system

Abstract

The system according to the embodiment aims to suggest routes and destinations that are enjoyable to drive, taking into account parameters such as weather forecast, season, and time of day. [Solution] A system according to an embodiment includes a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit accepts input of a departure point and a destination. The acquisition unit acquires weather forecast, season, and time parameters based on the information accepted by the reception unit. The generation unit generates a route based on the parameters acquired by the acquisition unit. The provision unit provides information on tourist spots or restaurants based on the route generated by the generation 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 does not take into account parameters such as weather forecasts, seasons, and time of day when proposing routes, so there is room for improvement.

[0005] The system according to the embodiment aims to suggest routes and destinations that are enjoyable to drive, taking into account parameters such as weather forecast, season, and time of day. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit receives input of a departure point and a destination. The acquisition unit acquires parameters such as weather forecast, season, and time based on the information received by the reception unit. The generation unit generates a route based on the parameters acquired by the acquisition unit. The provision unit provides information on tourist spots or restaurants based on the route generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest routes and destinations that are enjoyable to drive, taking into account parameters such as weather forecast, season, and time of day. [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 car navigation app according to an embodiment of the present invention is a system that suggests optimal routes and destinations for a user to enjoy driving. This car navigation app suggests routes and destinations for a user to enjoy driving based on current parameters such as the weather forecast, season, and time of day, as well as conditions entered by the user. For example, the user inputs the starting point, destination, and driving conditions (e.g., a scenic route, avoiding crowds, etc.). The app then acquires parameters such as the weather forecast, season, and time of day and generates an optimal route based on this information. For example, the app suggests a scenic route on a sunny day and a safe route on a rainy day. It can also suggest routes that pass through famous cherry blossom viewing spots or autumn foliage viewing spots depending on the season. Furthermore, the app provides information on tourist spots and restaurants that can be visited along the way, based on the user's preferences. This allows the user to enjoy driving while arriving at their destination. This car navigation app offers a new experience for users to enjoy driving, setting it apart from conventional car navigation apps.

[0029] A car navigation app according to an embodiment includes a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit receives input from a user of a departure point and a destination. The user's input conditions include, but are not limited to, a scenic route and avoiding crowds. The reception unit allows the user to input the departure point and destination using, for example, a smartphone or tablet. The reception unit can also input the departure point and destination using voice input. The acquisition unit acquires weather forecast, season, and time parameters based on the information received by the reception unit. The acquisition unit acquires weather forecasts from, for example, data from the Japan Meteorological Agency or private weather forecast services. The acquisition unit can also reference calendars and weather data to acquire seasonal information. The acquisition unit can also reference an internal system clock or an Internet time server to acquire time information. The generation unit generates a route based on the parameters acquired by the acquisition unit. For example, the generation unit generates a scenic route for sunny days and a safe route for rainy days based on the weather forecast. The generation unit can also generate routes that pass through famous cherry blossom viewing spots or famous autumn foliage viewing spots depending on the season. Furthermore, the generation unit can generate routes taking into account information about tourist spots and restaurants that the user can stop at along the way, according to the user's preferences. The provision unit provides information about tourist spots or restaurants based on the route generated by the generation unit. For example, the provision unit displays information about tourist spots and restaurants that the user can stop at during a drive. The provision unit can also provide information about specific genres or highly rated restaurants, according to the user's preferences. This allows the car navigation app according to the embodiment to suggest optimal routes and destinations for the user to enjoy a drive.

[0030] The acquisition unit can acquire parameters such as weather forecast, season, and time. The acquisition unit can acquire weather forecasts, for example, from data from the Japan Meteorological Agency or private weather forecast services. For example, the acquisition unit can use the Japan Meteorological Agency's API via the Internet to acquire the latest weather forecast data. The acquisition unit can also refer to a calendar or weather data to acquire seasonal information. For example, the acquisition unit can determine the current season based on calendar data and acquire information corresponding to the season. The acquisition unit can also refer to the system's internal clock or an Internet time server to acquire time information. For example, the acquisition unit can acquire the current time based on the system's internal clock and provide information corresponding to the time. In this way, by acquiring parameters such as weather forecast, season, and time, information for generating an optimal route can be provided.

[0031] The generation unit can generate a route based on the acquired parameters. For example, the generation unit can generate a scenic route on sunny days and a safe route on rainy days based on a weather forecast. For example, the generation unit can analyze weather forecast data and select a scenic route on sunny days and a safe route with low traffic on rainy days. The generation unit can also generate routes that pass through famous cherry blossom viewing spots and famous autumn foliage viewing spots depending on the season. For example, the generation unit can select a route that passes through famous cherry blossom viewing spots in spring and a route that passes through famous autumn foliage viewing spots in autumn based on seasonal data. Furthermore, the generation unit can generate a route taking into account information about tourist spots and restaurants that can be visited along the way, according to the user's preferences. For example, the generation unit can generate a route that incorporates information about tourist spots and restaurants based on conditions entered by the user. In this way, by generating an optimal route based on the acquired parameters, it is possible to provide a route that allows the user to enjoy a drive.

[0032] The providing unit can provide information about tourist spots or restaurants based on the generated route. The providing unit, for example, displays information about tourist spots and restaurants that the user can stop at during a drive. For example, the providing unit can display information about tourist spots and restaurants on the generated route on a map, allowing the user to easily access them. The providing unit can also provide information about restaurants in a specific genre or with high ratings according to the user's preferences. For example, the providing unit can preferentially display restaurants with high ratings or restaurants in a specific genre based on conditions entered by the user. In this way, by providing information about tourist spots and restaurants based on the generated route, it is possible to provide information for the user to enjoy their drive.

[0033] The generation unit can generate a scenic route on a sunny day and a safe route on a rainy day. The generation unit can, for example, analyze weather forecast data and select a scenic route on a sunny day and a safe route with less traffic on a rainy day. For example, the generation unit can suggest a scenic route along the mountains or the sea on a sunny day and a route with less traffic that avoids major roads on a rainy day. The generation unit can also select a route that avoids slippery roads on a rainy day based on the weather forecast data. For example, the generation unit can suggest a route with fewer sharp curves and slopes on a rainy day. In this way, the optimal route can be generated according to the weather, thereby providing the user with a safe and enjoyable route.

[0034] The providing unit can provide routes that pass through famous cherry blossom viewing spots or famous autumn foliage viewing spots according to the season. For example, the providing unit can select a route that passes through famous cherry blossom viewing spots in spring and a route that passes through famous autumn foliage viewing spots in autumn based on seasonal data. For example, the providing unit can suggest a route that passes through parks and temples known as famous cherry blossom viewing spots in spring, and a route that passes through mountains and valleys known as famous autumn foliage viewing spots in autumn. The providing unit can also provide information on tourist spots according to the season. For example, the providing unit can provide information on cherry blossom viewing spots in spring and information on autumn leaf viewing spots in autumn. In this way, by providing routes that pass through tourist spots according to the season, the user can enjoy the seasons.

[0035] The reception unit can analyze the user's past driving history and suggest an input method. For example, the reception unit automatically displays as candidates starting points and destinations that the user has frequently input in the past. For example, the reception unit analyzes the user's past driving history and displays frequently visited places as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest starting points and destinations to be used during a specific time period based on the user's past driving history. For example, if the user has visited a specific place during a specific time period in the past, the reception unit suggests starting points and destinations appropriate for that time period. In this way, the optimal input method can be suggested by analyzing the user's past driving history.

