks and schedules, emails and messages, and location-based information provision. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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] 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 receiving 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 receiving 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 AI ​​assistant for smartphones according to an embodiment of the present invention is a system having functions for automatically managing and notifying the user of daily tasks and schedules, summarizing, translating, and providing speech-to-text functionality for emails and messages, and sensing the user's location and situation to provide necessary information and services. The AI ​​assistant for smartphones automatically manages and notifies the user of daily tasks and schedules. For example, the AI ​​analyzes appointments and reminders entered by the user in their calendar and provides notifications at the appropriate time. This allows the user to efficiently complete daily tasks without forgetting important appointments. Next, the AI ​​assistant for smartphones provides functions for summarizing, translating, and providing speech-to-text functionality for emails and messages. For example, the AI ​​summarizes long emails and provides the user with concise content. The AI ​​also translates foreign language messages and provides them in a format that is easy for the user to understand. Furthermore, it provides a speech-to-text function that converts voice input into text, allowing users to easily create messages. In addition, the AI ​​assistant for smartphones senses the user's location and situation to provide necessary information and services. For example, if the user is out and about, the AI ​​provides weather information for their current location and information on nearby restaurants. This allows the user to quickly obtain the information they need on the spot. Thus, smartphone AI assistants are multi-functional assistants designed to support and streamline users' daily lives, making them more efficient and convenient. This allows smartphone AI assistants to efficiently manage users' daily tasks and schedules, and provide necessary information and services.

[0029] The AI ​​assistant for smartphones according to this embodiment comprises a management unit, a provision unit, and a sensing unit. The management unit automatically manages and notifies the user of their daily tasks and schedules. For example, the management unit analyzes appointments and reminders entered by the user in their calendar and provides notifications at the appropriate time. For example, if the user enters a work task in their calendar, the management unit will notify the user when the deadline for that task is approaching. The management unit can also provide notifications for household tasks and personal tasks in a similar manner. For example, if the user enters a shopping list as a household task, the management unit can notify the user of the timing for shopping based on the contents of the list. For example, if the user enters an exercise schedule as a personal task, the management unit can notify the user when the time for that exercise is approaching. The provision unit provides email and message summarization, translation, and speech-to-text functionality. For example, the provision unit can summarize long emails and provide the user with concise content. For example, the provision unit can analyze long emails, extract important points, and create summaries. Furthermore, the service provider can translate foreign language messages and provide them to the user in an easily understandable format. For example, the service provider can translate an English message into Japanese and provide it to the user. For example, the service provider can translate a French message into English and provide it to the user. The service provider provides a speech-to-text function that converts voice input into text. For example, the service provider can convert what the user inputs by voice into text and save it as a message. For example, the service provider can convert what the user inputs by voice into text in real time and display it on the screen. For example, the service provider can convert what the user inputs by voice into text and send that text as an email or message. The sensing unit senses the user's location and situation and provides necessary information and services. For example, the sensing unit can provide weather information for the user's current location if the user is out. For example, the sensing unit can provide information about nearby restaurants if the user is out. For example, the sensing unit can provide weather information for the area around the user's home if the user is at home.The sensing unit can, for example, provide information about restaurants near the user's home if the user is at home. This allows the AI ​​assistant in the smartphone according to the embodiment to efficiently manage the user's daily tasks and schedule and provide necessary information and services.

[0030] The management unit automatically manages and notifies users of their daily tasks and schedules. For example, the management unit analyzes appointments and reminders entered by users in their calendar and sends notifications at the appropriate time. Specifically, the management unit works in conjunction with the user's calendar application and analyzes the entered appointments. For example, if a user enters a work task in their calendar, the management unit will send a notification when the deadline for that task is approaching. The management unit can adjust the timing of notifications, taking into account the importance and urgency of the task. For example, it can send a reminder the day before an important meeting to ensure the user doesn't forget to prepare. The management unit can also provide notifications for household and personal tasks in a similar manner. For example, if a user enters a shopping list as a household task, the management unit can notify the user of the timing for shopping based on the contents of the list. The management unit can learn the user's past behavior patterns and suggest the optimal notification timing. For example, if a user has a habit of shopping every Saturday, the management unit will send a notification based on that habit. The management unit can also notify users when the time for exercise approaches if a user enters an exercise plan as a personal task. The management department provides features that help users manage their health, including the ability to send reminders based on the frequency and type of exercise. For example, if a user enters a jogging schedule, the system can suggest the optimal time, taking weather forecasts into consideration. This allows the management department to efficiently support users' daily lives and make task management easier.

[0031] The service provider offers email and message summarization, translation, and speech-to-text functionality. For example, it can summarize long emails, providing users with concise content. Specifically, it uses natural language processing to analyze email content, extract key points, and create summaries. This allows users to quickly understand long emails, such as meeting minutes or project progress reports. Furthermore, the service provider can translate foreign language messages and provide them to users in an easily understandable format. For example, it can translate English messages into Japanese and provide them to users. Using a translation engine, the service provider can perform real-time translation between multiple languages. For example, it can translate French messages into English and provide them to users. In addition, the service provider offers a speech-to-text function that converts voice input into text. For example, it can convert user voice input into text and save it as a message. Using speech recognition technology, the service provider can convert user speech into text with high accuracy. For example, it can convert user voice input into text in real time and display it on the screen. The service provider can, for example, convert user voice input into text and send that text as an email or message. This allows the service provider to facilitate user communication and efficiently transmit information.

