System

Abstract

To provide a system allowing a user to efficiently acquire real-time traffic information and weather information on the basis of the current location.SOLUTION: A system according to an embodiment comprises an acquisition unit, a traffic information collection unit, a weather information collection unit, a determination unit, and a provision unit. The acquisition unit acquires current location information or destination information of a user. The traffic information collection unit collects traffic information. The weather information collection unit collects weather information. The determination unit determines traffic information and weather information offered to the user in real time on the basis of the information collected by the acquisition unit, traffic information collection unit, and weather information collection unit. The provision unit provides the user with the traffic information and weather information determined by the determination unit in real time.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult for users to efficiently obtain real-time traffic and weather information based on their current location and destination.

[0005] The system according to the embodiment aims to enable a user to efficiently obtain real-time traffic and weather information based on a current location and a destination. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a traffic information collection unit, a weather information collection unit, a determination unit, and a provision unit. The acquisition unit acquires information on a user's current location or destination. The traffic information collection unit collects traffic information. The weather information collection unit collects weather information. The determination unit determines in real time the traffic information and weather information to be provided to the user based on the information collected by the acquisition unit, the traffic information collection unit, and the weather information collection unit. The provision unit provides the traffic information and weather information determined by the determination unit to the user in real time. Effect of the Invention

[0007] The system according to the embodiment allows a user to efficiently obtain real-time traffic and weather information based on a current location and a destination. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​assistant system according to the embodiment of the present invention is a system that provides real-time traffic and weather information based on the user's current location and destination. The system acquires information on the user's current location or destination, collects traffic information such as traffic congestion, traffic accidents, and construction information, and collects weather information such as current weather and weather forecasts. These pieces of information are analyzed in real time and provided to the user. For example, if a traffic jam or accident occurs while the user is moving, the system analyzes the information in real time and notifies the user. In addition, if a sudden heavy rain or storm is expected, the system issues a warning to the user. Furthermore, the system determines and provides traffic and weather information on the user's movement route in real time. For example, it proposes an alternative route to avoid traffic jams and a movement plan that corresponds to changes in the weather. The system can also collect real-time data on traffic and weather from social media, acquire sensor information from other vehicles and drones, and use that information to complement the traffic and weather information. Furthermore, the system can adjust the movement route based on the user's health condition. For example, if the user is not feeling well, the system proposes an optimal movement route to reduce the burden on the user. This allows the AI ​​assistant system to provide users with optimal traffic and weather information in real time, improving the safety and efficiency of travel. This allows the AI ​​assistant system to provide real-time traffic and weather information based on the user's current location and destination.

[0029] The AI ​​assistant system according to the embodiment includes an acquisition unit, a traffic information collection unit, a weather information collection unit, a determination unit, and a provision unit. The acquisition unit acquires information on the user's current location or destination. The information on the user's current location or destination includes, for example, GPS coordinates, an address, a landmark, and the like, but is not limited to such examples. The acquisition unit acquires the user's current location using, for example, a GPS device. The acquisition unit can also acquire address information input by the user. Furthermore, the acquisition unit can specify the user's destination using landmark information. For example, the acquisition unit acquires location information based on the landmark name input by the user. The traffic information collection unit collects traffic information. The traffic information includes, for example, congestion information, accident information, and the operation status of public transportation, but is not limited to such examples. The traffic information collection unit collects traffic congestion status using, for example, a traffic sensor. Furthermore, the traffic information collection unit can also collect information on traffic accidents. Furthermore, the traffic information collection unit can collect the operation status of public transportation. For example, the traffic information collecting unit monitors the congestion status of roads in real time using a traffic sensor and collects congestion information. Information on traffic accidents is collected based on data provided by the police or a traffic management center. The operation status of public transportation is acquired from a system that provides operation information for buses and trains. The weather information collecting unit collects weather information. Weather information includes, for example, temperature, precipitation, wind speed, weather forecast, etc., but is not limited to such examples. The weather information collecting unit collects current weather information using, for example, a weather sensor. In addition, the weather information collecting unit can also acquire weather forecast information from a weather forecast system. Furthermore, the weather information collecting unit can also collect weather information using data from a weather satellite. For example, the weather information collecting unit measures temperature and precipitation in real time using a weather sensor and collects weather information. From the weather forecast system, future weather forecasts are acquired and provided to the user. The data from the weather satellite is used to collect weather information over a wide area. The determining unit determines the traffic information and weather information to be provided to the user in real time based on the information collected by the acquiring unit, the traffic information collecting unit, and the weather information collecting unit.The determination unit, for example, analyzes the collected information using AI and determines the information to be provided to the user. The determination unit, for example, proposes an optimal travel route to the user based on traffic congestion and accident information. The determination unit can also provide appropriate weather information to the user based on weather information. For example, the determination unit uses AI to analyze data from a traffic sensor and identify locations of congestion and accidents. Furthermore, the determination unit analyzes data from a weather sensor and determines the current weather and future weather forecast. The provision unit provides the traffic information and weather information determined by the determination unit to the user in real time. The provision unit provides information to the user through, for example, a smartphone app. Furthermore, the provision unit can also provide information through an in-vehicle display. Furthermore, the provision unit can also provide information through a voice assistant. For example, the provision unit notifies the user of traffic information and weather information in real time using a smartphone app. The in-vehicle display provides information visually to the user while driving. The voice assistant provides information by voice, allowing the user while driving to obtain information without taking their eyes off the vehicle. As a result, the AI ​​assistant system according to the embodiment can provide real-time traffic and weather information based on the user's current location and destination.

[0030] The acquisition unit acquires information on the user's current location or destination. The information on the user's current location or destination includes, for example, GPS coordinates, addresses, landmarks, etc., but is not limited to such examples. The acquisition unit acquires the user's current location using, for example, a GPS device. The acquisition unit can also acquire address information input by the user. Furthermore, the acquisition unit can identify the user's destination using landmark information. For example, the acquisition unit acquires location information based on the landmark name input by the user. The acquisition unit can integrate multiple data sources to acquire these pieces of information. For example, data from a GPS device tracks the user's location in real time and provides highly accurate location information. The address information can be acquired by the user manually entering it or using voice input. The landmark information is linked to a map database to identify the exact location of the landmark specified by the user. This allows the acquisition unit to quickly and accurately acquire various information on the user's current location and destination, and improve the accuracy and reliability of the entire system. Furthermore, the acquisition unit has a function of encrypting and safely managing the acquired data in order to protect the user's privacy. This allows the user to use the system with peace of mind.

[0031] The traffic information collecting unit collects traffic information. The traffic information includes, for example, congestion information, accident information, and the operation status of public transportation, but is not limited to such examples. The traffic information collecting unit collects traffic congestion status, for example, using a traffic sensor. The traffic information collecting unit can also collect information on traffic accidents. Furthermore, the traffic information collecting unit can also collect the operation status of public transportation. For example, the traffic information collecting unit monitors the congestion status of roads in real time using a traffic sensor and collects congestion information. The traffic accident information is collected based on data provided by the police or a traffic management center. The operation status of public transportation is acquired from a system that provides operation information of buses and trains. The traffic information collecting unit has a function of centrally managing these information and updating them in real time. For example, the traffic sensor monitors the flow of vehicles on the road and grasps the congestion status in real time. This allows the traffic information collecting unit to quickly and accurately grasp the traffic status and improve the accuracy of the information provided to the user. Furthermore, the traffic information collecting unit can use an anomaly detection algorithm to detect unusual traffic patterns and abnormal situations early and issue a warning. This enables the traffic information gathering unit to provide the user with prompt and appropriate information, thereby improving traffic safety and efficiency.

[0032] The weather information collecting unit collects weather information. The weather information includes, for example, temperature, precipitation, wind speed, weather forecast, etc., but is not limited to such examples. The weather information collecting unit collects current weather information using, for example, a weather sensor. The weather information collecting unit can also obtain weather forecast information from a weather forecast system. Furthermore, the weather information collecting unit can also collect weather information using data from a weather satellite. For example, the weather information collecting unit measures temperature and precipitation in real time using a weather sensor to collect weather information. From the weather forecast system, future weather forecasts are obtained and provided to the user. The data from the weather satellite is used to collect weather information over a wide range. The weather information collecting unit has a function of integrating these pieces of information and updating them in real time. For example, the weather sensor collects environmental data such as temperature, humidity, and wind speed in real time to improve the accuracy of the weather information. The data from the weather forecast system provides short-term and long-term weather forecasts and provides appropriate information to the user. The data from the weather satellite is used to grasp the weather conditions over a wide range and provide detailed weather information for each region. This allows the weather information gathering unit to provide the user with accurate and up-to-date weather information, which can be useful for travel and planning.

[0033] The determination unit determines the traffic information and weather information to be provided to the user in real time based on the information collected by the acquisition unit, the traffic information collection unit, and the weather information collection unit. The determination unit, for example, uses AI to analyze the collected information and determines the information to be provided to the user. The determination unit proposes an optimal travel route to the user based on, for example, traffic congestion and accident information. The determination unit can also provide appropriate weather information to the user based on the weather information. For example, the determination unit uses AI to analyze data from a traffic sensor and identify the location of a traffic jam or an accident. Furthermore, the determination unit analyzes data from a weather sensor and determines the current weather and future weather forecast. The determination unit has a function of integrating these pieces of information and providing the most useful information to the user in real time. For example, the AI ​​uses a machine learning algorithm to learn past data and predict future traffic conditions and weather fluctuations. As a result, the determination unit can provide the user with the optimal travel route and weather information, improving the efficiency and safety of travel. Furthermore, the determination unit can learn the user's past behavior and preferences and provide personalized information. This enables the determination unit to provide the user with a more personalized service, thereby improving the user's level of satisfaction.

[0034] The providing unit provides the traffic information and weather information determined by the determining unit to the user in real time. The providing unit provides the information to the user through, for example, a smartphone app. The providing unit can also provide the information through an in-vehicle display. Furthermore, the providing unit can also provide the information through a voice assistant. For example, the providing unit notifies the user of traffic information and weather information in real time using a smartphone app. The in-vehicle display visually provides information to the user while driving. The voice assistant provides information by voice, allowing the user while driving to obtain information without taking their eyes off the road. The providing unit has a function of integrating these information providing means and providing information to the user in an optimal manner. For example, the smartphone app can quickly convey important information to the user using push notifications and widgets. The in-vehicle display provides an intuitive interface to the user while driving, allowing the user to visually confirm information. The voice assistant uses natural language processing technology to respond to the user's questions and provide the necessary information. This allows the providing unit to quickly provide accurate information to the user, thereby improving the efficiency and safety of travel. Furthermore, the providing unit can collect feedback from users and continuously improve the accuracy and effectiveness of the information provided, thereby enabling the providing unit to always provide the latest and most appropriate information to users and improve user satisfaction.

[0035] The AI ​​assistant system includes a warning unit that issues a warning about sudden climate change. The warning unit issues a warning about sudden climate change. Sudden climate change includes, but is not limited to, sudden temperature changes, wind gusts, heavy rain, and the like. For example, the warning unit detects a sudden temperature change based on data from a weather sensor. The warning unit can also detect wind gusts using a wind speed sensor. Furthermore, the warning unit can detect heavy rain using a precipitation sensor. For example, the warning unit analyzes data from the weather sensor in real time and issues a warning to the user when a sudden temperature change occurs. The wind speed sensor detects the occurrence of a wind gust and notifies the user of the information. The precipitation sensor detects the occurrence of heavy rain and issues a warning to the user. This makes it possible to provide the user with a warning about sudden climate change.

[0036] The AI ​​assistant system includes a suggestion unit that proposes a travel plan to the user based on the judgment result by the judgment unit. The suggestion unit proposes a travel plan to the user based on the judgment result by the judgment unit. The travel plan includes, for example, an optimal route, a selection of a means of transportation, a required time, and the like, but is not limited to such examples. The suggestion unit proposes an optimal route to the user based on, for example, traffic congestion and accident information. The suggestion unit can also propose an appropriate means of transportation to the user based on weather information. Furthermore, the suggestion unit can propose an optimal travel plan to the user taking into account the required time. For example, the suggestion unit analyzes data from a traffic sensor and proposes an alternative route to avoid congestion. Based on weather information, it can also propose the use of public transportation in rainy weather. Taking into account the required time, a route to reach the destination in the shortest time is proposed. This makes it possible to propose an optimal travel plan to the user.

[0037] In the AI ​​assistant system, the acquisition unit collects real-time data on traffic and weather from social media, and the determination unit uses the data to complement the traffic information and weather information. The acquisition unit collects real-time data on traffic and weather from social media. The real-time data from social media includes, but is not limited to, Twitter, Facebook, and Instagram posting data. The acquisition unit collects posts on traffic information and weather information, for example, using Twitter's API. The acquisition unit can also analyze Facebook posting data to collect traffic information and weather information. The acquisition unit can also use Instagram posting data to collect traffic information and weather information. For example, the acquisition unit collects posts related to a specific hashtag using Twitter's API to acquire traffic information and weather information. The Facebook posting data is analyzed to collect traffic accident and weather information shared by users. The Instagram posting data is used to acquire traffic information and weather information from photos and comments posted by users. The determination unit uses the real-time data acquired by the acquisition unit to complement the traffic information and weather information. The determination unit uses, for example, AI to analyze data from social media to complement the traffic information and weather information. The determination unit, for example, analyzes Twitter posting data to complement information on traffic congestion and accidents. The determination unit can also analyze Facebook posting data to complement information on weather changes. The determination unit can also analyze Instagram posting data to complement traffic information and weather information. For example, the determination unit uses AI to analyze Twitter posting data to identify locations of traffic congestion and accidents. The determination unit analyzes Facebook posting data to complement information on sudden weather changes. The determination unit analyzes Instagram posting data to complement traffic information and weather information from photos and comments posted by users. This makes it possible to complement traffic information and weather information using data from social media.