[0036] The reception unit can select an input means according to the user's input method when inputting the departure point and destination. For example, the reception unit automatically sets the departure point and destination when the user simply inputs "from home to the station" by voice. For example, when the user specifies the departure point and destination using voice input, the reception unit automatically sets the input content using voice recognition technology. The reception unit can also allow the user to easily set the departure point and destination by performing specific gestures on the smartphone screen. For example, the reception unit provides an interface that allows the user to set the departure point and destination by performing gestures such as swiping or tapping on the screen. Furthermore, the reception unit can allow the user to set the departure point and destination more intuitively by combining voice input and gesture input. For example, the reception unit provides an interface that allows the user to intuitively input by specifying the departure point by voice and the destination by gesture. This allows the user to intuitively set the departure point and destination by selecting the optimal input means according to the user's input method.

[0037] When inputting a departure point and a destination, the reception unit can prioritize displaying highly relevant candidate locations by taking into account the user's geographical location information. The reception unit, for example, prioritizes displaying places close to the user's current location. For example, the reception unit prioritizes displaying nearby tourist spots and restaurants based on the user's current location. The reception unit can also prioritize displaying places the user has visited in the past. For example, the reception unit displays places the user would like to revisit as candidates based on the user's past visit history. Furthermore, the reception unit can also suggest optimal candidate locations by taking into account the distance from the user's current location. For example, the reception unit prioritizes displaying candidate locations that can be reached in a short time based on the distance from the user's current location. In this way, highly relevant candidate locations can be prioritized by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when the departure point and destination are input, and suggest related candidate locations. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. For example, the reception unit can suggest places visited in the past as candidate locations based on the user's social media check-in history. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. For example, the reception unit can analyze the content of the user's posts and suggest places of interest as candidate locations. Furthermore, the reception unit can suggest related places by referring to the activities of the user's friends on social media. For example, the reception unit can suggest places that the user may be interested in as candidate locations based on places visited by the user's friends. In this way, related candidate locations can be suggested by analyzing the user's social media activity.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting the departure point and destination. The reception unit, for example, preferentially suggests an input method that the user has preferred in the past. For example, if the user has preferred voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit provides an easy-to-use interface based on the user's feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. For example, the reception unit can simplify the input procedure based on the user's feedback to enable efficient input. In this way, the input method can be customized by reflecting the user's past feedback.

[0040] The acquisition unit can analyze past weather data and seasonal data and select an acquisition method. The acquisition unit, for example, suggests an optimal route for a specific season and weather based on past weather data. For example, the acquisition unit analyzes past weather data and selects a route suitable for a specific season and weather. The acquisition unit can also analyze past seasonal data and suggest tourist spots for each season. For example, the acquisition unit suggests famous cherry blossom viewing spots in spring and famous autumn foliage viewing spots in autumn based on past seasonal data. Furthermore, the acquisition unit can combine past weather data and seasonal data to predict the best weather for a drive. For example, the acquisition unit predicts sunny days and days with comfortable temperatures based on past weather data and seasonal data and suggests the best days for a drive. In this way, the optimal acquisition method can be selected by analyzing past data.

[0041] When acquiring the parameters, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is traveling with their family, the acquisition unit can prioritize suggesting family-friendly tourist spots. For example, if the acquisition unit estimates that the user is traveling with their family, the acquisition unit can prioritize suggesting attractions for children and family-friendly restaurants. Furthermore, if the user likes the outdoors, the acquisition unit can prioritize suggesting routes rich in nature. For example, if the acquisition unit estimates that the user likes the outdoors, the acquisition unit can suggest routes rich in nature, such as mountains and the sea. Furthermore, if the user likes to eat out, the acquisition unit can prioritize suggesting gourmet spots. For example, if the acquisition unit estimates that the user likes to eat out, the acquisition unit can suggest local specialty dishes and popular restaurants. In this way, by filtering the parameters according to the user's living situation and areas of interest, optimal information can be provided.

[0042] When acquiring parameters, the acquisition unit can select an optimal acquisition means according to the user's input method. For example, when a user wants to check the weather forecast by voice, the acquisition unit acquires a weather forecast corresponding to the voice input. For example, when a user wants to check the weather forecast using voice input, the acquisition unit acquires weather forecast data using voice recognition technology and provides it as voice. Furthermore, when a user wants to check seasonal information using text, the acquisition unit can also acquire seasonal information corresponding to text input. For example, when a user wants to check seasonal information using text input, the acquisition unit acquires text data and provides it as text. Furthermore, when a user wants to check time information using an image, the acquisition unit can also acquire time information corresponding to the image input. For example, when a user wants to check time information using image input, the acquisition unit acquires image data and provides it as an image. This allows the user to intuitively acquire information by selecting an optimal acquisition means according to the user's input method.

[0043] When acquiring parameters, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring weather forecasts for locations close to the user's current location. For example, the acquisition unit prioritizes acquiring nearby weather forecasts based on the user's current location. The acquisition unit can also prioritize acquiring seasonal information for tourist spots close to the user's current location. For example, the acquisition unit prioritizes acquiring seasonal information for nearby tourist spots based on the user's current location. Furthermore, the acquisition unit can also acquire optimal data by taking into account the distance from the user's current location. For example, the acquisition unit prioritizes acquiring data for locations that can be reached in a short time based on the distance from the user's current location. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0044] When acquiring the parameters, the acquisition unit can analyze the user's social media activities and acquire related data. The acquisition unit, for example, acquires weather forecasts for places the user has checked in to on social media. For example, the acquisition unit acquires weather forecasts for places the user has visited in the past based on the user's social media check-in history. The acquisition unit can also analyze the content of the user's posts on social media to acquire related seasonal information. For example, the acquisition unit analyzes the content of the user's posts to acquire seasonal information for places the user is interested in. Furthermore, the acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires data that the user is likely to be interested in based on information about places the user's friends have visited. In this way, related data can be acquired by analyzing the user's social media activities.

[0045] When acquiring parameters, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit preferentially suggests an acquisition method that the user has preferred in the past. For example, if the user has preferred voice input in the past, the acquisition unit preferentially suggests voice input. The acquisition unit can also customize the acquisition interface based on the user's past feedback. For example, the acquisition unit provides an easy-to-use interface based on the user's feedback. Furthermore, the acquisition unit can optimize the acquisition procedure by reflecting the user's past feedback. For example, the acquisition unit simplifies the acquisition procedure based on the user's feedback, enabling information to be acquired efficiently. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0046] When generating a route, the generation unit can suggest an optimal route by referring to the user's past driving history. The generation unit, for example, suggests an optimal route based on routes the user has used in the past. For example, the generation unit analyzes the user's past driving history and suggests an optimal route based on frequently used routes. The generation unit can also suggest a route that avoids congestion based on the user's past driving history. For example, the generation unit suggests a route that avoids congestion based on the user's past driving history. Furthermore, the generation unit can analyze the user's past driving history and suggest the most efficient route. For example, the generation unit selects and suggests the most efficient route based on the user's past driving history. In this way, the optimal route can be suggested by referring to the user's past driving history.

