system

Abstract

The system according to the embodiment aims to automatically suggest the optimal means of transportation and travel time based on the user's schedule and current location. [Solution] A system according to an embodiment includes a schedule acquisition unit, a current location acquisition unit, a transportation means selection unit, a travel time calculation unit, a route proposal unit, and a notification unit. The schedule acquisition unit acquires a schedule. The current location acquisition unit acquires a current location. The transportation means selection unit selects an appropriate transportation means based on information acquired by the schedule acquisition unit and the current location acquisition unit. The travel time calculation unit calculates travel time based on the transportation means selected by the transportation means selection unit. The route proposal unit proposes an appropriate route based on the travel time calculated by the travel time calculation unit. The notification unit notifies the user of the route proposed by the route proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires users to manually calculate the optimal means of transportation and travel time based on their schedule, which makes it difficult to travel efficiently.

[0005] The system according to the embodiment aims to automatically suggest the optimal means of transportation and travel time based on the user's schedule and current location. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule acquisition unit, a current location acquisition unit, a transportation means selection unit, a travel time calculation unit, a route proposal unit, and a notification unit. The schedule acquisition unit acquires a schedule. The current location acquisition unit acquires a current location. The transportation means selection unit selects an appropriate transportation means based on information acquired by the schedule acquisition unit and the current location acquisition unit. The travel time calculation unit calculates the travel time based on the transportation means selected by the transportation means selection unit. The route proposal unit proposes an appropriate route based on the travel time calculated by the travel time calculation unit. The notification unit notifies the user of the route proposed by the route proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically suggest the optimal means of transportation and travel time based on the user's schedule and current location. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A mobility assistance system according to an embodiment of the present invention is a system in which an AI automatically determines travel time and travel method when a user inputs their schedule. The mobility assistance system grasps the user's schedule and current location and suggests the optimal means of transportation and travel time. For example, if a user inputs "meeting at 10 o'clock," the AI ​​calculates the optimal means of transportation (train, bus, taxi, etc.) from the current location to the meeting location and the travel time, and notifies the user. The AI ​​also takes traffic conditions and weather information into consideration and suggests the optimal route in real time. This allows the user to travel efficiently and makes schedule management easier. For example, in a mobility assistance system, when a user inputs their schedule, a schedule acquisition unit acquires the user's schedule. For example, if the user inputs "meeting at 10 o'clock," the schedule acquisition unit acquires that information. Next, a current location acquisition unit acquires the user's current location. For example, the user's current location is identified using GPS. Next, a transportation means selection unit selects the optimal transportation means. For example, it selects public transportation, taxi, or other transportation means. Furthermore, a travel time calculation unit calculates the travel time. For example, it calculates the travel time from the current location to the meeting location. Furthermore, a traffic information acquisition unit acquires traffic conditions in real time. For example, information such as traffic congestion and operation status is acquired. Next, the weather information acquisition unit acquires weather information. For example, weather information such as rain or snow is acquired. Based on this information, the route suggestion unit proposes the optimal route. For example, it proposes a route that avoids traffic congestion or a route that suits the weather. Finally, the notification unit notifies the user. For example, it notifies the user using the notification function of a smartphone. This allows the mobility assistance system to efficiently manage the user's schedule and travel. This allows the mobility assistance system to efficiently manage the user's schedule and travel. For example, if a user enters "meeting at 10 o'clock," the AI ​​calculates the optimal means of travel and travel time from the current location to the meeting location and notifies the user. This allows the user to travel efficiently and makes schedule management easier.

[0029] A travel assistance system according to an embodiment includes a schedule acquisition unit, a current location acquisition unit, a transportation means selection unit, a travel time calculation unit, a route suggestion unit, and a notification unit. The schedule acquisition unit acquires a schedule input by a user. For example, if a user inputs "meeting at 10 o'clock," the schedule acquisition unit acquires the information. The current location acquisition unit acquires the user's current location using a GPS. For example, the current location acquisition unit identifies the user's current location using a GPS device. The transportation means selection unit selects the optimal transportation means. For example, the transportation means selection unit selects a transportation means such as public transportation or a taxi. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without AI. For example, the transportation means selection unit may select a transportation means using an AI model that receives the user's schedule and current location as input and outputs the optimal transportation means. The travel time calculation unit calculates the travel time based on the transportation means selected by the transportation means selection unit. For example, the travel time calculation unit calculates the travel time from the current location to the destination. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit may calculate the travel time using an AI model that receives the travel mode selected by the travel mode selection unit, the current location, and the destination as input and outputs the travel time. The route proposal unit proposes an optimal route based on the travel time calculated by the travel time calculation unit. For example, the route proposal unit proposes an optimal route based on information acquired by the traffic information acquisition unit and the weather information acquisition unit. Some or all of the above-described processing in the route proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the route proposal unit may propose a route using an AI model that receives information acquired by the traffic information acquisition unit and the weather information acquisition unit as input and outputs the optimal route. The notification unit notifies the user of the route proposed by the route proposal unit. For example, the notification unit notifies the user using a notification function of a smartphone. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.For example, the notification unit can notify the user using an AI model that receives the route proposed by the route proposal unit and outputs the notification content. This allows the mobility assistance system according to the embodiment to efficiently manage the user's schedule and travel.

[0030] The schedule acquisition unit can acquire a schedule input by a user. For example, when a user inputs "meeting at 10 o'clock," the schedule acquisition unit acquires the information. The schedule acquisition unit can also automatically analyze a schedule input by a user and reflect it in the schedule. For example, the schedule acquisition unit analyzes a schedule input by a user and adds it to the schedule. The schedule acquisition unit can also convert a schedule input by voice by a user into text and reflect it in the schedule. For example, the schedule acquisition unit can use voice recognition technology to convert a schedule input by a user into text and add it to the schedule. This allows the schedule input by the user to be accurately acquired. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that analyzes a schedule input by a user and reflects it in the schedule.

[0031] The current location acquisition unit can acquire the user's current location using a GPS. The current location acquisition unit identifies the user's current location using, for example, a GPS device. The current location acquisition unit can also update the user's current location in real time. For example, the current location acquisition unit acquires location information from the GPS device at regular intervals and updates the user's current location. The current location acquisition unit can also display the user's current location on a map. For example, the current location acquisition unit combines the acquired location information with map data to display the user's current location on a map. This allows the user's current location to be accurately acquired. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that analyzes location information acquired from a GPS device and identifies the user's current location.

[0032] The transportation means selection unit can select at least one transportation means from public transportation and a taxi. The transportation means selection unit selects transportation means such as public transportation and a taxi. The transportation means selection unit can also select a transportation means taking into consideration the user's transportation means preference. For example, if the user prefers public transportation, the transportation means selection unit can preferentially select trains or buses. If the user prefers taxis, the transportation means selection unit can preferentially select taxis. The transportation means selection unit can also adjust the user's transportation means selection criteria. For example, if the user is in a hurry, the transportation means selection unit selects the fastest transportation means. This allows the optimal transportation means to be selected. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's schedule and current location and outputs the optimal transportation means.

[0033] The travel time calculation unit can calculate the travel time from the current location to the destination. For example, the travel time calculation unit calculates the travel time from the current location to the destination. The travel time calculation unit can also calculate the travel time taking traffic conditions into account. For example, the travel time calculation unit calculates the travel time based on information such as traffic congestion and operation status. The travel time calculation unit can also calculate the travel time taking the user's transportation preference into account. For example, if the user prefers public transportation, the travel time calculation unit calculates the travel time by train or bus. If the user prefers taxis, the travel time calculation unit can also calculate the travel time by taxi. This allows for accurate calculation of travel times. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that inputs the transportation mode selected by the transportation mode selection unit, the current location, and the destination, and outputs the travel time.

[0034] The route proposal unit can propose an appropriate route based on information acquired by the traffic information acquisition unit and the weather information acquisition unit. The route proposal unit proposes an optimal route based on, for example, information acquired by the traffic information acquisition unit and the weather information acquisition unit. The route proposal unit can also propose a route that avoids traffic congestion or a route that suits the weather. For example, the route proposal unit proposes a route that avoids traffic congestion. The route proposal unit can also propose a route taking into account weather information such as rain or snow. The route proposal unit can also propose a route taking into account the user's preferred means of transportation. For example, if the user prefers public transportation, the route proposal unit can propose a route using a train or bus. If the user prefers taxis, the route proposal unit can also propose a route using a taxi. This makes it possible to propose an optimal route that takes into account traffic information and weather information. Some or all of the above-described processing in the route proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the route proposal unit can propose a route using an AI model that inputs information acquired by the traffic information acquisition unit and the weather information acquisition unit and outputs an optimal route.

[0035] The notification unit can notify the user using a notification function of the smartphone. The notification unit notifies the user, for example, using the notification function of the smartphone. The notification unit can also adjust the notification method based on the user's notification settings. For example, if the user has set vibration notification, the notification unit can notify by vibration. If the user has set audio notification, the notification unit can also notify by audio. The notification unit can also adjust the notification method taking into account the user's current situation. For example, if the user is in a meeting, the notification unit can select a quiet notification method. If the user is traveling, the notification unit can also select a visual notification method. This allows the user to be notified quickly. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the user using an AI model that inputs the route proposed by the route suggestion unit and outputs notification content.

[0036] The device includes a traffic information acquisition unit, which can acquire traffic conditions in real time. The traffic information acquisition unit acquires traffic conditions in real time using, for example, a traffic sensor. The traffic information acquisition unit can also acquire traffic conditions using a traffic information service. For example, the traffic information acquisition unit acquires real-time traffic information from the traffic information service. The traffic information acquisition unit can also select an optimal acquisition method by referring to the user's past travel history. For example, the traffic information acquisition unit prioritizes acquiring traffic information for routes that the user has frequently used in the past. The traffic information acquisition unit can also adjust the acquisition method taking into account the user's current means of travel. For example, if the user is using public transportation, the traffic information acquisition unit prioritizes acquiring operation status. This allows traffic conditions to be grasped in real time. Some or all of the above-described processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that analyzes data acquired from a traffic sensor and identifies traffic conditions.

[0037] The weather information acquisition unit is provided with a weather information acquisition unit, and the weather information acquisition unit can acquire weather information. The weather information acquisition unit acquires weather information, for example, using a weather data provision service. The weather information acquisition unit can also acquire weather information using a weather sensor. For example, the weather information acquisition unit acquires real-time weather information from a weather sensor. The weather information acquisition unit can also select an optimal acquisition method by referring to the user's past travel history. For example, the weather information acquisition unit prioritizes acquiring weather information for routes that the user has frequently used in the past. The weather information acquisition unit can also adjust the acquisition method taking into account the user's current means of travel. For example, when the user is using public transportation, the weather information acquisition unit prioritizes acquiring weather information that affects the operation status. This allows weather information to be acquired. Some or all of the above-described processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that analyzes data acquired from a weather data provision service and identifies weather conditions.

[0038] The schedule acquisition unit can analyze the user's past schedule history and select the optimal acquisition method. For example, the schedule acquisition unit can automatically display schedules that the user frequently input in the past as candidates. The schedule acquisition unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The schedule acquisition unit can also predict and suggest plans to be used in a specific time period from the user's past schedule history. This makes it possible to select the optimal acquisition method based on the past schedule history. Some or all of the above-mentioned processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can select the acquisition method using an AI model that inputs the user's past schedule history and outputs the optimal acquisition method.