[0032] The sensing unit senses the user's location and situation and provides necessary information and services. For example, if the user is out, the sensing unit can provide weather information for their current location. Specifically, the sensing unit uses GPS to identify the user's current location and obtains the latest weather information for that area. For example, if the user is out, it can provide information on nearby restaurants. Based on the user's current location, the sensing unit can search for information on nearby restaurants and cafes and suggest recommended places according to the user's preferences. For example, if the user is at home, it can provide weather information for the area around their home. The sensing unit can register the user's home location and periodically update weather information for that area. For example, if the user is at home, it can provide information on restaurants around their home. The sensing unit can learn the user's past behavior history and preferences to provide more personalized information. For example, if the user frequently visits a particular restaurant, it can notify them of new menu items or special offers at that restaurant. Furthermore, the sensing unit can provide information tailored to the user's situation. For example, if the user is exercising, it can provide information on nearby parks or jogging courses. This allows the sensing unit to enrich the user's life and provide necessary information and services in a timely manner.

[0033] The sensing unit can provide weather information or nearby restaurant information based on the user's location information. For example, if the user is out, the sensing unit provides weather information for the user's current location. For example, if the user is out, the sensing unit provides nearby restaurant information. For example, if the user is at home, the sensing unit provides weather information for the area around the user's home. For example, if the user is at home, the sensing unit provides restaurant information for the area around the user's home. This allows the system to provide appropriate information based on the user's location information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's location information into a generating AI and have the generating AI perform the task of providing weather information or restaurant information.

[0034] The management unit can analyze appointments or reminders entered by users into their calendars and send notifications at appropriate times. For example, the management unit can send notifications when the deadline for an appointment entered by the user is approaching. For example, if a user has set a reminder, the management unit can send notifications when the time for that reminder is approaching. For example, the management unit can adjust the timing of notifications based on the priority of appointments entered by the user into their calendar. This allows users to efficiently complete their daily tasks without forgetting important appointments. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input appointments entered by users into a generating AI and have the generating AI execute the timing of notifications.

[0035] The service provider can summarize long emails and provide users with concise content. For example, the service provider can analyze long emails, extract key points, and create summaries. For example, the service provider can summarize the content of long emails and provide them to users in a concise form. For example, the service provider can input the summary of a long email into a generation AI and have the generation AI generate the summary. This allows users to understand long emails concisely. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a long email into a generation AI and have the generation AI generate the summary.

[0036] The service provider can translate foreign language messages and provide them to users in an easily understandable format. For example, the service provider can translate an English message into Japanese and provide it to the user. For example, the service provider can translate a French message into English and provide it to the user. For example, the service provider can input a foreign language message into a generation AI and have the generation AI perform the translation generation. This makes it easier for users to understand foreign language messages. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input a foreign language message into a generation AI and have the generation AI perform the translation generation.

[0037] The service provider can provide a speech-to-text function that converts voice input into text. For example, the service provider can convert what the user inputs by voice into text and save it as a message. For example, the service provider can convert what the user inputs by voice into text in real time and display it on the screen. For example, the service provider can convert what the user inputs by voice into text and send that text as an email or message. For example, the service provider can input voice input into a generation AI and have the generation AI perform the conversion to text. This allows users to easily create messages. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input voice input into a generation AI and have the generation AI perform the conversion to text.

[0038] The management department can analyze the user's past schedule history and select an appropriate notification method. For example, the management department may prioritize notification methods that the user has previously preferred (e.g., voice, vibration). For example, the management department may suggest a notification method suitable for a specific time period based on the user's past schedule history. For example, the management department may analyze the user's past responses and select the most effective notification method. This allows the management department to select the optimal notification method based on the user's past schedule history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department may input the user's past schedule history into a generating AI and have the generating AI perform the selection of notification methods.

[0039] The management unit can customize the content of reminders based on the user's current activity status when managing schedules. For example, if the user is in a meeting, the management unit will display a reminder after the meeting ends. For example, if the user is exercising, the management unit will display a reminder after the exercise is finished. For example, if the user is taking a break, the management unit will display a reminder after the break is finished. This allows the content of reminders to be customized based on the user's current activity status. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current activity status into a generating AI and have the generating AI perform the customization of the reminder content.

[0040] The management unit can prioritize highly relevant notifications based on the user's geographical location when managing schedules. For example, if the user is in a specific location, the management unit will prioritize notifications related to that location. For example, if the user is on the move, the management unit will prioritize notifications related to the destination. For example, if the user is at home, the management unit will prioritize notifications related to home. This allows the management unit to prioritize highly relevant notifications based on the user's geographical location. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of notifications.

[0041] The management unit can analyze users' social media activity and provide relevant reminders when managing schedules. For example, if a user posts on social media that they plan to attend a specific event, the management unit can provide a reminder related to that event. For example, if a user posts on social media that they plan to go to a specific place, the management unit can provide a reminder related to that place. For example, if a user posts on social media that they plan to perform a specific task, the management unit can provide a reminder related to that task. This allows the management unit to provide relevant reminders based on users' social media activity. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input users' social media activity into a generating AI and have the generating AI perform the task of providing reminders.