[0047] The generation unit can improve the route generation algorithm by reflecting user feedback when generating a route. The generation unit adjusts the route generation algorithm, for example, based on feedback previously provided by the user. For example, the generation unit changes the priority of a specific route based on user feedback. The generation unit can also change the priority of a specific route by reflecting user feedback. For example, the generation unit changes the priority of a specific route based on user feedback. Furthermore, the generation unit can develop a new route generation algorithm based on user feedback. For example, the generation unit develops a new route generation algorithm based on user feedback and provides a more optimal route. In this way, the route generation algorithm can be improved by reflecting user feedback.

[0048] When generating a route, the generation unit can propose an optimal route by taking into consideration the user's geographical location information. For example, the generation unit prioritizes proposing places close to the user's current location. For example, the generation unit prioritizes proposing nearby tourist spots and restaurants based on the user's current location. The generation unit can also prioritize proposing places the user has visited in the past. For example, the generation unit suggests places the user would like to revisit as candidates based on the user's past visit history. Furthermore, the generation unit can also propose an optimal route by taking into consideration the distance from the user's current location. For example, the generation unit prioritizes proposing routes that can be reached in the shortest time based on the distance from the user's current location. In this way, the optimal route can be proposed by taking into consideration the user's geographical location information.

[0049] When generating a route, the generation unit can analyze the user's social media activity and suggest related routes. The generation unit, for example, incorporates places the user has checked in to on social media into the route. For example, the generation unit incorporates places the user has visited in the past into the route based on the user's social media check-in history. The generation unit can also analyze the content of the user's social media posts and incorporate related places into the route. For example, the generation unit analyzes the content of the user's posts and incorporates places of interest into the route. Furthermore, the generation unit can incorporate related places into the route based on the activities of the user's friends on social media. For example, the generation unit incorporates places that the user is likely to be interested in into the route based on places the user's friends have visited. In this way, related routes can be suggested by analyzing the user's social media activity.

[0050] The generation unit can customize the route generation method by reflecting the user's past feedback when generating a route. For example, the generation unit preferentially suggests a route generation method that the user has previously preferred. For example, the generation unit preferentially suggests a route generation method that the user has previously preferred. The generation unit can also customize the route generation interface based on the user's past feedback. For example, the generation unit provides an easy-to-use interface based on the user's feedback. Furthermore, the generation unit can also optimize the route generation procedure by reflecting the user's past feedback. For example, the generation unit can simplify the route generation procedure based on the user's feedback, enabling efficient route generation. In this way, the route generation method can be customized by reflecting the user's past feedback.

[0051] When providing information, the providing unit can provide optimal information by referring to the user's past visit history. The providing unit, for example, suggests new related spots based on tourist spots that the user has visited in the past. For example, the providing unit suggests new related tourist spots based on the user's past visit history. The providing unit can also suggest tourist spots that avoid crowds based on the user's past visit history. For example, the providing unit suggests tourist spots that avoid crowds based on the user's past visit history. Furthermore, the providing unit can analyze the user's past visit history and suggest the most efficient tourist spots. For example, the providing unit suggests tourist spots that can be enjoyed most efficiently based on the user's past visit history. In this way, optimal information can be provided by referring to the user's past visit history.

[0052] The providing unit can improve the information providing algorithm by reflecting user feedback when providing information. The providing unit adjusts the information providing algorithm, for example, based on feedback previously provided by the user. For example, the providing unit changes the priority of specific information based on user feedback. The providing unit can also change the priority of specific information by reflecting user feedback. For example, the providing unit changes the priority of specific information based on user feedback. Furthermore, the providing unit can develop a new information providing algorithm based on user feedback. For example, the providing unit develops a new information providing algorithm based on user feedback and provides more optimal information. In this way, the information providing algorithm can be improved by reflecting user feedback.

[0053] When providing information, the providing unit can provide optimal information by taking into consideration the user's geographical location information. For example, the providing unit can prioritize providing information about tourist spots close to the user's current location. For example, the providing unit can prioritize providing information about nearby tourist spots based on the user's current location. The providing unit can also prioritize providing information about places the user has visited in the past. For example, the providing unit can prioritize providing information about places the user wants to revisit based on the user's past visit history. Furthermore, the providing unit can also provide optimal information by taking into consideration the distance from the user's current location. For example, the providing unit can prioritize providing information about tourist spots that can be reached in a short time based on the distance from the user's current location. In this way, optimal information can be provided by taking into consideration the user's geographical location information.

[0054] When providing information, the providing unit can analyze the user's social media activity and provide related information. The providing unit, for example, provides information on places the user has checked in on social media. For example, the providing unit provides information on places the user has visited in the past based on the user's social media check-in history. The providing unit can also analyze the content of the user's posts on social media and provide information on related tourist spots and stores. For example, the providing unit analyzes the content of the user's posts and provides information on tourist spots and stores that the user is interested in. Furthermore, the providing unit can provide information on related places and events based on the activities of the user's friends on social media. For example, the providing unit provides information on places and events that the user may be interested in based on information on places the user's friends have visited and events they have attended. In this way, it is possible to provide related information by analyzing the user's social media activity.

[0055] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. For example, the providing unit preferentially suggests an information provision method that the user has previously preferred. For example, the providing unit preferentially suggests an information provision method that the user has previously preferred. The providing unit can also customize the information provision interface based on the user's past feedback. For example, the providing unit provides an easy-to-use interface based on the user's feedback. Furthermore, the providing unit can also optimize the information provision procedure by reflecting the user's past feedback. For example, the providing unit can simplify the information provision procedure based on the user's feedback, enabling information to be provided efficiently. In this way, the information provision method can be customized by reflecting the user's past feedback.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the user's past driving history and suggest input methods. For example, it can automatically display as candidates the departure point and destination that the user has frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest departure points and destinations to be used during a specific time period based on the user's past driving history. This makes it possible to suggest the optimal input method by analyzing the user's past driving history.

[0058] The acquisition unit can prioritize acquisition of highly relevant data by taking into account the user's geographical location information. For example, the acquisition unit can prioritize acquisition of weather forecasts for locations close to the user's current location. The acquisition unit can also prioritize acquisition of seasonal information for tourist spots close to the user's current location. Furthermore, the acquisition unit can also acquire optimal data by taking into account the distance from the user's current location. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0059] When acquiring the parameters, the acquisition unit can analyze the user's social media activities and acquire related data. For example, the acquisition unit can acquire the weather forecast for the location where the user checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related seasonal information. Furthermore, the acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. In this way, it is possible to acquire related data by analyzing the user's social media activities.

[0060] When generating a route, the generation unit can suggest an optimal route by referring to the user's past driving history. For example, the generation unit can suggest an optimal route based on routes the user has used in the past. The generation unit can also suggest a route that avoids congestion based on the user's past driving history. Furthermore, the generation unit can analyze the user's past driving history and suggest the most efficient route. In this way, the generation unit can suggest an optimal route by referring to the user's past driving history.

