system

Abstract

The system according to the embodiment aims to comprehensively support users in their daily lives, travel, learning, and health management. [Solution] A system according to an embodiment includes a unit that provides daily life assistance, a unit that provides travel support, a unit that promotes learning, and a unit that supports health and happiness. The unit that provides daily life assistance supports the user's daily life. The unit that provides travel support supports the user's travel. The unit that promotes learning promotes the user's learning. The unit that supports health and happiness supports the user's health and happiness.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately provided a single system with support for a wide range of areas, such as daily life, travel, learning, and health management, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support users in their daily lives, travel, learning, and health management. [Means for solving the problem]

[0006] The system according to the embodiment includes a unit for assisting daily life, a unit for supporting travel, a unit for promoting learning, and a unit for supporting health and happiness. The unit for assisting daily life supports the user's daily life. The unit for supporting travel supports the user's travel. The unit for promoting learning promotes the user's learning. The unit for supporting health and happiness supports the user's health and happiness. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively support users in their daily lives, travel, learning, and health management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a smartphone-based multifunctional audio guide app. This system is an intelligent personal assistant that supports users in a range of activities, from daily life to travel, learning, and health management. Specifically, the system provides functions for assisting with daily life, travel support, promoting learning, and supporting health and well-being. For example, as a daily life assistant, the system provides functions for inputting the user's schedule by voice and setting reminders, creating a shopping list by voice and adding necessary items to the list, providing weather information for the user's current location, and providing advice when going out. As a travel support function, the system provides functions for proposing itineraries based on the user's preferences, providing information on tourist spots, providing voice directions from the user's current location to their destination, and providing real-time foreign language translation to support communication during travel. As a learning support function, the system provides functions for checking the user's pronunciation and providing guidance on correct pronunciation, providing quizzes based on the learning content to check the user's learning progress, and providing e-book reading support. Health and well-being support includes functions such as recording the user's health data and monitoring their health status, providing exercise guidance and helping the user achieve their fitness goals, and providing relaxation music and meditation guides to support stress management. This allows the system to improve the user's quality of life and provide support for living a more efficient and comfortable life. This allows the system to support the user's life in many ways and improve their quality of life.

[0029] A system according to an embodiment includes a daily life assistance unit, a travel support unit, a learning promotion unit, and a health and happiness support unit. The daily life assistance unit allows a user to input their schedule via voice and set reminders. For example, if a user inputs "Meeting tomorrow at 10:00 AM," the system records the information and notifies the user of a reminder at the specified time. The daily life assistance unit also creates a shopping list via voice and adds necessary items to the list. For example, if a user inputs "Buy milk," the system adds "Milk" to the shopping list. The daily life assistance unit also provides weather information for the user's current location and advice when going out. For example, if a user asks, "What's the weather like today?" the system provides weather information for the user's current location and advises, "It's going to rain today, so please bring an umbrella." The travel support unit suggests travel plans based on the user's preferences and provides information about tourist attractions. For example, if a user says, "I want to go on a trip next weekend," the system suggests travel plans based on the user's preferences and budget and provides information about tourist attractions. The travel support unit also provides voice guidance from the user's current location to the destination. For example, when the user says, "Tell me the way to the station," the system provides voice guidance from the user's current location to the station. The travel support unit also provides real-time foreign language translation to support communication during travel. For example, when the user says, "Translate this phrase into English," the system translates the phrase into English and provides it via voice. The learning promotion unit checks the user's pronunciation and provides guidance on correct pronunciation. For example, when the user says, "Check the pronunciation of this word," the system checks the user's pronunciation and provides guidance on correct pronunciation. The learning promotion unit also provides quizzes based on the learning content to check the user's learning progress. For example, when the user says, "Give me a math quiz," the system provides a math quiz to check the user's learning progress. The learning promotion unit also provides an e-book reading function to support reading. For example, when the user says, "Read this book aloud," the system reads the e-book aloud to support the user's reading.The health and well-being support unit records the user's health data and monitors their health condition. For example, when a user says, "Record my weight today," the system records the user's weight and monitors their health condition. The health and well-being support unit also provides exercise guidance and supports the user in achieving their fitness goals. For example, when a user says, "Tell me what exercise to do today," the system suggests an exercise menu based on the user's fitness goals and provides guidance. The health and well-being support unit also provides relaxation music and meditation guides to support stress management. For example, when a user says, "Play relaxing music," the system plays relaxation music to support the user's stress management. In this way, the system according to the embodiment can support the user's life in many ways and improve their quality of life.

[0030] The daily life assistant unit can input the user's schedule by voice and set a reminder. For example, if the user voice-inputs "Meeting tomorrow at 10:00 AM," the system records the information and notifies the user of the reminder at the specified time. The daily life assistant unit can also convert the user's voice into text using voice recognition technology and set a reminder. For example, if the user says, "I'll take my medicine at 3:00 PM," the system converts the information into text and sets a reminder. Furthermore, the daily life assistant unit can analyze the user's past schedule history and suggest the optimal method for setting reminders. For example, it can set an earlier reminder for an appointment that the user has frequently been late to in the past. This can improve the efficiency of the user's schedule management.

[0031] The daily life assistant unit can create a shopping list using voice and add necessary items to the list. For example, when the user vocally inputs "buy milk," the system adds "milk" to the shopping list. The daily life assistant unit can also use voice recognition technology to convert the user's voice into text and create a shopping list. For example, when the user says "buy eggs," the system converts the information into text and adds "eggs" to the shopping list. Furthermore, the daily life assistant unit can automatically generate a list by referring to the user's past purchase history. For example, items that the user frequently purchased in the past can be automatically added to the list. This simplifies the creation of a shopping list.

[0032] The daily life assistant unit can provide weather information for the current location and give advice when going out. For example, when the user asks, "What's the weather like today?", the system provides weather information for the current location and advises, "It's going to rain today, so please take an umbrella." The daily life assistant unit can also obtain the latest weather information from the Internet and provide it to the user as a method of obtaining weather data. For example, the system periodically updates weather information to provide the user with the latest information. Furthermore, the daily life assistant unit can customize advice when going out by taking into account the user's past behavioral patterns. For example, if the user has refrained from going out on rainy days in the past, the system can advise the user to refrain from going out on rainy days. This allows the user to make appropriate decisions when going out.

[0033] The travel support unit can propose a travel plan based on the user's request and provide information on tourist spots. For example, when the user says, "I want to go on a trip next weekend," the system proposes a travel plan based on the user's preferences and budget and provides information on tourist spots. As a specific method of proposing a travel plan, the travel support unit can also propose an optimal plan by referring to the user's past travel history. For example, it can suggest similar places based on places the user has visited in the past. Furthermore, the travel support unit can estimate the user's emotions and adjust the proposed travel plan based on the estimated user's emotions. For example, if the user is relaxed, it can suggest a leisurely travel plan. This can efficiently support the user's travel planning.

[0034] The travel support unit can provide voice guidance from the current location to the destination. For example, when the user says, "Tell me the way to the station," the travel support unit can provide voice guidance from the current location to the station. The travel support unit can also use a navigation system to provide guidance on the optimal route by reflecting the user's current traffic conditions in real time. For example, the optimal route can be suggested based on real-time traffic congestion information. Furthermore, the travel support unit can also suggest optimal tourist spots taking into account the user's geographical location information. For example, the travel support unit can suggest tourist spots close to the user's current location. This allows the user to reach the destination without getting lost.

[0035] The travel support unit can instantly translate foreign languages ​​to support communication during travel. For example, when a user says, "Translate this phrase into English," the system translates the phrase into English and provides it audibly. The travel support unit can also use a translation engine to provide the optimal translation method by referring to the user's past translation history. For example, it can suggest similar phrases based on translation phrases the user has used in the past. Furthermore, the travel support unit can analyze the user's social media activity and provide related translations. For example, if a user posts in a specific language on social media, it can provide a translation in that language. This can enable users to communicate smoothly in foreign countries.

