System

Abstract

An object of a system according to an embodiment is to provide route guidance in consideration of the shade.SOLUTION: A system includes a reception unit, a generation unit, and a navigation unit. The reception unit receives input of a departure place and a destination. The generation unit predicts the position of the sun and the shade of a building or a row of trees on the basis of the information received by the reception unit, and generates an appropriate route. The navigation unit performs navigation based on the route generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 provide route guidance that takes shade into account, making it difficult to travel comfortably, especially in the summer.

[0005] The system according to the embodiment aims to provide route guidance that takes into account shade. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit receives input of a departure point and a destination. The generation unit predicts the position of the sun and the shadow of buildings or trees based on the information received by the reception unit, and generates an appropriate route. The navigation unit performs navigation based on the route generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide route guidance that takes into account shade. [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 navigation system according to an embodiment of the present invention accepts input of a departure point and a destination, predicts the position of the sun and the shade of buildings and trees, generates an optimal route, and performs navigation. The navigation system accepts input of a departure point and a destination, predicts the position of the sun and the shade of buildings and trees, and generates an optimal route. For example, the navigation system generates an optimal route by simply inputting a user's departure point and destination. The generated route also offers enjoyment depending on the time of day. For example, the position of the sun is different in the morning and afternoon, so the route also differs. This allows the user to enjoy a different route each time. Furthermore, the navigation system can be switched to Sunny Navigation in winter. This provides a route in the sun during cold seasons. For example, in winter, a route with a lot of sun is generated, taking the position of the sun into consideration. The navigation system also has a thermo function that displays the different surface temperatures for dirt, grass, and asphalt. This allows the user to select a route while checking the surface temperature. For example, if the asphalt temperature is high, the user can select a grass route. Furthermore, the navigation system also provides functions that alert users when it's time to hydrate and indicate the locations of vending machines. This allows users to hydrate at the appropriate time. For example, hydration alerts are displayed at regular intervals, and the locations of vending machines are displayed on a map. In this way, the navigation system provides a navigation function that is useful when users want to walk in the shade as much as possible during dog walks, jogging, commuting, etc. in the summer, and supports users' comfortable travel. In this way, the navigation system can support users' comfortable travel. For example, it can provide users with the optimal route for walking in the shade, supporting a comfortable journey. Users can also select a route while checking the ground surface temperature, allowing them to hydrate at the appropriate time. This allows users to enjoy a comfortable and healthy journey.

[0029] A navigation system according to an embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit receives input of a departure point and a destination. For example, a user simply inputs the departure point and the destination, and the reception unit receives the information. The generation unit predicts the position of the sun and the shade of buildings and trees based on the information received by the reception unit, and generates an optimal route. For example, the generation unit uses a generation AI to predict the position of the sun and the shade of buildings and trees to generate an optimal route. The generation AI calculates an optimal route based on map data and weather data, and generates navigation along that route. For example, the generation AI receives a prompt such as, "Please predict the optimal shade for this route," and predicts the optimal shade for the route. The navigation unit performs navigation based on the route generated by the generation unit. For example, the navigation unit provides navigation to a user based on the generated route. This allows the user to be provided with an optimal route for walking in the shade, supporting a comfortable journey. This allows the navigation system according to an embodiment to support a user's comfortable journey. For example, it provides the user with an optimal route for walking in the shade, supporting a comfortable journey.

[0030] The generation unit can use the generation AI to predict the position of the sun and the shade of buildings or trees, and generate an appropriate route. The generation unit, for example, uses the generation AI to predict the position of the sun and the shade of buildings or trees. For example, the generation AI calculates an optimal route based on map data and weather data, and generates navigation along that route. The generation unit can also use the generation AI to predict the position of the sun and the shade of buildings or trees, and generate an appropriate route. For example, the generation AI receives a prompt such as "Please predict the optimal shade for this route," and predicts the optimal shade for the route. This makes it possible to provide an optimal route that takes shade into consideration by using the generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input map data and weather data into the generation AI and cause the generation AI to generate an optimal route.

[0031] The generation unit can generate different routes depending on the time of day. The generation unit, for example, generates different routes depending on the time of day. For example, the route differs because the position of the sun is different in the morning and afternoon. The generation unit can also generate different routes depending on the time of day. For example, the generation unit generates an optimal route depending on the time of day. This improves the user's travel experience by providing an optimal route depending on the time of day. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input time of day information into the generation AI and cause the generation AI to generate an optimal route depending on the time of day.