[0042] The service provider can adjust the level of detail in an email or message summary based on its importance. For example, it might provide a detailed summary for an important email, a concise summary for an email of low importance, or a moderately detailed summary for an email of moderate importance. This allows the service provider to adjust the level of detail in a summary based on the importance of the email or message. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the email or message into a generating AI and have the generating AI adjust the level of detail in the summary.

[0043] The service provider can apply different summarization algorithms depending on the category when summarizing emails or messages. For example, in the case of business emails, the service provider provides a summary that highlights the key points. For example, in the case of private emails, the service provider provides a summary that allows the recipient to grasp the overall flow. For example, in the case of advertising emails, the service provider provides a summary that highlights important offers and discount information. This allows the service provider to apply an appropriate summarization algorithm depending on the category of the email or message. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the category of the email or message into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0044] The service provider can determine the priority of summaries based on the submission date when summarizing emails and messages. For example, the service provider will provide a summary of an urgent email with the highest priority. For example, the service provider will provide a summary of an email with an approaching submission deadline with priority. For example, the service provider will provide a summary of an email with a far-off submission deadline with priority. This allows the service provider to determine the priority of summaries based on the submission date of emails and messages. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the submission date of emails and messages into a generating AI and have the generating AI perform the determination of the summary priority.

[0045] The service provider can adjust the order of summaries based on relevance when summarizing emails and messages. For example, the service provider may prioritize summarizing emails related to the user's current task. For example, the service provider may prioritize summarizing emails that are highly relevant based on the user's past behavior history. For example, the service provider may prioritize summarizing emails that are highly relevant based on the user's current situation. This allows the order of summaries to be adjusted based on the relevance of emails and messages. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the relevance of emails and messages into a generating AI and have the generating AI perform the adjustment of the summaries order.

[0046] The sensing unit can provide optimal information based on the user's past behavior history when it detects something. For example, the sensing unit can provide relevant information based on places the user has visited in the past. For example, the sensing unit can analyze the user's past behavior patterns and provide optimal information. For example, the sensing unit can provide relevant information based on the user's past search history. This allows the sensing unit to provide optimal information based on the user's past behavior history. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's past behavior history into a generating AI and have the generating AI perform the task of providing optimal information.

[0047] The sensing unit can customize how information is provided based on the user's current activity status when it detects something. For example, if the user is exercising, the sensing unit provides information via voice. For example, if the user is in a meeting, the sensing unit provides information via text. For example, if the user is resting, the sensing unit provides information via visuals. This allows the way information is provided to be customized based on the user's current activity status. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's current activity status into a generating AI and have the generating AI perform the customization of how information is provided.

[0048] The sensing unit can provide optimal information based on the user's geographical location information upon detection. For example, if the user is in a specific location, the sensing unit provides information related to that location. For example, if the user is on the move, the sensing unit provides information related to the destination. For example, if the user is at home, the sensing unit provides information related to home. This allows the sensing unit to provide optimal information based on the user's geographical location information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information.

[0049] The sensing unit can analyze the user's social media activity and provide relevant information upon detection. For example, if the user posts on social media that they plan to attend a specific event, the sensing unit will provide information related to that event. For example, if the user posts on social media that they plan to go to a specific place, the sensing unit will provide information related to that place. For example, if the user posts on social media that they plan to perform a specific task, the sensing unit will provide information related to that task. In this way, relevant information can be provided based on the user's social media activity. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's social media activity into a generating AI and have the generating AI perform the task of providing relevant information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The management department can analyze users' past behavior patterns to predict and notify them of future tasks. For example, if a user attends a specific meeting every Monday, the management department can automatically add the meeting to the calendar and notify them at the appropriate time. Similarly, if a user has a habit of submitting a specific report at the end of each month, the management department can predict and notify them of the report's deadline. Furthermore, if a user has a habit of exercising at a specific time, the management department can automatically set an exercise reminder for that time. This allows for the prediction and efficient management of future tasks based on users' past behavior patterns.

[0052] The sensing unit can monitor the user's current health status and provide necessary information and services. For example, it can monitor the user's heart rate and blood pressure, and if an abnormality is detected, it can send a notification prompting the user to contact a medical institution. Furthermore, if the user is exercising, it can provide real-time feedback on their exercise progress and offer appropriate advice. Additionally, if the user is relaxing, it can provide music or meditation guides to promote relaxation. This allows the system to provide appropriate information and services according to the user's health condition.

[0053] The service provider can analyze a user's past message history and automatically summarize similar messages. For example, it can analyze similar emails a user has received in the past and create a summary of a new email based on those summaries. It can also analyze the content of messages a user has sent in the past and automatically summarize similar messages. Furthermore, it can analyze the content of replies to messages a user has received in the past and suggest appropriate replies. This allows for efficient message summarization based on the user's past message history.

[0054] The management department can automatically suggest tasks for specific locations based on the user's geographical location. For example, if a user is in a particular supermarket, it can list and notify them of items they should purchase there. Similarly, if a user is in a specific gym, it can suggest exercise routines they should do there. Furthermore, if a user is in a particular cafe, it can suggest ways to relax there. This allows for efficient task suggestions based on the user's geographical location.