[0038] In the AI ​​assistant system, the acquisition unit acquires sensor information of other vehicles, and the determination unit uses the information to complement traffic information and weather information. The acquisition unit acquires sensor information of other vehicles. The sensor information of other vehicles includes, for example, the speed of the vehicle, position information, surrounding obstacle information, and the like, but is not limited to such examples. The acquisition unit acquires speed information of other vehicles, for example, using a speed sensor of the vehicle. The acquisition unit can also acquire position information of other vehicles using a GPS device of the vehicle. Furthermore, the acquisition unit can also acquire surrounding obstacle information using an obstacle sensor of the vehicle. For example, the acquisition unit collects speed data of other vehicles in real time using a speed sensor of the vehicle. The acquisition unit acquires position information of other vehicles using a GPS device of the vehicle to complement traffic information. The acquisition unit acquires surrounding obstacle information using an obstacle sensor of the vehicle to complement traffic information. The determination unit uses the sensor information of other vehicles acquired by the acquisition unit to complement traffic information and weather information. The determination unit analyzes sensor information of other vehicles using, for example, AI, to complement traffic information and weather information. The determination unit, for example, analyzes the speed information of other vehicles and identifies the location of the traffic jam. The determination unit can also analyze the position information of other vehicles and identify the location of the traffic accident. Furthermore, the determination unit can analyze obstacle information of other vehicles and complement the traffic information. For example, the determination unit uses AI to analyze the speed data of other vehicles and identify the location of the traffic jam. The determination unit analyzes the position information of other vehicles and identifies the location of the traffic accident. The determination unit analyzes surrounding obstacle information and complements the traffic information. This makes it possible to complement traffic information and weather information using sensor information of other vehicles.

[0039] In the AI ​​assistant system, the acquisition unit acquires sensor information of the drone, and the determination unit uses the information to complement the traffic information and weather information. The acquisition unit acquires the sensor information of the drone. The sensor information of the drone includes, for example, aerial images, weather data, flight route information, etc., but is not limited to such examples. The acquisition unit acquires aerial images, for example, using a camera of the drone. The acquisition unit can also acquire weather data using a weather sensor of the drone. Furthermore, the acquisition unit can also acquire flight route information using a GPS device of the drone. For example, the acquisition unit acquires aerial images of traffic congestion and accidents using a camera of the drone. The acquisition unit acquires weather data in the sky using a weather sensor of the drone to complement the weather information. The acquisition unit acquires flight route information of the drone using a GPS device of the drone to complement the traffic information. The determination unit uses the sensor information of the drone acquired by the acquisition unit to complement the traffic information and weather information. The determination unit analyzes the sensor information of the drone using, for example, AI, and complements the traffic information and weather information. The determination unit analyzes the aerial images of the drone, for example, to identify the location of the traffic congestion or accident. The determination unit can also analyze the drone's weather data to complement the weather conditions in the sky. Furthermore, the determination unit can also analyze the drone's flight path information to complement traffic information. For example, the determination unit uses AI to analyze aerial images taken by the drone to identify the locations of traffic congestion and accidents. The determination unit analyzes the drone's weather data to complement the weather conditions in the sky. The determination unit analyzes the drone's flight path information to complement traffic information. This makes it possible to complement traffic information and weather information using drone sensor information.

[0040] The AI ​​assistant system includes an adjustment unit that adjusts the moving route based on the health condition of the user. The adjustment unit adjusts the moving route based on the health condition of the user. The health condition includes, for example, heart rate, blood pressure, pedometer data, etc., but is not limited to such examples. For example, the adjustment unit measures the user's heart rate using a heart rate sensor. The adjustment unit can also measure the user's blood pressure using a blood pressure monitor. Furthermore, the adjustment unit can measure the user's step count using a pedometer. For example, the adjustment unit uses a heart rate sensor to collect the user's heart rate data in real time. The blood pressure monitor is used to obtain the user's blood pressure data and grasp the health condition. The pedometer is used to collect the user's step count data and adjust the moving route. The adjustment unit proposes an optimal moving route to the user based on the obtained health condition data. For example, if the user is in poor health, the adjustment unit proposes the shortest route and reduces the user's burden. Furthermore, if the user is in good health, the adjustment unit can also propose a scenic route. Furthermore, if the user is in a hurry, the adjustment unit can also propose a route that avoids traffic congestion. For example, the adjustment unit analyzes heart rate data and proposes a movement route according to the user's physical condition. Based on blood pressure data, the adjustment unit proposes a movement route that takes into account the user's health condition. Based on step count data, the adjustment unit proposes an optimal route based on the user's movement pattern. In this way, it is possible to propose an optimal movement route according to the user's health condition.

[0041] In the AI ​​assistant system, the acquisition unit analyzes the user's past movement history and selects the optimal acquisition method. The past movement history includes, for example, movement data for the past year, movement patterns for a specific period, etc., but is not limited to such examples. The acquisition unit collects the user's past movement history using, for example, GPS data. The acquisition unit can also analyze the user's movement pattern and select the optimal acquisition method. Furthermore, the acquisition unit can optimize the acquisition frequency of location information in a specific time period. For example, the acquisition unit identifies a place that the user frequently visits based on the past movement history, and adjusts the acquisition timing of location information at that place. The acquisition unit analyzes the user's movement pattern and proposes the optimal acquisition method. The acquisition frequency of location information in a specific time period is optimized to provide efficient information. This makes it possible to select the optimal location information acquisition method based on the user's past movement history.

[0042] In the AI ​​assistant system, when the acquisition unit acquires location information, the acquisition unit performs filtering based on the user's current activity status and field of interest. The current activity status includes, for example, exercising, resting, working, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, topics of interest, past search history, etc., but is not limited to such examples. For example, the acquisition unit monitors the user's activity status in real time and adjusts the timing of acquiring location information. In addition, the acquisition unit can also preferentially acquire related location information based on the user's field of interest. For example, when the user is exercising, the acquisition unit increases the frequency of acquiring location information and records the exercise route. When the user is sightseeing, the acquisition unit preferentially acquires location information related to tourist spots. When the user is at work, the acquisition unit preferentially acquires location information related to work. This makes it possible to filter location information based on the user's activity status and field of interest.

[0043] In the AI ​​assistant system, when the acquisition unit acquires location information, the acquisition unit preferentially acquires highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, landmarks, etc., but is not limited to such examples. The acquisition unit acquires the geographical location information of the user using, for example, a GPS device. The acquisition unit can also acquire address information input by the user. Furthermore, the acquisition unit can identify the user's location using landmark information. For example, when the user is in a specific area, the acquisition unit preferentially acquires location information related to the area. When the user is traveling, the acquisition unit preferentially acquires tourist information about the travel destination. When the user is participating in an event, the acquisition unit preferentially acquires location information around the event venue. This makes it possible to preferentially acquire highly relevant information based on the geographical location information of the user.

[0044] In the AI ​​assistant system, when the acquisition unit acquires the location information, the acquisition unit analyzes the user's social media activity and acquires related information. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The acquisition unit acquires, for example, the user's check-in information on social media. The acquisition unit can also acquire event information shared by the user. Furthermore, the acquisition unit can analyze the content of the user's post and acquire related location information. For example, the acquisition unit preferentially acquires location information of the place where the user checked in on social media. The acquisition unit preferentially acquires location information of the event shared by the user. The content of the user's post is analyzed and related location information is acquired. This makes it possible to acquire related information based on the user's social media activity.

[0045] In the AI ​​assistant system, when the traffic information collection unit collects traffic information, the traffic information collection unit optimizes the collection algorithm by referring to past traffic data. The past traffic data includes, for example, traffic data from the past year, traffic patterns for a specific period, and the like, but is not limited to such examples. The traffic information collection unit sets an optimal collection algorithm, for example, based on past traffic congestion data. The traffic information collection unit can also refer to past traffic accident data and preferentially collect information on places where accidents are likely to occur. Furthermore, the traffic information collection unit can also collect information on places where construction is likely to be performed based on past construction information. For example, the traffic information collection unit analyzes past traffic congestion data, identifies places where congestion is likely to occur, and preferentially collects information on those places. The traffic accident data is analyzed, and information on places where accidents are likely to occur is preferentially collected. The past construction information is analyzed, and information on places where construction is likely to be performed is collected. This makes it possible to optimize the collection algorithm of traffic information based on past traffic data.

[0046] In the AI ​​assistant system, when the traffic information collection unit collects traffic information, the traffic information collection unit adjusts the collection range based on a specific traffic event. The specific traffic event includes, but is not limited to, a sporting event, a concert, and the like. For example, when a sporting event is held, the traffic information collection unit preferentially collects traffic information around the event. In addition, when a concert is held, the traffic information collection unit can also collect traffic information around the venue. Furthermore, when a large-scale event is scheduled, the traffic information collection unit can also collect traffic information within the range of the event's influence. For example, when a sporting event is held, the traffic information collection unit preferentially collects traffic information around the event. When a concert is held, traffic information around the venue is collected. When a large-scale event is scheduled, traffic information within the range of the event's influence is collected. This makes it possible to adjust the collection range of traffic information based on a specific traffic event.

[0047] In the AI ​​assistant system, when the traffic information collecting unit collects traffic information, the traffic information collecting unit preferentially collects highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The traffic information collecting unit acquires the geographical location information of the user using, for example, a GPS device. The traffic information collecting unit can also acquire address information input by the user. Furthermore, the traffic information collecting unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the traffic information collecting unit preferentially collects traffic information related to the area. When the user is traveling, the traffic information collecting unit preferentially collects traffic information for the travel destination. When the user is participating in an event, the traffic information around the event venue is preferentially collected. This makes it possible to preferentially collect highly relevant traffic information based on the geographical location information of the user.

[0048] In the AI ​​assistant system, when the traffic information collection unit collects traffic information, the traffic information collection unit collects information on other transportation means in an integrated manner. The information on other transportation means includes, for example, operation information on buses, trains, and taxis, but is not limited to such examples. The traffic information collection unit collects, for example, the operation status of buses and integrates it into the traffic information. The traffic information collection unit can also collect the operation status of trains and integrate it into the traffic information. Furthermore, the traffic information collection unit can collect the operation status of taxis and integrate it into the traffic information. For example, the traffic information collection unit collects the operation status of buses in real time and integrates it into the traffic information. The traffic information collection unit collects the operation status of trains and integrates it into the traffic information. The traffic information collection unit collects the operation status of taxis and integrates it into the traffic information. In this way, comprehensive traffic information can be provided by integrating and collecting information on other transportation means.

[0049] In the AI ​​assistant system, when the weather information collection unit collects weather information, the weather information collection unit optimizes the collection algorithm by referring to past weather data. The past weather data includes, for example, weather data from the past year, weather patterns for a specific period, and the like, but is not limited to such examples. The weather information collection unit, for example, sets an optimal collection algorithm based on the past weather data. The weather information collection unit can also refer to past weather event data and preferentially collect information under specific weather conditions. Furthermore, the weather information collection unit can analyze the accuracy of past weather forecasts and optimize the collection algorithm. For example, the weather information collection unit analyzes past weather data and preferentially collects information under specific weather conditions. The weather event data is referred to and information under specific weather conditions is preferentially collected. The accuracy of past weather forecasts is analyzed and the collection algorithm is optimized. This makes it possible to optimize the collection algorithm of weather information based on past weather data.

[0050] In the AI ​​assistant system, when the weather information collecting unit collects weather information, the collection range is adjusted based on a specific weather event. The specific weather event includes, for example, a typhoon, a snowstorm, and the like, but is not limited to such examples. For example, when a typhoon is approaching, the weather information collecting unit collects weather information in its influence range with priority. In addition, when a snowstorm is expected, the weather information collecting unit can also collect weather information in its influence range. Furthermore, when a large-scale weather event is expected, the weather information collecting unit can also collect weather information in its influence range. For example, when a typhoon is approaching, the weather information collecting unit collects weather information in its influence range with priority. When a snowstorm is expected, the weather information collecting unit collects weather information in its influence range. When a large-scale weather event is expected, the weather information collecting unit collects weather information in its influence range. This makes it possible to adjust the collection range of weather information based on a specific weather event.

[0051] In the AI ​​assistant system, when the weather information collecting unit collects weather information, the weather information collecting unit preferentially collects highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The weather information collecting unit, for example, acquires the geographical location information of the user using a GPS device. The weather information collecting unit can also acquire address information input by the user. Furthermore, the weather information collecting unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the weather information collecting unit preferentially collects weather information related to the area. When the user is traveling, the weather information collecting unit preferentially collects weather information for the travel destination. When the user is participating in an event, the weather information around the event venue is preferentially collected. This makes it possible to preferentially collect highly relevant weather information based on the geographical location information of the user.

[0052] In the AI ​​assistant system, when the weather information collection unit collects weather information, the weather information collection unit collects information from other meteorological organizations in an integrated manner. The information from other meteorological organizations includes, for example, data from the Japan Meteorological Agency and private weather services, but is not limited to such examples. The weather information collection unit, for example, collects weather forecasts from the Japan Meteorological Agency and integrates them into weather information. The weather information collection unit can also collect weather forecasts from private weather services and integrate them into weather information. Furthermore, the weather information collection unit can collect weather forecasts from other meteorological organizations and provide comprehensive weather information. For example, the weather information collection unit collects weather forecasts from the Japan Meteorological Agency and integrates them into weather information. The weather information collection unit collects weather forecasts from private weather services and integrates them into weather information. Weather forecasts from other meteorological organizations are collected and comprehensive weather information is provided. This makes it possible to provide comprehensive weather information by integrating and collecting information from other meteorological organizations.

[0053] In the AI ​​assistant system, the judgment unit optimizes the algorithm by referring to past judgment results when making a judgment. Past judgment results include, for example, judgment results from the past year, judgment patterns from a specific period, and the like, but are not limited to such examples. For example, the judgment unit improves the accuracy of the judgment algorithm based on the past judgment results. The judgment unit can also analyze the past judgment results, find a specific pattern, and optimize the algorithm. Furthermore, the judgment unit can also adjust parameters of the judgment algorithm by referring to the past judgment results. For example, the judgment unit analyzes the past judgment results, finds a specific pattern, and optimizes the algorithm. The accuracy of the judgment algorithm is improved based on the past judgment results. The parameters of the judgment algorithm are adjusted by referring to the past judgment results. This makes it possible to optimize the judgment algorithm based on the past judgment results.