[0036] The learning promotion unit can check the user's pronunciation and provide guidance on correct pronunciation. For example, when the user says, "Check the pronunciation of this word," the system checks the user's pronunciation and provides guidance on correct pronunciation. The learning promotion unit can also evaluate the user's pronunciation using speech recognition technology and provide feedback. For example, when the user says, "Check the pronunciation of this sentence," the system evaluates the pronunciation and points out areas for improvement. Furthermore, the learning promotion unit can refer to the user's past pronunciation history to provide the most appropriate teaching method. For example, it can provide guidance on pronunciation that the user has previously struggled with. This can effectively support the user's language learning.

[0037] The learning promotion unit can provide quizzes based on the learning content and check the learning progress of the user. For example, when the user says, "Give me a math quiz," the system provides a math quiz and checks the user's learning progress. As a specific method of providing quizzes, the learning promotion unit can also provide optimal questions that reflect the user's learning progress in real time. For example, the learning promotion unit can provide questions with increased difficulty based on questions that the user has answered correctly in the past. Furthermore, the learning promotion unit can estimate the user's emotions and adjust the difficulty of the quiz based on the estimated user's emotions. For example, if the user is relaxed, the system can provide quizzes of normal difficulty. This makes it possible to effectively check the user's learning progress.

[0038] The learning promotion unit can provide an e-book reading function to support reading. For example, when the user says, "Read this book aloud," the system reads the e-book aloud to support the user's reading. The learning promotion unit can also use a text-to-speech engine to provide an optimal reading method by referring to the user's past reading history. For example, the unit can read at a similar speed based on the user's preferred reading speed in the past. Furthermore, the learning promotion unit can analyze the user's social media activity and suggest related reading content. For example, if the user posts about a specific genre on social media, the unit can suggest reading content in that genre. This can improve the user's reading experience.

[0039] The health and well-being support unit can record a user's health data and monitor their health condition. For example, when a user says, "Record my weight today," the health and well-being support unit records the user's weight and monitors their health condition. The health and well-being support unit can also provide an optimal monitoring method for recording health data by referring to the user's past health history. For example, it can provide a similar monitoring method based on health data previously recorded by the user. Furthermore, the health and well-being support unit can estimate the user's emotions and adjust the method for recording health data based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple input method to make it easier to record health data. This can effectively support the user's health management.

[0040] The health and well-being support unit can provide exercise guidance and support the user in achieving their fitness goals. For example, when the user says, "Tell me today's exercise menu," the health and well-being support unit can suggest an exercise menu based on the user's fitness goals and provide guidance. Furthermore, as a specific method of exercise guidance, the health and well-being support unit can also provide optimal exercise guidance by reflecting the user's current physical condition in real time. For example, if the user is tired, it can suggest light exercise. Furthermore, the health and well-being support unit can estimate the user's emotions and prioritize fitness goals based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize fitness goals that emphasize relaxation. This can effectively support the user in achieving their fitness goals.

[0041] The health and well-being support unit can provide relaxation music and meditation guides to support stress management. For example, when the user says, "Play relaxing music," the health and well-being support unit can play relaxation music to support the user's stress management. The health and well-being support unit can also refer to the user's past stress history to provide an optimal relaxation method as a specific method of meditation guidance. For example, the unit can re-suggest relaxation methods that were effective for the user in the past. Furthermore, the health and well-being support unit can analyze the user's social media activity and suggest related relaxation methods. For example, if the user posts about stress on social media, the unit can suggest relaxation methods based on those posts. This can effectively support the user's stress management.

[0042] The daily life assistant unit can analyze the user's past schedule history and suggest an optimal reminder setting method. For example, the daily life assistant unit can set an earlier reminder for appointments that the user has frequently been late for in the past. Furthermore, as a specific method of analyzing the schedule history, the daily life assistant unit can also collect and analyze the user's past schedule data. For example, it can set a regular reminder for appointments that the user has previously completed smoothly. Furthermore, the daily life assistant unit can estimate the user's emotions and adjust the timing of reminder notifications based on the estimated user emotions. For example, if the user is feeling stressed, the reminder notification can be delayed slightly to allow the user time to relax. This makes it possible to optimize reminder settings based on the user's past schedule history.

[0043] When creating a shopping list, the unit that performs daily life assistance can automatically generate the list by referring to the user's past purchase history. For example, the unit that performs daily life assistance can automatically add items that the user frequently purchased in the past to the list. Furthermore, as a specific method of referring to the purchase history, the unit that performs daily life assistance can also collect and analyze the user's past purchase data. For example, items that the user purchased in a particular season can be added to the list. Furthermore, the unit that performs daily life assistance can estimate the user's emotions and determine the priorities of the shopping list based on the estimated user's emotions. For example, if the user is feeling stressed, items that help them relax can be preferentially added to the list. In this way, a shopping list can be automatically generated based on the user's past purchase history.

[0044] The daily life assistant unit can customize advice for going out based on the user's past behavioral patterns when providing weather information. For example, if the user has refrained from going out on rainy days in the past, the daily life assistant unit can advise the user to refrain from going out on rainy days. Furthermore, as a specific method of taking behavioral patterns into consideration, the daily life assistant unit can collect and analyze the user's past behavioral data. For example, if the user has preferred going out on sunny days in the past, the unit can recommend going out on sunny days. Furthermore, the daily life assistant unit can analyze the user's social media activity and provide related advice. For example, if the user posts about the weather on social media, the unit can provide advice based on the post. This allows advice for going out to be optimized based on the user's past behavioral patterns.

[0045] The daily life assistant unit can set optimal reminders during schedule management, taking into account the user's geographical location information. For example, when the user is in a specific location, the daily life assistant unit sets a reminder for an appointment related to that location. As a specific method of taking geographical location information into consideration, the daily life assistant unit can also acquire and analyze the user's location information. For example, when the user is traveling, the unit can set a reminder for an appointment related to the user's destination. Furthermore, the daily life assistant unit can analyze the user's past schedule history and suggest an optimal reminder setting method. For example, the unit can set an earlier reminder for an appointment for which the user was frequently late in the past. This makes it possible to optimize reminders based on the user's geographical location information.

[0046] The daily life assistant unit can analyze the user's social media activity and provide related advice when providing weather information. For example, if the user posts about the weather on social media, the daily life assistant unit can provide advice based on the post. Furthermore, as a specific method of analyzing social media activity, the daily life assistant unit can collect and analyze the user's social media data. For example, if the user posts about a specific activity on social media, the unit can provide weather information related to that activity. Furthermore, the daily life assistant unit can customize advice for going out by taking into account the user's past behavioral patterns. For example, if the user has avoided going out on rainy days in the past, the unit can advise the user to avoid going out on rainy days. This allows weather information to be customized based on the user's social media activity.

[0047] When creating a travel plan, the travel support unit can suggest an optimal plan by referring to the user's past travel history. For example, the travel support unit can suggest similar places based on places the user has visited in the past. Furthermore, as a specific method of referring to the travel history, the travel support unit can collect and analyze the user's past travel data. For example, based on activities the user has previously preferred, the travel support unit can suggest a plan that includes similar activities. Furthermore, the travel support unit can estimate the user's emotions and adjust the suggested travel plan based on the estimated user's emotions. For example, if the user is relaxed, a leisurely travel plan can be suggested. In this way, an optimal travel plan can be suggested based on the user's past travel history.

[0048] During navigation, the travel support unit can guide the user to the optimal route by reflecting the user's current traffic conditions in real time. For example, the travel support unit can suggest the optimal route based on real-time traffic congestion information. As a specific method of reflecting traffic conditions, the travel support unit can also obtain the latest traffic information from the Internet and provide it to the user. For example, the system can periodically update traffic information to provide the user with the latest information. Furthermore, the travel support unit can suggest the optimal tourist destinations by taking into account the user's geographical location information. For example, the system can suggest tourist destinations close to the user's current location. This makes it possible to guide the user to the optimal route based on real-time traffic conditions.

[0049] When using the translation function, the travel support unit can refer to the user's past translation history and provide the optimal translation method. For example, the travel support unit can suggest similar phrases based on translation phrases the user has used in the past. As a specific method of referring to the translation history, the travel support unit can also collect and analyze the user's past translation data. For example, it can provide related translations based on content that the user has translated in the past. Furthermore, the travel support unit can analyze the user's social media activity and provide related translations. For example, if the user posts on social media in a specific language, it can provide a translation in that language. This makes it possible to provide the optimal translation method based on the user's past translation history.