[0055] The sensing unit can analyze the user's past search history and prioritize providing relevant information. For example, it can suggest restaurants near the user's current location based on information the user has previously searched for. It can also provide weather forecasts for the user's current location based on weather information the user has previously searched for. Furthermore, it can suggest tourist destinations near the user's current location based on information the user has previously searched for. This allows for the efficient provision of relevant information based on the user's past search history.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The management system automatically manages and notifies users of their daily tasks and schedules. For example, it analyzes appointments and reminders entered by the user in their calendar and sends notifications at the appropriate time. Notifications can be sent for work tasks, household tasks, and personal tasks in the same way. Step 2: The service provides email and message summarization, translation, and speech-to-text functionality. For example, it can summarize long emails, extract key points, and provide users with concise content. It can translate foreign language messages and present them in an easy-to-understand format. It can convert voice input into text, which can then be saved as a message, displayed on the screen in real time, or sent as an email or message. Step 3: The sensing unit senses the user's location and situation and provides necessary information and services. For example, if the user is out, it provides weather information for their current location and information on nearby restaurants. If the user is at home, it provides weather information for the area around their home and information on restaurants.

[0058] (Example of form 2) The AI ​​assistant for smartphones according to an embodiment of the present invention is a system having functions for automatically managing and notifying the user of daily tasks and schedules, summarizing, translating, and providing speech-to-text functionality for emails and messages, and sensing the user's location and situation to provide necessary information and services. The AI ​​assistant for smartphones automatically manages and notifies the user of daily tasks and schedules. For example, the AI ​​analyzes appointments and reminders entered by the user in their calendar and provides notifications at the appropriate time. This allows the user to efficiently complete daily tasks without forgetting important appointments. Next, the AI ​​assistant for smartphones provides functions for summarizing, translating, and providing speech-to-text functionality for emails and messages. For example, the AI ​​summarizes long emails and provides the user with concise content. The AI ​​also translates foreign language messages and provides them in a format that is easy for the user to understand. Furthermore, it provides a speech-to-text function that converts voice input into text, allowing users to easily create messages. In addition, the AI ​​assistant for smartphones senses the user's location and situation to provide necessary information and services. For example, if the user is out and about, the AI ​​provides weather information for their current location and information on nearby restaurants. This allows the user to quickly obtain the information they need on the spot. Thus, smartphone AI assistants are multi-functional assistants designed to support and streamline users' daily lives, making them more efficient and convenient. This allows smartphone AI assistants to efficiently manage users' daily tasks and schedules, and provide necessary information and services.

[0059] The AI ​​assistant for smartphones according to this embodiment comprises a management unit, a provision unit, and a sensing unit. The management unit automatically manages and notifies the user of their daily tasks and schedules. For example, the management unit analyzes appointments and reminders entered by the user in their calendar and provides notifications at the appropriate time. For example, if the user enters a work task in their calendar, the management unit will notify the user when the deadline for that task is approaching. The management unit can also provide notifications for household tasks and personal tasks in a similar manner. For example, if the user enters a shopping list as a household task, the management unit can notify the user of the timing for shopping based on the contents of the list. For example, if the user enters an exercise schedule as a personal task, the management unit can notify the user when the time for that exercise is approaching. The provision unit provides email and message summarization, translation, and speech-to-text functionality. For example, the provision unit can summarize long emails and provide the user with concise content. For example, the provision unit can analyze long emails, extract important points, and create summaries. Furthermore, the service provider can translate foreign language messages and provide them to the user in an easily understandable format. For example, the service provider can translate an English message into Japanese and provide it to the user. For example, the service provider can translate a French message into English and provide it to the user. The service provider provides a speech-to-text function that converts voice input into text. For example, the service provider can convert what the user inputs by voice into text and save it as a message. For example, the service provider can convert what the user inputs by voice into text in real time and display it on the screen. For example, the service provider can convert what the user inputs by voice into text and send that text as an email or message. The sensing unit senses the user's location and situation and provides necessary information and services. For example, the sensing unit can provide weather information for the user's current location if the user is out. For example, the sensing unit can provide information about nearby restaurants if the user is out. For example, the sensing unit can provide weather information for the area around the user's home if the user is at home.The sensing unit can, for example, provide information about restaurants near the user's home if the user is at home. This allows the AI ​​assistant in the smartphone according to the embodiment to efficiently manage the user's daily tasks and schedule and provide necessary information and services.

[0060] The management unit automatically manages and notifies users of their daily tasks and schedules. For example, the management unit analyzes appointments and reminders entered by users in their calendar and sends notifications at the appropriate time. Specifically, the management unit works in conjunction with the user's calendar application and analyzes the entered appointments. For example, if a user enters a work task in their calendar, the management unit will send a notification when the deadline for that task is approaching. The management unit can adjust the timing of notifications, taking into account the importance and urgency of the task. For example, it can send a reminder the day before an important meeting to ensure the user doesn't forget to prepare. The management unit can also provide notifications for household and personal tasks in a similar manner. For example, if a user enters a shopping list as a household task, the management unit can notify the user of the timing for shopping based on the contents of the list. The management unit can learn the user's past behavior patterns and suggest the optimal notification timing. For example, if a user has a habit of shopping every Saturday, the management unit will send a notification based on that habit. The management unit can also notify users when the time for exercise approaches if a user enters an exercise plan as a personal task. The management department provides features that help users manage their health, including the ability to send reminders based on the frequency and type of exercise. For example, if a user enters a jogging schedule, the system can suggest the optimal time, taking weather forecasts into consideration. This allows the management department to efficiently support users' daily lives and make task management easier.