[0054] In the AI ​​assistant system, the judgment unit adjusts the judgment criteria based on a specific condition when making a judgment. The specific conditions include, for example, an emergency situation, an event being held, and the like, but are not limited to such examples. For example, when an emergency situation occurs, the judgment unit prioritizes information with a high level of urgency. In addition, when an event is held, the judgment unit can also prioritize information within the scope of the event being held. Furthermore, when a specific condition is met, the judgment unit can dynamically adjust the judgment criteria. For example, when an emergency situation occurs, the judgment unit prioritizes information with a high level of urgency. In addition, when an event is held, the judgment unit prioritizes information within the scope of the event being held. In addition, when a specific condition is met, the judgment criteria are dynamically adjusted. This makes it possible to adjust the judgment criteria based on a specific condition.

[0055] In the AI ​​assistant system, the determination unit customizes the determination result by taking into consideration the geographical location information of the user when making a determination. The geographical location information includes, for example, GPS data, address information, landmarks, and the like, but is not limited to such examples. The determination unit acquires the geographical location information of the user using, for example, a GPS device. The determination unit can also acquire address information input by the user. Furthermore, the determination unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the determination unit provides a determination result related to the area. When the user is traveling, the determination unit provides a determination result including information about the travel destination. When the user is participating in an event, the determination unit provides a determination result including information about the area around the event venue. This makes it possible to customize the determination result based on the geographical location information of the user.

[0056] In the AI ​​assistant system, when the judgment unit makes a judgment, the judgment unit integrates data from other information sources. The data from other information sources includes, for example, news, social media, sensor data, user-provided data, and the like, but is not limited to such examples. The judgment unit, for example, integrates news information and reflects it in the judgment result. The judgment unit can also integrate social media data and reflect it in the judgment result. Furthermore, the judgment unit can integrate data from other information sources and provide a comprehensive judgment result. For example, the judgment unit integrates news information and reflects it in the judgment result. The judgment unit integrates social media data and reflects it in the judgment result. The data from other information sources is integrated to provide a comprehensive judgment result. As a result, a comprehensive judgment result can be provided by integrating data from other information sources to make a judgment.

[0057] In the AI ​​assistant system, when the providing unit provides information, the providing unit refers to the past provision history to select the optimal providing method. When the providing unit provides information, the providing unit refers to the past provision history to select the optimal providing method. The past provision history includes, for example, a provision history for the past year, a provision pattern for a specific period, and the like, but is not limited to such examples. For example, the providing unit selects the optimal information providing method based on the past provision history. In addition, the providing unit can also analyze the past provision history, find a specific pattern, and optimize the providing method. Furthermore, the providing unit can also adjust the parameters of the providing method by referring to the past provision history. For example, the providing unit analyzes the past provision history, finds a specific pattern, and optimizes the providing method. The optimal information providing method is selected based on the past provision history. The parameters of the providing method are adjusted by referring to the past provision history. This makes it possible to select the optimal information providing method based on the past provision history.

[0058] In the AI ​​assistant system, when the providing unit provides information, the providing content is adjusted based on a specific condition. The specific condition includes, for example, an emergency, an event, and the like, but is not limited to such examples. For example, when an emergency occurs, the providing unit provides information with a high degree of urgency with priority. In addition, when an event is held, the providing unit can also provide information within the range of its influence with priority. Furthermore, when a specific condition is met, the providing unit can dynamically adjust the provided content. For example, when an emergency occurs, the providing unit provides information with a high degree of urgency with priority. When an event is held, information within the range of its influence with priority. When a specific condition is met, the provided content is dynamically adjusted. This makes it possible to adjust the information provided content based on a specific condition.

[0059] In the AI ​​assistant system, when the providing unit provides information, the providing unit preferentially provides highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. For example, the providing unit acquires the geographical location information of the user using a GPS device. The providing unit can also acquire address information input by the user. Furthermore, the providing unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the providing unit preferentially provides information related to the area. When the user is traveling, the providing unit preferentially provides information about the travel destination. When the user is participating in an event, the providing unit preferentially provides information about the area around the event venue. This makes it possible to preferentially provide highly relevant information based on the geographical location information of the user.

[0060] In the AI ​​assistant system, when the providing unit provides information, the providing unit integrates data from other information sources and provides the information. The data from other information sources includes, for example, news, social media, sensor data, user-provided data, and the like, but is not limited to such examples. For example, the providing unit integrates news information and reflects it in the information provided. The providing unit can also integrate social media data and reflect it in the information provided. Furthermore, the providing unit can integrate data from other information sources and provide comprehensive information. For example, the providing unit integrates news information and reflects it in the information provided. The providing unit integrates social media data and reflects it in the information provided. The data from other information sources is integrated and provides comprehensive information. As a result, comprehensive information can be provided by integrating and providing data from other information sources.

[0061] In the AI ​​assistant system, when the warning unit issues a warning, the warning unit refers to past warning history and selects the optimal warning method. The past warning history includes, for example, warning history for the past year, warning patterns for a specific period, etc., but is not limited to such examples. The warning unit selects the optimal warning method based on, for example, the past warning history. The warning unit can also analyze the past warning history, find a specific pattern, and optimize the warning method. Furthermore, the warning unit can also refer to the past warning history and adjust the parameters of the warning method. For example, the warning unit analyzes the past warning history, finds a specific pattern, and optimizes the warning method. The optimal warning method is selected based on the past warning history. The parameters of the warning method are adjusted by referring to the past warning history. This makes it possible to select the optimal warning method based on the past warning history.

[0062] When the warning unit issues a warning, the AI ​​assistant system preferentially provides a highly relevant warning in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The warning unit, for example, acquires the geographical location information of the user using a GPS device. The warning unit can also acquire address information input by the user. Furthermore, the warning unit can identify the user's location using landmark information. For example, when the user is in a specific area, the warning unit preferentially provides a warning related to the area. When the user is traveling, the warning unit preferentially provides a warning for the travel destination. When the user is participating in an event, the warning unit preferentially provides a warning around the event venue. This makes it possible to preferentially provide a highly relevant warning based on the geographical location information of the user.

[0063] In the AI ​​assistant system, when the suggestion unit makes a suggestion, the suggestion unit selects an optimal suggestion method by referring to the past proposal history. The past proposal history includes, for example, a proposal history for the past year, a proposal pattern for a specific period, and the like, but is not limited to such examples. The suggestion unit selects an optimal suggestion method based on, for example, the past proposal history. The suggestion unit can also analyze the past proposal history, find a specific pattern, and optimize the proposal method. Furthermore, the suggestion unit can also adjust parameters of the proposal method by referring to the past proposal history. For example, the suggestion unit analyzes the past proposal history, finds a specific pattern, and optimizes the proposal method. The optimal suggestion method is selected based on the past proposal history. The parameters of the proposal method are adjusted by referring to the past proposal history. This makes it possible to select an optimal suggestion method based on the past proposal history.

[0064] In the AI ​​assistant system, when the suggestion unit makes a proposal, the suggestion unit adjusts the proposal content based on a specific condition. The specific condition includes, for example, an emergency, an event being held, and the like, but is not limited to such examples. For example, when an emergency occurs, the suggestion unit prioritizes a proposal with a high urgency. In addition, when an event is held, the suggestion unit can also prioritize a proposal of the extent of the impact. Furthermore, when a specific condition is met, the suggestion unit can dynamically adjust the proposal content. For example, when an emergency occurs, the suggestion unit prioritizes a proposal with a high urgency. In the event of an event being held, the suggestion unit prioritizes a proposal of the extent of the impact. When a specific condition is met, the suggestion content is dynamically adjusted. This makes it possible to adjust the proposal content based on a specific condition.

[0065] When the suggestion unit makes a suggestion, the AI ​​assistant system preferentially provides highly relevant suggestions taking into consideration the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The suggestion unit acquires the geographical location information of the user using, for example, a GPS device. The suggestion unit can also acquire address information input by the user. Furthermore, the suggestion unit can identify the user's location using landmark information. For example, when the user is in a specific area, the suggestion unit preferentially provides suggestions related to the area. When the user is traveling, the suggestion unit preferentially provides suggestions for travel destinations. When the user is participating in an event, the suggestion unit preferentially provides suggestions around the event venue. This makes it possible to preferentially provide highly relevant suggestions based on the geographical location information of the user.

[0066] In the AI ​​assistant system, when the suggestion unit makes a suggestion, the suggestion unit integrates data from other information sources. The data from other information sources includes, for example, news, social media, sensor data, user-provided data, and the like, but is not limited to such examples. The suggestion unit, for example, integrates news information and reflects it in the proposal content. The suggestion unit can also integrate social media data and reflect it in the proposal content. Furthermore, the suggestion unit can also integrate data from other information sources and provide a comprehensive proposal. For example, the suggestion unit integrates news information and reflects it in the proposal content. The suggestion unit integrates social media data and reflects it in the proposal content. The suggestion unit integrates data from other information sources and provides a comprehensive proposal. In this way, a comprehensive proposal can be provided by integrating data from other information sources to make a proposal.

[0067] In the AI ​​assistant system, when the adjustment unit adjusts the movement route, the adjustment unit refers to the past movement history and selects the optimal adjustment method. The past movement history includes, for example, movement data for the past year, movement patterns for a specific period, etc., but is not limited to such examples. The adjustment unit, for example, proposes an optimal movement route based on the past movement history. The adjustment unit can also analyze the past movement history, find a specific pattern, and optimize the movement route. Furthermore, the adjustment unit can also adjust the parameters of the movement route by referring to the past movement history. For example, the adjustment unit analyzes the past movement history, finds a specific pattern, and optimizes the movement route. The optimal movement route is proposed based on the past movement history. The parameters of the movement route are adjusted by referring to the past movement history. This makes it possible to propose an optimal movement route based on the past movement history.

[0068] In the AI ​​assistant system, when the adjustment unit adjusts the movement route, the adjustment content is adjusted based on a specific condition. The specific condition includes, for example, an emergency, an event, and the like, but is not limited to such examples. For example, when an emergency occurs, the adjustment unit preferentially proposes a movement route with high urgency. In addition, when an event is held, the adjustment unit can also preferentially propose a movement route within the range of influence of the event. Furthermore, the adjustment unit can dynamically adjust the movement route when a specific condition is met. For example, when an emergency occurs, the adjustment unit preferentially proposes a movement route with high urgency. In the event of an event, the adjustment unit preferentially proposes a movement route within the range of influence of the event. When a specific condition is met, the movement route is dynamically adjusted. This makes it possible to adjust the movement route based on a specific condition.

[0069] In the AI ​​assistant system, when the adjustment unit adjusts a travel route, the adjustment unit takes into consideration the geographical location information of the user and preferentially provides a highly relevant route. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The adjustment unit acquires the geographical location information of the user using, for example, a GPS device. The adjustment unit can also acquire address information input by the user. Furthermore, the adjustment unit can identify the user's location using landmark information. For example, when the user is in a specific area, the adjustment unit preferentially provides a travel route related to the area. When the user is traveling, the adjustment unit preferentially provides a travel route of the travel destination. When the user is participating in an event, the adjustment unit preferentially provides a travel route around the event venue. This makes it possible to preferentially provide a highly relevant travel route based on the geographical location information of the user.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] When acquiring information on the user's current location or destination, the acquisition unit can also select the optimal acquisition method by referring to the user's past movement history. For example, based on the past movement history, the acquisition unit can identify locations frequently visited by the user and adjust the timing of acquiring location information at those locations. The acquisition unit can also analyze the user's movement pattern and suggest the optimal acquisition method. Furthermore, the acquisition frequency of location information in a specific time period can be optimized to provide efficient information provision. This makes it possible to select the optimal method of acquiring location information based on the user's past movement history.

[0072] The suggestion unit can also adjust the suggested content based on the user's current activity status and areas of interest. For example, when the user is exercising, the suggestion unit can suggest a route suitable for exercise. When the user is sightseeing, information about tourist spots can be provided preferentially. When the user is at work, information related to work can be provided preferentially. In this way, the suggested content can be adjusted based on the user's activity status and areas of interest.

[0073] When acquiring sensor information of other vehicles, the acquisition unit can also prioritize acquiring highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific area, sensor information of other vehicles related to that area is acquired preferentially. Also, when the user is traveling, traffic information of the travel destination can be acquired preferentially. Furthermore, when the user is participating in an event, traffic information around the event venue can be acquired preferentially. This makes it possible to prioritize acquiring highly relevant information based on the geographical location information of the user.

[0074] When acquiring drone sensor information, the acquisition unit can also select the optimal acquisition method by referring to past drone data. For example, based on past drone data, traffic congestion and accident occurrence patterns in a specific area are identified, and that information is acquired preferentially. Also, by referring to past weather data, it is possible to preferentially acquire information under specific weather conditions. Furthermore, it is also possible to select the optimal flight path by referring to past drone flight path information. This makes it possible to select the optimal acquisition method based on past drone data.

[0075] The acquisition unit can also analyze the user's social media activity and acquire related information. For example, the acquisition unit can preferentially acquire location information of a place where the user has checked in on social media. The acquisition unit can also preferentially acquire location information of an event shared by the user. Furthermore, the acquisition unit can analyze the content posted by the user and acquire related location information. This makes it possible to acquire related information based on the user's social media activity.

[0076] The providing unit can also integrate data from other information sources when providing information. For example, news information can be integrated and reflected in the provided information. Also, data from social media can be integrated and reflected in the provided information. Furthermore, data from other information sources can be integrated to provide comprehensive information. In this way, by integrating and providing data from other information sources, comprehensive information can be provided.