[0032] The generation unit can generate a route that can be switched to sunshine navigation in winter. The generation unit, for example, generates a route that can be switched to sunshine navigation in winter. For example, a route with a lot of sunshine is generated in winter, taking into account the position of the sun. The generation unit can also generate a route that can be switched to sunshine navigation in winter. For example, the generation unit generates a route with a lot of sunshine based on winter information. This supports comfortable travel even in cold seasons by providing a route that walks in sunny areas in winter. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input winter information into the generation AI and cause the generation AI to generate a route that can be switched to sunshine navigation.

[0033] The navigation unit may be equipped with a thermo function that displays the ground surface temperature for each of soil, grass, and asphalt. The navigation unit may be equipped with a thermo function that displays the ground surface temperature for each of soil, grass, and asphalt. For example, the navigation unit may select a route while checking the ground surface temperature. The navigation unit may also be equipped with a thermo function that displays the ground surface temperature for each of soil, grass, and asphalt. For example, the navigation unit may select a grass route if the asphalt temperature is high. This allows for a more comfortable journey by selecting a route while checking the ground surface temperature. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit may input ground surface temperature data into a generation AI and have the generation AI display the ground surface temperature.

[0034] The navigation unit may have a function for notifying the user when it is time to hydrate. The navigation unit may have, for example, a function for notifying the user when it is time to hydrate. For example, the navigation unit may display a hydration alert at regular intervals. The navigation unit may also have a function for notifying the user when it is time to hydrate. For example, the navigation unit may display a hydration alert at a timing based on the temperature or the amount of exercise. This allows the user to hydrate at the appropriate time, supporting their health. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit may input the timing for hydration into the generation AI and cause the generation AI to execute a hydration notification.

[0035] The navigation unit may have a function to display the installation location of a beverage vending machine. The navigation unit may have a function to display the installation location of a beverage vending machine. For example, the navigation unit may display the installation location of the vending machine on a map. The navigation unit may also have a function to display the installation location of the beverage vending machine. For example, the navigation unit may update and display the installation location of the vending machine in real time. This allows a user to hydrate when needed by checking the installation location of the vending machine. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit may input data on the installation location of the vending machine into a generation AI and cause the generation AI to display the installation location.

[0036] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an appropriate input method. For example, the reception unit automatically displays departure points and destinations that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest departure points and destinations to be used during a specific time period based on the user's past input history. This makes it possible to provide the user with the optimal input method by referring to the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data to a generation AI and have the generation AI suggest the optimal input method.

[0037] The reception unit can simplify the input by automatically acquiring the user's current location information when inputting the departure point and destination. For example, the reception unit can simplify the input by automatically acquiring the user's current location information when inputting the departure point and destination. For example, when the user opens the app, the reception unit can automatically acquire the user's current location and set it as the departure point. Furthermore, when the user inputs a destination, the reception unit can also suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the departure point. This simplifies the input work by automatically acquiring the current location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the current location information to the generation AI and cause the generation AI to simplify the input.

[0038] The reception unit can support voice input or gesture input when inputting the starting point and destination. The reception unit supports voice input or gesture input when inputting the starting point and destination, for example. For example, the reception unit can automatically set the starting point and destination by the user simply inputting "from home to the park" by voice. The reception unit can also allow the user to easily set the starting point and destination by performing specific gestures on the smartphone screen. The reception unit can also combine voice input and gesture input to set the starting point and destination more intuitively. In this way, supporting voice input and gesture input enables intuitive input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data or gesture data to a generation AI and have the generation AI perform input support.

[0039] The reception unit can automatically suggest candidate locations by referring to the user's past travel history when the departure point and destination are input. For example, the reception unit can automatically suggest candidate locations by referring to the user's past travel history when the departure point and destination are input. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. The reception unit can also analyze the user's past travel patterns and suggest optimal candidate locations. In this way, the optimal candidate locations can be suggested to the user by referring to the past travel history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past travel history data into a generation AI and cause the generation AI to suggest candidate locations.

[0040] The reception unit can make schedule-based suggestions based on the user's calendar information when the departure point and destination are input. For example, the reception unit can make schedule-based suggestions based on the user's calendar information when the departure point and destination are input. For example, the reception unit can refer to plans registered in the user's calendar and automatically set the departure point and destination. The reception unit can also suggest locations related to specific events as candidate locations based on the user's calendar information. The reception unit can also suggest an optimal route based on the schedule based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the calendar information. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input calendar information to a generation AI and cause the generation AI to execute schedule-based suggestions.