[0061] The service provider offers email and message summarization, translation, and speech-to-text functionality. For example, it can summarize long emails, providing users with concise content. Specifically, it uses natural language processing to analyze email content, extract key points, and create summaries. This allows users to quickly understand long emails, such as meeting minutes or project progress reports. Furthermore, the service provider can translate foreign language messages and provide them to users in an easily understandable format. For example, it can translate English messages into Japanese and provide them to users. Using a translation engine, the service provider can perform real-time translation between multiple languages. For example, it can translate French messages into English and provide them to users. In addition, the service provider offers a speech-to-text function that converts voice input into text. For example, it can convert user voice input into text and save it as a message. Using speech recognition technology, the service provider can convert user speech into text with high accuracy. For example, it can convert user voice input into text in real time and display it on the screen. The service provider can, for example, convert user voice input into text and send that text as an email or message. This allows the service provider to facilitate user communication and efficiently transmit information.

[0062] The sensing unit senses the user's location and situation and provides necessary information and services. For example, if the user is out, the sensing unit can provide weather information for their current location. Specifically, the sensing unit uses GPS to identify the user's current location and obtains the latest weather information for that area. For example, if the user is out, it can provide information on nearby restaurants. Based on the user's current location, the sensing unit can search for information on nearby restaurants and cafes and suggest recommended places according to the user's preferences. For example, if the user is at home, it can provide weather information for the area around their home. The sensing unit can register the user's home location and periodically update weather information for that area. For example, if the user is at home, it can provide information on restaurants around their home. The sensing unit can learn the user's past behavior history and preferences to provide more personalized information. For example, if the user frequently visits a particular restaurant, it can notify them of new menu items or special offers at that restaurant. Furthermore, the sensing unit can provide information tailored to the user's situation. For example, if the user is exercising, it can provide information on nearby parks or jogging courses. This allows the sensing unit to enrich the user's life and provide necessary information and services in a timely manner.

[0063] The sensing unit can provide weather information or nearby restaurant information based on the user's location information. For example, if the user is out, the sensing unit provides weather information for the user's current location. For example, if the user is out, the sensing unit provides nearby restaurant information. For example, if the user is at home, the sensing unit provides weather information for the area around the user's home. For example, if the user is at home, the sensing unit provides restaurant information for the area around the user's home. This allows the system to provide appropriate information based on the user's location information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's location information into a generating AI and have the generating AI perform the task of providing weather information or restaurant information.

[0064] The management unit can analyze appointments or reminders entered by users into their calendars and send notifications at appropriate times. For example, the management unit can send notifications when the deadline for an appointment entered by the user is approaching. For example, if a user has set a reminder, the management unit can send notifications when the time for that reminder is approaching. For example, the management unit can adjust the timing of notifications based on the priority of appointments entered by the user into their calendar. This allows users to efficiently complete their daily tasks without forgetting important appointments. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input appointments entered by users into a generating AI and have the generating AI execute the timing of notifications.

[0065] The service provider can summarize long emails and provide users with concise content. For example, the service provider can analyze long emails, extract key points, and create summaries. For example, the service provider can summarize the content of long emails and provide them to users in a concise form. For example, the service provider can input the summary of a long email into a generation AI and have the generation AI generate the summary. This allows users to understand long emails concisely. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a long email into a generation AI and have the generation AI generate the summary.

[0066] The service provider can translate foreign language messages and provide them to users in an easily understandable format. For example, the service provider can translate an English message into Japanese and provide it to the user. For example, the service provider can translate a French message into English and provide it to the user. For example, the service provider can input a foreign language message into a generation AI and have the generation AI perform the translation generation. This makes it easier for users to understand foreign language messages. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input a foreign language message into a generation AI and have the generation AI perform the translation generation.

[0067] The service provider can provide a speech-to-text function that converts voice input into text. For example, the service provider can convert what the user inputs by voice into text and save it as a message. For example, the service provider can convert what the user inputs by voice into text in real time and display it on the screen. For example, the service provider can convert what the user inputs by voice into text and send that text as an email or message. For example, the service provider can input voice input into a generation AI and have the generation AI perform the conversion to text. This allows users to easily create messages. Some or all of the above processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input voice input into a generation AI and have the generation AI perform the conversion to text.

[0068] The management unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the management unit may reduce the frequency of notifications and send them at a more relaxed time. For example, if the user is focused, the management unit may prioritize only important notifications and postpone others. For example, if the user is relaxed, the management unit may send notifications at the usual time. This allows for more appropriate timing of notifications 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0069] The management department can analyze the user's past schedule history and select an appropriate notification method. For example, the management department may prioritize notification methods that the user has previously preferred (e.g., voice, vibration). For example, the management department may suggest a notification method suitable for a specific time period based on the user's past schedule history. For example, the management department may analyze the user's past responses and select the most effective notification method. This allows the management department to select the optimal notification method based on the user's past schedule history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department may input the user's past schedule history into a generating AI and have the generating AI perform the selection of notification methods.