[0077] The process flow of the first embodiment will be briefly described below.

[0078] Step 1: The acquisition unit acquires information on the user's current location or destination. The information on the user's current location or destination includes, for example, GPS coordinates, an address, a landmark, etc. The acquisition unit acquires the user's current location using a GPS device, and identifies the destination based on the address information and landmark information input by the user. Step 2: The traffic information collection unit collects traffic information. Traffic information includes congestion information, accident information, and the operation status of public transportation. The traffic information collection unit collects traffic congestion information using traffic sensors, and collects traffic accident information based on data provided by the police and traffic management center. In addition, the operation status of public transportation is obtained from a system that provides bus and train operation information. Step 3: The weather information collection unit collects weather information. The weather information includes temperature, precipitation, wind speed, weather forecast, etc. The weather information collection unit collects current weather information using a weather sensor and obtains weather forecast information from a weather forecast system. In addition, the weather information collection unit collects a wide range of weather information using data from weather satellites. Step 4: The determination unit determines the traffic and weather information to be provided to the user in real time based on the information collected by the acquisition unit, the traffic information collection unit, and the weather information collection unit. The determination unit analyzes the collected information using AI and provides the user with the optimal travel route and appropriate weather information. Step 5: The providing unit provides the traffic information and weather information determined by the determining unit to the user in real time. The providing unit provides the information through a smartphone app, an in-vehicle display, and a voice assistant.

[0079] (Example 2) The AI ​​assistant system according to the embodiment of the present invention is a system that provides real-time traffic and weather information based on the user's current location and destination. The system acquires information on the user's current location or destination, collects traffic information such as traffic congestion, traffic accidents, and construction information, and collects weather information such as current weather and weather forecasts. These pieces of information are analyzed in real time and provided to the user. For example, if a traffic jam or accident occurs while the user is moving, the system analyzes the information in real time and notifies the user. In addition, if a sudden heavy rain or storm is expected, the system issues a warning to the user. Furthermore, the system determines and provides traffic and weather information on the user's movement route in real time. For example, it proposes an alternative route to avoid traffic jams and a movement plan that corresponds to changes in the weather. The system can also collect real-time data on traffic and weather from social media, acquire sensor information from other vehicles and drones, and use that information to complement the traffic and weather information. Furthermore, the system can adjust the movement route based on the user's health condition. For example, if the user is not feeling well, the system proposes an optimal movement route to reduce the burden on the user. This allows the AI ​​assistant system to provide users with optimal traffic and weather information in real time, improving the safety and efficiency of travel. This allows the AI ​​assistant system to provide real-time traffic and weather information based on the user's current location and destination.

[0080] The AI ​​assistant system according to the embodiment includes an acquisition unit, a traffic information collection unit, a weather information collection unit, a determination unit, and a provision unit. The acquisition unit acquires information on the user's current location or destination. The information on the user's current location or destination includes, for example, GPS coordinates, an address, a landmark, and the like, but is not limited to such examples. The acquisition unit acquires the user's current location using, for example, a GPS device. The acquisition unit can also acquire address information input by the user. Furthermore, the acquisition unit can specify the user's destination using landmark information. For example, the acquisition unit acquires location information based on the landmark name input by the user. The traffic information collection unit collects traffic information. The traffic information includes, for example, congestion information, accident information, and the operation status of public transportation, but is not limited to such examples. The traffic information collection unit collects traffic congestion status using, for example, a traffic sensor. Furthermore, the traffic information collection unit can also collect information on traffic accidents. Furthermore, the traffic information collection unit can collect the operation status of public transportation. For example, the traffic information collecting unit monitors the congestion status of roads in real time using a traffic sensor and collects congestion information. Information on traffic accidents is collected based on data provided by the police or a traffic management center. The operation status of public transportation is acquired from a system that provides operation information for buses and trains. The weather information collecting unit collects weather information. Weather information includes, for example, temperature, precipitation, wind speed, weather forecast, etc., but is not limited to such examples. The weather information collecting unit collects current weather information using, for example, a weather sensor. In addition, the weather information collecting unit can also acquire weather forecast information from a weather forecast system. Furthermore, the weather information collecting unit can also collect weather information using data from a weather satellite. For example, the weather information collecting unit measures temperature and precipitation in real time using a weather sensor and collects weather information. From the weather forecast system, future weather forecasts are acquired and provided to the user. The data from the weather satellite is used to collect weather information over a wide area. The determining unit determines the traffic information and weather information to be provided to the user in real time based on the information collected by the acquiring unit, the traffic information collecting unit, and the weather information collecting unit.The determination unit, for example, analyzes the collected information using AI and determines the information to be provided to the user. The determination unit, for example, proposes an optimal travel route to the user based on traffic congestion and accident information. The determination unit can also provide appropriate weather information to the user based on weather information. For example, the determination unit uses AI to analyze data from a traffic sensor and identify locations of congestion and accidents. Furthermore, the determination unit analyzes data from a weather sensor and determines the current weather and future weather forecast. The provision unit provides the traffic information and weather information determined by the determination unit to the user in real time. The provision unit provides information to the user through, for example, a smartphone app. Furthermore, the provision unit can also provide information through an in-vehicle display. Furthermore, the provision unit can also provide information through a voice assistant. For example, the provision unit notifies the user of traffic information and weather information in real time using a smartphone app. The in-vehicle display provides information visually to the user while driving. The voice assistant provides information by voice, allowing the user while driving to obtain information without taking their eyes off the vehicle. As a result, the AI ​​assistant system according to the embodiment can provide real-time traffic and weather information based on the user's current location and destination.

[0081] The acquisition unit acquires information on the user's current location or destination. The information on the user's current location or destination includes, for example, GPS coordinates, addresses, landmarks, etc., but is not limited to such examples. The acquisition unit acquires the user's current location using, for example, a GPS device. The acquisition unit can also acquire address information input by the user. Furthermore, the acquisition unit can identify the user's destination using landmark information. For example, the acquisition unit acquires location information based on the landmark name input by the user. The acquisition unit can integrate multiple data sources to acquire these pieces of information. For example, data from a GPS device tracks the user's location in real time and provides highly accurate location information. The address information can be acquired by the user manually entering it or using voice input. The landmark information is linked to a map database to identify the exact location of the landmark specified by the user. This allows the acquisition unit to quickly and accurately acquire various information on the user's current location and destination, and improve the accuracy and reliability of the entire system. Furthermore, the acquisition unit has a function of encrypting and safely managing the acquired data in order to protect the user's privacy. This allows the user to use the system with peace of mind.

[0082] The traffic information collecting unit collects traffic information. The traffic information includes, for example, congestion information, accident information, and the operation status of public transportation, but is not limited to such examples. The traffic information collecting unit collects traffic congestion status, for example, using a traffic sensor. The traffic information collecting unit can also collect information on traffic accidents. Furthermore, the traffic information collecting unit can also collect the operation status of public transportation. For example, the traffic information collecting unit monitors the congestion status of roads in real time using a traffic sensor and collects congestion information. The traffic accident information is collected based on data provided by the police or a traffic management center. The operation status of public transportation is acquired from a system that provides operation information of buses and trains. The traffic information collecting unit has a function of centrally managing these information and updating them in real time. For example, the traffic sensor monitors the flow of vehicles on the road and grasps the congestion status in real time. This allows the traffic information collecting unit to quickly and accurately grasp the traffic status and improve the accuracy of the information provided to the user. Furthermore, the traffic information collecting unit can use an anomaly detection algorithm to detect unusual traffic patterns and abnormal situations early and issue a warning. This enables the traffic information gathering unit to provide the user with prompt and appropriate information, thereby improving traffic safety and efficiency.

[0083] The weather information collecting unit collects weather information. The weather information includes, for example, temperature, precipitation, wind speed, weather forecast, etc., but is not limited to such examples. The weather information collecting unit collects current weather information using, for example, a weather sensor. The weather information collecting unit can also obtain weather forecast information from a weather forecast system. Furthermore, the weather information collecting unit can also collect weather information using data from a weather satellite. For example, the weather information collecting unit measures temperature and precipitation in real time using a weather sensor to collect weather information. From the weather forecast system, future weather forecasts are obtained and provided to the user. The data from the weather satellite is used to collect weather information over a wide range. The weather information collecting unit has a function of integrating these pieces of information and updating them in real time. For example, the weather sensor collects environmental data such as temperature, humidity, and wind speed in real time to improve the accuracy of the weather information. The data from the weather forecast system provides short-term and long-term weather forecasts and provides appropriate information to the user. The data from the weather satellite is used to grasp the weather conditions over a wide range and provide detailed weather information for each region. This allows the weather information gathering unit to provide the user with accurate and up-to-date weather information, which can be useful for travel and planning.

[0084] The determination unit determines the traffic information and weather information to be provided to the user in real time based on the information collected by the acquisition unit, the traffic information collection unit, and the weather information collection unit. The determination unit, for example, uses AI to analyze the collected information and determines the information to be provided to the user. The determination unit proposes an optimal travel route to the user based on, for example, traffic congestion and accident information. The determination unit can also provide appropriate weather information to the user based on the weather information. For example, the determination unit uses AI to analyze data from a traffic sensor and identify the location of a traffic jam or an accident. Furthermore, the determination unit analyzes data from a weather sensor and determines the current weather and future weather forecast. The determination unit has a function of integrating these pieces of information and providing the most useful information to the user in real time. For example, the AI ​​uses a machine learning algorithm to learn past data and predict future traffic conditions and weather fluctuations. As a result, the determination unit can provide the user with the optimal travel route and weather information, improving the efficiency and safety of travel. Furthermore, the determination unit can learn the user's past behavior and preferences and provide personalized information. This enables the determination unit to provide the user with a more personalized service, thereby improving the user's level of satisfaction.

[0085] The providing unit provides the traffic information and weather information determined by the determining unit to the user in real time. The providing unit provides the information to the user through, for example, a smartphone app. The providing unit can also provide the information through an in-vehicle display. Furthermore, the providing unit can also provide the information through a voice assistant. For example, the providing unit notifies the user of traffic information and weather information in real time using a smartphone app. The in-vehicle display visually provides information to the user while driving. The voice assistant provides information by voice, allowing the user while driving to obtain information without taking their eyes off the road. The providing unit has a function of integrating these information providing means and providing information to the user in an optimal manner. For example, the smartphone app can quickly convey important information to the user using push notifications and widgets. The in-vehicle display provides an intuitive interface to the user while driving, allowing the user to visually confirm information. The voice assistant uses natural language processing technology to respond to the user's questions and provide the necessary information. This allows the providing unit to quickly provide accurate information to the user, thereby improving the efficiency and safety of travel. Furthermore, the providing unit can collect feedback from users and continuously improve the accuracy and effectiveness of the information provided, thereby enabling the providing unit to always provide the latest and most appropriate information to users and improve user satisfaction.

[0086] The AI ​​assistant system includes a warning unit that issues a warning about sudden climate change. The warning unit issues a warning about sudden climate change. Sudden climate change includes, but is not limited to, sudden temperature changes, wind gusts, heavy rain, and the like. For example, the warning unit detects a sudden temperature change based on data from a weather sensor. The warning unit can also detect wind gusts using a wind speed sensor. Furthermore, the warning unit can detect heavy rain using a precipitation sensor. For example, the warning unit analyzes data from the weather sensor in real time and issues a warning to the user when a sudden temperature change occurs. The wind speed sensor detects the occurrence of a wind gust and notifies the user of the information. The precipitation sensor detects the occurrence of heavy rain and issues a warning to the user. This makes it possible to provide the user with a warning about sudden climate change.

[0087] The AI ​​assistant system includes a suggestion unit that proposes a travel plan to the user based on the judgment result by the judgment unit. The suggestion unit proposes a travel plan to the user based on the judgment result by the judgment unit. The travel plan includes, for example, an optimal route, a selection of a means of transportation, a required time, and the like, but is not limited to such examples. The suggestion unit proposes an optimal route to the user based on, for example, traffic congestion and accident information. The suggestion unit can also propose an appropriate means of transportation to the user based on weather information. Furthermore, the suggestion unit can propose an optimal travel plan to the user taking into account the required time. For example, the suggestion unit analyzes data from a traffic sensor and proposes an alternative route to avoid congestion. Based on weather information, it can also propose the use of public transportation in rainy weather. Taking into account the required time, a route to reach the destination in the shortest time is proposed. This makes it possible to propose an optimal travel plan to the user.

[0088] In the AI ​​assistant system, the acquisition unit collects real-time data on traffic and weather from social media, and the determination unit uses the data to complement the traffic information and weather information. The acquisition unit collects real-time data on traffic and weather from social media. The real-time data from social media includes, but is not limited to, Twitter, Facebook, and Instagram posting data. The acquisition unit collects posts on traffic information and weather information, for example, using Twitter's API. The acquisition unit can also analyze Facebook posting data to collect traffic information and weather information. The acquisition unit can also use Instagram posting data to collect traffic information and weather information. For example, the acquisition unit collects posts related to a specific hashtag using Twitter's API to acquire traffic information and weather information. The Facebook posting data is analyzed to collect traffic accident and weather information shared by users. The Instagram posting data is used to acquire traffic information and weather information from photos and comments posted by users. The determination unit uses the real-time data acquired by the acquisition unit to complement the traffic information and weather information. The determination unit uses, for example, AI to analyze data from social media to complement the traffic information and weather information. The determination unit, for example, analyzes Twitter posting data to complement information on traffic congestion and accidents. The determination unit can also analyze Facebook posting data to complement information on weather changes. The determination unit can also analyze Instagram posting data to complement traffic information and weather information. For example, the determination unit uses AI to analyze Twitter posting data to identify locations of traffic congestion and accidents. The determination unit analyzes Facebook posting data to complement information on sudden weather changes. The determination unit analyzes Instagram posting data to complement traffic information and weather information from photos and comments posted by users. This makes it possible to complement traffic information and weather information using data from social media.