[0061] When providing information, the providing unit can provide optimal information by referring to the user's past visit history. For example, the providing unit can suggest new related spots based on tourist spots that the user has visited in the past. The providing unit can also suggest tourist spots that avoid crowds based on the user's past visit history. Furthermore, the providing unit can analyze the user's past visit history and suggest the most efficient tourist spots. In this way, optimal information can be provided by referring to the user's past visit history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The user inputs their departure point and destination into the reception unit. The user can input their departure point and destination using a smartphone or tablet, or by voice input. Furthermore, the user can input conditions such as a scenic route or wanting to avoid crowds. Step 2: The acquisition unit acquires weather forecast, season, and time parameters based on the information received by the reception unit. The acquisition unit acquires weather forecasts from data from the Japan Meteorological Agency and private weather forecast services, and acquires seasonal information by referring to calendars and weather data. It also acquires time information by referencing the system's internal clock and an Internet time server. Step 3: The generation unit generates a route based on the parameters acquired by the acquisition unit. Based on the weather forecast, the generation unit generates a scenic route for sunny days and a safe route for rainy days. It can also generate routes that pass through famous cherry blossom viewing spots or autumn foliage viewing spots depending on the season. Furthermore, the generation unit generates a route taking into account information about tourist spots and restaurants that can be visited along the way, depending on the user's preferences. Step 4: The providing unit provides information about tourist attractions or restaurants based on the route generated by the generating unit. The providing unit displays information about tourist attractions and restaurants that the user can stop at during the drive, and can also provide information about specific genres or highly rated restaurants according to the user's preferences.

[0064] (Example 2) A car navigation app according to an embodiment of the present invention is a system that suggests optimal routes and destinations for a user to enjoy driving. This car navigation app suggests routes and destinations for a user to enjoy driving based on current parameters such as the weather forecast, season, and time of day, as well as conditions entered by the user. For example, the user inputs the starting point, destination, and driving conditions (e.g., a scenic route, avoiding crowds, etc.). The app then acquires parameters such as the weather forecast, season, and time of day and generates an optimal route based on this information. For example, the app suggests a scenic route on a sunny day and a safe route on a rainy day. It can also suggest routes that pass through famous cherry blossom viewing spots or autumn foliage viewing spots depending on the season. Furthermore, the app provides information on tourist spots and restaurants that can be visited along the way, based on the user's preferences. This allows the user to enjoy driving while arriving at their destination. This car navigation app offers a new experience for users to enjoy driving, setting it apart from conventional car navigation apps.

[0065] A car navigation app according to an embodiment includes a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit receives input from a user of a departure point and a destination. The user's input conditions include, but are not limited to, a scenic route and avoiding crowds. The reception unit allows the user to input the departure point and destination using, for example, a smartphone or tablet. The reception unit can also input the departure point and destination using voice input. The acquisition unit acquires weather forecast, season, and time parameters based on the information received by the reception unit. The acquisition unit acquires weather forecasts from, for example, data from the Japan Meteorological Agency or private weather forecast services. The acquisition unit can also reference calendars and weather data to acquire seasonal information. The acquisition unit can also reference an internal system clock or an Internet time server to acquire time information. The generation unit generates a route based on the parameters acquired by the acquisition unit. For example, the generation unit generates a scenic route for sunny days and a safe route for rainy days based on the weather forecast. The generation unit can also generate routes that pass through famous cherry blossom viewing spots or famous autumn foliage viewing spots depending on the season. Furthermore, the generation unit can generate routes taking into account information about tourist spots and restaurants that the user can stop at along the way, according to the user's preferences. The provision unit provides information about tourist spots or restaurants based on the route generated by the generation unit. For example, the provision unit displays information about tourist spots and restaurants that the user can stop at during a drive. The provision unit can also provide information about specific genres or highly rated restaurants, according to the user's preferences. This allows the car navigation app according to the embodiment to suggest optimal routes and destinations for the user to enjoy a drive.

[0066] The acquisition unit can acquire parameters such as weather forecast, season, and time. The acquisition unit can acquire weather forecasts, for example, from data from the Japan Meteorological Agency or private weather forecast services. For example, the acquisition unit can use the Japan Meteorological Agency's API via the Internet to acquire the latest weather forecast data. The acquisition unit can also refer to a calendar or weather data to acquire seasonal information. For example, the acquisition unit can determine the current season based on calendar data and acquire information corresponding to the season. The acquisition unit can also refer to the system's internal clock or an Internet time server to acquire time information. For example, the acquisition unit can acquire the current time based on the system's internal clock and provide information corresponding to the time. In this way, by acquiring parameters such as weather forecast, season, and time, information for generating an optimal route can be provided.

[0067] The generation unit can generate a route based on the acquired parameters. For example, the generation unit can generate a scenic route on sunny days and a safe route on rainy days based on a weather forecast. For example, the generation unit can analyze weather forecast data and select a scenic route on sunny days and a safe route with low traffic on rainy days. The generation unit can also generate routes that pass through famous cherry blossom viewing spots and famous autumn foliage viewing spots depending on the season. For example, the generation unit can select a route that passes through famous cherry blossom viewing spots in spring and a route that passes through famous autumn foliage viewing spots in autumn based on seasonal data. Furthermore, the generation unit can generate a route taking into account information about tourist spots and restaurants that can be visited along the way, according to the user's preferences. For example, the generation unit can generate a route that incorporates information about tourist spots and restaurants based on conditions entered by the user. In this way, by generating an optimal route based on the acquired parameters, it is possible to provide a route that allows the user to enjoy a drive.

[0068] The providing unit can provide information about tourist spots or restaurants based on the generated route. The providing unit, for example, displays information about tourist spots and restaurants that the user can stop at during a drive. For example, the providing unit can display information about tourist spots and restaurants on the generated route on a map, allowing the user to easily access them. The providing unit can also provide information about restaurants in a specific genre or with high ratings according to the user's preferences. For example, the providing unit can preferentially display restaurants with high ratings or restaurants in a specific genre based on conditions entered by the user. In this way, by providing information about tourist spots and restaurants based on the generated route, it is possible to provide information for the user to enjoy their drive.

[0069] The generation unit can generate a scenic route on a sunny day and a safe route on a rainy day. The generation unit can, for example, analyze weather forecast data and select a scenic route on a sunny day and a safe route with less traffic on a rainy day. For example, the generation unit can suggest a scenic route along the mountains or the sea on a sunny day and a route with less traffic that avoids major roads on a rainy day. The generation unit can also select a route that avoids slippery roads on a rainy day based on the weather forecast data. For example, the generation unit can suggest a route with fewer sharp curves and slopes on a rainy day. In this way, the optimal route can be generated according to the weather, thereby providing the user with a safe and enjoyable route.

[0070] The providing unit can provide routes that pass through famous cherry blossom viewing spots or famous autumn foliage viewing spots according to the season. For example, the providing unit can select a route that passes through famous cherry blossom viewing spots in spring and a route that passes through famous autumn foliage viewing spots in autumn based on seasonal data. For example, the providing unit can suggest a route that passes through parks and temples known as famous cherry blossom viewing spots in spring, and a route that passes through mountains and valleys known as famous autumn foliage viewing spots in autumn. The providing unit can also provide information on tourist spots according to the season. For example, the providing unit can provide information on cherry blossom viewing spots in spring and information on autumn leaf viewing spots in autumn. In this way, by providing routes that pass through tourist spots according to the season, the user can enjoy the seasons.