[0050] The travel support unit can suggest optimal tourist destinations by taking into consideration the user's geographical location information when creating a travel plan. For example, the travel support unit can suggest tourist destinations close to the user's current location. As a specific method of considering geographical location information, the travel support unit can also acquire and analyze the user's location information. For example, if the user is in a specific area, it can suggest tourist destinations related to that area. Furthermore, the travel support unit can also suggest optimal plans by referring to the user's past travel history. For example, it can suggest similar places based on places the user has visited in the past. This makes it possible to suggest optimal tourist destinations based on the user's geographical location information.

[0051] When using the translation function, the travel support unit can analyze the user's social media activity and provide relevant translations. For example, if the user posts on social media in a specific language, the travel support unit can provide translations in that language. As a specific method of analyzing social media activity, the travel support unit can also collect and analyze the user's social media data. For example, if the user posts on social media about a specific phrase, it can provide translations related to that phrase. Furthermore, the travel support unit can refer to the user's past translation history to provide the optimal translation method. For example, it can suggest similar phrases based on translation phrases the user has used in the past. This makes it possible to provide the optimal translation based on the user's social media activity.

[0052] The learning promotion unit can provide an optimal teaching method by referring to the user's past pronunciation history when checking pronunciation. For example, the learning promotion unit can focus on teaching pronunciation that the user has previously struggled with. As a specific method of referring to the pronunciation history, the learning promotion unit can also collect and analyze the user's past pronunciation data. For example, based on pronunciation that the user has previously excelled at, the learning promotion unit can provide similar pronunciation instruction. Furthermore, the learning promotion unit can also provide optimal pronunciation instruction by taking into account the user's geographical location information. For example, if the user is in a specific region, the learning promotion unit can provide pronunciation instruction that takes into account the accent and dialect of that region. This makes it possible to provide optimal pronunciation instruction based on the user's past pronunciation history.

[0053] The learning promotion unit can provide optimal questions by reflecting the user's learning progress in real time when providing quizzes. For example, the learning promotion unit can provide questions of increased difficulty based on questions that the user has answered correctly in the past. As a specific method for reflecting learning progress, the learning promotion unit can also collect and analyze the user's learning data in real time. For example, the learning promotion unit can re-provide similar questions based on questions that the user has answered incorrectly in the past. Furthermore, the learning promotion unit can estimate the user's emotions and adjust the difficulty of the quiz based on the estimated user's emotions. For example, if the user is relaxed, the unit can provide a quiz of normal difficulty. This allows optimal questions to be provided based on the user's learning progress.

[0054] When providing reading support, the learning promotion unit can provide an optimal reading method by referring to the user's past reading history. For example, the learning promotion unit can read at a similar speed based on the user's previously preferred reading speed. Furthermore, as a specific method of referring to the reading history, the learning promotion unit can collect and analyze the user's past reading data. For example, the learning promotion unit can read in a similar tone based on the user's previously preferred tone of voice. Furthermore, the learning promotion unit can analyze the user's social media activity and suggest related reading content. For example, if the user posts about a specific genre on social media, reading content in that genre can be suggested. This makes it possible to provide an optimal reading method based on the user's past reading history.

[0055] The learning promotion unit can provide optimal pronunciation instruction by taking into account the user's geographical location information when checking pronunciation. For example, if the user is in a specific region, the learning promotion unit can provide pronunciation instruction that takes into account the accent and dialect of that region. Furthermore, as a specific method of taking geographical location information into account, the learning promotion unit can also acquire and analyze the user's location information. For example, if the user is traveling, pronunciation instruction tailored to the language of the destination can be provided. Furthermore, the learning promotion unit can also provide optimal instruction methods by referring to the user's past pronunciation history. For example, instruction can be focused on pronunciation that the user has previously struggled with. This makes it possible to provide optimal pronunciation instruction based on the user's geographical location information.

[0056] When providing reading support, the learning promotion unit can analyze the user's social media activity and suggest related reading content. For example, if the user posts about a specific genre on social media, the learning promotion unit can suggest reading content in that genre. The learning promotion unit can also collect and analyze the user's social media data as a specific method of analyzing social media activity. For example, if the user posts about a specific author on social media, the unit can suggest works by that author. Furthermore, the learning promotion unit can refer to the user's past reading history to provide the optimal reading method. For example, the unit can read at a similar speed based on the user's preferred reading speed in the past. This makes it possible to suggest optimal reading content based on the user's social media activity.

[0057] The health and well-being support unit can provide an optimal monitoring method by referring to the user's past health history when recording health data. For example, the health and well-being support unit can provide a similar monitoring method based on health data previously recorded by the user. Furthermore, as a specific method of referring to the health history, the health and well-being support unit can collect and analyze the user's past health data. For example, it can provide a monitoring method that aims for further improvement based on the user's previously improved health status. Furthermore, the health and well-being support unit can estimate the user's emotions and adjust the health data recording method based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide a simple input method to facilitate the recording of health data. This makes it possible to provide an optimal monitoring method based on the user's past health history.

[0058] The health and well-being support unit can provide optimal exercise guidance by reflecting the user's current physical condition in real time during fitness guidance. For example, if the user is tired, the health and well-being support unit can suggest light exercise. As a specific method for reflecting the user's physical condition, the health and well-being support unit can also collect and analyze the user's physical condition data in real time. For example, if the user is in good health, it can suggest high-intensity exercise. Furthermore, the health and well-being support unit can estimate the user's emotions and prioritize fitness goals based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize fitness goals that emphasize relaxation. This makes it possible to provide optimal exercise guidance based on the user's current physical condition.

[0059] The health and well-being support unit can provide an optimal relaxation method by referring to the user's past stress history when providing mental health support. For example, the health and well-being support unit can re-suggest a relaxation method that was effective for the user in the past. As a specific method for referring to the stress history, the health and well-being support unit can also collect and analyze the user's past stress data. For example, it can suggest a different relaxation method based on a relaxation method that the user avoided in the past. Furthermore, the health and well-being support unit can estimate the user's emotions and provide a relaxation method based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest a method that emphasizes relaxation. This makes it possible to provide an optimal relaxation method based on the user's past stress history.

[0060] The health and well-being support unit can provide an optimal monitoring method by taking into account the user's geographical location information when recording health data. For example, if the user is in a specific area, the health and well-being support unit can provide a monitoring method that takes into account the climate and environment of that area. Furthermore, as a specific method of taking into account the geographical location information, the health and well-being support unit can also acquire and analyze the user's location information. For example, if the user is traveling, the health and well-being support unit can provide a monitoring method that suits the environment of the travel destination. Furthermore, the health and well-being support unit can also provide an optimal monitoring method by referring to the user's past health history. For example, the health and well-being support unit can provide a similar monitoring method based on health data previously recorded by the user. This makes it possible to provide an optimal monitoring method based on the user's geographical location information.

[0061] The health and well-being support unit can analyze the user's social media activity and suggest related relaxation methods when providing mental health support. For example, if the user posts about stress on social media, the health and well-being support unit can suggest relaxation methods based on those posts. As a specific method for analyzing social media activity, the health and well-being support unit can also collect and analyze the user's social media data. For example, if the user posts about a specific relaxation method on social media, the health and well-being support unit can suggest that method. Furthermore, the health and well-being support unit can refer to the user's past stress history to provide the optimal relaxation method. For example, the health and well-being support unit can re-suggest a relaxation method that was effective for the user in the past. This makes it possible to suggest the optimal relaxation method based on the user's social media activity.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The daily life assistant can set optimal reminders taking into account the user's geographic location information. For example, if the user is in a specific location, it can set reminders for appointments related to that location. Also, if the user is traveling, it can set reminders for appointments related to the user's destination. Furthermore, it can analyze the user's past schedule history and suggest the optimal method for setting reminders. This allows reminders to be optimized based on the user's geographic location information.