[0089] In the AI ​​assistant system, the acquisition unit acquires sensor information of other vehicles, and the determination unit uses the information to complement traffic information and weather information. The acquisition unit acquires sensor information of other vehicles. The sensor information of other vehicles includes, for example, the speed of the vehicle, position information, surrounding obstacle information, and the like, but is not limited to such examples. The acquisition unit acquires speed information of other vehicles, for example, using a speed sensor of the vehicle. The acquisition unit can also acquire position information of other vehicles using a GPS device of the vehicle. Furthermore, the acquisition unit can also acquire surrounding obstacle information using an obstacle sensor of the vehicle. For example, the acquisition unit collects speed data of other vehicles in real time using a speed sensor of the vehicle. The acquisition unit acquires position information of other vehicles using a GPS device of the vehicle to complement traffic information. The acquisition unit acquires surrounding obstacle information using an obstacle sensor of the vehicle to complement traffic information. The determination unit uses the sensor information of other vehicles acquired by the acquisition unit to complement traffic information and weather information. The determination unit analyzes sensor information of other vehicles using, for example, AI, to complement traffic information and weather information. The determination unit, for example, analyzes the speed information of other vehicles and identifies the location of the traffic jam. The determination unit can also analyze the position information of other vehicles and identify the location of the traffic accident. Furthermore, the determination unit can analyze obstacle information of other vehicles and complement the traffic information. For example, the determination unit uses AI to analyze the speed data of other vehicles and identify the location of the traffic jam. The determination unit analyzes the position information of other vehicles and identifies the location of the traffic accident. The determination unit analyzes surrounding obstacle information and complements the traffic information. This makes it possible to complement traffic information and weather information using sensor information of other vehicles.

[0090] In the AI ​​assistant system, the acquisition unit acquires sensor information of the drone, and the determination unit uses the information to complement the traffic information and weather information. The acquisition unit acquires the sensor information of the drone. The sensor information of the drone includes, for example, aerial images, weather data, flight route information, etc., but is not limited to such examples. The acquisition unit acquires aerial images, for example, using a camera of the drone. The acquisition unit can also acquire weather data using a weather sensor of the drone. Furthermore, the acquisition unit can also acquire flight route information using a GPS device of the drone. For example, the acquisition unit acquires aerial images of traffic congestion and accidents using a camera of the drone. The acquisition unit acquires weather data in the sky using a weather sensor of the drone to complement the weather information. The acquisition unit acquires flight route information of the drone using a GPS device of the drone to complement the traffic information. The determination unit uses the sensor information of the drone acquired by the acquisition unit to complement the traffic information and weather information. The determination unit analyzes the sensor information of the drone using, for example, AI, and complements the traffic information and weather information. The determination unit analyzes the aerial images of the drone, for example, to identify the location of the traffic congestion or accident. The determination unit can also analyze the drone's weather data to complement the weather conditions in the sky. Furthermore, the determination unit can also analyze the drone's flight path information to complement traffic information. For example, the determination unit uses AI to analyze aerial images taken by the drone to identify the locations of traffic congestion and accidents. The determination unit analyzes the drone's weather data to complement the weather conditions in the sky. The determination unit analyzes the drone's flight path information to complement traffic information. This makes it possible to complement traffic information and weather information using drone sensor information.

[0091] The AI ​​assistant system includes an adjustment unit that adjusts the moving route based on the health condition of the user. The adjustment unit adjusts the moving route based on the health condition of the user. The health condition includes, for example, heart rate, blood pressure, pedometer data, etc., but is not limited to such examples. For example, the adjustment unit measures the user's heart rate using a heart rate sensor. The adjustment unit can also measure the user's blood pressure using a blood pressure monitor. Furthermore, the adjustment unit can measure the user's step count using a pedometer. For example, the adjustment unit uses a heart rate sensor to collect the user's heart rate data in real time. The blood pressure monitor is used to obtain the user's blood pressure data and grasp the health condition. The pedometer is used to collect the user's step count data and adjust the moving route. The adjustment unit proposes an optimal moving route to the user based on the obtained health condition data. For example, if the user is in poor health, the adjustment unit proposes the shortest route and reduces the user's burden. Furthermore, if the user is in good health, the adjustment unit can also propose a scenic route. Furthermore, if the user is in a hurry, the adjustment unit can also propose a route that avoids traffic congestion. For example, the adjustment unit analyzes heart rate data and proposes a movement route according to the user's physical condition. Based on blood pressure data, the adjustment unit proposes a movement route that takes into account the user's health condition. Based on step count data, the adjustment unit proposes an optimal route based on the user's movement pattern. In this way, it is possible to propose an optimal movement route according to the user's health condition.

[0092] In the AI ​​assistant system, the acquisition unit estimates the user's emotion and adjusts the timing of acquiring location information based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the acquisition unit estimates the user's emotion using facial expression recognition technology. The acquisition unit can also estimate the user's emotion using voice analysis technology. Furthermore, the acquisition unit can estimate the user's emotion using text analysis technology. For example, the acquisition unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The acquisition unit adjusts the timing of acquiring location information based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit reduces the frequency of acquiring location information and reduces notifications. Also, when the user is relaxed, the acquisition unit can increase the frequency of acquiring location information and provide detailed information. Furthermore, when the user is in a hurry, the acquisition unit can acquire location information in real time and provide information quickly. For example, the acquisition unit analyzes the user's emotion data, and when the user is feeling stressed, reduces the frequency of acquiring location information. When the user is relaxed, increases the frequency of acquiring location information and provides detailed information. When the user is in a hurry, acquires location information in real time and provides information quickly. This makes it possible to adjust the timing of acquiring location information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] In the AI ​​assistant system, the acquisition unit analyzes the user's past movement history and selects the optimal acquisition method. The past movement history includes, for example, movement data for the past year, movement patterns for a specific period, etc., but is not limited to such examples. The acquisition unit collects the user's past movement history using, for example, GPS data. The acquisition unit can also analyze the user's movement pattern and select the optimal acquisition method. Furthermore, the acquisition unit can optimize the acquisition frequency of location information in a specific time period. For example, the acquisition unit identifies a place that the user frequently visits based on the past movement history, and adjusts the acquisition timing of location information at that place. The acquisition unit analyzes the user's movement pattern and proposes the optimal acquisition method. The acquisition frequency of location information in a specific time period is optimized to provide efficient information. This makes it possible to select the optimal location information acquisition method based on the user's past movement history.

[0094] In the AI ​​assistant system, when the acquisition unit acquires location information, the acquisition unit performs filtering based on the user's current activity status and field of interest. The current activity status includes, for example, exercising, resting, working, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, topics of interest, past search history, etc., but is not limited to such examples. For example, the acquisition unit monitors the user's activity status in real time and adjusts the timing of acquiring location information. In addition, the acquisition unit can also preferentially acquire related location information based on the user's field of interest. For example, when the user is exercising, the acquisition unit increases the frequency of acquiring location information and records the exercise route. When the user is sightseeing, the acquisition unit preferentially acquires location information related to tourist spots. When the user is at work, the acquisition unit preferentially acquires location information related to work. This makes it possible to filter location information based on the user's activity status and field of interest.

[0095] In the AI ​​assistant system, the acquisition unit estimates the user's emotion and determines the priority of the location information to be acquired based on the estimated user's emotion. The estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the acquisition unit estimates the user's emotion using facial expression recognition technology. The acquisition unit can also estimate the user's emotion using voice analysis technology. Furthermore, the acquisition unit can estimate the user's emotion using text analysis technology. For example, the acquisition unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The acquisition unit determines the priority of the location information to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring only important location information. Furthermore, when the user is relaxed, the acquisition unit can also prioritize acquiring detailed location information. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring location information with high urgency. For example, the acquisition unit analyzes the user's emotion data, and when the user is feeling stressed, it prioritizes acquiring only important location information. When the user is relaxed, it prioritizes acquiring detailed location information. When the user is in a hurry, it prioritizes acquiring location information with high urgency. This allows the priority of location information to be determined based on the user's emotion. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] In the AI ​​assistant system, when the acquisition unit acquires location information, the acquisition unit preferentially acquires highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, landmarks, etc., but is not limited to such examples. The acquisition unit acquires the geographical location information of the user using, for example, a GPS device. The acquisition unit can also acquire address information input by the user. Furthermore, the acquisition unit can identify the user's location using landmark information. For example, when the user is in a specific area, the acquisition unit preferentially acquires location information related to the area. When the user is traveling, the acquisition unit preferentially acquires tourist information about the travel destination. When the user is participating in an event, the acquisition unit preferentially acquires location information around the event venue. This makes it possible to preferentially acquire highly relevant information based on the geographical location information of the user.

[0097] In the AI ​​assistant system, when the acquisition unit acquires the location information, the acquisition unit analyzes the user's social media activity and acquires related information. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The acquisition unit acquires, for example, the user's check-in information on social media. The acquisition unit can also acquire event information shared by the user. Furthermore, the acquisition unit can analyze the content of the user's post and acquire related location information. For example, the acquisition unit preferentially acquires location information of the place where the user checked in on social media. The acquisition unit preferentially acquires location information of the event shared by the user. The content of the user's post is analyzed and related location information is acquired. This makes it possible to acquire related information based on the user's social media activity.

[0098] In the AI ​​assistant system, the traffic information collection unit estimates the user's emotion, and adjusts the method of collecting traffic information based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the traffic information collection unit estimates the user's emotion using facial expression recognition technology. In addition, the traffic information collection unit can also estimate the user's emotion using voice analysis technology. In addition, the traffic information collection unit can also estimate the user's emotion using text analysis technology. For example, the traffic information collection unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. Using voice analysis technology, the tone and speed of the user's voice are analyzed and the emotion is estimated. Using text analysis technology, text data input by the user is analyzed and the emotion is estimated. The traffic information collection unit adjusts the method of collecting traffic information based on the estimated user's emotion. For example, when the user is stressed, the traffic information collection unit collects only important traffic information. Also, when the user is relaxed, the traffic information collection unit can also collect detailed traffic information. Furthermore, when the user is in a hurry, the traffic information collection unit can also collect traffic information with high urgency with priority. For example, the traffic information collection unit analyzes the user's emotion data, and when the user is feeling stressed, collects only important traffic information. When the user is relaxed, collects detailed traffic information. When the user is in a hurry, collects traffic information with high urgency with priority. This allows the traffic information collection method to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] In the AI ​​assistant system, when the traffic information collection unit collects traffic information, the traffic information collection unit optimizes the collection algorithm by referring to past traffic data. The past traffic data includes, for example, traffic data from the past year, traffic patterns for a specific period, and the like, but is not limited to such examples. The traffic information collection unit sets an optimal collection algorithm, for example, based on past traffic congestion data. The traffic information collection unit can also refer to past traffic accident data and preferentially collect information on places where accidents are likely to occur. Furthermore, the traffic information collection unit can also collect information on places where construction is likely to be performed based on past construction information. For example, the traffic information collection unit analyzes past traffic congestion data, identifies places where congestion is likely to occur, and preferentially collects information on those places. The traffic accident data is analyzed, and information on places where accidents are likely to occur is preferentially collected. The past construction information is analyzed, and information on places where construction is likely to be performed is collected. This makes it possible to optimize the collection algorithm of traffic information based on past traffic data.

[0100] In the AI ​​assistant system, when the traffic information collection unit collects traffic information, the traffic information collection unit adjusts the collection range based on a specific traffic event. The specific traffic event includes, but is not limited to, a sporting event, a concert, and the like. For example, when a sporting event is held, the traffic information collection unit preferentially collects traffic information around the event. In addition, when a concert is held, the traffic information collection unit can also collect traffic information around the venue. Furthermore, when a large-scale event is scheduled, the traffic information collection unit can also collect traffic information within the range of the event's influence. For example, when a sporting event is held, the traffic information collection unit preferentially collects traffic information around the event. When a concert is held, traffic information around the venue is collected. When a large-scale event is scheduled, traffic information within the range of the event's influence is collected. This makes it possible to adjust the collection range of traffic information based on a specific traffic event.

[0101] In the AI ​​assistant system, the traffic information collection unit estimates the user's emotion and determines the priority of the traffic information to be collected based on the estimated user's emotion. The estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the traffic information collection unit estimates the user's emotion using facial expression recognition technology. The traffic information collection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the traffic information collection unit can also estimate the user's emotion using text analysis technology. For example, the traffic information collection unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The traffic information collection unit determines the priority of the traffic information to be collected based on the estimated user's emotion. For example, when the user is stressed, the traffic information collection unit preferentially collects only important traffic information. Also, when the user is relaxed, the traffic information collection unit can preferentially collect detailed traffic information. Furthermore, when the user is in a hurry, the traffic information collecting unit can also collect traffic information with high urgency with priority. For example, the traffic information collecting unit analyzes the user's emotion data, and when the user is feeling stressed, it collects only important traffic information with priority. When the user is relaxed, it collects detailed traffic information with priority. When the user is in a hurry, it collects traffic information with high urgency with priority. This makes it possible to determine the priority of traffic information based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] In the AI ​​assistant system, when the traffic information collecting unit collects traffic information, the traffic information collecting unit preferentially collects highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The traffic information collecting unit acquires the geographical location information of the user using, for example, a GPS device. The traffic information collecting unit can also acquire address information input by the user. Furthermore, the traffic information collecting unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the traffic information collecting unit preferentially collects traffic information related to the area. When the user is traveling, the traffic information collecting unit preferentially collects traffic information for the travel destination. When the user is participating in an event, the traffic information around the event venue is preferentially collected. This makes it possible to preferentially collect highly relevant traffic information based on the geographical location information of the user.