[0070] The management unit can customize the content of reminders based on the user's current activity status when managing schedules. For example, if the user is in a meeting, the management unit will display a reminder after the meeting ends. For example, if the user is exercising, the management unit will display a reminder after the exercise is finished. For example, if the user is taking a break, the management unit will display a reminder after the break is finished. This allows the content of reminders to be customized based on the user's current activity status. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current activity status into a generating AI and have the generating AI perform the customization of the reminder content.

[0071] The management unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize only important notifications and postpone other notifications. For example, if the user is relaxed, the management unit will send notifications with the usual priority. For example, if the user is focused, the management unit will prioritize only important notifications and postpone other notifications. This allows important notifications to be prioritized by determining the priority of notifications 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. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0072] The management unit can prioritize highly relevant notifications based on the user's geographical location when managing schedules. For example, if the user is in a specific location, the management unit will prioritize notifications related to that location. For example, if the user is on the move, the management unit will prioritize notifications related to the destination. For example, if the user is at home, the management unit will prioritize notifications related to home. This allows the management unit to prioritize highly relevant notifications based on the user's geographical location. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of notifications.

[0073] The management unit can analyze users' social media activity and provide relevant reminders when managing schedules. For example, if a user posts on social media that they plan to attend a specific event, the management unit can provide a reminder related to that event. For example, if a user posts on social media that they plan to go to a specific place, the management unit can provide a reminder related to that place. For example, if a user posts on social media that they plan to perform a specific task, the management unit can provide a reminder related to that task. This allows the management unit to provide relevant reminders based on users' social media activity. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input users' social media activity into a generating AI and have the generating AI perform the task of providing reminders.

[0074] The service provider can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the service provider will provide a concise and to-the-point summary. For example, if the user is relaxed, the service provider will provide a detailed summary. For example, if the user is focused, the service provider will provide a summary that highlights important information. By adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the way the summary is presented.

[0075] The service provider can adjust the level of detail in an email or message summary based on its importance. For example, it might provide a detailed summary for an important email, a concise summary for an email of low importance, or a moderately detailed summary for an email of moderate importance. This allows the service provider to adjust the level of detail in a summary based on the importance of the email or message. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the email or message into a generating AI and have the generating AI adjust the level of detail in the summary.

[0076] The service provider can apply different summarization algorithms depending on the category when summarizing emails or messages. For example, in the case of business emails, the service provider provides a summary that highlights the key points. For example, in the case of private emails, the service provider provides a summary that allows the recipient to grasp the overall flow. For example, in the case of advertising emails, the service provider provides a summary that highlights important offers and discount information. This allows the service provider to apply an appropriate summarization algorithm depending on the category of the email or message. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the category of the email or message into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0077] The service provider 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 service provider will provide a short, concise summary. For example, if the user is relaxed, the service provider will provide a detailed summary. For example, if the user is focused, the service provider will provide a summary that highlights important information. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the length of the summary.

[0078] The service provider can determine the priority of summaries based on the submission date when summarizing emails and messages. For example, the service provider will provide a summary of an urgent email with the highest priority. For example, the service provider will provide a summary of an email with an approaching submission deadline with priority. For example, the service provider will provide a summary of an email with a far-off submission deadline with priority. This allows the service provider to determine the priority of summaries based on the submission date of emails and messages. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the submission date of emails and messages into a generating AI and have the generating AI perform the determination of the summary priority.

[0079] The service provider can adjust the order of summaries based on relevance when summarizing emails and messages. For example, the service provider may prioritize summarizing emails related to the user's current task. For example, the service provider may prioritize summarizing emails that are highly relevant based on the user's past behavior history. For example, the service provider may prioritize summarizing emails that are highly relevant based on the user's current situation. This allows the order of summaries to be adjusted based on the relevance of emails and messages. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the relevance of emails and messages into a generating AI and have the generating AI perform the adjustment of the summaries order.

[0080] The sensing unit can estimate the user's emotions and adjust the content of the information it provides based on the estimated emotions. For example, if the user is stressed, the sensing unit provides relaxing information. For example, if the user is relaxed, the sensing unit provides interesting information. For example, if the user is focused, the sensing unit provides important information. In this way, by adjusting the content of the information provided according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input user emotion data into a generative AI and have the generative AI adjust the content of the information provided.

[0081] The sensing unit can provide optimal information based on the user's past behavior history when it detects something. For example, the sensing unit can provide relevant information based on places the user has visited in the past. For example, the sensing unit can analyze the user's past behavior patterns and provide optimal information. For example, the sensing unit can provide relevant information based on the user's past search history. This allows the sensing unit to provide optimal information based on the user's past behavior history. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's past behavior history into a generating AI and have the generating AI perform the task of providing optimal information.

[0082] The sensing unit can customize how information is provided based on the user's current activity status when it detects something. For example, if the user is exercising, the sensing unit provides information via voice. For example, if the user is in a meeting, the sensing unit provides information via text. For example, if the user is resting, the sensing unit provides information via visuals. This allows the way information is provided to be customized based on the user's current activity status. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's current activity status into a generating AI and have the generating AI perform the customization of how information is provided.