[0064] The daily life assistant unit can automatically generate a shopping list by referring to the user's past purchase history. For example, items that the user frequently purchased in the past can be automatically added to the list. Items purchased during a particular season can also be added to the list. Furthermore, the unit can estimate the user's emotions and prioritize the shopping list based on the estimated emotions. This makes it possible to automatically generate a shopping list based on the user's past purchase history.

[0065] The travel support unit can suggest optimal tourist destinations by taking into account the user's geographical location information. For example, it can suggest tourist destinations close to the user's current location. If the user is in a specific area, it can also suggest tourist destinations related to that area. Furthermore, it can suggest optimal plans by referring to the user's past travel history. This makes it possible to suggest optimal tourist destinations based on the user's geographical location information.

[0066] The learning promotion unit can provide the optimal teaching method by referring to the user's past pronunciation history. For example, it can focus on teaching pronunciation that the user has previously struggled with. It can also provide similar pronunciation based on pronunciation that the user has excelled at. It can also provide optimal pronunciation instruction by taking into account the user's geographical location information. This makes it possible to provide optimal pronunciation instruction based on the user's past pronunciation history.

[0067] The health and well-being support unit can provide an optimal monitoring method by referring to the user's past health history. For example, it can provide a similar monitoring method based on health data recorded by the user in the past. It can also provide a monitoring method that aims for further improvement based on the user's past improvements in health status. It can also estimate the user's emotions and adjust the health data recording method based on the estimated emotions. This makes it possible to provide an optimal monitoring method based on the user's past health history.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The daily life assistant section allows the user to input their schedule via voice and set reminders. For example, if the user voice-inputs "Meeting tomorrow at 10 a.m.", the system records that information and notifies the user of the reminder at the specified time. The system also creates a shopping list via voice and adds the necessary items to the list. For example, if the user voice-inputs "Buy milk," the system adds "Milk" to the shopping list. The system also provides weather information for the user's current location and gives advice when going out. For example, if the user asks "What's the weather like today?", the system provides weather information for the user's current location and advises, "It's going to rain today, so please take an umbrella." Step 2: The travel support section proposes a travel plan based on the user's wishes and provides information on tourist spots. For example, if the user says, "I want to go on a trip next weekend," the system will propose a travel plan based on the user's preferences and budget and provide information on tourist spots. The system also provides voice guidance from the user's current location to the destination. For example, if the user says, "Tell me the way to the station," the system will provide voice guidance from the user's current location to the station. Furthermore, the system provides real-time foreign language translation to support communication during the trip. For example, if the user says, "Translate this phrase into English," the system will translate the phrase into English and provide it via voice. Step 3: The learning promotion unit checks the user's pronunciation and provides guidance on correct pronunciation. For example, when the user says, "Check the pronunciation of this word," the system checks the user's pronunciation and provides guidance on correct pronunciation. The system also provides quizzes based on the learning content to check the user's learning progress. For example, when the user says, "Give me a math quiz," the system provides a math quiz to check the user's learning progress. The system also provides a function to read e-books aloud to support reading. For example, when the user says, "Read this book aloud," the system reads the e-book aloud to support the user's reading. Step 4: The health and well-being support section records the user's health data and monitors their health status. For example, when a user says, "Record my weight today," the system records the user's weight and monitors their health status. It also provides exercise guidance and helps the user achieve their fitness goals. For example, when a user says, "Tell me what exercise to do today," the system suggests and provides guidance on an exercise menu based on the user's fitness goals. It also provides relaxation music and meditation guides to support stress management. For example, when a user says, "Play relaxing music," the system plays relaxation music to help the user manage their stress.

[0070] (Example 2) A system according to an embodiment of the present invention is a smartphone-based multifunctional audio guide app. This system is an intelligent personal assistant that supports users in a range of activities, from daily life to travel, learning, and health management. Specifically, the system provides functions for assisting with daily life, travel support, promoting learning, and supporting health and well-being. For example, as a daily life assistant, the system provides functions for inputting the user's schedule by voice and setting reminders, creating a shopping list by voice and adding necessary items to the list, providing weather information for the user's current location, and providing advice when going out. As a travel support function, the system provides functions for proposing itineraries based on the user's preferences, providing information on tourist spots, providing voice directions from the user's current location to their destination, and providing real-time foreign language translation to support communication during travel. As a learning support function, the system provides functions for checking the user's pronunciation and providing guidance on correct pronunciation, providing quizzes based on the learning content to check the user's learning progress, and providing e-book reading support. Health and well-being support includes functions such as recording the user's health data and monitoring their health status, providing exercise guidance and helping the user achieve their fitness goals, and providing relaxation music and meditation guides to support stress management. This allows the system to improve the user's quality of life and provide support for living a more efficient and comfortable life. This allows the system to support the user's life in many ways and improve their quality of life.

[0071] A system according to an embodiment includes a daily life assistance unit, a travel support unit, a learning promotion unit, and a health and happiness support unit. The daily life assistance unit allows a user to input their schedule via voice and set reminders. For example, if a user inputs "Meeting tomorrow at 10:00 AM," the system records the information and notifies the user of a reminder at the specified time. The daily life assistance unit also creates a shopping list via voice and adds necessary items to the list. For example, if a user inputs "Buy milk," the system adds "Milk" to the shopping list. The daily life assistance unit also provides weather information for the user's current location and advice when going out. For example, if a user asks, "What's the weather like today?" the system provides weather information for the user's current location and advises, "It's going to rain today, so please bring an umbrella." The travel support unit suggests travel plans based on the user's preferences and provides information about tourist attractions. For example, if a user says, "I want to go on a trip next weekend," the system suggests travel plans based on the user's preferences and budget and provides information about tourist attractions. The travel support unit also provides voice guidance from the user's current location to the destination. For example, when the user says, "Tell me the way to the station," the system provides voice guidance from the user's current location to the station. The travel support unit also provides real-time foreign language translation to support communication during travel. For example, when the user says, "Translate this phrase into English," the system translates the phrase into English and provides it via voice. The learning promotion unit checks the user's pronunciation and provides guidance on correct pronunciation. For example, when the user says, "Check the pronunciation of this word," the system checks the user's pronunciation and provides guidance on correct pronunciation. The learning promotion unit also provides quizzes based on the learning content to check the user's learning progress. For example, when the user says, "Give me a math quiz," the system provides a math quiz to check the user's learning progress. The learning promotion unit also provides an e-book reading function to support reading. For example, when the user says, "Read this book aloud," the system reads the e-book aloud to support the user's reading.The health and well-being support unit records the user's health data and monitors their health condition. For example, when a user says, "Record my weight today," the system records the user's weight and monitors their health condition. The health and well-being support unit also provides exercise guidance and supports the user in achieving their fitness goals. For example, when a user says, "Tell me what exercise to do today," the system suggests an exercise menu based on the user's fitness goals and provides guidance. The health and well-being support unit also provides relaxation music and meditation guides to support stress management. For example, when a user says, "Play relaxing music," the system plays relaxation music to support the user's stress management. In this way, the system according to the embodiment can support the user's life in many ways and improve their quality of life.

[0072] The daily life assistant unit can input the user's schedule by voice and set a reminder. For example, if the user voice-inputs "Meeting tomorrow at 10:00 AM," the system records the information and notifies the user of the reminder at the specified time. The daily life assistant unit can also convert the user's voice into text using voice recognition technology and set a reminder. For example, if the user says, "I'll take my medicine at 3:00 PM," the system converts the information into text and sets a reminder. Furthermore, the daily life assistant unit can analyze the user's past schedule history and suggest the optimal method for setting reminders. For example, it can set an earlier reminder for an appointment that the user has frequently been late to in the past. This can improve the efficiency of the user's schedule management.

[0073] The daily life assistant unit can create a shopping list using voice and add necessary items to the list. For example, when the user vocally inputs "buy milk," the system adds "milk" to the shopping list. The daily life assistant unit can also use voice recognition technology to convert the user's voice into text and create a shopping list. For example, when the user says "buy eggs," the system converts the information into text and adds "eggs" to the shopping list. Furthermore, the daily life assistant unit can automatically generate a list by referring to the user's past purchase history. For example, items that the user frequently purchased in the past can be automatically added to the list. This simplifies the creation of a shopping list.