[0103] In the AI ​​assistant system, when the traffic information collection unit collects traffic information, the traffic information collection unit collects information on other transportation means in an integrated manner. The information on other transportation means includes, for example, operation information on buses, trains, and taxis, but is not limited to such examples. The traffic information collection unit collects, for example, the operation status of buses and integrates it into the traffic information. The traffic information collection unit can also collect the operation status of trains and integrate it into the traffic information. Furthermore, the traffic information collection unit can collect the operation status of taxis and integrate it into the traffic information. For example, the traffic information collection unit collects the operation status of buses in real time and integrates it into the traffic information. The traffic information collection unit collects the operation status of trains and integrates it into the traffic information. The traffic information collection unit collects the operation status of taxis and integrates it into the traffic information. In this way, comprehensive traffic information can be provided by integrating and collecting information on other transportation means.

[0104] In the AI ​​assistant system, the weather information collection unit estimates the user's emotion, and adjusts the weather information collection method based on the estimated user's emotion. The estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the weather information collection unit estimates the user's emotion using facial expression recognition technology. The weather information collection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the weather information collection unit can also estimate the user's emotion using text analysis technology. For example, the weather information collection unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The weather information collection unit adjusts the weather information collection method based on the estimated user's emotion. For example, when the user is stressed, the weather information collection unit collects only important weather information. Also, when the user is relaxed, the weather information collection unit can collect detailed weather information. Furthermore, when the user is in a hurry, the weather information collection unit can also collect weather information with high urgency with priority. For example, the weather information collection unit analyzes the user's emotion data, and when the user is feeling stressed, collects only important weather information. When the user is relaxed, collects detailed weather information. When the user is in a hurry, collects weather information with high urgency with priority. This allows the weather information collection method to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] In the AI ​​assistant system, when the weather information collection unit collects weather information, the weather information collection unit optimizes the collection algorithm by referring to past weather data. The past weather data includes, for example, weather data from the past year, weather patterns for a specific period, and the like, but is not limited to such examples. The weather information collection unit, for example, sets an optimal collection algorithm based on the past weather data. The weather information collection unit can also refer to past weather event data and preferentially collect information under specific weather conditions. Furthermore, the weather information collection unit can analyze the accuracy of past weather forecasts and optimize the collection algorithm. For example, the weather information collection unit analyzes past weather data and preferentially collects information under specific weather conditions. The weather event data is referred to and information under specific weather conditions is preferentially collected. The accuracy of past weather forecasts is analyzed and the collection algorithm is optimized. This makes it possible to optimize the collection algorithm of weather information based on past weather data.

[0106] In the AI ​​assistant system, when the weather information collecting unit collects weather information, the collection range is adjusted based on a specific weather event. The specific weather event includes, for example, a typhoon, a snowstorm, and the like, but is not limited to such examples. For example, when a typhoon is approaching, the weather information collecting unit collects weather information in its influence range with priority. In addition, when a snowstorm is expected, the weather information collecting unit can also collect weather information in its influence range. Furthermore, when a large-scale weather event is expected, the weather information collecting unit can also collect weather information in its influence range. For example, when a typhoon is approaching, the weather information collecting unit collects weather information in its influence range with priority. When a snowstorm is expected, the weather information collecting unit collects weather information in its influence range. When a large-scale weather event is expected, the weather information collecting unit collects weather information in its influence range. This makes it possible to adjust the collection range of weather information based on a specific weather event.

[0107] In the AI ​​assistant system, the weather information collection unit estimates the user's emotion and determines the priority of the weather information to be collected based on the estimated user's emotion. The estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the weather information collection unit estimates the user's emotion using facial expression recognition technology. The weather information collection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the weather information collection unit can also estimate the user's emotion using text analysis technology. For example, the weather information collection unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The weather information collection unit determines the priority of the weather information to be collected based on the estimated user's emotion. For example, when the user is stressed, the weather information collection unit preferentially collects only important weather information. Also, when the user is relaxed, the weather information collection unit can preferentially collect detailed weather information. Furthermore, when the user is in a hurry, the weather information collecting unit can also collect weather information with high urgency with priority. For example, the weather information collecting unit analyzes the user's emotion data, and when the user is feeling stressed, it collects only important weather information with priority. When the user is relaxed, it collects detailed weather information with priority. When the user is in a hurry, it collects weather information with high urgency with priority. This makes it possible to determine the priority of weather information based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] In the AI ​​assistant system, when the weather information collecting unit collects weather information, the weather information collecting unit preferentially collects highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The weather information collecting unit, for example, acquires the geographical location information of the user using a GPS device. The weather information collecting unit can also acquire address information input by the user. Furthermore, the weather information collecting unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the weather information collecting unit preferentially collects weather information related to the area. When the user is traveling, the weather information collecting unit preferentially collects weather information for the travel destination. When the user is participating in an event, the weather information around the event venue is preferentially collected. This makes it possible to preferentially collect highly relevant weather information based on the geographical location information of the user.

[0109] In the AI ​​assistant system, when the weather information collection unit collects weather information, the weather information collection unit collects information from other meteorological organizations in an integrated manner. The information from other meteorological organizations includes, for example, data from the Japan Meteorological Agency and private weather services, but is not limited to such examples. The weather information collection unit, for example, collects weather forecasts from the Japan Meteorological Agency and integrates them into weather information. The weather information collection unit can also collect weather forecasts from private weather services and integrate them into weather information. Furthermore, the weather information collection unit can collect weather forecasts from other meteorological organizations and provide comprehensive weather information. For example, the weather information collection unit collects weather forecasts from the Japan Meteorological Agency and integrates them into weather information. The weather information collection unit collects weather forecasts from private weather services and integrates them into weather information. Weather forecasts from other meteorological organizations are collected and comprehensive weather information is provided. This makes it possible to provide comprehensive weather information by integrating and collecting information from other meteorological organizations.

[0110] In the AI ​​assistant system, the determination unit estimates the user's emotion and adjusts the determination algorithm based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the determination unit estimates the user's emotion using facial expression recognition technology. The determination unit can also estimate the user's emotion using voice analysis technology. Furthermore, the determination unit can estimate the user's emotion using text analysis technology. For example, the determination unit uses a camera to analyze the user's facial expression in real time and estimates the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The determination unit adjusts the determination algorithm based on the estimated user's emotion. For example, when the user is feeling stressed, the determination unit uses a concise and quick determination algorithm. Furthermore, when the user is relaxed, the determination unit can also use a determination algorithm that includes detailed information. Furthermore, when the user is in a hurry, the determination unit can also use an algorithm that prioritizes information with high urgency. For example, the determination unit analyzes the user's emotion data, and if the user is feeling stressed, a simple and quick determination algorithm is used. If the user is relaxed, a determination algorithm including detailed information is used. If the user is in a hurry, an algorithm that prioritizes determining information with high urgency is used. This allows the determination algorithm to be adjusted based on the user's emotion. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] In the AI ​​assistant system, the judgment unit optimizes the algorithm by referring to past judgment results when making a judgment. Past judgment results include, for example, judgment results from the past year, judgment patterns from a specific period, and the like, but are not limited to such examples. For example, the judgment unit improves the accuracy of the judgment algorithm based on the past judgment results. The judgment unit can also analyze the past judgment results, find a specific pattern, and optimize the algorithm. Furthermore, the judgment unit can also adjust parameters of the judgment algorithm by referring to the past judgment results. For example, the judgment unit analyzes the past judgment results, finds a specific pattern, and optimizes the algorithm. The accuracy of the judgment algorithm is improved based on the past judgment results. The parameters of the judgment algorithm are adjusted by referring to the past judgment results. This makes it possible to optimize the judgment algorithm based on the past judgment results.

[0112] In the AI ​​assistant system, the judgment unit adjusts the judgment criteria based on a specific condition when making a judgment. The specific conditions include, for example, an emergency situation, an event being held, and the like, but are not limited to such examples. For example, when an emergency situation occurs, the judgment unit prioritizes information with a high level of urgency. In addition, when an event is held, the judgment unit can also prioritize information within the scope of the event being held. Furthermore, when a specific condition is met, the judgment unit can dynamically adjust the judgment criteria. For example, when an emergency situation occurs, the judgment unit prioritizes information with a high level of urgency. In addition, when an event is held, the judgment unit prioritizes information within the scope of the event being held. In addition, when a specific condition is met, the judgment criteria are dynamically adjusted. This makes it possible to adjust the judgment criteria based on a specific condition.

[0113] In the AI ​​assistant system, the determination unit estimates the user's emotion and adjusts the display method of the determination result based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the determination unit estimates the user's emotion using facial expression recognition technology. The determination unit can also estimate the user's emotion using voice analysis technology. Furthermore, the determination unit can estimate the user's emotion using text analysis technology. For example, the determination unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The determination unit adjusts the display method of the determination result based on the estimated user's emotion. For example, if the user is nervous, the determination unit provides a simple and highly visible display method. Furthermore, if the user is relaxed, the determination unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the determination unit can also provide a display method that focuses on the main points. For example, the determination unit analyzes the user's emotion data, and if the user is nervous, provides a simple, highly visible display method. If the user is relaxed, provides a display method including detailed information. If the user is in a hurry, provides a display method that focuses on the main points. This makes it possible to adjust the display method of the determination result based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0114] In the AI ​​assistant system, the determination unit customizes the determination result by taking into consideration the geographical location information of the user when making a determination. The geographical location information includes, for example, GPS data, address information, landmarks, and the like, but is not limited to such examples. The determination unit acquires the geographical location information of the user using, for example, a GPS device. The determination unit can also acquire address information input by the user. Furthermore, the determination unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the determination unit provides a determination result related to the area. When the user is traveling, the determination unit provides a determination result including information about the travel destination. When the user is participating in an event, the determination unit provides a determination result including information about the area around the event venue. This makes it possible to customize the determination result based on the geographical location information of the user.

[0115] In the AI ​​assistant system, when the judgment unit makes a judgment, the judgment unit integrates data from other information sources. The data from other information sources includes, for example, news, social media, sensor data, user-provided data, and the like, but is not limited to such examples. The judgment unit, for example, integrates news information and reflects it in the judgment result. The judgment unit can also integrate social media data and reflect it in the judgment result. Furthermore, the judgment unit can integrate data from other information sources and provide a comprehensive judgment result. For example, the judgment unit integrates news information and reflects it in the judgment result. The judgment unit integrates social media data and reflects it in the judgment result. The data from other information sources is integrated to provide a comprehensive judgment result. As a result, a comprehensive judgment result can be provided by integrating data from other information sources to make a judgment.

[0116] In the AI ​​assistant system, the providing unit estimates the user's emotion and adjusts the method of providing information based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the providing unit estimates the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. Furthermore, the providing unit can estimate the user's emotion using text analysis technology. For example, the providing unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The providing unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The providing unit uses text analysis technology to analyze text data input by the user and estimate the emotion. The providing unit adjusts the method of providing information based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides concise and prompt information. Furthermore, when the user is relaxed, the providing unit can also provide detailed information. Furthermore, when the user is in a hurry, the providing unit can provide information with high urgency preferentially. For example, the providing unit analyzes the user's emotion data, and provides concise and prompt information when the user is feeling stressed. When the user is relaxed, detailed information is provided. When the user is in a hurry, information with high urgency is provided preferentially. This makes it possible to adjust the method of providing information based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0117] In the AI ​​assistant system, when the providing unit provides information, the providing unit refers to the past provision history to select the optimal providing method. When the providing unit provides information, the providing unit refers to the past provision history to select the optimal providing method. The past provision history includes, for example, a provision history for the past year, a provision pattern for a specific period, and the like, but is not limited to such examples. For example, the providing unit selects the optimal information providing method based on the past provision history. In addition, the providing unit can also analyze the past provision history, find a specific pattern, and optimize the providing method. Furthermore, the providing unit can also adjust the parameters of the providing method by referring to the past provision history. For example, the providing unit analyzes the past provision history, finds a specific pattern, and optimizes the providing method. The optimal information providing method is selected based on the past provision history. The parameters of the providing method are adjusted by referring to the past provision history. This makes it possible to select the optimal information providing method based on the past provision history.

[0118] In the AI ​​assistant system, when the providing unit provides information, the providing content is adjusted based on a specific condition. The specific condition includes, for example, an emergency, an event, and the like, but is not limited to such examples. For example, when an emergency occurs, the providing unit provides information with a high degree of urgency with priority. In addition, when an event is held, the providing unit can also provide information within the range of its influence with priority. Furthermore, when a specific condition is met, the providing unit can dynamically adjust the provided content. For example, when an emergency occurs, the providing unit provides information with a high degree of urgency with priority. When an event is held, information within the range of its influence with priority. When a specific condition is met, the provided content is dynamically adjusted. This makes it possible to adjust the information provided content based on a specific condition.

[0119] In the AI ​​assistant system, the providing unit estimates the user's emotion and determines the priority of information provision based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the providing unit estimates the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. Furthermore, the providing unit can estimate the user's emotion using text analysis technology. For example, the providing unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The providing unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The providing unit uses text analysis technology to analyze text data input by the user and estimate the emotion. The providing unit determines the priority of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides only important information with priority. Furthermore, when the user is relaxed, the providing unit can also provide detailed information with priority. Furthermore, when the user is in a hurry, the providing unit can also provide information with high urgency with priority. For example, the providing unit analyzes the emotion data of the user, and when the user is feeling stressed, it provides only important information preferentially. When the user is relaxed, it provides detailed information preferentially. When the user is in a hurry, it provides information with high urgency preferentially. This makes it possible to determine the priority of information provision based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0120] In the AI ​​assistant system, when the providing unit provides information, the providing unit preferentially provides highly relevant information in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. For example, the providing unit acquires the geographical location information of the user using a GPS device. The providing unit can also acquire address information input by the user. Furthermore, the providing unit can identify the location of the user using landmark information. For example, when the user is in a specific area, the providing unit preferentially provides information related to the area. When the user is traveling, the providing unit preferentially provides information about the travel destination. When the user is participating in an event, the providing unit preferentially provides information about the area around the event venue. This makes it possible to preferentially provide highly relevant information based on the geographical location information of the user.