[0071] The reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the reception unit estimates that the user is feeling stressed, it simplifies the input of the departure point and destination, allowing input with minimal operations. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the reception unit estimates that the user is relaxed, it can provide an option to set detailed stopovers and route conditions in addition to inputting the departure point and destination. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly input the departure point and destination. For example, if the reception unit estimates that the user is in a hurry, it can allow the user to quickly input the departure point and destination using voice input. In this way, the input method can be adjusted according to the user's emotions, allowing the user to comfortably input the departure point and destination.

[0072] The reception unit can analyze the user's past driving history and suggest an input method. For example, the reception unit automatically displays as candidates starting points and destinations that the user has frequently input in the past. For example, the reception unit analyzes the user's past driving history and displays frequently visited places as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest starting points and destinations to be used during a specific time period based on the user's past driving history. For example, if the user has visited a specific place during a specific time period in the past, the reception unit suggests starting points and destinations appropriate for that time period. In this way, the optimal input method can be suggested by analyzing the user's past driving history.

[0073] When the user inputs the departure point and destination, the reception unit can filter the input content based on the user's current mood and physical condition. For example, if the user is tired, the reception unit prioritizes displaying destinations that can be reached in a short time. For example, if the reception unit estimates that the user is tired, the reception unit prioritizes displaying nearby tourist spots and restaurants. The reception unit can also suggest places rich in nature as destinations if the user wants to refresh themselves. For example, if the reception unit estimates that the user wants to refresh themselves, the reception unit suggests places rich in nature, such as mountains and the sea, as destinations. Furthermore, if the user is feeling unwell, the reception unit can also suggest nearby medical facilities as destinations. For example, if the reception unit estimates that the user is feeling unwell, the reception unit suggests nearby hospitals and clinics as destinations. In this way, by filtering the input content according to the user's mood and physical condition, more appropriate destinations can be suggested.

[0074] The reception unit can select an input means according to the user's input method when inputting the departure point and destination. For example, the reception unit automatically sets the departure point and destination when the user simply inputs "from home to the station" by voice. For example, when the user specifies the departure point and destination using voice input, the reception unit automatically sets the input content using voice recognition technology. The reception unit can also allow the user to easily set the departure point and destination by performing specific gestures on the smartphone screen. For example, the reception unit provides an interface that allows the user to set the departure point and destination by performing gestures such as swiping or tapping on the screen. Furthermore, the reception unit can allow the user to set the departure point and destination more intuitively by combining voice input and gesture input. For example, the reception unit provides an interface that allows the user to intuitively input by specifying the departure point by voice and the destination by gesture. This allows the user to intuitively set the departure point and destination by selecting the optimal input means according to the user's input method.

[0075] The reception unit can estimate the user's emotions and determine the priority of the departure point and destination to be input based on the estimated user's emotions. For example, if the user is relaxed, the reception unit can preferentially suggest a scenic route. For example, if the reception unit estimates that the user is relaxed, the reception unit can preferentially suggest a scenic route. Furthermore, if the user is in a hurry, the reception unit can preferentially suggest the shortest route. For example, if the reception unit estimates that the user is in a hurry, the reception unit can preferentially suggest a route that can be reached in the shortest time. Furthermore, if the user has a spirit of adventure, the reception unit can preferentially suggest new routes or unexplored places. For example, if the reception unit estimates that the user has a spirit of adventure, the reception unit can suggest unvisited tourist spots or new routes. In this way, by prioritizing the departure point and destination according to the user's emotions, the user can select the optimal route.

[0076] When inputting a departure point and a destination, the reception unit can prioritize displaying highly relevant candidate locations by taking into account the user's geographical location information. The reception unit, for example, prioritizes displaying places close to the user's current location. For example, the reception unit prioritizes displaying nearby tourist spots and restaurants based on the user's current location. The reception unit can also prioritize displaying places the user has visited in the past. For example, the reception unit displays places the user would like to revisit as candidates based on the user's past visit history. Furthermore, the reception unit can also suggest optimal candidate locations by taking into account the distance from the user's current location. For example, the reception unit prioritizes displaying candidate locations that can be reached in a short time based on the distance from the user's current location. In this way, highly relevant candidate locations can be prioritized by taking into account the user's geographical location information.

[0077] The reception unit can analyze the user's social media activity when the departure point and destination are input, and suggest related candidate locations. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. For example, the reception unit can suggest places visited in the past as candidate locations based on the user's social media check-in history. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. For example, the reception unit can analyze the content of the user's posts and suggest places of interest as candidate locations. Furthermore, the reception unit can suggest related places by referring to the activities of the user's friends on social media. For example, the reception unit can suggest places that the user may be interested in as candidate locations based on places visited by the user's friends. In this way, related candidate locations can be suggested by analyzing the user's social media activity.

[0078] The reception unit can customize the input method by reflecting the user's past feedback when inputting the departure point and destination. The reception unit, for example, preferentially suggests an input method that the user has preferred in the past. For example, if the user has preferred voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit provides an easy-to-use interface based on the user's feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. For example, the reception unit can simplify the input procedure based on the user's feedback to enable efficient input. In this way, the input method can be customized by reflecting the user's past feedback.

[0079] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring parameters such as weather forecasts, seasons, and time of day based on the estimated user's emotions. For example, if the user is relaxed, the acquisition unit frequently acquires weather forecasts and suggests optimal driving conditions. For example, if the acquisition unit estimates that the user is relaxed, the acquisition unit frequently updates the weather forecast and suggests sunny days. Furthermore, if the user is in a hurry, the acquisition unit can acquire the minimum necessary parameters and quickly suggest a route. For example, if the acquisition unit estimates that the user is in a hurry, the acquisition unit can acquire minimal weather forecast and time information and quickly generate a route. Furthermore, if the user is adventurous, the acquisition unit can acquire parameters to suggest special routes according to the season and time of day. For example, if the acquisition unit estimates that the user is adventurous, the acquisition unit can acquire information on special tourist spots and events according to the season and time of day. This allows the optimal information to be provided by adjusting the timing of parameter acquisition according to the user's emotions.

[0080] The acquisition unit can analyze past weather data and seasonal data and select an acquisition method. The acquisition unit, for example, suggests an optimal route for a specific season and weather based on past weather data. For example, the acquisition unit analyzes past weather data and selects a route suitable for a specific season and weather. The acquisition unit can also analyze past seasonal data and suggest tourist spots for each season. For example, the acquisition unit suggests famous cherry blossom viewing spots in spring and famous autumn foliage viewing spots in autumn based on past seasonal data. Furthermore, the acquisition unit can combine past weather data and seasonal data to predict the best weather for a drive. For example, the acquisition unit predicts sunny days and days with comfortable temperatures based on past weather data and seasonal data and suggests the best days for a drive. In this way, the optimal acquisition method can be selected by analyzing past data.

[0081] When acquiring the parameters, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is traveling with their family, the acquisition unit can prioritize suggesting family-friendly tourist spots. For example, if the acquisition unit estimates that the user is traveling with their family, the acquisition unit can prioritize suggesting attractions for children and family-friendly restaurants. Furthermore, if the user likes the outdoors, the acquisition unit can prioritize suggesting routes rich in nature. For example, if the acquisition unit estimates that the user likes the outdoors, the acquisition unit can suggest routes rich in nature, such as mountains and the sea. Furthermore, if the user likes to eat out, the acquisition unit can prioritize suggesting gourmet spots. For example, if the acquisition unit estimates that the user likes to eat out, the acquisition unit can suggest local specialty dishes and popular restaurants. In this way, by filtering the parameters according to the user's living situation and areas of interest, optimal information can be provided.