[0074] The daily life assistant unit can provide weather information for the current location and give advice when going out. For example, when the user asks, "What's the weather like today?", the system provides weather information for the current location and advises, "It's going to rain today, so please take an umbrella." The daily life assistant unit can also obtain the latest weather information from the Internet and provide it to the user as a method of obtaining weather data. For example, the system periodically updates weather information to provide the user with the latest information. Furthermore, the daily life assistant unit can customize advice when going out by taking into account the user's past behavioral patterns. For example, if the user has refrained from going out on rainy days in the past, the system can advise the user to refrain from going out on rainy days. This allows the user to make appropriate decisions when going out.

[0075] The travel support unit can propose a travel plan based on the user's request and provide information on tourist spots. For example, when the user says, "I want to go on a trip next weekend," the system proposes a travel plan based on the user's preferences and budget and provides information on tourist spots. As a specific method of proposing a travel plan, the travel support unit can also propose an optimal plan by referring to the user's past travel history. For example, it can suggest similar places based on places the user has visited in the past. Furthermore, the travel support unit can estimate the user's emotions and adjust the proposed travel plan based on the estimated user's emotions. For example, if the user is relaxed, it can suggest a leisurely travel plan. This can efficiently support the user's travel planning.

[0076] The travel support unit can provide voice guidance from the current location to the destination. For example, when the user says, "Tell me the way to the station," the travel support unit can provide voice guidance from the current location to the station. The travel support unit can also use a navigation system to provide guidance on the optimal route by reflecting the user's current traffic conditions in real time. For example, the optimal route can be suggested based on real-time traffic congestion information. Furthermore, the travel support unit can also suggest optimal tourist spots taking into account the user's geographical location information. For example, the travel support unit can suggest tourist spots close to the user's current location. This allows the user to reach the destination without getting lost.

[0077] The travel support unit can instantly translate foreign languages ​​to support communication during travel. For example, when a user says, "Translate this phrase into English," the system translates the phrase into English and provides it audibly. The travel support unit can also use a translation engine to provide the optimal translation method by referring to the user's past translation history. For example, it can suggest similar phrases based on translation phrases the user has used in the past. Furthermore, the travel support unit can analyze the user's social media activity and provide related translations. For example, if a user posts in a specific language on social media, it can provide a translation in that language. This can enable users to communicate smoothly in foreign countries.

[0078] The learning promotion unit can check the user's pronunciation and provide guidance on correct pronunciation. For example, when the user says, "Check the pronunciation of this word," the system checks the user's pronunciation and provides guidance on correct pronunciation. The learning promotion unit can also evaluate the user's pronunciation using speech recognition technology and provide feedback. For example, when the user says, "Check the pronunciation of this sentence," the system evaluates the pronunciation and points out areas for improvement. Furthermore, the learning promotion unit can refer to the user's past pronunciation history to provide the most appropriate teaching method. For example, it can provide guidance on pronunciation that the user has previously struggled with. This can effectively support the user's language learning.

[0079] The learning promotion unit can provide quizzes based on the learning content and check the learning progress of the user. For example, when the user says, "Give me a math quiz," the system provides a math quiz and checks the user's learning progress. As a specific method of providing quizzes, the learning promotion unit can also provide optimal questions that reflect the user's learning progress in real time. For example, the learning promotion unit can provide questions with increased difficulty based on questions that the user has answered correctly in the past. Furthermore, the learning promotion unit can estimate the user's emotions and adjust the difficulty of the quiz based on the estimated user's emotions. For example, if the user is relaxed, the system can provide quizzes of normal difficulty. This makes it possible to effectively check the user's learning progress.

[0080] The learning promotion unit can provide an e-book reading function to support reading. For example, when the user says, "Read this book aloud," the system reads the e-book aloud to support the user's reading. The learning promotion unit can also use a text-to-speech engine to provide an optimal reading method by referring to the user's past reading history. For example, the unit can read at a similar speed based on the user's preferred reading speed in the past. Furthermore, the learning promotion unit can analyze the user's social media activity and suggest related reading content. For example, if the user posts about a specific genre on social media, the unit can suggest reading content in that genre. This can improve the user's reading experience.

[0081] The health and well-being support unit can record a user's health data and monitor their health condition. For example, when a user says, "Record my weight today," the health and well-being support unit records the user's weight and monitors their health condition. The health and well-being support unit can also provide an optimal monitoring method for recording health data by referring to the user's past health history. For example, it can provide a similar monitoring method based on health data previously recorded by the user. Furthermore, the health and well-being support unit can estimate the user's emotions and adjust the method for recording health data based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple input method to make it easier to record health data. This can effectively support the user's health management.

[0082] The health and well-being support unit can provide exercise guidance and support the user in achieving their fitness goals. For example, when the user says, "Tell me today's exercise menu," the health and well-being support unit can suggest an exercise menu based on the user's fitness goals and provide guidance. Furthermore, as a specific method of exercise guidance, the health and well-being support unit can also provide optimal exercise guidance by reflecting the user's current physical condition in real time. For example, if the user is tired, it can suggest light exercise. Furthermore, the health and well-being support unit can estimate the user's emotions and prioritize fitness goals based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize fitness goals that emphasize relaxation. This can effectively support the user in achieving their fitness goals.

[0083] The health and well-being support unit can provide relaxation music and meditation guides to support stress management. For example, when the user says, "Play relaxing music," the health and well-being support unit can play relaxation music to support the user's stress management. The health and well-being support unit can also refer to the user's past stress history to provide an optimal relaxation method as a specific method of meditation guidance. For example, the unit can re-suggest relaxation methods that were effective for the user in the past. Furthermore, the health and well-being support unit can analyze the user's social media activity and suggest related relaxation methods. For example, if the user posts about stress on social media, the unit can suggest relaxation methods based on those posts. This can effectively support the user's stress management.

[0084] The daily life assistant unit can estimate the user's emotions and adjust the timing of reminder notifications based on the estimated user emotions. For example, if the user is feeling stressed, the daily life assistant unit can delay reminder notifications slightly to allow the user time to relax. The daily life assistant unit can also estimate the user's emotions using an emotion recognition algorithm and adjust the timing of reminder notifications. For example, if the user is relaxed, the unit can set reminder notifications as usual to ensure the schedule proceeds smoothly. Furthermore, the daily life assistant unit can analyze the user's past schedule history and suggest an optimal reminder setting method. For example, the unit can set reminders earlier for appointments that the user has frequently been late to in the past. This makes it possible to optimize the timing of reminder notifications according to the user's emotions.

[0085] The daily life assistant unit can analyze the user's past schedule history and suggest an optimal reminder setting method. For example, the daily life assistant unit can set an earlier reminder for appointments that the user has frequently been late for in the past. Furthermore, as a specific method of analyzing the schedule history, the daily life assistant unit can also collect and analyze the user's past schedule data. For example, it can set a regular reminder for appointments that the user has previously completed smoothly. Furthermore, the daily life assistant unit can estimate the user's emotions and adjust the timing of reminder notifications based on the estimated user emotions. For example, if the user is feeling stressed, the reminder notification can be delayed slightly to allow the user time to relax. This makes it possible to optimize reminder settings based on the user's past schedule history.

[0086] When creating a shopping list, the unit that performs daily life assistance can automatically generate the list by referring to the user's past purchase history. For example, the unit that performs daily life assistance can automatically add items that the user frequently purchased in the past to the list. Furthermore, as a specific method of referring to the purchase history, the unit that performs daily life assistance can also collect and analyze the user's past purchase data. For example, items that the user purchased in a particular season can be added to the list. Furthermore, the unit that performs daily life assistance can estimate the user's emotions and determine the priorities of the shopping list based on the estimated user's emotions. For example, if the user is feeling stressed, items that help them relax can be preferentially added to the list. In this way, a shopping list can be automatically generated based on the user's past purchase history.