[0121] In the AI ​​assistant system, when the providing unit provides information, the providing unit integrates data from other information sources and provides the information. The data from other information sources includes, for example, news, social media, sensor data, user-provided data, and the like, but is not limited to such examples. For example, the providing unit integrates news information and reflects it in the information provided. The providing unit can also integrate social media data and reflect it in the information provided. Furthermore, the providing unit can integrate data from other information sources and provide comprehensive information. For example, the providing unit integrates news information and reflects it in the information provided. The providing unit integrates social media data and reflects it in the information provided. The data from other information sources is integrated and provides comprehensive information. As a result, comprehensive information can be provided by integrating and providing data from other information sources.

[0122] In the AI ​​assistant system, the warning unit estimates the user's emotion and adjusts the warning method based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the warning unit estimates the user's emotion using facial expression recognition technology. The warning unit can also estimate the user's emotion using voice analysis technology. Furthermore, the warning unit can estimate the user's emotion using text analysis technology. For example, the warning unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The warning unit adjusts the warning method based on the estimated user's emotion. For example, if the user is feeling stressed, the warning unit issues a concise and quick warning. Also, if the user is relaxed, the warning unit can issue a detailed warning. Furthermore, if the user is in a hurry, the warning unit can prioritize a warning with a high urgency. For example, the warning unit analyzes the user's emotion data, and if the user is feeling stressed, a concise and quick warning is issued. If the user is relaxed, a detailed warning is issued. If the user is in a hurry, a warning with high urgency is given priority. This allows the warning method to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0123] In the AI ​​assistant system, when the warning unit issues a warning, the warning unit refers to past warning history and selects the optimal warning method. The past warning history includes, for example, warning history for the past year, warning patterns for a specific period, etc., but is not limited to such examples. The warning unit selects the optimal warning method based on, for example, the past warning history. The warning unit can also analyze the past warning history, find a specific pattern, and optimize the warning method. Furthermore, the warning unit can also refer to the past warning history and adjust the parameters of the warning method. For example, the warning unit analyzes the past warning history, finds a specific pattern, and optimizes the warning method. The optimal warning method is selected based on the past warning history. The parameters of the warning method are adjusted by referring to the past warning history. This makes it possible to select the optimal warning method based on the past warning history.

[0124] In the AI ​​assistant system, the warning unit estimates the user's emotion and determines the priority of the warning based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the warning unit estimates the user's emotion using facial expression recognition technology. The warning unit can also estimate the user's emotion using voice analysis technology. Furthermore, the warning unit can estimate the user's emotion using text analysis technology. For example, the warning unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze text data input by the user and estimate the emotion. The warning unit determines the priority of the warning based on the estimated user's emotion. For example, when the user is feeling stressed, the warning unit prioritizes only important warnings. Furthermore, when the user is relaxed, the warning unit can also prioritize detailed warnings. Furthermore, when the user is in a hurry, the warning unit can also prioritize warnings with high urgency. For example, the warning unit analyzes the user's emotion data, and when the user is feeling stressed, it prioritizes only important warnings. When the user is relaxed, it prioritizes detailed warnings. When the user is in a hurry, it prioritizes highly urgent warnings. This allows the priority of warnings to be determined based on the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0125] When the warning unit issues a warning, the AI ​​assistant system preferentially provides a highly relevant warning in consideration of the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The warning unit, for example, acquires the geographical location information of the user using a GPS device. The warning unit can also acquire address information input by the user. Furthermore, the warning unit can identify the user's location using landmark information. For example, when the user is in a specific area, the warning unit preferentially provides a warning related to the area. When the user is traveling, the warning unit preferentially provides a warning for the travel destination. When the user is participating in an event, the warning unit preferentially provides a warning around the event venue. This makes it possible to preferentially provide a highly relevant warning based on the geographical location information of the user.

[0126] In the AI ​​assistant system, the suggestion unit estimates the user's emotion and adjusts the method of suggestion based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the suggestion unit estimates the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit can estimate the user's emotion using text analysis technology. For example, the suggestion unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The suggestion unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The suggestion unit uses text analysis technology to analyze text data input by the user and estimate the emotion. The suggestion unit adjusts the method of suggestion based on the estimated user's emotion. For example, if the user is feeling stressed, the suggestion unit makes a concise and quick suggestion. Also, if the user is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions with high urgency. For example, the suggestion unit analyzes the user's emotion data, and when the user is feeling stressed, the suggestion unit makes a concise and quick suggestion. When the user is relaxed, the suggestion unit makes a detailed suggestion. When the user is in a hurry, the suggestion unit gives priority to suggestions with high urgency. This allows the suggestion method to be adjusted based on the user's emotion. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0127] In the AI ​​assistant system, when the suggestion unit makes a suggestion, the suggestion unit selects an optimal suggestion method by referring to the past proposal history. The past proposal history includes, for example, a proposal history for the past year, a proposal pattern for a specific period, and the like, but is not limited to such examples. The suggestion unit selects an optimal suggestion method based on, for example, the past proposal history. The suggestion unit can also analyze the past proposal history, find a specific pattern, and optimize the proposal method. Furthermore, the suggestion unit can also adjust parameters of the proposal method by referring to the past proposal history. For example, the suggestion unit analyzes the past proposal history, finds a specific pattern, and optimizes the proposal method. The optimal suggestion method is selected based on the past proposal history. The parameters of the proposal method are adjusted by referring to the past proposal history. This makes it possible to select an optimal suggestion method based on the past proposal history.

[0128] In the AI ​​assistant system, when the suggestion unit makes a proposal, the suggestion unit adjusts the proposal content based on a specific condition. The specific condition includes, for example, an emergency, an event being held, and the like, but is not limited to such examples. For example, when an emergency occurs, the suggestion unit prioritizes a proposal with a high urgency. In addition, when an event is held, the suggestion unit can also prioritize a proposal of the extent of the impact. Furthermore, when a specific condition is met, the suggestion unit can dynamically adjust the proposal content. For example, when an emergency occurs, the suggestion unit prioritizes a proposal with a high urgency. In the event of an event being held, the suggestion unit prioritizes a proposal of the extent of the impact. When a specific condition is met, the suggestion content is dynamically adjusted. This makes it possible to adjust the proposal content based on a specific condition.

[0129] In the AI ​​assistant system, the suggestion unit estimates the user's emotion and determines the priority of the suggestions based on the estimated user's emotion. The estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the suggestion unit estimates the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit can also estimate the user's emotion using text analysis technology. For example, the suggestion unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The suggestion unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The suggestion unit uses text analysis technology to analyze text data input by the user and estimate the emotion. The suggestion unit determines the priority of the suggestions based on the estimated user's emotion. For example, when the user is feeling stressed, the suggestion unit prioritizes only important suggestions. Furthermore, when the user is relaxed, the suggestion unit can also prioritize detailed suggestions. Furthermore, when the user is in a hurry, the suggestion unit can also prioritize suggestions with high urgency. For example, the suggestion unit analyzes the user's emotion data, and when the user is feeling stressed, prioritizes only important suggestions. When the user is relaxed, prioritizes detailed suggestions. When the user is in a hurry, prioritizes urgent suggestions. This allows the priority of suggestions to be determined based on the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0130] When the suggestion unit makes a suggestion, the AI ​​assistant system preferentially provides highly relevant suggestions taking into consideration the geographical location information of the user. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The suggestion unit acquires the geographical location information of the user using, for example, a GPS device. The suggestion unit can also acquire address information input by the user. Furthermore, the suggestion unit can identify the user's location using landmark information. For example, when the user is in a specific area, the suggestion unit preferentially provides suggestions related to the area. When the user is traveling, the suggestion unit preferentially provides suggestions for travel destinations. When the user is participating in an event, the suggestion unit preferentially provides suggestions around the event venue. This makes it possible to preferentially provide highly relevant suggestions based on the geographical location information of the user.

[0131] In the AI ​​assistant system, when the suggestion unit makes a suggestion, the suggestion unit integrates data from other information sources. The data from other information sources includes, for example, news, social media, sensor data, user-provided data, and the like, but is not limited to such examples. The suggestion unit, for example, integrates news information and reflects it in the proposal content. The suggestion unit can also integrate social media data and reflect it in the proposal content. Furthermore, the suggestion unit can also integrate data from other information sources and provide a comprehensive proposal. For example, the suggestion unit integrates news information and reflects it in the proposal content. The suggestion unit integrates social media data and reflects it in the proposal content. The suggestion unit integrates data from other information sources and provides a comprehensive proposal. In this way, a comprehensive proposal can be provided by integrating data from other information sources to make a proposal.

[0132] In the AI ​​assistant system, the adjustment unit estimates the user's emotion and adjusts the adjustment method of the travel route based on the estimated user's emotion. Estimation of emotion includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the adjustment unit estimates the user's emotion using facial expression recognition technology. The adjustment unit can also estimate the user's emotion using voice analysis technology. Furthermore, the adjustment unit can estimate the user's emotion using text analysis technology. For example, the adjustment unit uses a camera to analyze the user's facial expression in real time and estimate the emotion. The voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotion. The text analysis technology is used to analyze the text data input by the user and estimate the emotion. The adjustment unit adjusts the adjustment method of the travel route based on the estimated user's emotion. For example, if the user is feeling stressed, the adjustment unit preferentially suggests the shortest route. Also, if the user is relaxed, the adjustment unit can also suggest a route with a good view. Furthermore, if the user is in a hurry, the adjustment unit can also preferentially suggest a route that avoids traffic congestion. For example, the adjustment unit analyzes the emotion data of the user, and when the user is feeling stressed, it preferentially suggests the shortest route. When the user is relaxed, it suggests a route with a nice view. When the user is in a hurry, it preferentially suggests a route that avoids traffic congestion. This allows the adjustment method of the travel route to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0133] In the AI ​​assistant system, when the adjustment unit adjusts the movement route, the adjustment unit refers to the past movement history and selects the optimal adjustment method. The past movement history includes, for example, movement data for the past year, movement patterns for a specific period, etc., but is not limited to such examples. The adjustment unit, for example, proposes an optimal movement route based on the past movement history. The adjustment unit can also analyze the past movement history, find a specific pattern, and optimize the movement route. Furthermore, the adjustment unit can also adjust the parameters of the movement route by referring to the past movement history. For example, the adjustment unit analyzes the past movement history, finds a specific pattern, and optimizes the movement route. The optimal movement route is proposed based on the past movement history. The parameters of the movement route are adjusted by referring to the past movement history. This makes it possible to propose an optimal movement route based on the past movement history.

[0134] In the AI ​​assistant system, when the adjustment unit adjusts the movement route, the adjustment content is adjusted based on a specific condition. The specific condition includes, for example, an emergency, an event, and the like, but is not limited to such examples. For example, when an emergency occurs, the adjustment unit preferentially proposes a movement route with high urgency. In addition, when an event is held, the adjustment unit can also preferentially propose a movement route within the range of influence of the event. Furthermore, the adjustment unit can dynamically adjust the movement route when a specific condition is met. For example, when an emergency occurs, the adjustment unit preferentially proposes a movement route with high urgency. In the event of an event, the adjustment unit preferentially proposes a movement route within the range of influence of the event. When a specific condition is met, the movement route is dynamically adjusted. This makes it possible to adjust the movement route based on a specific condition.

[0135] In the AI ​​assistant system, the adjustment unit estimates the user's emotions and determines the priority of the travel route based on the estimated user's emotions. Estimation of emotions includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the adjustment unit estimates the user's emotions using facial expression recognition technology. The adjustment unit can also estimate the user's emotions using voice analysis technology. Furthermore, the adjustment unit can estimate the user's emotions using text analysis technology. For example, the adjustment unit uses a camera to analyze the user's facial expressions in real time and estimate the emotions. The adjustment unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. The adjustment unit uses text analysis technology to analyze text data input by the user and estimate the emotions. The adjustment unit determines the priority of the travel route based on the estimated user's emotions. For example, when the user is feeling stressed, the adjustment unit preferentially proposes only important travel routes. Furthermore, when the user is relaxed, the adjustment unit can also preferentially propose detailed travel routes. Furthermore, when the user is in a hurry, the adjustment unit can also preferentially propose travel routes with high urgency. For example, the adjustment unit analyzes the emotion data of the user, and when the user is feeling stressed, it preferentially proposes only important travel routes. When the user is relaxed, it preferentially proposes detailed travel routes. When the user is in a hurry, it preferentially proposes travel routes with high urgency. This makes it possible to determine the priority of travel routes based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0136] In the AI ​​assistant system, when the adjustment unit adjusts a travel route, the adjustment unit takes into consideration the geographical location information of the user and preferentially provides a highly relevant route. The geographical location information includes, but is not limited to, for example, GPS data, address information, and landmarks. The adjustment unit acquires the geographical location information of the user using, for example, a GPS device. The adjustment unit can also acquire address information input by the user. Furthermore, the adjustment unit can identify the user's location using landmark information. For example, when the user is in a specific area, the adjustment unit preferentially provides a travel route related to the area. When the user is traveling, the adjustment unit preferentially provides a travel route of the travel destination. When the user is participating in an event, the adjustment unit preferentially provides a travel route around the event venue. This makes it possible to preferentially provide a highly relevant travel route based on the geographical location information of the user.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] When acquiring information on the user's current location or destination, the acquisition unit can also select the optimal acquisition method by referring to the user's past movement history. For example, based on the past movement history, the acquisition unit can identify locations frequently visited by the user and adjust the timing of acquiring location information at those locations. The acquisition unit can also analyze the user's movement pattern and suggest the optimal acquisition method. Furthermore, the acquisition frequency of location information in a specific time period can be optimized to provide efficient information provision. This makes it possible to select the optimal method of acquiring location information based on the user's past movement history.

[0139] The warning unit can also estimate the user's emotion and adjust the warning method based on the estimated user's emotion. For example, if the user is stressed, the warning unit can issue a concise and quick warning. If the user is relaxed, the warning unit can also issue a detailed warning. Furthermore, if the user is in a hurry, the warning unit can also give priority to a warning with a high degree of urgency. In this way, the warning method can be adjusted based on the user's emotion.