[0083] The sensing unit can estimate the user's emotions and determine the priority of information to provide based on the estimated emotions. For example, if the user is stressed, the sensing unit will prioritize providing information that helps them relax. For example, if the user is relaxed, the sensing unit will prioritize providing information that is interesting. For example, if the user is focused, the sensing unit will prioritize providing information that is important. In this way, by determining the priority of information to provide according to the user's emotions, more appropriate information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0084] The sensing unit can provide optimal information based on the user's geographical location information upon detection. For example, if the user is in a specific location, the sensing unit provides information related to that location. For example, if the user is on the move, the sensing unit provides information related to the destination. For example, if the user is at home, the sensing unit provides information related to home. This allows the sensing unit to provide optimal information based on the user's geographical location information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information.

[0085] The sensing unit can analyze the user's social media activity and provide relevant information upon detection. For example, if the user posts on social media that they plan to attend a specific event, the sensing unit will provide information related to that event. For example, if the user posts on social media that they plan to go to a specific place, the sensing unit will provide information related to that place. For example, if the user posts on social media that they plan to perform a specific task, the sensing unit will provide information related to that task. In this way, relevant information can be provided based on the user's social media activity. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's social media activity into a generating AI and have the generating AI perform the task of providing relevant information.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The management department can analyze users' past behavior patterns to predict and notify them of future tasks. For example, if a user attends a specific meeting every Monday, the management department can automatically add the meeting to the calendar and notify them at the appropriate time. Similarly, if a user has a habit of submitting a specific report at the end of each month, the management department can predict and notify them of the report's deadline. Furthermore, if a user has a habit of exercising at a specific time, the management department can automatically set an exercise reminder for that time. This allows for the prediction and efficient management of future tasks based on users' past behavior patterns.

[0088] The translation system can estimate the user's emotions and adjust the tone of the translation based on that estimation. For example, if the user is stressed, the translation tone can be softened and relaxing expressions can be used. If the user is relaxed, the translation tone can be made more casual and friendly. Furthermore, if the user is focused, the translation tone can be made more formal to emphasize important information. By adjusting the translation tone according to the user's emotions, a more appropriate translation can be provided.

[0089] The sensing unit can monitor the user's current health status and provide necessary information and services. For example, it can monitor the user's heart rate and blood pressure, and if an abnormality is detected, it can send a notification prompting the user to contact a medical institution. Furthermore, if the user is exercising, it can provide real-time feedback on their exercise progress and offer appropriate advice. Additionally, if the user is relaxing, it can provide music or meditation guides to promote relaxation. This allows the system to provide appropriate information and services according to the user's health condition.

[0090] The management department can estimate the user's emotions and adjust schedule priorities based on those estimates. For example, if a user is stressed, less important tasks can be postponed, and relaxation time can be prioritized. If a user is relaxed, high-priority tasks can be prioritized in the schedule. Furthermore, if a user is focused, tasks requiring concentration can be prioritized in the schedule. This allows for more efficient task management by adjusting schedule priorities according to the user's emotions.

[0091] The service provider can analyze a user's past message history and automatically summarize similar messages. For example, it can analyze similar emails a user has received in the past and create a summary of a new email based on those summaries. It can also analyze the content of messages a user has sent in the past and automatically summarize similar messages. Furthermore, it can analyze the content of replies to messages a user has received in the past and suggest appropriate replies. This allows for efficient message summarization based on the user's past message history.

[0092] The sensing unit can estimate the user's emotions and adjust the format of the information provided based on those emotions. For example, if the user is stressed, it can provide visually relaxing images or videos. If the user is relaxed, it can provide detailed text information. Furthermore, if the user is focused, it can provide information in a bulleted list format that highlights important points. In this way, by adjusting the format of the information provided according to the user's emotions, more appropriate information can be delivered.

[0093] The management department can automatically suggest tasks for specific locations based on the user's geographical location. For example, if a user is in a particular supermarket, it can list and notify them of items they should purchase there. Similarly, if a user is in a specific gym, it can suggest exercise routines they should do there. Furthermore, if a user is in a particular cafe, it can suggest ways to relax there. This allows for efficient task suggestions based on the user's geographical location.

[0094] The system can estimate the user's emotions and adjust the accuracy of speech-to-text based on those emotions. For example, if the user is stressed, the accuracy of speech-to-text can be increased to reduce misrecognition. If the user is relaxed, the accuracy of speech-to-text can be maintained at normal levels. Furthermore, if the user is focused, the accuracy of speech-to-text can be increased to accurately recognize important information. By adjusting the accuracy of speech-to-text according to the user's emotions, a more appropriate text conversion can be provided.

[0095] The sensing unit can analyze the user's past search history and prioritize providing relevant information. For example, it can suggest restaurants near the user's current location based on information the user has previously searched for. It can also provide weather forecasts for the user's current location based on weather information the user has previously searched for. Furthermore, it can suggest tourist destinations near the user's current location based on information the user has previously searched for. This allows for the efficient provision of relevant information based on the user's past search history.