[0082] When acquiring parameters, the acquisition unit can select an optimal acquisition means according to the user's input method. For example, when a user wants to check the weather forecast by voice, the acquisition unit acquires a weather forecast corresponding to the voice input. For example, when a user wants to check the weather forecast using voice input, the acquisition unit acquires weather forecast data using voice recognition technology and provides it as voice. Furthermore, when a user wants to check seasonal information using text, the acquisition unit can also acquire seasonal information corresponding to text input. For example, when a user wants to check seasonal information using text input, the acquisition unit acquires text data and provides it as text. Furthermore, when a user wants to check time information using an image, the acquisition unit can also acquire time information corresponding to the image input. For example, when a user wants to check time information using image input, the acquisition unit acquires image data and provides it as an image. This allows the user to intuitively acquire information by selecting an optimal acquisition means according to the user's input method.

[0083] The acquisition unit can estimate the user's emotions and determine the priority of parameters to be acquired based on the estimated user's emotions. For example, if the user is relaxed, the acquisition unit prioritizes acquiring a weather forecast. For example, if the acquisition unit estimates that the user is relaxed, the acquisition unit prioritizes acquiring a weather forecast and suggests sunny days. The acquisition unit can also prioritize acquiring time information if the user is in a hurry. For example, if the acquisition unit estimates that the user is in a hurry, the acquisition unit prioritizes acquiring time information and quickly generates a route. Furthermore, the acquisition unit can prioritize acquiring seasonal information if the user has a sense of adventure. For example, if the acquisition unit estimates that the user has a sense of adventure, the acquisition unit prioritizes acquiring seasonal information and provides information on special tourist spots and events. In this way, by determining the priority of parameters according to the user's emotions, optimal information can be provided.

[0084] When acquiring parameters, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring weather forecasts for locations close to the user's current location. For example, the acquisition unit prioritizes acquiring nearby weather forecasts based on the user's current location. The acquisition unit can also prioritize acquiring seasonal information for tourist spots close to the user's current location. For example, the acquisition unit prioritizes acquiring seasonal information for nearby tourist spots based on the user's current location. Furthermore, the acquisition unit can also acquire optimal data by taking into account the distance from the user's current location. For example, the acquisition unit prioritizes acquiring data for locations that can be reached in a short time based on the distance from the user's current location. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0085] When acquiring the parameters, the acquisition unit can analyze the user's social media activities and acquire related data. The acquisition unit, for example, acquires weather forecasts for places the user has checked in to on social media. For example, the acquisition unit acquires weather forecasts for places the user has visited in the past based on the user's social media check-in history. The acquisition unit can also analyze the content of the user's posts on social media to acquire related seasonal information. For example, the acquisition unit analyzes the content of the user's posts to acquire seasonal information for places the user is interested in. Furthermore, the acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires data that the user is likely to be interested in based on information about places the user's friends have visited. In this way, related data can be acquired by analyzing the user's social media activities.

[0086] When acquiring parameters, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit preferentially suggests an acquisition method that the user has preferred in the past. For example, if the user has preferred voice input in the past, the acquisition unit preferentially suggests voice input. The acquisition unit can also customize the acquisition interface based on the user's past feedback. For example, the acquisition unit provides an easy-to-use interface based on the user's feedback. Furthermore, the acquisition unit can optimize the acquisition procedure by reflecting the user's past feedback. For example, the acquisition unit simplifies the acquisition procedure based on the user's feedback, enabling information to be acquired efficiently. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0087] The generation unit can estimate the user's emotions and adjust the route generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit prioritizes generating a scenic route. For example, if the generation unit estimates that the user is relaxed, the generation unit prioritizes generating a scenic route. Furthermore, if the user is in a hurry, the generation unit can prioritize generating the shortest route. For example, if the generation unit estimates that the user is in a hurry, the generation unit prioritizes generating a route that can be reached in the shortest time. Furthermore, if the user has a spirit of adventure, the generation unit can prioritize generating new routes or unexplored places. For example, if the generation unit estimates that the user has a spirit of adventure, the generation unit generates unvisited tourist spots or new routes. In this way, by adjusting the route generation method according to the user's emotions, it is possible to provide a route that allows the user to enjoy a comfortable drive.

[0088] When generating a route, the generation unit can suggest an optimal route by referring to the user's past driving history. The generation unit, for example, suggests an optimal route based on routes the user has used in the past. For example, the generation unit analyzes the user's past driving history and suggests an optimal route based on frequently used routes. The generation unit can also suggest a route that avoids congestion based on the user's past driving history. For example, the generation unit suggests a route that avoids congestion based on the user's past driving history. Furthermore, the generation unit can analyze the user's past driving history and suggest the most efficient route. For example, the generation unit selects and suggests the most efficient route based on the user's past driving history. In this way, the optimal route can be suggested by referring to the user's past driving history.

[0089] When generating a route, the generation unit can customize the route based on the user's current mood and physical condition. For example, if the user is tired, the generation unit prioritizes generating a route that can reach the destination in a short time. For example, if the generation unit estimates that the user is tired, the generation unit generates a route that can reach the destination in a short time that passes through nearby tourist spots and restaurants. Furthermore, if the user wants to refresh themselves, the generation unit can prioritize generating a route that is rich in nature. For example, if the generation unit estimates that the user wants to refresh themselves, the generation unit generates a route that is rich in nature, such as mountains or the sea. Furthermore, if the user is feeling unwell, the generation unit can prioritize generating a route that passes through nearby medical facilities. For example, if the generation unit estimates that the user is feeling unwell, the generation unit generates a route that passes through nearby hospitals and clinics. In this way, by customizing the route according to the user's mood and physical condition, a more appropriate route can be provided.

[0090] The generation unit can improve the route generation algorithm by reflecting user feedback when generating a route. The generation unit adjusts the route generation algorithm, for example, based on feedback previously provided by the user. For example, the generation unit changes the priority of a specific route based on user feedback. The generation unit can also change the priority of a specific route by reflecting user feedback. For example, the generation unit changes the priority of a specific route based on user feedback. Furthermore, the generation unit can develop a new route generation algorithm based on user feedback. For example, the generation unit develops a new route generation algorithm based on user feedback and provides a more optimal route. In this way, the route generation algorithm can be improved by reflecting user feedback.

[0091] The generation unit can estimate the user's emotions and determine the priority of routes to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit preferentially suggests routes with beautiful scenery. For example, if the generation unit estimates that the user is relaxed, the generation unit preferentially suggests routes with beautiful scenery. Furthermore, if the user is in a hurry, the generation unit can preferentially suggest the shortest route. For example, if the generation unit estimates that the user is in a hurry, the generation unit preferentially suggests a route that can be reached in the shortest time. Furthermore, if the user has a spirit of adventure, the generation unit can preferentially suggest new routes or unexplored places. For example, if the generation unit estimates that the user has a spirit of adventure, the generation unit suggests unvisited tourist spots or new routes. In this way, by determining the priority of routes to be generated according to the user's emotions, it is possible to provide an optimal route.