[0087] The daily life assistant unit can customize advice for going out based on the user's past behavioral patterns when providing weather information. For example, if the user has refrained from going out on rainy days in the past, the daily life assistant unit can advise the user to refrain from going out on rainy days. Furthermore, as a specific method of taking behavioral patterns into consideration, the daily life assistant unit can collect and analyze the user's past behavioral data. For example, if the user has preferred going out on sunny days in the past, the unit can recommend going out on sunny days. Furthermore, the daily life assistant unit can analyze the user's social media activity and provide related advice. For example, if the user posts about the weather on social media, the unit can provide advice based on the post. This allows advice for going out to be optimized based on the user's past behavioral patterns.

[0088] The daily life assistant unit can estimate the user's emotions and determine the priorities of the shopping list based on the estimated user emotions. For example, if the user is feeling stressed, the daily life assistant unit can prioritize adding relaxing items to the list. The daily life assistant unit can also estimate the user's emotions using an emotion recognition algorithm and determine the priorities of the shopping list. For example, if the user is feeling relaxed, the normal shopping list can be prioritized. Furthermore, the daily life assistant unit can automatically generate the list by referring to the user's past purchase history. For example, items that the user frequently purchased in the past can be automatically added to the list. This makes it possible to optimize the priorities of the shopping list according to the user's emotions.

[0089] The daily life assistant unit can set optimal reminders during schedule management, taking into account the user's geographical location information. For example, when the user is in a specific location, the daily life assistant unit sets a reminder for an appointment related to that location. As a specific method of taking geographical location information into consideration, the daily life assistant unit can also acquire and analyze the user's location information. For example, when the user is traveling, the unit can set a reminder for an appointment related to the user's destination. Furthermore, the daily life assistant unit can analyze the user's past schedule history and suggest an optimal reminder setting method. For example, the unit can set an earlier reminder for an appointment for which the user was frequently late in the past. This makes it possible to optimize reminders based on the user's geographical location information.

[0090] The daily life assistant unit can analyze the user's social media activity and provide related advice when providing weather information. For example, if the user posts about the weather on social media, the daily life assistant unit can provide advice based on the post. Furthermore, as a specific method of analyzing social media activity, the daily life assistant unit can collect and analyze the user's social media data. For example, if the user posts about a specific activity on social media, the unit can provide weather information related to that activity. Furthermore, the daily life assistant unit can customize advice for going out by taking into account the user's past behavioral patterns. For example, if the user has avoided going out on rainy days in the past, the unit can advise the user to avoid going out on rainy days. This allows weather information to be customized based on the user's social media activity.

[0091] The travel support unit can estimate the user's emotional state and adjust the proposed travel plan based on the estimated emotional state of the user. For example, if the user is relaxed, the travel support unit can suggest a leisurely travel plan. The travel support unit can also estimate the user's emotions using an emotion recognition algorithm and adjust the proposed travel plan. For example, if the user is excited, the travel support unit can suggest an active travel plan. Furthermore, the travel support unit can also suggest an optimal plan by referring to the user's past travel history. For example, similar places can be suggested based on places the user has visited in the past. This makes it possible to suggest an optimal travel plan according to the user's emotions.

[0092] When creating a travel plan, the travel support unit can suggest an optimal plan by referring to the user's past travel history. For example, the travel support unit can suggest similar places based on places the user has visited in the past. Furthermore, as a specific method of referring to the travel history, the travel support unit can collect and analyze the user's past travel data. For example, based on activities the user has previously preferred, the travel support unit can suggest a plan that includes similar activities. Furthermore, the travel support unit can estimate the user's emotions and adjust the suggested travel plan based on the estimated user's emotions. For example, if the user is relaxed, a leisurely travel plan can be suggested. In this way, an optimal travel plan can be suggested based on the user's past travel history.

[0093] During navigation, the travel support unit can guide the user to the optimal route by reflecting the user's current traffic conditions in real time. For example, the travel support unit can suggest the optimal route based on real-time traffic congestion information. As a specific method of reflecting traffic conditions, the travel support unit can also obtain the latest traffic information from the Internet and provide it to the user. For example, the system can periodically update traffic information to provide the user with the latest information. Furthermore, the travel support unit can suggest the optimal tourist destinations by taking into account the user's geographical location information. For example, the system can suggest tourist destinations close to the user's current location. This makes it possible to guide the user to the optimal route based on real-time traffic conditions.

[0094] When using the translation function, the travel support unit can refer to the user's past translation history and provide the optimal translation method. For example, the travel support unit can suggest similar phrases based on translation phrases the user has used in the past. As a specific method of referring to the translation history, the travel support unit can also collect and analyze the user's past translation data. For example, it can provide related translations based on content that the user has translated in the past. Furthermore, the travel support unit can analyze the user's social media activity and provide related translations. For example, if the user posts on social media in a specific language, it can provide a translation in that language. This makes it possible to provide the optimal translation method based on the user's past translation history.

[0095] The travel support unit can estimate the user's emotional state and adjust the display order of tourist destination information based on the estimated emotional state of the user. For example, if the user is relaxed, the travel support unit can prioritize displaying relaxing tourist destinations. The travel support unit can also estimate the user's emotions using an emotion recognition algorithm and adjust the display order of tourist destination information. For example, if the user is excited, active tourist destinations can be prioritized. Furthermore, the travel support unit can suggest an optimal plan by referring to the user's past travel history. For example, similar places can be suggested based on places the user has visited in the past. This makes it possible to optimize the display order of tourist destination information according to the user's emotions.

[0096] The travel support unit can suggest optimal tourist destinations by taking into consideration the user's geographical location information when creating a travel plan. For example, the travel support unit can suggest tourist destinations close to the user's current location. As a specific method of considering geographical location information, the travel support unit can also acquire and analyze the user's location information. For example, if the user is in a specific area, it can suggest tourist destinations related to that area. Furthermore, the travel support unit can also suggest optimal plans by referring to the user's past travel history. For example, it can suggest similar places based on places the user has visited in the past. This makes it possible to suggest optimal tourist destinations based on the user's geographical location information.

[0097] When using the translation function, the travel support unit can analyze the user's social media activity and provide relevant translations. For example, if the user posts on social media in a specific language, the travel support unit can provide translations in that language. As a specific method of analyzing social media activity, the travel support unit can also collect and analyze the user's social media data. For example, if the user posts on social media about a specific phrase, it can provide translations related to that phrase. Furthermore, the travel support unit can refer to the user's past translation history to provide the optimal translation method. For example, it can suggest similar phrases based on translation phrases the user has used in the past. This makes it possible to provide the optimal translation based on the user's social media activity.

[0098] The learning promotion unit can estimate the user's emotional state and adjust the presentation method of the learning content based on the estimated emotional state of the user. For example, if the user is relaxed, the learning promotion unit presents learning content with detailed explanations. The learning promotion unit can also estimate the user's emotions using an emotion recognition algorithm and adjust the presentation method of the learning content. For example, if the user is nervous, the learning promotion unit presents simple, highly visible learning content. Furthermore, the learning promotion unit can refer to the user's past learning history to provide optimal learning content. For example, the unit can present content that the user found difficult in the past. This makes it possible to optimize the presentation method of the learning content according to the user's emotions.

[0099] The learning promotion unit can provide an optimal teaching method by referring to the user's past pronunciation history when checking pronunciation. For example, the learning promotion unit can focus on teaching pronunciation that the user has previously struggled with. As a specific method of referring to the pronunciation history, the learning promotion unit can also collect and analyze the user's past pronunciation data. For example, based on pronunciation that the user has previously excelled at, the learning promotion unit can provide similar pronunciation instruction. Furthermore, the learning promotion unit can also provide optimal pronunciation instruction by taking into account the user's geographical location information. For example, if the user is in a specific region, the learning promotion unit can provide pronunciation instruction that takes into account the accent and dialect of that region. This makes it possible to provide optimal pronunciation instruction based on the user's past pronunciation history.