[0039] When acquiring a schedule, the schedule acquisition unit can perform filtering based on the user's current project or areas of interest. For example, the schedule acquisition unit prioritizes acquiring only plans related to the user's ongoing project. The schedule acquisition unit can also prioritize acquiring events and meetings related to the user's areas of interest. The schedule acquisition unit can also analyze trends in events the user has previously attended and acquire related plans. This allows schedules related to the user's areas of interest to be acquired preferentially. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that inputs the user's current project or areas of interest and outputs a filtered schedule.

[0040] When acquiring a schedule, the schedule acquisition unit can select the optimal acquisition means depending on the user's input method. For example, when the user inputs a schedule by voice, the schedule acquisition unit acquires the schedule using voice recognition technology. Furthermore, when the user inputs a schedule by text, the schedule acquisition unit can also acquire the schedule using text analysis technology. Furthermore, when the user inputs a schedule by image, the schedule acquisition unit can also acquire the schedule using image recognition technology. In this way, a schedule can be acquired depending on the user's input method. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that receives the user's input method as input and outputs the optimal acquisition means.

[0041] When acquiring a schedule, the schedule acquisition unit can prioritize acquiring highly relevant schedules by taking into account the user's geographical location information. For example, the schedule acquisition unit prioritizes acquiring plans for locations close to the user's current location. Furthermore, when the user is in a specific area, the schedule acquisition unit can also prioritize acquiring plans related to that area. Furthermore, when the user is traveling, the schedule acquisition unit can also prioritize acquiring plans related to the user's destination. This makes it possible to acquire a highly relevant schedule based on the user's geographical location information. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that inputs the user's geographical location information and outputs a highly relevant schedule.

[0042] The schedule acquisition unit can analyze the user's social media activities when acquiring a schedule and acquire related schedules. For example, the schedule acquisition unit reflects events that the user plans to attend on social media in the schedule. The schedule acquisition unit can also analyze the content of the user's social media posts and acquire related schedules. The schedule acquisition unit can also acquire related schedules by referring to the activities of the user's friends on social media. In this way, related schedules can be acquired based on the user's social media activities. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that inputs the user's social media activities and outputs related schedules.

[0043] When acquiring a schedule, the schedule acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the schedule acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The schedule acquisition unit can also improve the acquisition method based on the user's past feedback. The schedule acquisition unit can also avoid an acquisition method that the user has previously been dissatisfied with. This allows the acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0044] When acquiring the current location, the current location acquisition unit can select the optimal acquisition method by referring to the user's past movement history. The current location acquisition unit optimizes the current location acquisition method based on, for example, places the user has frequently visited in the past. The current location acquisition unit can also select the most efficient current location acquisition method from the user's past movement history. The current location acquisition unit can also analyze the user's past movement patterns and suggest the optimal current location acquisition method. This makes it possible to select the optimal current location acquisition method based on the user's past movement history. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the user's past movement history and outputs the optimal acquisition method.

[0045] When acquiring a current location, the current location acquisition unit can adjust the acquisition method taking into account the battery status of the user's device. For example, when the battery of the user's device is low, the current location acquisition unit reduces the frequency of acquiring the current location to reduce battery consumption. Furthermore, when the battery of the user's device is sufficient, the current location acquisition unit can increase the frequency of acquiring the current location to provide more accurate location information. Furthermore, the current location acquisition unit can automatically adjust the current location acquisition method according to the battery status of the user's device. This allows the current location acquisition method to be adjusted according to the battery status of the user's device. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the battery status of the user's device and outputs an acquisition method.

[0046] When acquiring the current location, the current location acquisition unit can adjust the acquisition accuracy according to the user's means of transportation. For example, when the user is traveling on foot, the current location acquisition unit acquires the current location with high accuracy. Furthermore, when the user is traveling by bicycle, the current location acquisition unit can acquire the current location with moderate accuracy. Furthermore, when the user is traveling by car, the current location acquisition unit can quickly acquire the current location with low accuracy. This allows the acquisition accuracy of the current location to be adjusted according to the user's means of transportation. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the user's means of transportation and outputs the acquisition accuracy.

[0047] When acquiring the current location, the current location acquisition unit can adjust the acquisition method depending on the type of device of the user. For example, if the user is using a smartphone, the current location acquisition unit acquires the current location with high accuracy using GPS. Furthermore, if the user is using a tablet, the current location acquisition unit can also acquire the current location using Wi-Fi or Bluetooth (registered trademark). Furthermore, if the user is using a wearable device, the current location acquisition unit can also acquire the current location using a low-power sensor. This allows the current location acquisition method to be adjusted depending on the type of device of the user. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the type of device of the user and outputs an acquisition method.

[0048] When acquiring the current location, the current location acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the current location acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The current location acquisition unit can also improve the acquisition method based on the user's past feedback. The current location acquisition unit can also avoid acquisition methods that the user has previously dissatisfied with. This allows the current location acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0049] When selecting a transportation mode, the transportation mode selection unit can select the optimal transportation mode by referring to the user's past travel history. For example, the transportation mode selection unit prioritizes the selection of transportation modes that the user has frequently used in the past. The transportation mode selection unit can also select the most efficient transportation mode from the user's past travel history. The transportation mode selection unit can also analyze the user's past travel patterns and suggest the optimal transportation mode. This makes it possible to select the optimal transportation mode based on the user's past travel history. Some or all of the above-described processing in the transportation mode selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation mode selection unit can select a transportation mode using an AI model that inputs the user's past travel history and outputs the optimal transportation mode.

[0050] When selecting a transportation means, the transportation means selection unit can adjust the selection criteria taking into account the user's current physical condition and mood. For example, if the user is tired, the transportation means selection unit will preferentially select a comfortable transportation means (such as a taxi). Furthermore, if the user is seeking healthy exercise, the transportation means selection unit can also select a transportation means such as walking or cycling. Furthermore, if the user is feeling unwell, the transportation means selection unit can also select a means that will allow the user to travel in the shortest time. This makes it possible to adjust the selection criteria for a transportation means according to the user's physical condition and mood. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's physical condition and mood and outputs selection criteria.

[0051] The transportation means selection unit can select a transportation means taking into consideration the user's transportation means preference. For example, if the user prefers public transportation, the transportation means selection unit can preferentially select trains or buses. Furthermore, if the user prefers taxis, the transportation means selection unit can also preferentially select taxis. Furthermore, if the user prefers bicycles, the transportation means selection unit can also select routes where bicycles can be used. This makes it possible to select the optimal transportation means according to the user's transportation means preference. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's transportation means preference and outputs the optimal transportation means.

[0052] When selecting a transportation means, the transportation means selection unit can select the optimal transportation means by taking into account the user's geographical location information. For example, if the user is in an urban area, the transportation means selection unit can preferentially select public transportation. Furthermore, if the user is in a suburban area, the transportation means selection unit can also preferentially select a taxi or private car. Furthermore, if the user is in a specific area, the transportation means selection unit can also select transportation means available in that area. This makes it possible to select the optimal transportation means based on the user's geographical location information. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's geographical location information and outputs the optimal transportation means.

[0053] When selecting a transportation means, the transportation means selection unit can analyze the user's social media activity and reflect the results in the selection of the transportation means. For example, the transportation means selection unit selects a transportation means based on the location where the user checked in on social media. The transportation means selection unit can also analyze the content of the user's social media posts and select a related transportation means. The transportation means selection unit can also select a transportation means based on the activity of the user's friends on social media. This makes it possible to select the optimal transportation means based on the user's social media activity. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's social media activity and outputs the optimal transportation means.

[0054] The transportation means selection unit can customize the selection criteria by reflecting the user's past feedback when selecting a transportation means. For example, the transportation means selection unit preferentially suggests transportation means that the user has previously preferred. The transportation means selection unit can also improve the selection criteria based on the user's past feedback. The transportation means selection unit can also avoid transportation means that the user has previously been dissatisfied with. This allows the transportation means selection criteria to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that customizes the selection criteria using the user's past feedback as input.

[0055] When calculating travel time, the travel time calculation unit can improve calculation accuracy by referring to the user's past travel history. The travel time calculation unit calculates travel time based on, for example, routes used by the user in the past. The travel time calculation unit can also calculate a route that avoids congestion based on the user's past travel history. The travel time calculation unit can also analyze the user's past travel history and calculate the most efficient travel time. This improves the calculation accuracy of travel time based on the user's past travel history. Some or all of the above-mentioned processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate travel time using an AI model that inputs the user's past travel history and improves calculation accuracy.

[0056] The travel time calculation unit can adjust the calculation method when calculating travel time, taking into account the user's current physical condition and mood. For example, if the user is tired, the travel time calculation unit calculates a travel time with some leeway. Furthermore, if the user is seeking healthy exercise, the travel time calculation unit can also calculate a normal travel time. Furthermore, if the user is in poor health, the travel time calculation unit can calculate a time that allows travel in the shortest time. This allows the travel time calculation method to be adjusted according to the user's physical condition and mood. Some or all of the above-mentioned processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate travel time using an AI model that inputs the user's physical condition and mood and outputs a calculation method.

[0057] The travel time calculation unit can take into account the user's transportation preference when calculating the travel time. For example, if the user prefers public transportation, the travel time calculation unit can calculate the train or bus travel time. Furthermore, if the user prefers taxis, the travel time calculation unit can also calculate the taxi travel time. Furthermore, if the user prefers bicycles, the travel time calculation unit can also calculate the bicycle travel time. This makes it possible to calculate travel times according to the user's transportation preference. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate travel times using an AI model that inputs the user's transportation preference and outputs travel times.

[0058] The travel time calculation unit can improve the calculation accuracy when calculating the travel time by taking into account the user's geographical location information. For example, if the user is in an urban area, the travel time calculation unit can calculate the travel time by taking into account traffic congestion. Furthermore, if the user is in a suburban area, the travel time calculation unit can also calculate the travel time by taking into account a route with less traffic. Furthermore, if the user is in a specific area, the travel time calculation unit can also calculate the travel time by taking into account the traffic conditions in that area. This improves the calculation accuracy of the travel time based on the user's geographical location information. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that inputs the user's geographical location information and improves calculation accuracy.

[0059] The travel time calculation unit can analyze the user's social media activity and reflect it in the calculation when calculating the travel time. For example, the travel time calculation unit calculates the travel time based on the location where the user checked in on social media. The travel time calculation unit can also analyze the content of the user's social media posts and calculate the related travel time. The travel time calculation unit can also calculate the travel time by referring to the activities of the user's friends on social media. In this way, the travel time can be calculated based on the user's social media activity. Some or all of the above-mentioned processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that inputs the user's social media activity and outputs the travel time.

[0060] The travel time calculation unit can customize the calculation method by reflecting the user's past feedback when calculating the travel time. For example, the travel time calculation unit preferentially suggests calculation methods that the user has previously preferred. The travel time calculation unit can also improve the calculation method based on the user's past feedback. The travel time calculation unit can also avoid calculation methods that the user has previously dissatisfied with. This allows the travel time calculation method to be customized based on the user's past feedback. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that uses the user's past feedback as input and customizes the calculation method.