[0092] When generating a route, the generation unit can propose an optimal route by taking into consideration the user's geographical location information. For example, the generation unit prioritizes proposing places close to the user's current location. For example, the generation unit prioritizes proposing nearby tourist spots and restaurants based on the user's current location. The generation unit can also prioritize proposing places the user has visited in the past. For example, the generation unit suggests places the user would like to revisit as candidates based on the user's past visit history. Furthermore, the generation unit can also propose an optimal route by taking into consideration the distance from the user's current location. For example, the generation unit prioritizes proposing routes that can be reached in the shortest time based on the distance from the user's current location. In this way, the optimal route can be proposed by taking into consideration the user's geographical location information.

[0093] When generating a route, the generation unit can analyze the user's social media activity and suggest related routes. The generation unit, for example, incorporates places the user has checked in to on social media into the route. For example, the generation unit incorporates places the user has visited in the past into the route based on the user's social media check-in history. The generation unit can also analyze the content of the user's social media posts and incorporate related places into the route. For example, the generation unit analyzes the content of the user's posts and incorporates places of interest into the route. Furthermore, the generation unit can incorporate related places into the route based on the activities of the user's friends on social media. For example, the generation unit incorporates places that the user is likely to be interested in into the route based on places the user's friends have visited. In this way, related routes can be suggested by analyzing the user's social media activity.

[0094] The generation unit can customize the route generation method by reflecting the user's past feedback when generating a route. For example, the generation unit preferentially suggests a route generation method that the user has previously preferred. For example, the generation unit preferentially suggests a route generation method that the user has previously preferred. The generation unit can also customize the route generation interface based on the user's past feedback. For example, the generation unit provides an easy-to-use interface based on the user's feedback. Furthermore, the generation unit can also optimize the route generation procedure by reflecting the user's past feedback. For example, the generation unit can simplify the route generation procedure based on the user's feedback, enabling efficient route generation. In this way, the route generation method can be customized by reflecting the user's past feedback.

[0095] The providing unit can estimate the user's emotions and adjust the method of providing information about tourist spots and restaurants based on the estimated user's emotions. For example, if the user is relaxed, the providing unit provides information about tourist spots that can be progressed at a leisurely pace. For example, if the providing unit estimates that the user is relaxed, it provides information about tourist spots that can be enjoyed at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can also provide information about tourist spots that can be enjoyed in the shortest time. For example, if the providing unit estimates that the user is in a hurry, it provides information about tourist spots that can be enjoyed in a short amount of time. Furthermore, if the user is excited, the providing unit can also provide information about visually stimulating tourist spots. For example, if the providing unit estimates that the user is excited, it provides information about visually attractive tourist spots. In this way, by adjusting the method of providing information according to the user's emotions, the user can receive information comfortably.

[0096] When providing information, the providing unit can provide optimal information by referring to the user's past visit history. The providing unit, for example, suggests new related spots based on tourist spots that the user has visited in the past. For example, the providing unit suggests new related tourist spots based on the user's past visit history. The providing unit can also suggest tourist spots that avoid crowds based on the user's past visit history. For example, the providing unit suggests tourist spots that avoid crowds based on the user's past visit history. Furthermore, the providing unit can analyze the user's past visit history and suggest the most efficient tourist spots. For example, the providing unit suggests tourist spots that can be enjoyed most efficiently based on the user's past visit history. In this way, optimal information can be provided by referring to the user's past visit history.

[0097] The providing unit can customize the information based on the user's current mood and physical condition when providing information. For example, if the user is tired, the providing unit provides information on tourist spots where the user can relax. For example, if the providing unit estimates that the user is tired, the providing unit provides information on tourist spots where the user can relax. Furthermore, if the user wants to refresh themselves, the providing unit can also provide information on tourist spots rich in nature. For example, if the providing unit estimates that the user wants to refresh themselves, the providing unit provides information on tourist spots rich in nature. Furthermore, if the user is feeling unwell, the providing unit can also provide information on nearby medical facilities. For example, if the providing unit estimates that the user is feeling unwell, the providing unit provides information on nearby hospitals and clinics. In this way, by customizing the information according to the user's mood and physical condition, more appropriate information can be provided.

[0098] The providing unit can improve the information providing algorithm by reflecting user feedback when providing information. The providing unit adjusts the information providing algorithm, for example, based on feedback previously provided by the user. For example, the providing unit changes the priority of specific information based on user feedback. The providing unit can also change the priority of specific information by reflecting user feedback. For example, the providing unit changes the priority of specific information based on user feedback. Furthermore, the providing unit can develop a new information providing algorithm based on user feedback. For example, the providing unit develops a new information providing algorithm based on user feedback and provides more optimal information. In this way, the information providing algorithm can be improved by reflecting user feedback.

[0099] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing information about tourist spots with beautiful scenery. For example, when the providing unit estimates that the user is relaxed, the providing unit can prioritize providing information about tourist spots with beautiful scenery. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information about tourist spots that can be enjoyed in the shortest time. For example, when the providing unit estimates that the user is in a hurry, the providing unit can prioritize providing information about tourist spots that can be enjoyed in a short time. Furthermore, when the user has a spirit of adventure, the providing unit can prioritize providing information about new tourist spots. For example, when the providing unit estimates that the user has a spirit of adventure, the providing unit can prioritize providing information about unvisited tourist spots or new tourist spots. In this way, by determining the priority of information to be provided according to the user's emotions, it is possible to provide optimal information.

[0100] When providing information, the providing unit can provide optimal information by taking into consideration the user's geographical location information. For example, the providing unit can prioritize providing information about tourist spots close to the user's current location. For example, the providing unit can prioritize providing information about nearby tourist spots based on the user's current location. The providing unit can also prioritize providing information about places the user has visited in the past. For example, the providing unit can prioritize providing information about places the user wants to revisit based on the user's past visit history. Furthermore, the providing unit can also provide optimal information by taking into consideration the distance from the user's current location. For example, the providing unit can prioritize providing information about tourist spots that can be reached in a short time based on the distance from the user's current location. In this way, optimal information can be provided by taking into consideration the user's geographical location information.

[0101] When providing information, the providing unit can analyze the user's social media activity and provide related information. The providing unit, for example, provides information on places the user has checked in on social media. For example, the providing unit provides information on places the user has visited in the past based on the user's social media check-in history. The providing unit can also analyze the content of the user's posts on social media and provide information on related tourist spots and stores. For example, the providing unit analyzes the content of the user's posts and provides information on tourist spots and stores that the user is interested in. Furthermore, the providing unit can provide information on related places and events based on the activities of the user's friends on social media. For example, the providing unit provides information on places and events that the user may be interested in based on information on places the user's friends have visited and events they have attended. In this way, it is possible to provide related information by analyzing the user's social media activity.