[0100] The learning promotion unit can provide optimal questions by reflecting the user's learning progress in real time when providing quizzes. For example, the learning promotion unit can provide questions of increased difficulty based on questions that the user has answered correctly in the past. As a specific method for reflecting learning progress, the learning promotion unit can also collect and analyze the user's learning data in real time. For example, the learning promotion unit can re-provide similar questions based on questions that the user has answered incorrectly in the past. Furthermore, the learning promotion unit can estimate the user's emotions and adjust the difficulty of the quiz based on the estimated user's emotions. For example, if the user is relaxed, the unit can provide a quiz of normal difficulty. This allows optimal questions to be provided based on the user's learning progress.

[0101] When providing reading support, the learning promotion unit can provide an optimal reading method by referring to the user's past reading history. For example, the learning promotion unit can read at a similar speed based on the user's previously preferred reading speed. Furthermore, as a specific method of referring to the reading history, the learning promotion unit can collect and analyze the user's past reading data. For example, the learning promotion unit can read in a similar tone based on the user's previously preferred tone of voice. Furthermore, the learning promotion unit can analyze the user's social media activity and suggest related reading content. For example, if the user posts about a specific genre on social media, reading content in that genre can be suggested. This makes it possible to provide an optimal reading method based on the user's past reading history.

[0102] The unit for promoting learning can estimate the emotional state of the user and adjust the difficulty of the quiz based on the estimated emotional state of the user. For example, if the user is relaxed, the unit for promoting learning can present a quiz of normal difficulty. The unit for promoting learning can also estimate the user's emotions using an emotion recognition algorithm and adjust the difficulty of the quiz. For example, if the user is nervous, it can present an easy quiz. Furthermore, the unit for promoting learning can provide an optimal quiz by referring to the user's past learning history. For example, it can present questions of increased difficulty based on questions that the user has answered correctly in the past. This makes it possible to optimize the difficulty of the quiz according to the user's emotions.

[0103] The learning promotion unit can provide optimal pronunciation instruction by taking into account the user's geographical location information when checking pronunciation. For example, if the user is in a specific region, the learning promotion unit can provide pronunciation instruction that takes into account the accent and dialect of that region. Furthermore, as a specific method of taking geographical location information into account, the learning promotion unit can also acquire and analyze the user's location information. For example, if the user is traveling, pronunciation instruction tailored to the language of the destination can be provided. Furthermore, the learning promotion unit can also provide optimal instruction methods by referring to the user's past pronunciation history. For example, instruction can be focused on pronunciation that the user has previously struggled with. This makes it possible to provide optimal pronunciation instruction based on the user's geographical location information.

[0104] When providing reading support, the learning promotion unit can analyze the user's social media activity and suggest related reading content. For example, if the user posts about a specific genre on social media, the learning promotion unit can suggest reading content in that genre. The learning promotion unit can also collect and analyze the user's social media data as a specific method of analyzing social media activity. For example, if the user posts about a specific author on social media, the unit can suggest works by that author. Furthermore, the learning promotion unit can refer to the user's past reading history to provide the optimal reading method. For example, the unit can read at a similar speed based on the user's preferred reading speed in the past. This makes it possible to suggest optimal reading content based on the user's social media activity.

[0105] The health and well-being support unit can estimate the user's emotional state and adjust the health data recording method based on the estimated user's emotional state. For example, if the user is feeling stressed, the health and well-being support unit can provide a simple input method to facilitate health data recording. The health and well-being support unit can also estimate the user's emotions using an emotion recognition algorithm and adjust the health data recording method. For example, if the user is relaxed, detailed input options can be provided to enhance the health data recording. Furthermore, the health and well-being support unit can refer to the user's past health history to provide an optimal monitoring method. For example, a similar monitoring method can be provided based on health data previously recorded by the user. This makes it possible to optimize the health data recording method according to the user's emotions.

[0106] The health and well-being support unit can provide an optimal monitoring method by referring to the user's past health history when recording health data. For example, the health and well-being support unit can provide a similar monitoring method based on health data previously recorded by the user. Furthermore, as a specific method of referring to the health history, the health and well-being support unit can collect and analyze the user's past health data. For example, it can provide a monitoring method that aims for further improvement based on the user's previously improved health status. Furthermore, the health and well-being support unit can estimate the user's emotions and adjust the health data recording method based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide a simple input method to facilitate the recording of health data. This makes it possible to provide an optimal monitoring method based on the user's past health history.

[0107] The health and well-being support unit can provide optimal exercise guidance by reflecting the user's current physical condition in real time during fitness guidance. For example, if the user is tired, the health and well-being support unit can suggest light exercise. As a specific method for reflecting the user's physical condition, the health and well-being support unit can also collect and analyze the user's physical condition data in real time. For example, if the user is in good health, it can suggest high-intensity exercise. Furthermore, the health and well-being support unit can estimate the user's emotions and prioritize fitness goals based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize fitness goals that emphasize relaxation. This makes it possible to provide optimal exercise guidance based on the user's current physical condition.

[0108] The health and well-being support unit can provide an optimal relaxation method by referring to the user's past stress history when providing mental health support. For example, the health and well-being support unit can re-suggest a relaxation method that was effective for the user in the past. As a specific method for referring to the stress history, the health and well-being support unit can also collect and analyze the user's past stress data. For example, it can suggest a different relaxation method based on a relaxation method that the user avoided in the past. Furthermore, the health and well-being support unit can estimate the user's emotions and provide a relaxation method based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest a method that emphasizes relaxation. This makes it possible to provide an optimal relaxation method based on the user's past stress history.

[0109] The health and well-being support unit can estimate the user's emotional state and prioritize fitness goals based on the estimated emotional state of the user. For example, if the user is feeling stressed, the health and well-being support unit can prioritize fitness goals that emphasize relaxation. The health and well-being support unit can also estimate the user's emotions using an emotion recognition algorithm and prioritize fitness goals. For example, if the user is relaxed, it can prioritize regular fitness goals. Furthermore, the health and well-being support unit can set optimal goals by referring to the user's past fitness history. For example, it can set the next goal based on goals the user has achieved in the past. This makes it possible to optimize the priority of fitness goals according to the user's emotions.

[0110] The health and well-being support unit can provide an optimal monitoring method by taking into account the user's geographical location information when recording health data. For example, if the user is in a specific area, the health and well-being support unit can provide a monitoring method that takes into account the climate and environment of that area. Furthermore, as a specific method of taking into account the geographical location information, the health and well-being support unit can also acquire and analyze the user's location information. For example, if the user is traveling, the health and well-being support unit can provide a monitoring method that suits the environment of the travel destination. Furthermore, the health and well-being support unit can also provide an optimal monitoring method by referring to the user's past health history. For example, the health and well-being support unit can provide a similar monitoring method based on health data previously recorded by the user. This makes it possible to provide an optimal monitoring method based on the user's geographical location information.