[0061] When proposing a route, the route suggestion unit can suggest an optimal route by referring to the user's past movement history. The route suggestion unit can, for example, suggest an optimal route based on routes the user has used in the past. The route suggestion unit can also suggest a route that avoids congestion based on the user's past movement history. The route suggestion unit can also analyze the user's past movement history and suggest the most efficient route. This makes it possible to suggest an optimal route based on the user's past movement history. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can suggest a route using an AI model that inputs the user's past movement history and outputs an optimal route.

[0062] When proposing a route, the route suggestion unit can adjust the suggestion method taking into account the user's current physical condition and mood. For example, if the user is tired, the route suggestion unit can preferentially suggest a comfortable route. Furthermore, if the user is seeking healthy exercise, the route suggestion unit can also suggest a slightly longer route. Furthermore, if the user is not feeling well, the route suggestion unit can also suggest the shortest route. This allows the route suggestion method to be adjusted according to the user's physical condition and mood. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can suggest a route using an AI model that inputs the user's physical condition and mood and outputs a suggestion method.

[0063] When proposing a route, the route suggestion unit can take into consideration the user's transportation preference. For example, if the user prefers public transportation, the route suggestion unit can suggest a route using a train or bus. Furthermore, if the user prefers taxis, the route suggestion unit can also suggest a route using a taxi. Furthermore, if the user prefers bicycles, the route suggestion unit can also suggest a route using bicycles. This makes it possible to propose an optimal route according to the user's transportation preference. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can propose a route using an AI model that inputs the user's transportation preference and outputs an optimal route.

[0064] When proposing a route, the route suggestion unit can propose an optimal route by taking into account the user's geographical location information. For example, if the user is in an urban area, the route suggestion unit can propose a route that avoids traffic congestion. Furthermore, if the user is in a suburban area, the route suggestion unit can also propose a route with less traffic. Furthermore, if the user is in a specific area, the route suggestion unit can also propose a route by taking into account the traffic conditions in that area. This makes it possible to propose an optimal route based on the user's geographical location information. Some or all of the above-described processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can propose a route using an AI model that inputs the user's geographical location information and outputs an optimal route.

[0065] When proposing a route, the route suggestion unit can analyze the user's social media activities and reflect them in the route suggestion. For example, the route suggestion unit can suggest a route based on locations where the user has checked in on social media. The route suggestion unit can also analyze the content of the user's social media posts and suggest related routes. The route suggestion unit can also suggest related routes based on the activities of the user's friends on social media. This makes it possible to suggest an optimal route based on the user's social media activities. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can suggest a route using an AI model that inputs the user's social media activities and outputs an optimal route.

[0066] When proposing a route, the route suggestion unit can customize the suggestion method by reflecting the user's past feedback. For example, the route suggestion unit preferentially suggests route suggestion methods that the user has previously preferred. The route suggestion unit can also improve the suggestion method based on the user's past feedback. The route suggestion unit can also avoid route suggestion methods that the user has previously been dissatisfied with. This allows the route suggestion method to be customized based on the user's past feedback. Some or all of the above-described processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can propose a route using an AI model that uses the user's past feedback as input and customizes the suggestion method.

[0067] The notification unit can select the optimal notification method by referring to the user's past notification history when providing a notification. For example, the notification unit preferentially suggests notification methods (audio, vibration, etc.) that the user has previously preferred. The notification unit can also select the most effective notification method from the user's past notification history. The notification unit can also analyze the user's past notification patterns and suggest the optimal notification method. This allows the optimal notification method to be selected based on the user's past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's past notification history and outputs the optimal notification method.

[0068] The notification unit can adjust the notification method taking into account the user's current situation when notifying. For example, when the user is in a meeting, the notification unit selects vibration or a quiet notification method. Furthermore, when the user is on the move, the notification unit can select audio notification or a visual notification method. Furthermore, when the user is taking a break, the notification unit can select a normal notification method. This allows the notification method to be adjusted according to the user's current situation. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's current situation and outputs the notification method.

[0069] When notifying, the notification unit can select a notification method depending on the type of device used by the user. For example, if the user is using a smartphone, the notification unit may notify by voice or vibration. If the user is using a tablet, the notification unit can also select a visual notification method. If the user is using a smartwatch, the notification unit can also select vibration or a short voice notification. This allows the optimal notification method to be selected depending on the type of device used by the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may perform notification using an AI model that inputs the type of device used by the user and outputs a notification method.

[0070] The notification unit can select the optimal notification method by taking into consideration the user's geographical location information when making a notification. For example, if the user is in a specific location, the notification unit notifies the user of information related to that location. Furthermore, if the user is traveling, the notification unit can also notify the user of information related to their destination. Furthermore, if the user is at home, the notification unit can also notify the user of information related to their home. This makes it possible to select the optimal notification method based on the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's geographical location information and outputs a notification method.

[0071] The notification unit can customize the notification content by analyzing the user's social media activity at the time of notification. For example, the notification unit notifies the user of events that the user plans to attend on social media. The notification unit can also analyze the content posted by the user on social media and notify the user of related information. The notification unit can also notify the user of related information by referring to the activities of the user's friends on social media. This allows the notification content to be customized based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's social media activity and outputs notification content.

[0072] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. For example, the notification unit preferentially suggests notification methods that the user has previously preferred. The notification unit can also improve the notification method based on the user's past feedback. The notification unit can also avoid notification methods that the user has previously dissatisfied with. This allows the notification method to be customized based on the user's past feedback. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can provide a notification using an AI model that uses the user's past feedback as input and customizes the notification method.

[0073] When acquiring traffic information, the traffic information acquisition unit can select the optimal acquisition method by referring to the user's past travel history. For example, the traffic information acquisition unit prioritizes acquiring traffic information for routes that the user has frequently used in the past. The traffic information acquisition unit can also select the most efficient traffic information acquisition method from the user's past travel history. The traffic information acquisition unit can also analyze the user's past travel patterns and suggest the optimal traffic information acquisition method. This makes it possible to select the optimal traffic information acquisition method based on the user's past travel history. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's past travel history and outputs the optimal acquisition method.

[0074] When acquiring traffic information, the traffic information acquisition unit can adjust the acquisition method taking into account the user's current means of transportation. For example, if the user is using public transportation, the traffic information acquisition unit prioritizes acquiring operation status. Furthermore, if the user is using a private car, the traffic information acquisition unit can also prioritize acquiring road traffic conditions. Furthermore, if the user is using a bicycle, the traffic information acquisition unit can also prioritize acquiring information on bicycle-only roads. This makes it possible to adjust the traffic information acquisition method according to the user's current means of transportation. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's current means of transportation and outputs an acquisition method.

[0075] When acquiring traffic information, the traffic information acquisition unit can adjust the acquisition method taking into account the battery status of the user's device. For example, when the battery of the user's device is low, the traffic information acquisition unit reduces the frequency of traffic information acquisition to reduce battery consumption. Furthermore, when the battery of the user's device is sufficient, the traffic information acquisition unit can increase the frequency of traffic information acquisition to provide more accurate information. Furthermore, the traffic information acquisition unit can automatically adjust the traffic information acquisition method according to the battery status of the user's device. This makes it possible to adjust the traffic information acquisition method according to the battery status of the user's device. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the battery status of the user's device and outputs an acquisition method.

[0076] When acquiring traffic information, the traffic information acquisition unit can select the optimal acquisition method taking into account the user's geographical location information. For example, if the user is in an urban area, the traffic information acquisition unit can prioritize acquiring traffic congestion information. Furthermore, if the user is in a suburban area, the traffic information acquisition unit can also prioritize acquiring information about routes with less traffic volume. Furthermore, if the user is in a specific area, the traffic information acquisition unit can also acquire information taking into account the traffic conditions in that area. This makes it possible to select the optimal traffic information acquisition method based on the user's geographical location information. Some or all of the above-described processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's geographical location information and outputs an acquisition method.

[0077] When acquiring traffic information, the traffic information acquisition unit can customize the acquisition method by analyzing the user's social media activity. For example, the traffic information acquisition unit acquires traffic information based on the location where the user checked in on social media. The traffic information acquisition unit can also analyze the content of the user's social media posts to acquire related traffic information. The traffic information acquisition unit can also acquire traffic information by referring to the activities of the user's friends on social media. This allows the traffic information acquisition method to be customized based on the user's social media activity. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's social media activity and outputs an acquisition method.

[0078] The traffic information acquisition unit can customize the traffic information acquisition method by reflecting the user's past feedback when acquiring traffic information. For example, the traffic information acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The traffic information acquisition unit can also improve the acquisition method based on the user's past feedback. The traffic information acquisition unit can also avoid an acquisition method that the user has previously been dissatisfied with. This allows the traffic information acquisition method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0079] When acquiring weather information, the weather information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. For example, the weather information acquisition unit prioritizes acquiring weather information for routes that the user has frequently used in the past. The weather information acquisition unit can also select the most efficient method of acquiring weather information from the user's past movement history. The weather information acquisition unit can also analyze the user's past movement patterns and suggest the optimal method of acquiring weather information. This makes it possible to select the optimal method of acquiring weather information based on the user's past movement history. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's past movement history and outputs the optimal acquisition method.

[0080] When acquiring weather information, the weather information acquisition unit can adjust the acquisition method taking into account the user's current means of transportation. For example, if the user is using public transportation, the weather information acquisition unit prioritizes acquiring weather information that affects operation conditions. Furthermore, if the user is using a private car, the weather information acquisition unit can also prioritize acquiring weather information that affects road conditions. Furthermore, if the user is using a bicycle, the weather information acquisition unit can also prioritize acquiring weather information that affects the conditions of bicycle-only roads. This allows the weather information acquisition method to be adjusted according to the user's current means of transportation. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's current means of transportation and outputs an acquisition method.

[0081] When acquiring weather information, the weather information acquisition unit can adjust the acquisition method taking into account the battery status of the user's device. For example, if the battery of the user's device is low, the weather information acquisition unit can reduce the frequency of weather information acquisition to reduce battery consumption. Furthermore, if the battery of the user's device is sufficient, the weather information acquisition unit can increase the frequency of weather information acquisition to provide more accurate information. Furthermore, the weather information acquisition unit can automatically adjust the weather information acquisition method according to the battery status of the user's device. This allows the weather information acquisition method to be adjusted according to the battery status of the user's device. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the battery status of the user's device and outputs an acquisition method.

[0082] When acquiring weather information, the weather information acquisition unit can select the optimal acquisition method taking into account the user's geographical location information. For example, if the user is in an urban area, the weather information acquisition unit can prioritize acquiring city-specific weather information. Also, if the user is in a suburban area, the weather information acquisition unit can also prioritize acquiring suburban-specific weather information. Also, if the user is in a specific area, the weather information acquisition unit can acquire information taking into account the weather conditions in that area. This makes it possible to select the optimal weather information acquisition method based on the user's geographical location information. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's geographical location information and outputs an acquisition method.