[0140] The suggestion unit can also adjust the suggested content based on the user's current activity status and areas of interest. For example, when the user is exercising, the suggestion unit can suggest a route suitable for exercise. When the user is sightseeing, information about tourist spots can be provided preferentially. When the user is at work, information related to work can be provided preferentially. In this way, the suggested content can be adjusted based on the user's activity status and areas of interest.

[0141] The acquisition unit can also estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user's emotions. For example, when the user is feeling stressed, the acquisition unit can reduce the frequency of acquiring location information and reduce notifications. When the user is relaxed, the acquisition unit can increase the frequency of acquiring location information and provide more detailed information. Furthermore, when the user is in a hurry, the acquisition unit can acquire location information in real time and provide information quickly. This makes it possible to adjust the timing of acquiring location information according to the user's emotions.

[0142] When acquiring sensor information of other vehicles, the acquisition unit can also prioritize acquiring highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific area, sensor information of other vehicles related to that area is acquired preferentially. Also, when the user is traveling, traffic information of the travel destination can be acquired preferentially. Furthermore, when the user is participating in an event, traffic information around the event venue can be acquired preferentially. This makes it possible to prioritize acquiring highly relevant information based on the geographical location information of the user.

[0143] When acquiring drone sensor information, the acquisition unit can also select the optimal acquisition method by referring to past drone data. For example, based on past drone data, traffic congestion and accident occurrence patterns in a specific area are identified, and that information is acquired preferentially. Also, by referring to past weather data, it is possible to preferentially acquire information under specific weather conditions. Furthermore, it is also possible to select the optimal flight path by referring to past drone flight path information. This makes it possible to select the optimal acquisition method based on past drone data.

[0144] The adjustment unit can also estimate the user's emotions and adjust the adjustment method of the travel route based on the estimated user's emotions. For example, if the user is feeling stressed, the adjustment unit can preferentially suggest the shortest route. Also, if the user is relaxed, the adjustment unit can also suggest a route with a nice view. Furthermore, if the user is in a hurry, the adjustment unit can also preferentially suggest a route that avoids traffic congestion. In this way, the adjustment method of the travel route can be adjusted based on the user's emotions.

[0145] The acquisition unit can also analyze the user's social media activity and acquire related information. For example, the acquisition unit can preferentially acquire location information of a place where the user has checked in on social media. The acquisition unit can also preferentially acquire location information of an event shared by the user. Furthermore, the acquisition unit can analyze the content posted by the user and acquire related location information. This makes it possible to acquire related information based on the user's social media activity.

[0146] The determination unit can also estimate the user's emotions and adjust the display method of the determination result based on the estimated user's emotions. For example, if the user is nervous, the determination unit provides a simple and highly visible display method. If the user is relaxed, the determination unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the determination unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the determination result based on the user's emotions.

[0147] The providing unit can also integrate data from other information sources when providing information. For example, news information can be integrated and reflected in the provided information. Also, data from social media can be integrated and reflected in the provided information. Furthermore, data from other information sources can be integrated to provide comprehensive information. In this way, by integrating and providing data from other information sources, comprehensive information can be provided.

[0148] The process flow of the second embodiment will be briefly described below.

[0149] Step 1: The acquisition unit acquires information on the user's current location or destination. The information on the user's current location or destination includes, for example, GPS coordinates, an address, a landmark, etc. The acquisition unit acquires the user's current location using a GPS device, and identifies the destination based on the address information and landmark information input by the user. Step 2: The traffic information collection unit collects traffic information. Traffic information includes congestion information, accident information, and the operation status of public transportation. The traffic information collection unit collects traffic congestion information using traffic sensors, and collects traffic accident information based on data provided by the police and traffic management center. In addition, the operation status of public transportation is obtained from a system that provides bus and train operation information. Step 3: The weather information collection unit collects weather information. The weather information includes temperature, precipitation, wind speed, weather forecast, etc. The weather information collection unit collects current weather information using a weather sensor and obtains weather forecast information from a weather forecast system. In addition, the weather information collection unit collects a wide range of weather information using data from weather satellites. Step 4: The determination unit determines the traffic and weather information to be provided to the user in real time based on the information collected by the acquisition unit, the traffic information collection unit, and the weather information collection unit. The determination unit analyzes the collected information using AI and provides the user with the optimal travel route and appropriate weather information. Step 5: The providing unit provides the traffic information and weather information determined by the determining unit to the user in real time. The providing unit provides the information through a smartphone app, an in-vehicle display, and a voice assistant.

[0150] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0151] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0152] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the traffic information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the weather information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0157] 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.

[0158] 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 the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0160] 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 performed in a secure state.

[0161] Fig. 4 shows an example of 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.

[0162] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0169] For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the traffic information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the weather information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0173] 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.

[0174] 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 the voice according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0176] 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 performed in a secure state.

[0177] 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.

[0178] 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.

[0179] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0180] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0181] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0182] 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 voice indicating a user input for 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.

[0183] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0184] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0185] For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. For example, the traffic information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. For example, the weather information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 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.

[0188] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0189] 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.

[0190] 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 the voice according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0192] 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 performed in a secure state.

[0193] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0194] 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.

[0195] 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.

[0196] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0197] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0198] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0199] 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 a voice indicating a user input for 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.

[0200] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0201] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0202] For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the traffic information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the weather information collection unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0203] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0204] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0206] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0208] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0210] 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.

[0211] 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 Universal Serial Bus (USB) 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.

[0212] In addition, 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 upon request from the data processing device 12.

[0213] 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.

[0214] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0215] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0216] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0217] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0219] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0221] (Appendix 1) A system comprising: an acquisition unit that acquires information on a user's current location or destination; a traffic information collection unit that collects traffic information; a weather information collection unit that collects weather information; a determination unit that determines in real time traffic information and weather information to be provided to the user based on information collected by the acquisition unit, the traffic information collection unit, and the weather information collection unit; and a provision unit that provides the traffic information and weather information determined by the determination unit to the user in real time. (Appendix 2) Equipped with a warning section to warn of sudden climate change 2. The system of claim 1, (Appendix 3) The system described in claim 1, further comprising a proposal unit that proposes a travel plan to the user based on a determination result by the determination unit. (Appendix 4) The acquisition unit collects real-time traffic and weather data from social media, and the determination unit complements the traffic information and the weather information using the real-time data acquired by the acquisition unit. (Appendix 5) The acquisition unit is Acquire sensor information from other vehicles, The determination unit is The traffic information and the weather information are complemented by using the sensor information of the other vehicle acquired by the acquisition unit. 2. The system of claim 1, (Appendix 6) The acquisition unit is Acquire drone sensor information, The determination unit is The traffic information and the weather information are complemented by using the sensor information of the drone acquired by the acquisition unit. 2. The system of claim 1, (Appendix 7) an adjustment unit that adjusts a moving route based on the health condition of the user; 2. The system of claim 1, (Appendix 8) The acquisition unit is Estimate the user's emotions, Adjusting the timing of location information acquisition based on estimated user emotions 2. The system of claim 1, (Appendix 9) The acquisition unit is Analyze the user's past movement history, Select the best acquisition method 2. The system of claim 1, (Appendix 10) The system described in Appendix 1, characterized in that the acquisition unit performs filtering based on the user's current activity status or areas of interest when acquiring location information. (Appendix 11) The acquisition unit is Estimate the user's emotions, Determine the priority of location information to be acquired based on the estimated user's emotions 2. The system of claim 1, (Appendix 12) The acquisition unit is When obtaining location information, Prioritize the retrieval of relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 13) The acquisition unit is When obtaining location information, Analyze users' social media activities and obtain relevant information 2. The system of claim 1, (Appendix 14) The traffic information collection unit, Estimate the user's emotions, Adjusting the method of collecting traffic information based on estimated user emotions 2. The system of claim 1, (Appendix 15) The traffic information collection unit, When collecting traffic information, Optimize collection algorithms by referencing historical traffic data 2. The system of claim 1, (Appendix 16) The traffic information collection unit, When collecting traffic information, Adjust collection range based on specific traffic events 2. The system of claim 1, (Appendix 17) The traffic information collection unit, Estimate the user's emotions, Prioritize traffic information collection based on estimated user sentiment 2. The system of claim 1, (Appendix 18) The traffic information collection unit, When collecting traffic information, Prioritize collection of relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 19) The traffic information collection unit, When collecting traffic information, Integrate and collect information from other transportation agencies 2. The system of claim 1, (Appendix 20) The weather information collection unit is Estimate the user's emotions, Adjusting weather information collection methods based on estimated user emotions 2. The system of claim 1, (Appendix 21) The weather information collection unit is When collecting weather information, Referencing historical weather data to optimize collection algorithms 2. The system of claim 1, (Appendix 22) The weather information collection unit is When collecting weather information, Adjust collection coverage based on specific weather events 2. The system of claim 1, (Appendix 23) The weather information collection unit is Estimate the user's emotions, Prioritize weather information collection based on estimated user sentiment 2. The system of claim 1, (Appendix 24) The weather information collection unit is When collecting weather information, Prioritize collection of relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 25) The weather information collection unit is When collecting weather information, Integrate and collect information from other meteorological agencies 2. The system of claim 1, (Appendix 26) The determination unit is Estimate the user's emotions, Adjust the judgment algorithm based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The determination unit is At the time of judgment, Optimize the algorithm by referring to past judgment results 2. The system of claim 1, (Appendix 28) The determination unit is At the time of judgment, Adjusting the criteria based on specific conditions 2. The system of claim 1, (Appendix 29) The determination unit is Estimate the user's emotions, Adjust the way judgment results are displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The determination unit is At the time of judgment, Customize your results by taking into account the user's geographic location 2. The system of claim 1, (Appendix 31) The determination unit is At the time of judgment, Integrate data from other sources to make decisions 2. The system of claim 1, (Appendix 32) The providing unit is Estimate the user's emotions, Adjusting the way information is presented based on estimated user emotions 2. The system of claim 1, (Appendix 33) The providing unit is When providing information, Refer to past delivery history to select the optimal delivery method 2. The system of claim 1, (Appendix 34) The providing unit is When providing information, Tailor your offers based on specific conditions 2. The system of claim 1, (Appendix 35) The providing unit is Estimate the user's emotions, Decide the priority of information provision based on estimated user sentiment 2. The system of claim 1, (Appendix 36) The providing unit is When providing information, Prioritize relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 37) The providing unit is When providing information, Providing data integration from other sources 2. The system of claim 1, (Appendix 38) The warning unit is Estimate the user's emotions, Adjusting warning methods based on estimated user sentiment 2. The system of claim 1, (Appendix 39) The warning unit is When warning, Refer to past warning history to select the most appropriate warning method 2. The system of claim 1, (Appendix 40) The warning unit is Estimate the user's emotions, Prioritize alerts based on estimated user sentiment 2. The system of claim 1, (Appendix 41) The warning unit is When warning, Prioritize relevant alerts based on the user's geographic location 2. The system of claim 1, (Appendix 42) The suggestion unit, Estimate the user's emotions, Adjusting the suggestion method based on estimated user sentiment 2. The system of claim 1, (Appendix 43) The suggestion unit, When proposing, Select the optimal proposal method by referring to past proposal history 2. The system of claim 1, (Appendix 44) The suggestion unit, When proposing, Tailor your suggestions based on specific conditions 2. The system of claim 1, (Appendix 45) The suggestion unit, Estimate the user's emotions, Prioritize suggestions based on estimated user sentiment 2. The system of claim 1, (Appendix 46) The suggestion unit, When proposing, Prioritize relevant suggestions based on the user's geographic location 2. The system of claim 1, (Appendix 47) The suggestion unit, When proposing, Integrate data from other sources to make suggestions 2. The system of claim 1, (Appendix 48) The adjustment unit is Estimate the user's emotions, Adjust the travel route based on the estimated user's emotions 2. The system of claim 1, (Appendix 49) The adjustment unit is When adjusting the travel route, Refer to past movement history to select the optimal adjustment method 2. The system of claim 1, (Appendix 50) The adjustment unit is When adjusting the travel route, Adjust the adjustments based on specific conditions 2. The system of claim 1, (Appendix 51) The adjustment unit is Estimate the user's emotions, Prioritizing travel routes based on estimated user sentiment 2. The system of claim 1, (Appendix 52) The adjustment unit is When adjusting the travel route, Prioritize relevant routes based on the user's geographic location 2. The system of claim 1, [Explanation of symbols]

[0222] 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 comprising: an acquisition unit that acquires information on a user's current location or destination; a traffic information collection unit that collects traffic information; a weather information collection unit that collects weather information; a determination unit that determines in real time traffic information and weather information to be provided to the user based on information collected by the acquisition unit, the traffic information collection unit, and the weather information collection unit; and a provision unit that provides the traffic information and weather information determined by the determination unit to the user in real time.

2. Equipped with a warning section to warn of sudden climate change 2. The system of claim 1 .

3. The system according to claim 1 , further comprising a suggestion unit that suggests a travel plan to the user based on a result of the determination by the determination unit.

4. The acquisition unit collects real-time traffic and weather data from social media, and the determination unit complements the traffic information and the weather information using the real-time data acquired by the acquisition unit.

5. The acquisition unit is Acquire sensor information from other vehicles, The determination unit is The traffic information and the weather information are complemented by using the sensor information of the other vehicle acquired by the acquisition unit.

2. The system of claim 1 .

6. The acquisition unit is Acquire drone sensor information, The determination unit is The traffic information and the weather information are complemented by using the sensor information of the drone acquired by the acquisition unit.

2. The system of claim 1 .

7. an adjustment unit that adjusts a moving route based on the health condition of the user; 2. The system of claim 1 .

8. The acquisition unit is Estimate the user's emotions, Adjusting the timing of location information acquisition based on estimated user emotions 2. The system of claim 1 .

9. The acquisition unit is Analyze the user's past movement history, Select the best acquisition method 2. The system of claim 1 .

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