[0096] The management team can estimate the user's emotions and adjust the content of reminders based on those estimates. For example, if the user is stressed, the reminder content can be made concise, notifying only the important points. If the user is relaxed, the reminder content can be made more detailed, providing relevant information. Furthermore, if the user is focused, the reminder content can be provided in a concise format. By adjusting the content of reminders according to the user's emotions, more appropriate notifications can be delivered.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The management system automatically manages and notifies users of their daily tasks and schedules. For example, it analyzes appointments and reminders entered by the user in their calendar and sends notifications at the appropriate time. Notifications can be sent for work tasks, household tasks, and personal tasks in the same way. Step 2: The service provides email and message summarization, translation, and speech-to-text functionality. For example, it can summarize long emails, extract key points, and provide users with concise content. It can translate foreign language messages and present them in an easy-to-understand format. It can convert voice input into text, which can then be saved as a message, displayed on the screen in real time, or sent as an email or message. Step 3: The sensing unit senses the user's location and situation and provides necessary information and services. For example, if the user is out, it provides weather information for their current location and information on nearby restaurants. If the user is at home, it provides weather information for the area around their home and information on restaurants.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] For example, each of the multiple elements, including the management unit, the provision unit, and the sensing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart device 14, which analyzes appointments and reminders entered in the user's calendar and provides notifications at the appropriate time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides email and message summarization, translation, and speech-to-text functions. The sensing unit senses the user's location information and situation using the camera 42 and microphone 38B of the smart device 14, for example, and provides necessary information and services. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] For example, each of the multiple elements, including the management unit, the provision unit, and the sensing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart glasses 214, which analyzes appointments and reminders entered in the user's calendar and provides notifications at the appropriate time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides email and message summarization, translation, and speech-to-text functions. The sensing unit senses the user's location and situation using the camera 42 and microphone 238 of the smart glasses 214, for example, and provides necessary information and services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] For example, each of the multiple elements, including the management unit, the provision unit, and the sensing unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the headset terminal 314, which analyzes appointments and reminders entered in the user's calendar and provides notifications at the appropriate time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides email and message summarization, translation, and speech-to-text functions. The sensing unit senses the user's location and situation using the camera 42 and microphone 238 of the headset terminal 314, for example, and provides necessary information and services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] For example, each of the multiple elements, including the management unit, the provision unit, and the sensing unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the robot 414, which analyzes appointments and reminders entered into the user's calendar and provides notifications at the appropriate time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides email and message summarization, translation, and speech-to-text functions. The sensing unit senses the user's location and situation using the camera 42 and microphone 238 of the robot 414, for example, and provides necessary information and services. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A system characterized by comprising: a management unit that automatically manages and notifies users of their daily tasks or schedules; a provision unit that provides email or message summarization, translation, and speech-to-text functionality; and a sensing unit that senses the user's location or situation and provides necessary information or services. (Note 2) The system according to Appendix 1, characterized in that the sensing unit provides weather information or information on nearby restaurants based on the user's location information. (Note 3) The system described in Appendix 1 is characterized in that the management unit analyzes appointments or reminders entered by the user into the calendar and provides notifications at the appropriate time. (Note 4) The aforementioned supply unit is, Summarize lengthy emails and provide users with concise information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Translate foreign language messages and present them in a format that is easy for users to understand. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It provides a speech-to-text function that converts voice input into text. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The system described in Appendix 1 is characterized in that the management unit analyzes the user's past schedule history and selects an appropriate notification method. (Note 9) The aforementioned management department, When managing schedules, the content of reminders can be customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The system described in Appendix 1 is characterized in that the management unit prioritizes sending highly relevant notifications based on the user's geographical location information when managing schedules. (Note 12) The aforementioned management department, When managing schedules, the system analyzes users' social media activity and provides relevant reminders. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit 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 14) The system described in Appendix 1, wherein the providing unit adjusts the level of detail of the summary based on importance when summarizing an email or message. (Note 15) The system according to Appendix 1, characterized in that the providing unit applies different summarization algorithms depending on the category when summarizing emails or messages. (Note 16) The aforementioned supply unit 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 17) The aforementioned supply unit is, When summarizing emails and messages, prioritize summaries based on their submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When summarizing emails and messages, adjust the order of the summaries based on relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The sensing unit is It estimates the user's emotions and adjusts the content of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The system according to Appendix 1, characterized in that the sensing unit provides optimal information based on the user's past behavior history when sensing. (Note 21) The sensing unit is When detection occurs, the method of providing information is customized based on the user's current activity status. The smartphone AI assistant described in Appendix 1, characterized by the features described herein. (Note 22) The sensing unit is It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The system according to Appendix 1, characterized in that the sensing unit provides optimal information based on the user's geographical location information when sensing. (Note 24) The sensing unit is Upon detection, the system analyzes the user's social media activity and provides relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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 management unit automatically manages and notifies users of their daily tasks or schedules, A service that provides email or message summarization, translation, and speech-to-text functionality, It includes a sensing unit that senses the user's location or situation and provides necessary information or services. A system characterized by the following features.

2. The sensing unit is Based on the user's location, provide weather information or information about nearby restaurants. The system according to feature 1.

3. The aforementioned management department, The system analyzes appointments and reminders entered by the user in their calendar and sends notifications at the appropriate time. The system according to feature 1.

4. The aforementioned supply unit is, Summarize lengthy emails and provide users with concise information. The system according to feature 1.

5. The aforementioned supply unit is, Translate foreign language messages and present them in a format that is easy for users to understand. The system according to feature 1.

6. The aforementioned supply unit is, It provides a speech-to-text function that converts voice input into text. The system according to feature 1.

7. The aforementioned management department, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system according to feature 1.

8. The aforementioned management department, Analyze the user's past schedule history and select the appropriate notification method. The system according to feature 1.

9. The aforementioned management department, When managing schedules, the content of reminders can be customized based on the user's current activity status. The system according to feature 1.

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