[0083] When acquiring weather information, the weather information acquisition unit can analyze the user's social media activity and customize the acquisition method. The weather information acquisition unit can acquire weather information based on, for example, the location where the user checked in on social media. The weather information acquisition unit can also analyze the content of the user's social media posts and acquire related weather information. The weather information acquisition unit can also acquire weather information based on the activity of the user's friends on social media. This allows the weather information acquisition method to be customized based on the user's social media activity. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's social media activity and outputs an acquisition method.

[0084] When acquiring weather information, the weather information acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the weather information acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The weather information acquisition unit can also improve the acquisition method based on the user's past feedback. The weather information acquisition unit can also avoid acquisition methods that the user has previously dissatisfied with. This allows the weather information acquisition method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The mobility assistance system may further include a health condition acquisition unit that monitors the user's health condition. The health condition acquisition unit acquires vital data, such as the user's heart rate and blood pressure, and monitors the user's health condition in real time. The health condition acquisition unit may also adjust the means of transportation and route based on the user's health condition. For example, if the user is tired, the health condition acquisition unit may suggest a more comfortable means of transportation or a route that includes rest breaks. Furthermore, if the user is seeking healthy exercise, the health condition acquisition unit may suggest a route that involves walking or cycling. This makes it possible to provide the optimal means of transportation and route according to the user's health condition.

[0087] The mobility assistance system may further include a preference learning unit that learns the user's preferences. The preference learning unit may, for example, analyze transportation means and routes selected by the user in the past to learn the user's preferences. The preference learning unit may also suggest transportation means and routes based on the user's preferences. For example, it may preferentially suggest transportation means that the user has frequently used in the past. Furthermore, if the user prefers a particular route, it may preferentially suggest that route. This makes it possible to provide the optimal transportation means and routes according to the user's preferences.

[0088] The mobility assistance system may further include a battery management unit that takes into account the battery status of the user's device. For example, the battery management unit may monitor the remaining battery level of the user's device, and if the battery is low, may reduce the frequency of obtaining the current location to reduce battery consumption. Furthermore, if the battery is sufficient, the battery management unit may increase the frequency of obtaining the current location to provide more accurate location information. This makes it possible to provide an optimal method of obtaining the current location depending on the battery status of the user's device.

[0089] The mobility assistance system may further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit may, for example, analyze the locations where the user has checked in and the content of posts on social media, and suggest related means of transportation and routes. The social media analysis unit may also suggest related means of transportation and routes based on the activities of the user's friends. This makes it possible to provide optimal means of transportation and routes based on the user's social media activity.

[0090] The mobility assistance system may further include a feedback reflecting unit that reflects the user's past feedback. The feedback reflecting unit may, for example, preferentially suggest transportation means and routes that the user has previously preferred. The feedback reflecting unit may also improve the method of suggesting transportation means and routes based on the user's past feedback. This makes it possible to provide optimal transportation means and routes based on the user's past feedback.

[0091] The processing flow of the first embodiment will be briefly explained below.

[0092] Step 1: The schedule acquisition unit acquires the schedule entered by the user. For example, if the user enters "meeting at 10 o'clock," the schedule acquisition unit acquires that information. Step 2: The current location acquisition unit acquires the current location of the user using a GPS. For example, the current location acquisition unit identifies the current location of the user using a GPS device. Step 3: The transportation means selection unit selects the optimal transportation means based on the information acquired by the schedule acquisition unit and the current location acquisition unit. For example, the transportation means selection unit selects transportation means such as public transportation or taxi. The processing in the transportation means selection unit may be performed using AI or without AI. Step 4: The travel time calculation unit calculates the travel time based on the transportation mode selected by the transportation mode selection unit. For example, the travel time calculation unit calculates the travel time from the current location to the destination. The processing in the travel time calculation unit may be performed using AI or may be performed without using AI. Step 5: The route suggestion unit suggests an optimal route based on the travel time calculated by the travel time calculation unit. For example, the route suggestion unit suggests an optimal route based on information acquired by the traffic information acquisition unit and the weather information acquisition unit. The processing in the route suggestion unit may be performed using AI or may be performed without using AI. Step 6: The notification unit notifies the user of the route proposed by the route proposal unit. For example, the notification unit notifies the user using a notification function of a smartphone. The processing in the notification unit may be performed using AI or may be performed without using AI.

[0093] (Example 2) A mobility assistance system according to an embodiment of the present invention is a system in which an AI automatically determines travel time and travel method when a user inputs their schedule. The mobility assistance system grasps the user's schedule and current location and suggests the optimal means of transportation and travel time. For example, if a user inputs "meeting at 10 o'clock," the AI ​​calculates the optimal means of transportation (train, bus, taxi, etc.) from the current location to the meeting location and the travel time, and notifies the user. The AI ​​also takes traffic conditions and weather information into consideration and suggests the optimal route in real time. This allows the user to travel efficiently and makes schedule management easier. For example, in a mobility assistance system, when a user inputs their schedule, a schedule acquisition unit acquires the user's schedule. For example, if the user inputs "meeting at 10 o'clock," the schedule acquisition unit acquires that information. Next, a current location acquisition unit acquires the user's current location. For example, the user's current location is identified using GPS. Next, a transportation means selection unit selects the optimal transportation means. For example, it selects public transportation, taxi, or other transportation means. Furthermore, a travel time calculation unit calculates the travel time. For example, it calculates the travel time from the current location to the meeting location. Furthermore, a traffic information acquisition unit acquires traffic conditions in real time. For example, information such as traffic congestion and operation status is acquired. Next, the weather information acquisition unit acquires weather information. For example, weather information such as rain or snow is acquired. Based on this information, the route suggestion unit proposes the optimal route. For example, it proposes a route that avoids traffic congestion or a route that suits the weather. Finally, the notification unit notifies the user. For example, it notifies the user using the notification function of a smartphone. This allows the mobility assistance system to efficiently manage the user's schedule and travel. This allows the mobility assistance system to efficiently manage the user's schedule and travel. For example, if a user enters "meeting at 10 o'clock," the AI ​​calculates the optimal means of travel and travel time from the current location to the meeting location and notifies the user. This allows the user to travel efficiently and makes schedule management easier.

[0094] A travel assistance system according to an embodiment includes a schedule acquisition unit, a current location acquisition unit, a transportation means selection unit, a travel time calculation unit, a route suggestion unit, and a notification unit. The schedule acquisition unit acquires a schedule input by a user. For example, if a user inputs "meeting at 10 o'clock," the schedule acquisition unit acquires the information. The current location acquisition unit acquires the user's current location using a GPS. For example, the current location acquisition unit identifies the user's current location using a GPS device. The transportation means selection unit selects the optimal transportation means. For example, the transportation means selection unit selects a transportation means such as public transportation or a taxi. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without AI. For example, the transportation means selection unit may select a transportation means using an AI model that receives the user's schedule and current location as input and outputs the optimal transportation means. The travel time calculation unit calculates the travel time based on the transportation means selected by the transportation means selection unit. For example, the travel time calculation unit calculates the travel time from the current location to the destination. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit may calculate the travel time using an AI model that receives the travel mode selected by the travel mode selection unit, the current location, and the destination as input and outputs the travel time. The route proposal unit proposes an optimal route based on the travel time calculated by the travel time calculation unit. For example, the route proposal unit proposes an optimal route based on information acquired by the traffic information acquisition unit and the weather information acquisition unit. Some or all of the above-described processing in the route proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the route proposal unit may propose a route using an AI model that receives information acquired by the traffic information acquisition unit and the weather information acquisition unit as input and outputs the optimal route. The notification unit notifies the user of the route proposed by the route proposal unit. For example, the notification unit notifies the user using a notification function of a smartphone. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.For example, the notification unit can notify the user using an AI model that receives the route proposed by the route proposal unit and outputs the notification content. This allows the mobility assistance system according to the embodiment to efficiently manage the user's schedule and travel.

[0095] The schedule acquisition unit can acquire a schedule input by a user. For example, when a user inputs "meeting at 10 o'clock," the schedule acquisition unit acquires the information. The schedule acquisition unit can also automatically analyze a schedule input by a user and reflect it in the schedule. For example, the schedule acquisition unit analyzes a schedule input by a user and adds it to the schedule. The schedule acquisition unit can also convert a schedule input by voice by a user into text and reflect it in the schedule. For example, the schedule acquisition unit can use voice recognition technology to convert a schedule input by a user into text and add it to the schedule. This allows the schedule input by the user to be accurately acquired. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that analyzes a schedule input by a user and reflects it in the schedule.

[0096] The current location acquisition unit can acquire the user's current location using a GPS. The current location acquisition unit identifies the user's current location using, for example, a GPS device. The current location acquisition unit can also update the user's current location in real time. For example, the current location acquisition unit acquires location information from the GPS device at regular intervals and updates the user's current location. The current location acquisition unit can also display the user's current location on a map. For example, the current location acquisition unit combines the acquired location information with map data to display the user's current location on a map. This allows the user's current location to be accurately acquired. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that analyzes location information acquired from a GPS device and identifies the user's current location.

[0097] The transportation means selection unit can select at least one transportation means from public transportation and a taxi. The transportation means selection unit selects transportation means such as public transportation and a taxi. The transportation means selection unit can also select a transportation means taking into consideration the user's transportation means preference. For example, if the user prefers public transportation, the transportation means selection unit can preferentially select trains or buses. If the user prefers taxis, the transportation means selection unit can preferentially select taxis. The transportation means selection unit can also adjust the user's transportation means selection criteria. For example, if the user is in a hurry, the transportation means selection unit selects the fastest transportation means. This allows the optimal transportation means to be selected. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's schedule and current location and outputs the optimal transportation means.

[0098] The travel time calculation unit can calculate the travel time from the current location to the destination. For example, the travel time calculation unit calculates the travel time from the current location to the destination. The travel time calculation unit can also calculate the travel time taking traffic conditions into account. For example, the travel time calculation unit calculates the travel time based on information such as traffic congestion and operation status. The travel time calculation unit can also calculate the travel time taking the user's transportation preference into account. For example, if the user prefers public transportation, the travel time calculation unit calculates the travel time by train or bus. If the user prefers taxis, the travel time calculation unit can also calculate the travel time by taxi. This allows for accurate calculation of travel times. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that inputs the transportation mode selected by the transportation mode selection unit, the current location, and the destination, and outputs the travel time.

[0099] The route proposal unit can propose an appropriate route based on information acquired by the traffic information acquisition unit and the weather information acquisition unit. The route proposal unit proposes an optimal route based on, for example, information acquired by the traffic information acquisition unit and the weather information acquisition unit. The route proposal unit can also propose a route that avoids traffic congestion or a route that suits the weather. For example, the route proposal unit proposes a route that avoids traffic congestion. The route proposal unit can also propose a route taking into account weather information such as rain or snow. The route proposal unit can also propose a route taking into account the user's preferred means of transportation. For example, if the user prefers public transportation, the route proposal unit can propose a route using a train or bus. If the user prefers taxis, the route proposal unit can also propose a route using a taxi. This makes it possible to propose an optimal route that takes into account traffic information and weather information. Some or all of the above-described processing in the route proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the route proposal unit can propose a route using an AI model that inputs information acquired by the traffic information acquisition unit and the weather information acquisition unit and outputs an optimal route.