[0111] The health and well-being support unit can analyze the user's social media activity and suggest related relaxation methods when providing mental health support. For example, if the user posts about stress on social media, the health and well-being support unit can suggest relaxation methods based on those posts. As a specific method for analyzing social media activity, the health and well-being support unit can also collect and analyze the user's social media data. For example, if the user posts about a specific relaxation method on social media, the health and well-being support unit can suggest that method. Furthermore, the health and well-being support unit can refer to the user's past stress history to provide the optimal relaxation method. For example, the health and well-being support unit can re-suggest a relaxation method that was effective for the user in the past. This makes it possible to suggest the optimal relaxation method based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned daily life assistance unit, travel support unit, learning promotion unit, and health and happiness support unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the daily life assistance unit is realized by the control unit 46A of the smart device 14 and receives voice input from the user and sets reminders. The travel support unit is realized by the specific processing unit 290 of the data processing device 12 and suggests travel plans and provides route guidance. The learning promotion unit is realized by the control unit 46A of the smart device 14 and performs pronunciation checks and quizzes. The health and happiness support unit is realized by the specific processing unit 290 of the data processing device 12 and records health data and provides exercise guidance. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned daily life assistance unit, travel support unit, learning promotion unit, and health and happiness support unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the daily life assistance unit is realized by the control unit 46A of the smart glasses 214 and receives voice input from the user and sets reminders. The travel support unit is realized by the specific processing unit 290 of the data processing device 12 and suggests travel plans and provides route guidance. The learning promotion unit is realized by the control unit 46A of the smart glasses 214 and performs pronunciation checks and quizzes. The health and happiness support unit is realized by the specific processing unit 290 of the data processing device 12 and records health data and provides exercise guidance. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned daily life assistance unit, travel support unit, learning promotion unit, and health and happiness support unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the daily life assistance unit is realized by the control unit 46A of the headset-type terminal 314 and receives voice input from the user and sets reminders. The travel support unit is realized by the specific processing unit 290 of the data processing device 12 and suggests travel plans and provides route guidance. The learning promotion unit is realized by the control unit 46A of the headset-type terminal 314 and performs pronunciation checks and quizzes. The health and happiness support unit is realized by the specific processing unit 290 of the data processing device 12 and records health data and provides exercise guidance. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned daily life assistance unit, travel support unit, learning promotion unit, and health and happiness support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the daily life assistance unit is realized by the control unit 46A of the robot 414 and receives voice input from the user and sets reminders. The travel support unit is realized by the specific processing unit 290 of the data processing device 12 and suggests travel plans and provides route guidance. The learning promotion unit is realized by the control unit 46A of the robot 414 and performs pronunciation checks and quizzes. The health and happiness support unit is realized by the specific processing unit 290 of the data processing device 12 and records health data and provides exercise guidance.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The daily life assistant unit can also estimate the user's emotions and adjust the content of reminder notifications based on the estimated emotions. For example, if the user is feeling stressed, the reminder notification content can be changed to softer language to help the user relax. If the user is relaxed, the notification content can be set to normal to help the user proceed smoothly with their schedule. Furthermore, the unit can analyze the user's past emotional history and suggest the optimal reminder notification method. This allows the reminder notification content to be optimized according to the user's emotions.

[0114] The daily life assistance unit can also estimate the user's emotions and suggest shopping list items based on the estimated emotions. For example, if the user is feeling stressed, it can suggest items that will help them relax. If the user is relaxed, it can suggest a regular shopping list. Furthermore, it can analyze the user's past emotional history and generate an optimal shopping list. This allows the items on the shopping list to be optimized according to the user's emotions.

[0115] The daily life assistance unit can also estimate the user's emotions and adjust the way weather information is provided based on the estimated emotions. For example, if the user is feeling stressed, the unit can provide brief weather information and less advice on going out. On the other hand, if the user is relaxed, the unit can provide detailed weather information and advice. Furthermore, the unit can analyze the user's past emotional history and suggest the most appropriate way to provide weather information. This allows the unit to optimize the way weather information is provided according to the user's emotions.

[0116] The travel support unit can also estimate the user's emotions and adjust the proposed travel plan based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a relaxing travel plan. If the user is relaxed, it can suggest an active travel plan. Furthermore, it can analyze the user's past emotional history and suggest the most suitable travel plan. This makes it possible to optimize the proposed travel plan according to the user's emotions.

[0117] The learning promotion unit can also estimate the user's emotions and adjust the way in which learning content is presented based on the estimated emotions. For example, if the user is feeling stressed, simple, highly visible learning content can be presented. Alternatively, if the user is relaxed, learning content with detailed explanations can be presented. Furthermore, the unit can analyze the user's past emotional history and provide optimal learning content. This makes it possible to optimize the way in which learning content is presented according to the user's emotions.

[0118] The daily life assistant can set optimal reminders taking into account the user's geographic location information. For example, if the user is in a specific location, it can set reminders for appointments related to that location. Also, if the user is traveling, it can set reminders for appointments related to the user's destination. Furthermore, it can analyze the user's past schedule history and suggest the optimal method for setting reminders. This allows reminders to be optimized based on the user's geographic location information.

[0119] The daily life assistant unit can automatically generate a shopping list by referring to the user's past purchase history. For example, items that the user frequently purchased in the past can be automatically added to the list. Items purchased during a particular season can also be added to the list. Furthermore, the unit can estimate the user's emotions and prioritize the shopping list based on the estimated emotions. This makes it possible to automatically generate a shopping list based on the user's past purchase history.

[0120] The travel support unit can suggest optimal tourist destinations by taking into account the user's geographical location information. For example, it can suggest tourist destinations close to the user's current location. If the user is in a specific area, it can also suggest tourist destinations related to that area. Furthermore, it can suggest optimal plans by referring to the user's past travel history. This makes it possible to suggest optimal tourist destinations based on the user's geographical location information.

[0121] The learning promotion unit can provide the optimal teaching method by referring to the user's past pronunciation history. For example, it can focus on teaching pronunciation that the user has previously struggled with. It can also provide similar pronunciation based on pronunciation that the user has excelled at. It can also provide optimal pronunciation instruction by taking into account the user's geographical location information. This makes it possible to provide optimal pronunciation instruction based on the user's past pronunciation history.

[0122] The health and well-being support unit can provide an optimal monitoring method by referring to the user's past health history. For example, it can provide a similar monitoring method based on health data recorded by the user in the past. It can also provide a monitoring method that aims for further improvement based on the user's past improvements in health status. It can also estimate the user's emotions and adjust the health data recording method based on the estimated emotions. This makes it possible to provide an optimal monitoring method based on the user's past health history.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The daily life assistant section allows the user to input their schedule via voice and set reminders. For example, if the user voice-inputs "Meeting tomorrow at 10 a.m.", the system records that information and notifies the user of the reminder at the specified time. The system also creates a shopping list via voice and adds the necessary items to the list. For example, if the user voice-inputs "Buy milk," the system adds "Milk" to the shopping list. The system also provides weather information for the user's current location and gives advice when going out. For example, if the user asks "What's the weather like today?", the system provides weather information for the user's current location and advises, "It's going to rain today, so please take an umbrella." Step 2: The travel support section proposes a travel plan based on the user's wishes and provides information on tourist spots. For example, if the user says, "I want to go on a trip next weekend," the system will propose a travel plan based on the user's preferences and budget and provide information on tourist spots. The system also provides voice guidance from the user's current location to the destination. For example, if the user says, "Tell me the way to the station," the system will provide voice guidance from the user's current location to the station. Furthermore, the system provides real-time foreign language translation to support communication during the trip. For example, if the user says, "Translate this phrase into English," the system will translate the phrase into English and provide it via voice. Step 3: The learning promotion unit checks the user's pronunciation and provides guidance on correct pronunciation. For example, when the user says, "Check the pronunciation of this word," the system checks the user's pronunciation and provides guidance on correct pronunciation. The system also provides quizzes based on the learning content to check the user's learning progress. For example, when the user says, "Give me a math quiz," the system provides a math quiz to check the user's learning progress. The system also provides a function to read e-books aloud to support reading. For example, when the user says, "Read this book aloud," the system reads the e-book aloud to support the user's reading. Step 4: The health and well-being support section records the user's health data and monitors their health status. For example, when a user says, "Record my weight today," the system records the user's weight and monitors their health status. It also provides exercise guidance and helps the user achieve their fitness goals. For example, when a user says, "Tell me what exercise to do today," the system suggests and provides guidance on an exercise menu based on the user's fitness goals. It also provides relaxation music and meditation guides to support stress management. For example, when a user says, "Play relaxing music," the system plays relaxation music to help the user manage their stress.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] 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.

[0188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that provides a function to support a user's life, A department that assists with daily life, A department that provides travel support, A department that promotes learning, Health and Happiness Support Department, Equipped with A system characterized by:

2. The department that assists with daily life includes: Speak your events and set reminders The system of claim 1 .

3. The department that assists with daily life includes: Create a shopping list by voice and add items you need to the list The system of claim 1 .

4. The department that assists with daily life includes: Provides weather information for the current location and gives advice when going out The system of claim 1 .

5. The department that provides travel support is: Proposes travel plans based on user requests and provides information on tourist spots The system of claim 1 .

6. The department that provides travel support is: Provides voice guidance from your current location to your destination The system of claim 1 .

7. The department that provides travel support is: Instant translation of foreign languages ​​to support communication while traveling The system of claim 1 .

8. The unit that promotes learning includes: Check the user's pronunciation and teach them the correct pronunciation The system of claim 1 .

Images