[0102] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. For example, the providing unit preferentially suggests an information provision method that the user has previously preferred. For example, the providing unit preferentially suggests an information provision method that the user has previously preferred. The providing unit can also customize the information provision interface based on the user's past feedback. For example, the providing unit provides an easy-to-use interface based on the user's feedback. Furthermore, the providing unit can also optimize the information provision procedure by reflecting the user's past feedback. For example, the providing unit can simplify the information provision procedure based on the user's feedback, enabling information to be provided efficiently. In this way, the information provision method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit allows the user to input a departure point and a destination using a touch panel 38A or a microphone 38B of the smart device 14. The acquisition unit acquires, for example, parameters such as weather forecast, season, and time using the specific processing unit 290 of the data processing device 12. The generation unit generates a route based on, for example, the parameters acquired by the specific processing unit 290 of the data processing device 12. The provision unit can provide information on tourist spots and restaurants using, for example, a display 40A or a speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit allows the user to input a departure point and a destination using the microphone 238 of the smart glasses 214. The acquisition unit, for example, acquires parameters such as weather forecast, season, and time of day using the specific processing unit 290 of the data processing device 12. The generation unit, for example, generates a route based on the parameters acquired by the specific processing unit 290 of the data processing device 12. The provision unit, for example, can provide information on tourist attractions and restaurants using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit allows the user to input a departure point and a destination using the microphone 238 of the headset-type terminal 314. The acquisition unit acquires, for example, parameters such as weather forecast, season, and time using the specific processing unit 290 of the data processing device 12. The generation unit generates a route based on, for example, the parameters acquired by the specific processing unit 290 of the data processing device 12. The provision unit can provide information on tourist spots and restaurants using, for example, the display 343 or speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows the user to input a departure point and a destination using the microphone 238 of the robot 414. The acquisition unit acquires, for example, parameters such as weather forecast, season, and time of day using the specific processing unit 290 of the data processing device 12. The generation unit generates a route based on, for example, the parameters acquired by the specific processing unit 290 of the data processing device 12. The provision unit can provide information on tourist attractions and restaurants using, for example, the speaker 240 of the robot 414.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The reception unit can analyze the user's past driving history and suggest input methods. For example, it can automatically display as candidates the departure point and destination that the user has frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest departure points and destinations to be used during a specific time period based on the user's past driving history. This makes it possible to suggest the optimal input method by analyzing the user's past driving history.

[0105] The acquisition unit can prioritize acquisition of highly relevant data by taking into account the user's geographical location information. For example, the acquisition unit can prioritize acquisition of weather forecasts for locations close to the user's current location. The acquisition unit can also prioritize acquisition of seasonal information for tourist spots close to the user's current location. Furthermore, the acquisition unit can also acquire optimal data by taking into account the distance from the user's current location. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0106] The generation unit can estimate the user's emotions and adjust the route generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can prioritize generating a scenic route. If the user is in a hurry, the generation unit can also prioritize generating the shortest route. Furthermore, if the user is adventurous, the generation unit can also prioritize generating new routes or unexplored locations. In this way, by adjusting the route generation method according to the user's emotions, it is possible to provide a route that the user can enjoy driving comfortably.

[0107] The providing unit can estimate the user's emotions and adjust the method of providing information about tourist spots and restaurants based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide information about tourist spots that can be enjoyed at a leisurely pace. If the user is in a hurry, the providing unit can also provide information about tourist spots that can be enjoyed in the shortest time. Furthermore, if the user is excited, the providing unit can also provide information about visually stimulating tourist spots. In this way, the method of providing information can be adjusted according to the user's emotions, allowing the user to receive information comfortably.

[0108] The reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the departure point and destination. In this way, adjusting the input method according to the user's emotions allows the user to input the departure point and destination comfortably.

[0109] When acquiring the parameters, the acquisition unit can analyze the user's social media activities and acquire related data. For example, the acquisition unit can acquire the weather forecast for the location where the user checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related seasonal information. Furthermore, the acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. In this way, it is possible to acquire related data by analyzing the user's social media activities.

[0110] When generating a route, the generation unit can suggest an optimal route by referring to the user's past driving history. For example, the generation unit can suggest an optimal route based on routes the user has used in the past. The generation unit can also suggest a route that avoids congestion based on the user's past driving history. Furthermore, the generation unit can analyze the user's past driving history and suggest the most efficient route. In this way, the generation unit can suggest an optimal route by referring to the user's past driving history.

[0111] When providing information, the providing unit can provide optimal information by referring to the user's past visit history. For example, the providing unit can suggest new related spots based on tourist spots that the user has visited in the past. The providing unit can also suggest tourist spots that avoid crowds based on the user's past visit history. Furthermore, the providing unit can analyze the user's past visit history and suggest the most efficient tourist spots. In this way, optimal information can be provided by referring to the user's past visit history.

[0112] When providing information, the providing unit can customize the information based on the user's current mood and physical condition. For example, if the user is tired, the providing unit can provide information on tourist spots where the user can relax. If the user wants to refresh themselves, the providing unit can also provide information on tourist spots rich in nature. Furthermore, if the user is feeling unwell, the providing unit can also provide information on nearby medical facilities. In this way, by customizing the information according to the user's mood and physical condition, more appropriate information can be provided.

[0113] The acquisition unit can estimate the user's emotions and determine the priority of parameters to be acquired based on the estimated user's emotions. For example, if the user is relaxed, the acquisition unit can prioritize acquiring a weather forecast. If the user is in a hurry, the acquisition unit can also prioritize acquiring time information. Furthermore, if the user is adventurous, the acquisition unit can also prioritize acquiring seasonal information. In this way, by determining the priority of parameters according to the user's emotions, it is possible to provide optimal information.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The user inputs their departure point and destination into the reception unit. The user can input their departure point and destination using a smartphone or tablet, or by voice input. Furthermore, the user can input conditions such as a scenic route or wanting to avoid crowds. Step 2: The acquisition unit acquires weather forecast, season, and time parameters based on the information received by the reception unit. The acquisition unit acquires weather forecasts from data from the Japan Meteorological Agency and private weather forecast services, and acquires seasonal information by referring to calendars and weather data. It also acquires time information by referencing the system's internal clock and an Internet time server. Step 3: The generation unit generates a route based on the parameters acquired by the acquisition unit. Based on the weather forecast, the generation unit generates a scenic route for sunny days and a safe route for rainy days. It can also generate routes that pass through famous cherry blossom viewing spots or autumn foliage viewing spots depending on the season. Furthermore, the generation unit generates a route taking into account information about tourist spots and restaurants that can be visited along the way, depending on the user's preferences. Step 4: The providing unit provides information about tourist attractions or restaurants based on the route generated by the generating unit. The providing unit displays information about tourist attractions and restaurants that the user can stop at during the drive, and can also provide information about specific genres or highly rated restaurants according to the user's preferences.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 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 reception unit that receives input of a departure point and a destination; an acquisition unit that acquires parameters of a weather forecast, a season, and a time based on the information received by the reception unit; a generation unit that generates a route based on the parameters acquired by the acquisition unit; a provision unit that provides information about tourist spots or restaurants based on the route generated by the generation unit; Equipped with A system characterized by:

2. The acquisition unit Get weather forecast, season, and time parameters 2. The system of claim 1.

3. The generation unit Generate a route based on the parameters obtained 2. The system of claim 1.

4. The providing unit Providing tourist attraction or restaurant information based on the generated route 2. The system of claim 1.

5. The generation unit Generate scenic routes on sunny days and routes on rainy days 2. The system of claim 1.

6. The providing unit Providing routes that pass through famous cherry blossom or autumn foliage spots depending on the season 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the input method for departure and destination based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's driving history and suggests input methods 2. The system of claim 1.

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