[0100] The notification unit can notify the user using a notification function of the smartphone. The notification unit notifies the user, for example, using the notification function of the smartphone. The notification unit can also adjust the notification method based on the user's notification settings. For example, if the user has set vibration notification, the notification unit can notify by vibration. If the user has set audio notification, the notification unit can also notify by audio. The notification unit can also adjust the notification method taking into account the user's current situation. For example, if the user is in a meeting, the notification unit can select a quiet notification method. If the user is traveling, the notification unit can also select a visual notification method. This allows the user to be notified quickly. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the user using an AI model that inputs the route proposed by the route suggestion unit and outputs notification content.

[0101] The device includes a traffic information acquisition unit, which can acquire traffic conditions in real time. The traffic information acquisition unit acquires traffic conditions in real time using, for example, a traffic sensor. The traffic information acquisition unit can also acquire traffic conditions using a traffic information service. For example, the traffic information acquisition unit acquires real-time traffic information from the traffic information service. The traffic information acquisition unit can also select an optimal acquisition method by referring to the user's past travel history. For example, the traffic information acquisition unit prioritizes acquiring traffic information for routes that the user has frequently used in the past. The traffic information acquisition unit can also adjust the acquisition method taking into account the user's current means of travel. For example, if the user is using public transportation, the traffic information acquisition unit prioritizes acquiring operation status. This allows traffic conditions to be grasped in real time. Some or all of the above-described processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that analyzes data acquired from a traffic sensor and identifies traffic conditions.

[0102] The weather information acquisition unit is provided with a weather information acquisition unit, and the weather information acquisition unit can acquire weather information. The weather information acquisition unit acquires weather information, for example, using a weather data provision service. The weather information acquisition unit can also acquire weather information using a weather sensor. For example, the weather information acquisition unit acquires real-time weather information from a weather sensor. The weather information acquisition unit can also select an optimal acquisition method by referring to the user's past travel history. For example, the weather information acquisition unit prioritizes acquiring weather information for routes that the user has frequently used in the past. The weather information acquisition unit can also adjust the acquisition method taking into account the user's current means of travel. For example, when the user is using public transportation, the weather information acquisition unit prioritizes acquiring weather information that affects the operation status. This allows weather information to be acquired. Some or all of the above-described processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that analyzes data acquired from a weather data provision service and identifies weather conditions.

[0103] The schedule acquisition unit can estimate the user's emotions and adjust the timing of schedule acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the schedule acquisition unit can delay schedule acquisition to allow the user time to relax. Furthermore, if the user is relaxed, the schedule acquisition unit can also speed up schedule acquisition so that the user can check their plans with ample time to spare. Furthermore, if the user is in a hurry, the schedule acquisition unit can immediately acquire the schedule and quickly suggest the next action. This allows the schedule acquisition timing to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the schedule acquisition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the schedule acquisition unit can adjust the schedule acquisition timing using an AI model that inputs user emotion data and outputs the schedule acquisition timing.

[0104] The schedule acquisition unit can analyze the user's past schedule history and select the optimal acquisition method. For example, the schedule acquisition unit can automatically display schedules that the user frequently input in the past as candidates. The schedule acquisition unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The schedule acquisition unit can also predict and suggest plans to be used in a specific time period from the user's past schedule history. This makes it possible to select the optimal acquisition method based on the past schedule history. Some or all of the above-mentioned processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can select the acquisition method using an AI model that inputs the user's past schedule history and outputs the optimal acquisition method.

[0105] When acquiring a schedule, the schedule acquisition unit can perform filtering based on the user's current project or areas of interest. For example, the schedule acquisition unit prioritizes acquiring only plans related to the user's ongoing project. The schedule acquisition unit can also prioritize acquiring events and meetings related to the user's areas of interest. The schedule acquisition unit can also analyze trends in events the user has previously attended and acquire related plans. This allows schedules related to the user's areas of interest to be acquired preferentially. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that inputs the user's current project or areas of interest and outputs a filtered schedule.

[0106] When acquiring a schedule, the schedule acquisition unit can select the optimal acquisition means depending on the user's input method. For example, when the user inputs a schedule by voice, the schedule acquisition unit acquires the schedule using voice recognition technology. Furthermore, when the user inputs a schedule by text, the schedule acquisition unit can also acquire the schedule using text analysis technology. Furthermore, when the user inputs a schedule by image, the schedule acquisition unit can also acquire the schedule using image recognition technology. In this way, a schedule can be acquired depending on the user's input method. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that receives the user's input method as input and outputs the optimal acquisition means.

[0107] The schedule acquisition unit can estimate the user's emotions and determine the priority of schedules to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the schedule acquisition unit postpones less important events and prioritizes time for relaxation. Furthermore, when the user is relaxed, the schedule acquisition unit can prioritize acquiring more important events to efficiently manage the schedule. Furthermore, when the user is in a hurry, the schedule acquisition unit can prioritize acquiring upcoming events to enable the user to act quickly. This allows the schedule priority to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule acquisition unit can determine the schedule priority using an AI model that inputs the user's emotion data and outputs schedule priorities.

[0108] When acquiring a schedule, the schedule acquisition unit can prioritize acquiring highly relevant schedules by taking into account the user's geographical location information. For example, the schedule acquisition unit prioritizes acquiring plans for locations close to the user's current location. Furthermore, when the user is in a specific area, the schedule acquisition unit can also prioritize acquiring plans related to that area. Furthermore, when the user is traveling, the schedule acquisition unit can also prioritize acquiring plans related to the user's destination. This makes it possible to acquire a highly relevant schedule based on the user's geographical location information. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that inputs the user's geographical location information and outputs a highly relevant schedule.

[0109] The schedule acquisition unit can analyze the user's social media activities when acquiring a schedule and acquire related schedules. For example, the schedule acquisition unit reflects events that the user plans to attend on social media in the schedule. The schedule acquisition unit can also analyze the content of the user's social media posts and acquire related schedules. The schedule acquisition unit can also acquire related schedules by referring to the activities of the user's friends on social media. In this way, related schedules can be acquired based on the user's social media activities. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that inputs the user's social media activities and outputs related schedules.

[0110] When acquiring a schedule, the schedule acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the schedule acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The schedule acquisition unit can also improve the acquisition method based on the user's past feedback. The schedule acquisition unit can also avoid an acquisition method that the user has previously been dissatisfied with. This allows the acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the schedule acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule acquisition unit can acquire a schedule using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0111] The current location acquisition unit can estimate the user's emotions and adjust the frequency of current location acquisition based on the estimated user's emotions. For example, if the user is feeling stressed, the current location acquisition unit can reduce the frequency of current location acquisition to reduce battery consumption. Furthermore, if the user is relaxed, the current location acquisition unit can increase the frequency of current location acquisition to provide more accurate location information. Furthermore, if the user is in a hurry, the current location acquisition unit can maximize the frequency of current location acquisition to provide quick navigation. This allows the frequency of current location acquisition to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or without AI. For example, the current location acquisition unit can adjust the frequency of current location acquisition using an AI model that inputs user emotion data and outputs the frequency of current location acquisition.

[0112] When acquiring the current location, the current location acquisition unit can select the optimal acquisition method by referring to the user's past movement history. The current location acquisition unit optimizes the current location acquisition method based on, for example, places the user has frequently visited in the past. The current location acquisition unit can also select the most efficient current location acquisition method from the user's past movement history. The current location acquisition unit can also analyze the user's past movement patterns and suggest the optimal current location acquisition method. This makes it possible to select the optimal current location acquisition method based on the user's past movement history. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the user's past movement history and outputs the optimal acquisition method.

[0113] When acquiring a current location, the current location acquisition unit can adjust the acquisition method taking into account the battery status of the user's device. For example, when the battery of the user's device is low, the current location acquisition unit reduces the frequency of acquiring the current location to reduce battery consumption. Furthermore, when the battery of the user's device is sufficient, the current location acquisition unit can increase the frequency of acquiring the current location to provide more accurate location information. Furthermore, the current location acquisition unit can automatically adjust the current location acquisition method according to the battery status of the user's device. This allows the current location acquisition method to be adjusted according to the battery status of the user's device. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the battery status of the user's device and outputs an acquisition method.

[0114] When acquiring the current location, the current location acquisition unit can adjust the acquisition accuracy according to the user's means of transportation. For example, when the user is traveling on foot, the current location acquisition unit acquires the current location with high accuracy. Furthermore, when the user is traveling by bicycle, the current location acquisition unit can acquire the current location with moderate accuracy. Furthermore, when the user is traveling by car, the current location acquisition unit can quickly acquire the current location with low accuracy. This allows the acquisition accuracy of the current location to be adjusted according to the user's means of transportation. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the user's means of transportation and outputs the acquisition accuracy.

[0115] The current location acquisition unit can estimate the user's emotions and select a current location acquisition method based on the estimated user's emotions. For example, if the user is feeling stressed, the current location acquisition unit can acquire the current location less frequently to reduce battery consumption. Furthermore, if the user is relaxed, the current location acquisition unit can acquire the current location more frequently and provide accurate location information. Furthermore, if the user is in a hurry, the current location acquisition unit can acquire the current location in real time and provide quick navigation. This allows the current location acquisition method to be selected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the user's emotion data and outputs an acquisition method.

[0116] When acquiring the current location, the current location acquisition unit can adjust the acquisition method depending on the type of device of the user. For example, if the user is using a smartphone, the current location acquisition unit acquires the current location with high accuracy using GPS. Furthermore, if the user is using a tablet, the current location acquisition unit can also acquire the current location using Wi-Fi or Bluetooth. Furthermore, if the user is using a wearable device, the current location acquisition unit can also acquire the current location using a low-power sensor. This allows the current location acquisition method to be adjusted depending on the type of device of the user. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that inputs the type of device of the user and outputs an acquisition method.

[0117] When acquiring the current location, the current location acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the current location acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The current location acquisition unit can also improve the acquisition method based on the user's past feedback. The current location acquisition unit can also avoid acquisition methods that the user has previously dissatisfied with. This allows the current location acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the current location acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the current location acquisition unit can acquire the current location using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0118] The transportation means selection unit can estimate the user's emotions and adjust the transportation means selection criteria based on the estimated user emotions. For example, when the user is feeling stressed, the transportation means selection unit prioritizes selecting a comfortable transportation means (such as a taxi). Furthermore, when the user is relaxed, the transportation means selection unit can also select a cost-conscious transportation means (such as public transportation). Furthermore, when the user is in a hurry, the transportation means selection unit can also select the fastest transportation means (such as a train). This allows the transportation means selection criteria to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's emotion data and outputs selection criteria.

[0119] When selecting a transportation mode, the transportation mode selection unit can select the optimal transportation mode by referring to the user's past travel history. For example, the transportation mode selection unit prioritizes the selection of transportation modes that the user has frequently used in the past. The transportation mode selection unit can also select the most efficient transportation mode from the user's past travel history. The transportation mode selection unit can also analyze the user's past travel patterns and suggest the optimal transportation mode. This makes it possible to select the optimal transportation mode based on the user's past travel history. Some or all of the above-described processing in the transportation mode selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation mode selection unit can select a transportation mode using an AI model that inputs the user's past travel history and outputs the optimal transportation mode.

[0120] When selecting a transportation means, the transportation means selection unit can adjust the selection criteria taking into account the user's current physical condition and mood. For example, if the user is tired, the transportation means selection unit will preferentially select a comfortable transportation means (such as a taxi). Furthermore, if the user is seeking healthy exercise, the transportation means selection unit can also select a transportation means such as walking or cycling. Furthermore, if the user is feeling unwell, the transportation means selection unit can also select a means that will allow the user to travel in the shortest time. This makes it possible to adjust the selection criteria for a transportation means according to the user's physical condition and mood. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's physical condition and mood and outputs selection criteria.

[0121] The transportation means selection unit can select a transportation means taking into consideration the user's transportation means preference. For example, if the user prefers public transportation, the transportation means selection unit can preferentially select trains or buses. Furthermore, if the user prefers taxis, the transportation means selection unit can also preferentially select taxis. Furthermore, if the user prefers bicycles, the transportation means selection unit can also select routes where bicycles can be used. This makes it possible to select the optimal transportation means according to the user's transportation means preference. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's transportation means preference and outputs the optimal transportation means.

[0122] The transportation means selection unit can estimate the user's emotions and adjust the order in which the selected transportation means are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the transportation means selection unit can first display a comfortable transportation means. Furthermore, if the user is relaxed, the transportation means selection unit can first display a cost-effective transportation means. Furthermore, if the user is in a hurry, the transportation means selection unit can first display the fastest transportation means. This allows the order in which the selected transportation means are displayed to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transportation means selection unit can adjust the order in which the selected transportation means are displayed using an AI model that inputs the user's emotion data and outputs a display order.

[0123] When selecting a transportation means, the transportation means selection unit can select the optimal transportation means by taking into account the user's geographical location information. For example, if the user is in an urban area, the transportation means selection unit can preferentially select public transportation. Furthermore, if the user is in a suburban area, the transportation means selection unit can also preferentially select a taxi or private car. Furthermore, if the user is in a specific area, the transportation means selection unit can also select transportation means available in that area. This makes it possible to select the optimal transportation means based on the user's geographical location information. Some or all of the above-described processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's geographical location information and outputs the optimal transportation means.

[0124] When selecting a transportation means, the transportation means selection unit can analyze the user's social media activity and reflect the results in the selection of the transportation means. For example, the transportation means selection unit selects a transportation means based on the location where the user checked in on social media. The transportation means selection unit can also analyze the content of the user's social media posts and select a related transportation means. The transportation means selection unit can also select a transportation means based on the activity of the user's friends on social media. This makes it possible to select the optimal transportation means based on the user's social media activity. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that inputs the user's social media activity and outputs the optimal transportation means.

[0125] The transportation means selection unit can customize the selection criteria by reflecting the user's past feedback when selecting a transportation means. For example, the transportation means selection unit preferentially suggests transportation means that the user has previously preferred. The transportation means selection unit can also improve the selection criteria based on the user's past feedback. The transportation means selection unit can also avoid transportation means that the user has previously been dissatisfied with. This allows the transportation means selection criteria to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the transportation means selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation means selection unit can select a transportation means using an AI model that customizes the selection criteria using the user's past feedback as input.

[0126] The travel time calculation unit can estimate the user's emotions and adjust the travel time calculation method based on the estimated user emotions. For example, if the user is feeling stressed, the travel time calculation unit calculates a travel time with a margin of error. Furthermore, if the user is relaxed, the travel time calculation unit can also calculate a normal travel time. Furthermore, if the user is in a hurry, the travel time calculation unit can calculate a time that allows the user to travel in the shortest time. This allows the travel time calculation method to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the travel time calculation unit can calculate travel time using an AI model that inputs user emotion data and outputs a calculation method.

[0127] When calculating travel time, the travel time calculation unit can improve calculation accuracy by referring to the user's past travel history. The travel time calculation unit calculates travel time based on, for example, routes used by the user in the past. The travel time calculation unit can also calculate a route that avoids congestion based on the user's past travel history. The travel time calculation unit can also analyze the user's past travel history and calculate the most efficient travel time. This improves the calculation accuracy of travel time based on the user's past travel history. Some or all of the above-mentioned processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate travel time using an AI model that inputs the user's past travel history and improves calculation accuracy.

[0128] The travel time calculation unit can adjust the calculation method when calculating travel time, taking into account the user's current physical condition and mood. For example, if the user is tired, the travel time calculation unit calculates a travel time with some leeway. Furthermore, if the user is seeking healthy exercise, the travel time calculation unit can also calculate a normal travel time. Furthermore, if the user is in poor health, the travel time calculation unit can calculate a time that allows travel in the shortest time. This allows the travel time calculation method to be adjusted according to the user's physical condition and mood. Some or all of the above-mentioned processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate travel time using an AI model that inputs the user's physical condition and mood and outputs a calculation method.

[0129] The travel time calculation unit can take into account the user's transportation preference when calculating the travel time. For example, if the user prefers public transportation, the travel time calculation unit can calculate the train or bus travel time. Furthermore, if the user prefers taxis, the travel time calculation unit can also calculate the taxi travel time. Furthermore, if the user prefers bicycles, the travel time calculation unit can also calculate the bicycle travel time. This makes it possible to calculate travel times according to the user's transportation preference. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate travel times using an AI model that inputs the user's transportation preference and outputs travel times.

[0130] The travel time calculation unit can estimate the user's emotions and adjust the order in which the travel time calculation results are displayed based on the estimated user's emotions. For example, if the user is stressed, the travel time calculation unit can first display a generous travel time. Furthermore, if the user is relaxed, the travel time calculation unit can first display a normal travel time. Furthermore, if the user is in a hurry, the travel time calculation unit can first display the shortest travel time. This allows the order in which the travel time calculation results are displayed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the travel time calculation unit can be performed using, for example, AI, or without AI. For example, the travel time calculation unit can adjust the order in which the travel time calculation results are displayed using an AI model that inputs user emotion data and outputs a display order.

[0131] The travel time calculation unit can improve the calculation accuracy when calculating the travel time by taking into account the user's geographical location information. For example, if the user is in an urban area, the travel time calculation unit can calculate the travel time by taking into account traffic congestion. Furthermore, if the user is in a suburban area, the travel time calculation unit can also calculate the travel time by taking into account a route with less traffic. Furthermore, if the user is in a specific area, the travel time calculation unit can also calculate the travel time by taking into account the traffic conditions in that area. This improves the calculation accuracy of the travel time based on the user's geographical location information. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that inputs the user's geographical location information and improves calculation accuracy.

[0132] The travel time calculation unit can analyze the user's social media activity and reflect it in the calculation when calculating the travel time. For example, the travel time calculation unit calculates the travel time based on the location where the user checked in on social media. The travel time calculation unit can also analyze the content of the user's social media posts and calculate the related travel time. The travel time calculation unit can also calculate the travel time by referring to the activities of the user's friends on social media. In this way, the travel time can be calculated based on the user's social media activity. Some or all of the above-mentioned processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that inputs the user's social media activity and outputs the travel time.

[0133] The travel time calculation unit can customize the calculation method by reflecting the user's past feedback when calculating the travel time. For example, the travel time calculation unit preferentially suggests calculation methods that the user has previously preferred. The travel time calculation unit can also improve the calculation method based on the user's past feedback. The travel time calculation unit can also avoid calculation methods that the user has previously dissatisfied with. This allows the travel time calculation method to be customized based on the user's past feedback. Some or all of the above-described processing in the travel time calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel time calculation unit can calculate the travel time using an AI model that uses the user's past feedback as input and customizes the calculation method.

[0134] The route suggestion unit can estimate the user's emotions and adjust the route suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, the route suggestion unit can prioritize suggesting a comfortable route. Furthermore, if the user is relaxed, the route suggestion unit can also suggest an efficient route. Furthermore, if the user is in a hurry, the route suggestion unit can also suggest the shortest route. This allows the route suggestion method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the route suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the route suggestion unit can suggest a route using an AI model that inputs the user's emotion data and outputs a suggestion method.

[0135] When proposing a route, the route suggestion unit can suggest an optimal route by referring to the user's past movement history. The route suggestion unit can, for example, suggest an optimal route based on routes the user has used in the past. The route suggestion unit can also suggest a route that avoids congestion based on the user's past movement history. The route suggestion unit can also analyze the user's past movement history and suggest the most efficient route. This makes it possible to suggest an optimal route based on the user's past movement history. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can suggest a route using an AI model that inputs the user's past movement history and outputs an optimal route.

[0136] When proposing a route, the route suggestion unit can adjust the suggestion method taking into account the user's current physical condition and mood. For example, if the user is tired, the route suggestion unit can preferentially suggest a comfortable route. Furthermore, if the user is seeking healthy exercise, the route suggestion unit can also suggest a slightly longer route. Furthermore, if the user is not feeling well, the route suggestion unit can also suggest the shortest route. This allows the route suggestion method to be adjusted according to the user's physical condition and mood. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can suggest a route using an AI model that inputs the user's physical condition and mood and outputs a suggestion method.

[0137] When proposing a route, the route suggestion unit can take into consideration the user's transportation preference. For example, if the user prefers public transportation, the route suggestion unit can suggest a route using a train or bus. Furthermore, if the user prefers taxis, the route suggestion unit can also suggest a route using a taxi. Furthermore, if the user prefers bicycles, the route suggestion unit can also suggest a route using bicycles. This makes it possible to propose an optimal route according to the user's transportation preference. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can propose a route using an AI model that inputs the user's transportation preference and outputs an optimal route.

[0138] The route suggestion unit can estimate the user's emotions and adjust the order in which route suggestion results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the route suggestion unit can first display a comfortable route. Furthermore, if the user is relaxed, the route suggestion unit can first display an efficient route. Furthermore, if the user is in a hurry, the route suggestion unit can first display the shortest route. This allows the order in which route suggestion results are displayed to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the route suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the route suggestion unit can adjust the order in which route suggestion results are displayed using an AI model that receives user emotion data as input and outputs a display order.

[0139] When proposing a route, the route suggestion unit can propose an optimal route by taking into account the user's geographical location information. For example, if the user is in an urban area, the route suggestion unit can propose a route that avoids traffic congestion. Furthermore, if the user is in a suburban area, the route suggestion unit can also propose a route with less traffic. Furthermore, if the user is in a specific area, the route suggestion unit can also propose a route by taking into account the traffic conditions in that area. This makes it possible to propose an optimal route based on the user's geographical location information. Some or all of the above-described processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can propose a route using an AI model that inputs the user's geographical location information and outputs an optimal route.

[0140] When proposing a route, the route suggestion unit can analyze the user's social media activities and reflect them in the route suggestion. For example, the route suggestion unit can suggest a route based on locations where the user has checked in on social media. The route suggestion unit can also analyze the content of the user's social media posts and suggest related routes. The route suggestion unit can also suggest related routes based on the activities of the user's friends on social media. This makes it possible to suggest an optimal route based on the user's social media activities. Some or all of the above-mentioned processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can suggest a route using an AI model that inputs the user's social media activities and outputs an optimal route.

[0141] When proposing a route, the route suggestion unit can customize the suggestion method by reflecting the user's past feedback. For example, the route suggestion unit preferentially suggests route suggestion methods that the user has previously preferred. The route suggestion unit can also improve the suggestion method based on the user's past feedback. The route suggestion unit can also avoid route suggestion methods that the user has previously been dissatisfied with. This allows the route suggestion method to be customized based on the user's past feedback. Some or all of the above-described processing in the route suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the route suggestion unit can propose a route using an AI model that uses the user's past feedback as input and customizes the suggestion method.

[0142] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can delay the notification to allow the user time to relax. Furthermore, if the user is relaxed, the notification unit can also advance the notification to allow the user to confirm their schedule with ample time. Furthermore, if the user is in a hurry, the notification unit can immediately notify the user and quickly suggest the next action. This allows the timing of notifications to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can adjust the timing of notifications using an AI model that inputs user emotion data and outputs notification timing.

[0143] The notification unit can select the optimal notification method by referring to the user's past notification history when providing a notification. For example, the notification unit preferentially suggests notification methods (audio, vibration, etc.) that the user has previously preferred. The notification unit can also select the most effective notification method from the user's past notification history. The notification unit can also analyze the user's past notification patterns and suggest the optimal notification method. This allows the optimal notification method to be selected based on the user's past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's past notification history and outputs the optimal notification method.

[0144] The notification unit can adjust the notification method taking into account the user's current situation when notifying. For example, when the user is in a meeting, the notification unit selects vibration or a quiet notification method. Furthermore, when the user is on the move, the notification unit can select audio notification or a visual notification method. Furthermore, when the user is taking a break, the notification unit can select a normal notification method. This allows the notification method to be adjusted according to the user's current situation. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's current situation and outputs the notification method.

[0145] When notifying, the notification unit can select a notification method depending on the type of device used by the user. For example, if the user is using a smartphone, the notification unit may notify by voice or vibration. If the user is using a tablet, the notification unit can also select a visual notification method. If the user is using a smartwatch, the notification unit can also select vibration or a short voice notification. This allows the optimal notification method to be selected depending on the type of device used by the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may perform notification using an AI model that inputs the type of device used by the user and outputs a notification method.

[0146] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide a simple, relaxing notification. Furthermore, if the user is relaxed, the notification unit can provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a quick notification that focuses on the main points. This allows the content of the notification to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can provide a notification using an AI model that receives the user's emotion data as input and outputs notification content.

[0147] The notification unit can select the optimal notification method by taking into consideration the user's geographical location information when making a notification. For example, if the user is in a specific location, the notification unit notifies the user of information related to that location. Furthermore, if the user is traveling, the notification unit can also notify the user of information related to their destination. Furthermore, if the user is at home, the notification unit can also notify the user of information related to their home. This makes it possible to select the optimal notification method based on the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's geographical location information and outputs a notification method.

[0148] The notification unit can customize the notification content by analyzing the user's social media activity at the time of notification. For example, the notification unit notifies the user of events that the user plans to attend on social media. The notification unit can also analyze the content posted by the user on social media and notify the user of related information. The notification unit can also notify the user of related information by referring to the activities of the user's friends on social media. This allows the notification content to be customized based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs the user's social media activity and outputs notification content.

[0149] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. For example, the notification unit preferentially suggests notification methods that the user has previously preferred. The notification unit can also improve the notification method based on the user's past feedback. The notification unit can also avoid notification methods that the user has previously dissatisfied with. This allows the notification method to be customized based on the user's past feedback. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can provide a notification using an AI model that uses the user's past feedback as input and customizes the notification method.

[0150] The traffic information acquisition unit can estimate the user's emotions and adjust the frequency of traffic information acquisition based on the estimated user's emotions. For example, if the user is feeling stressed, the traffic information acquisition unit can reduce the frequency of traffic information acquisition to reduce battery consumption. Furthermore, if the user is relaxed, the traffic information acquisition unit can increase the frequency of traffic information acquisition to provide more accurate information. Furthermore, if the user is in a hurry, the traffic information acquisition unit can maximize the frequency of traffic information acquisition to provide quick navigation. This allows the frequency of traffic information acquisition to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the traffic information acquisition unit may be performed using, for example, AI, or without AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs user emotion data and outputs an acquisition frequency.

[0151] When acquiring traffic information, the traffic information acquisition unit can select the optimal acquisition method by referring to the user's past travel history. For example, the traffic information acquisition unit prioritizes acquiring traffic information for routes that the user has frequently used in the past. The traffic information acquisition unit can also select the most efficient traffic information acquisition method from the user's past travel history. The traffic information acquisition unit can also analyze the user's past travel patterns and suggest the optimal traffic information acquisition method. This makes it possible to select the optimal traffic information acquisition method based on the user's past travel history. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's past travel history and outputs the optimal acquisition method.

[0152] When acquiring traffic information, the traffic information acquisition unit can adjust the acquisition method taking into account the user's current means of transportation. For example, if the user is using public transportation, the traffic information acquisition unit prioritizes acquiring operation status. Furthermore, if the user is using a private car, the traffic information acquisition unit can also prioritize acquiring road traffic conditions. Furthermore, if the user is using a bicycle, the traffic information acquisition unit can also prioritize acquiring information on bicycle-only roads. This makes it possible to adjust the traffic information acquisition method according to the user's current means of transportation. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's current means of transportation and outputs an acquisition method.

[0153] When acquiring traffic information, the traffic information acquisition unit can adjust the acquisition method taking into account the battery status of the user's device. For example, when the battery of the user's device is low, the traffic information acquisition unit reduces the frequency of traffic information acquisition to reduce battery consumption. Furthermore, when the battery of the user's device is sufficient, the traffic information acquisition unit can increase the frequency of traffic information acquisition to provide more accurate information. Furthermore, the traffic information acquisition unit can automatically adjust the traffic information acquisition method according to the battery status of the user's device. This makes it possible to adjust the traffic information acquisition method according to the battery status of the user's device. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the battery status of the user's device and outputs an acquisition method.

[0154] The traffic information acquisition unit can estimate the user's emotions and select a traffic information acquisition method based on the estimated user's emotions. For example, when the user is stressed, the traffic information acquisition unit acquires traffic information less frequently to reduce battery consumption. Furthermore, when the user is relaxed, the traffic information acquisition unit can acquire traffic information more frequently and provide accurate information. Furthermore, when the user is in a hurry, the traffic information acquisition unit can acquire traffic information in real time and provide quick navigation. This allows the traffic information acquisition method to be selected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the traffic information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs user emotion data and outputs an acquisition method.

[0155] When acquiring traffic information, the traffic information acquisition unit can select the optimal acquisition method taking into account the user's geographical location information. For example, if the user is in an urban area, the traffic information acquisition unit can prioritize acquiring traffic congestion information. Furthermore, if the user is in a suburban area, the traffic information acquisition unit can also prioritize acquiring information about routes with less traffic volume. Furthermore, if the user is in a specific area, the traffic information acquisition unit can also acquire information taking into account the traffic conditions in that area. This makes it possible to select the optimal traffic information acquisition method based on the user's geographical location information. Some or all of the above-described processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's geographical location information and outputs an acquisition method.

[0156] When acquiring traffic information, the traffic information acquisition unit can customize the acquisition method by analyzing the user's social media activity. For example, the traffic information acquisition unit acquires traffic information based on the location where the user checked in on social media. The traffic information acquisition unit can also analyze the content of the user's social media posts to acquire related traffic information. The traffic information acquisition unit can also acquire traffic information by referring to the activities of the user's friends on social media. This allows the traffic information acquisition method to be customized based on the user's social media activity. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that inputs the user's social media activity and outputs an acquisition method.

[0157] The traffic information acquisition unit can customize the traffic information acquisition method by reflecting the user's past feedback when acquiring traffic information. For example, the traffic information acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The traffic information acquisition unit can also improve the acquisition method based on the user's past feedback. The traffic information acquisition unit can also avoid an acquisition method that the user has previously been dissatisfied with. This allows the traffic information acquisition method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the traffic information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information acquisition unit can acquire traffic information using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0158] The weather information acquisition unit can estimate the user's emotions and adjust the frequency of weather information acquisition based on the estimated user's emotions. For example, if the user is feeling stressed, the weather information acquisition unit can reduce the frequency of weather information acquisition to reduce battery consumption. Furthermore, if the user is relaxed, the weather information acquisition unit can increase the frequency of weather information acquisition to provide more accurate information. Furthermore, if the user is in a hurry, the weather information acquisition unit can maximize the frequency of weather information acquisition to provide quick navigation. This allows the frequency of weather information acquisition to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the weather information acquisition unit may be performed using, for example, AI, or without AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs user emotion data and outputs an acquisition frequency.

[0159] When acquiring weather information, the weather information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. For example, the weather information acquisition unit prioritizes acquiring weather information for routes that the user has frequently used in the past. The weather information acquisition unit can also select the most efficient method of acquiring weather information from the user's past movement history. The weather information acquisition unit can also analyze the user's past movement patterns and suggest the optimal method of acquiring weather information. This makes it possible to select the optimal method of acquiring weather information based on the user's past movement history. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's past movement history and outputs the optimal acquisition method.

[0160] When acquiring weather information, the weather information acquisition unit can adjust the acquisition method taking into account the user's current means of transportation. For example, if the user is using public transportation, the weather information acquisition unit prioritizes acquiring weather information that affects operation conditions. Furthermore, if the user is using a private car, the weather information acquisition unit can also prioritize acquiring weather information that affects road conditions. Furthermore, if the user is using a bicycle, the weather information acquisition unit can also prioritize acquiring weather information that affects the conditions of bicycle-only roads. This allows the weather information acquisition method to be adjusted according to the user's current means of transportation. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's current means of transportation and outputs an acquisition method.

[0161] When acquiring weather information, the weather information acquisition unit can adjust the acquisition method taking into account the battery status of the user's device. For example, if the battery of the user's device is low, the weather information acquisition unit can reduce the frequency of weather information acquisition to reduce battery consumption. Furthermore, if the battery of the user's device is sufficient, the weather information acquisition unit can increase the frequency of weather information acquisition to provide more accurate information. Furthermore, the weather information acquisition unit can automatically adjust the weather information acquisition method according to the battery status of the user's device. This allows the weather information acquisition method to be adjusted according to the battery status of the user's device. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the battery status of the user's device and outputs an acquisition method.

[0162] The weather information acquisition unit can estimate the user's emotions and select a weather information acquisition method based on the estimated user's emotions. For example, if the user is feeling stressed, the weather information acquisition unit can acquire weather information less frequently to reduce battery consumption. Furthermore, if the user is relaxed, the weather information acquisition unit can acquire weather information more frequently to provide accurate information. Furthermore, if the user is in a hurry, the weather information acquisition unit can acquire weather information in real time to provide quick navigation. This allows the weather information acquisition method to be selected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs user emotion data and outputs an acquisition method.

[0163] When acquiring weather information, the weather information acquisition unit can select the optimal acquisition method taking into account the user's geographical location information. For example, if the user is in an urban area, the weather information acquisition unit can prioritize acquiring city-specific weather information. Also, if the user is in a suburban area, the weather information acquisition unit can also prioritize acquiring suburban-specific weather information. Also, if the user is in a specific area, the weather information acquisition unit can acquire information taking into account the weather conditions in that area. This makes it possible to select the optimal weather information acquisition method based on the user's geographical location information. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's geographical location information and outputs an acquisition method.

[0164] When acquiring weather information, the weather information acquisition unit can analyze the user's social media activity and customize the acquisition method. The weather information acquisition unit can acquire weather information based on, for example, the location where the user checked in on social media. The weather information acquisition unit can also analyze the content of the user's social media posts and acquire related weather information. The weather information acquisition unit can also acquire weather information based on the activity of the user's friends on social media. This allows the weather information acquisition method to be customized based on the user's social media activity. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that inputs the user's social media activity and outputs an acquisition method.

[0165] When acquiring weather information, the weather information acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the weather information acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The weather information acquisition unit can also improve the acquisition method based on the user's past feedback. The weather information acquisition unit can also avoid acquisition methods that the user has previously dissatisfied with. This allows the weather information acquisition method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the weather information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather information acquisition unit can acquire weather information using an AI model that uses the user's past feedback as input and customizes the acquisition method. === Hard Collateral 1-1 === Each of the multiple elements, including the schedule acquisition unit, current location acquisition unit, transportation means selection unit, travel time calculation unit, route proposal unit, notification unit, traffic information acquisition unit, and weather information acquisition unit, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the schedule acquisition unit is realized by the control unit 46A of the smart device 14 and acquires a schedule input by the user. The current location acquisition unit identifies the user's current location using the GPS device of the smart device 14. The transportation means selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects the optimal transportation means. The travel time calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates travel time. The route proposal unit is realized by the identification processing unit 290 of the data processing device 12 and proposes the optimal route. The notification unit is realized by the control unit 46A of the smart device 14 and notifies the user. The traffic information acquisition unit is realized by the identification processing unit 290 of the data processing device 12 and acquires traffic conditions in real time. The weather information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires weather information. === Hard Collateral 1-2 === Each of the multiple elements, including the schedule acquisition unit, current location acquisition unit, transportation means selection unit, travel time calculation unit, route proposal unit, notification unit, traffic information acquisition unit, and weather information acquisition unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the schedule acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires a schedule input by the user. The current location acquisition unit identifies the user's current location using the GPS device of the smart glasses 214. The transportation means selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects the optimal transportation means. The travel time calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates the travel time. The route proposal unit is realized by the identification processing unit 290 of the data processing device 12 and proposes the optimal route. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the user. The traffic information acquisition unit is realized by the identification processing unit 290 of the data processing device 12 and acquires traffic conditions in real time. The weather information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires weather information. === Hard Collateral 1-3 === Each of the multiple elements, including the schedule acquisition unit, current location acquisition unit, transportation means selection unit, travel time calculation unit, route proposal unit, notification unit, traffic information acquisition unit, and weather information acquisition unit, is realized by, for example, at least one of the headset type terminal 314 and the data processing device 12. For example, the schedule acquisition unit is realized by the control unit 46A of the headset type terminal 314 and acquires a schedule input by the user. The current location acquisition unit identifies the user's current location using the GPS device of the headset type terminal 314. The transportation means selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects the optimal transportation means. The travel time calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates travel time. The route proposal unit is realized by the identification processing unit 290 of the data processing device 12 and proposes the optimal route. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user. The traffic information acquisition unit is realized by the identification processing unit 290 of the data processing device 12 and acquires traffic conditions in real time. The weather information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires weather information. === Hard Collateral 1-4 === Each of the multiple elements, including the schedule acquisition unit, current location acquisition unit, transportation means selection unit, travel time calculation unit, route proposal unit, notification unit, traffic information acquisition unit, and weather information acquisition unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the schedule acquisition unit is realized by the control unit 46A of the robot 414 and acquires a schedule input by the user. The current location acquisition unit identifies the user's current location using the GPS device of the robot 414. The transportation means selection unit is realized by the identification processing unit 290 of the data processing device 12 and selects the optimal transportation means. The travel time calculation unit is realized by the identification processing unit 290 of the data processing device 12 and calculates the travel time. The route proposal unit is realized by the identification processing unit 290 of the data processing device 12 and proposes the optimal route. The notification unit is realized by the control unit 46A of the robot 414 and notifies the user. The traffic information acquisition unit is realized by the identification processing unit 290 of the data processing device 12 and acquires traffic conditions in real time. The weather information acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires weather information.

[0166] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0167] The mobility assistance system may further include a health condition acquisition unit that monitors the user's health condition. The health condition acquisition unit acquires vital data, such as the user's heart rate and blood pressure, and monitors the user's health condition in real time. The health condition acquisition unit may also adjust the means of transportation and route based on the user's health condition. For example, if the user is tired, the health condition acquisition unit may suggest a more comfortable means of transportation or a route that includes rest breaks. Furthermore, if the user is seeking healthy exercise, the health condition acquisition unit may suggest a route that involves walking or cycling. This makes it possible to provide the optimal means of transportation and route according to the user's health condition.

[0168] The mobility assistance system may further include a preference learning unit that learns the user's preferences. The preference learning unit may, for example, analyze transportation means and routes selected by the user in the past to learn the user's preferences. The preference learning unit may also suggest transportation means and routes based on the user's preferences. For example, it may preferentially suggest transportation means that the user has frequently used in the past. Furthermore, if the user prefers a particular route, it may preferentially suggest that route. This makes it possible to provide the optimal transportation means and routes according to the user's preferences.

[0169] The mobility assistance system may further include a battery management unit that takes into account the battery status of the user's device. For example, the battery management unit may monitor the remaining battery level of the user's device, and if the battery is low, may reduce the frequency of obtaining the current location to reduce battery consumption. Furthermore, if the battery is sufficient, the battery management unit may increase the frequency of obtaining the current location to provide more accurate location information. This makes it possible to provide an optimal method of obtaining the current location depending on the battery status of the user's device.

[0170] The mobility assistance system may further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit may, for example, analyze the locations where the user has checked in and the content of posts on social media, and suggest related means of transportation and routes. The social media analysis unit may also suggest related means of transportation and routes based on the activities of the user's friends. This makes it possible to provide optimal means of transportation and routes based on the user's social media activity.

[0171] The mobility assistance system may further include a feedback reflecting unit that reflects the user's past feedback. The feedback reflecting unit may, for example, preferentially suggest transportation means and routes that the user has previously preferred. The feedback reflecting unit may also improve the method of suggesting transportation means and routes based on the user's past feedback. This makes it possible to provide optimal transportation means and routes based on the user's past feedback.

[0172] The mobility assistance system can also estimate the user's emotions and adjust the criteria for selecting a means of transportation based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize a comfortable means of transportation (such as a taxi). Also, if the user is relaxed, it can select a means of transportation that prioritizes cost (such as public transportation). This makes it possible to provide the optimal means of transportation according to the user's emotions.

[0173] The mobility assistance system can further estimate the user's emotions and adjust the travel time calculation method based on the estimated user emotions. For example, if the user is feeling stressed, it can calculate a travel time with ample time to spare. On the other hand, if the user is relaxed, it can calculate a normal travel time. This makes it possible to provide an optimal travel time according to the user's emotions.

[0174] The mobility assistance system can further estimate the user's emotions and adjust the route suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize the suggestion of a comfortable route. Also, if the user is relaxed, it can suggest an efficient route. In this way, it is possible to provide the optimal route according to the user's emotions.

[0175] The mobility assistance system can further estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the system can delay notifications to allow the user time to relax. Alternatively, if the user is relaxed, the system can advance notifications to allow the user ample time to check their schedule. This allows the system to provide optimal notification timing according to the user's emotions.

[0176] The mobility assistance system can further estimate the user's emotions and adjust the content of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the system can provide a simple, relaxing notification. Alternatively, if the user is relaxed, the system can provide a notification with detailed information. This allows the system to provide optimal notification content according to the user's emotions.

[0177] The processing flow of the second embodiment will be briefly explained below.

[0178] Step 1: The schedule acquisition unit acquires the schedule entered by the user. For example, if the user enters "meeting at 10 o'clock," the schedule acquisition unit acquires that information. Step 2: The current location acquisition unit acquires the current location of the user using a GPS. For example, the current location acquisition unit identifies the current location of the user using a GPS device. Step 3: The transportation means selection unit selects the optimal transportation means based on the information acquired by the schedule acquisition unit and the current location acquisition unit. For example, the transportation means selection unit selects transportation means such as public transportation or taxi. The processing in the transportation means selection unit may be performed using AI or without AI. Step 4: The travel time calculation unit calculates the travel time based on the transportation mode selected by the transportation mode selection unit. For example, the travel time calculation unit calculates the travel time from the current location to the destination. The processing in the travel time calculation unit may be performed using AI or may be performed without using AI. Step 5: The route suggestion unit suggests an optimal route based on the travel time calculated by the travel time calculation unit. For example, the route suggestion unit suggests an optimal route based on information acquired by the traffic information acquisition unit and the weather information acquisition unit. The processing in the route suggestion unit may be performed using AI or may be performed without using AI. Step 6: The notification unit notifies the user of the route proposed by the route proposal unit. For example, the notification unit notifies the user using a notification function of a smartphone. The processing in the notification unit may be performed using AI or may be performed without using AI.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0184] 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.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] 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.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] 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.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0200] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] 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.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] 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.

[0207] 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.

[0208] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0209] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0210] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0211] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0213] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0214] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0215] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0216] 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.

[0217] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0218] 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.

[0219] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0221] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0222] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0223] 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.

[0224] 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.

[0225] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0226] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0227] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0228] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0229] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0230] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0231] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0232] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0233] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0234] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0235] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0236] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0237] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0238] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0239] 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.

[0240] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0241] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0242] 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.

[0243] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0244] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0245] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0246] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0247] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0248] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0249] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0250] [Explanation of symbols]

[0251] 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 schedule acquisition unit that acquires a schedule; a current location acquisition unit that acquires a current location; a transportation means selection unit that selects an appropriate transportation means based on the information acquired by the schedule acquisition unit and the current location acquisition unit; a travel time calculation unit that calculates a travel time based on the transportation means selected by the transportation means selection unit; a route suggestion unit that suggests an appropriate route based on the travel time calculated by the travel time calculation unit; a notification unit that notifies a user of the route proposed by the route proposal unit. A system characterized by:

2. The schedule acquisition unit Get the appointments entered by the user 2. The system of claim 1.

3. The current location acquisition unit Get the user's current location using GPS 2. The system of claim 1.

4. The transportation means selection unit Choose at least one of the following transportation methods: public transport or taxi 2. The system of claim 1.

5. The travel time calculation unit Calculate travel time from current location to destination 2. The system of claim 1.

6. a traffic information acquisition unit that acquires traffic conditions; a weather information acquisition unit that acquires weather information, The route suggestion unit Proposing an appropriate route based on the information acquired by the traffic information acquisition unit and the weather information acquisition unit 2. The system of claim 1.

7. The notification unit Notify the user using the smartphone's notification function 2. The system of claim 1.

8. The traffic information acquisition unit Get real-time traffic conditions The system of claim 6 .

9. Get weather information The system of claim 6 .

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