[0041] The reception unit can analyze the user's social media activity and suggest related places when the departure point and destination are input. For example, when the departure point and destination are input, the reception unit analyzes the user's social media activity and suggests related places. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. In this way, related places can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data into a generation AI and cause the generation AI to suggest related places.

[0042] The generation unit can propose an appropriate route by referring to the user's past travel history when generating a route. For example, the generation unit can propose an appropriate route by referring to the user's past travel history when generating a route. For example, the generation unit can propose an optimal route based on routes the user has used in the past. The generation unit can also propose a route that avoids congestion based on the user's past travel history. The generation unit can also analyze the user's past travel history and propose the most efficient route. In this way, the optimal route can be proposed to the user by referring to the past travel history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past travel history data into the generation AI and cause the generation AI to propose an optimal route.

[0043] The generation unit can optimize the route based on real-time traffic information when generating the route. For example, the generation unit optimizes the route based on real-time traffic information when generating the route. For example, the generation unit proposes an optimal route based on real-time traffic congestion information. The generation unit can also propose an optimal route taking into account the real-time operation status of public transportation. The generation unit can also propose a detour route based on real-time road construction information. In this way, the optimal route can be provided by taking into account real-time traffic information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input real-time traffic information to the generation AI and cause the generation AI to optimize the route.

[0044] The generation unit can generate routes from different perspectives or styles according to the user's preferences when generating a route. For example, the generation unit generates routes from different perspectives or styles according to the user's preferences when generating a route. For example, the generation unit selects a viewpoint (bird's-eye view, first-person perspective, etc.) preferred by the user and generates the route. The generation unit can also select a style (animation, realistic footage, etc.) preferred by the user and generate the route. The generation unit can also suggest optimal viewpoints and styles based on the user's past selection history. This provides a more enjoyable travel experience by providing a route according to the user's preferences. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user preference data into the generation AI and cause the generation AI to adjust the viewpoint and style.

[0045] The generation unit can propose a route based on the user's current weather information when generating a route. For example, the generation unit proposes a route based on the user's current weather information when generating a route. For example, the generation unit prioritizes proposed covered routes and underground passages when it is raining. The generation unit can also propose scenic routes when it is sunny. The generation unit can also propose routes that are less slippery on snowy days. In this way, the optimal route can be provided to the user by taking current weather information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input current weather information into the generation AI and cause the generation AI to propose a route.

[0046] The generation unit can propose an appropriate walking route based on the user's health condition when generating a route. For example, the generation unit can propose an appropriate walking route based on the user's health condition when generating a route. For example, the generation unit can propose the shortest route if the user is tired. The generation unit can also propose a slightly longer route if the user is seeking healthy exercise. The generation unit can also propose a route that includes rest points if the user is not feeling well. This allows for a healthier travel experience by taking the user's health condition into consideration. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's health data into the generation AI and have the generation AI suggest a walking route.

[0047] The generation unit can incorporate tourist spots or stores into the route based on the user's interests when generating the route. For example, the generation unit incorporates tourist spots or stores into the route based on the user's interests when generating the route. For example, the generation unit incorporates tourist spots that the user is interested in into the route. The generation unit can also incorporate stores that the user frequently visits into the route. The generation unit can also incorporate places that the user is likely to be interested in into the route based on the user's past search history. This provides a route that matches the user's interests, thereby realizing a more enjoyable travel experience. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to incorporate tourist spots and stores.

[0048] The navigation unit can suggest an appropriate navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest an appropriate navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest an optimal navigation method based on routes the user has used in the past. The navigation unit can also suggest a navigation method that avoids congestion based on the user's past travel history. The navigation unit can also analyze the user's past travel history and suggest the most efficient navigation method. In this way, by referring to the past travel history, the optimal navigation method can be provided to the user. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input past travel history data into a generation AI and have the generation AI execute a navigation method suggestion.

[0049] The navigation unit can instantly update the user's current location information during navigation to perform navigation. For example, the navigation unit instantly updates the user's current location information during navigation to perform navigation. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the user's current location in real time as the user approaches the destination and suggest an optimal route. If the user gets lost, the navigation unit can update the user's current location in real time and perform navigation again. This enables more accurate navigation by updating the location information in real time. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the current location information to the generation AI and have the generation AI perform navigation updates.

[0050] The navigation unit can instantly detect the user's walking speed or direction during navigation and adjust the navigation accordingly. For example, the navigation unit instantly detects the user's walking speed or direction during navigation and adjusts the navigation accordingly. For example, the navigation unit adjusts the navigation progress speed in real time if the user changes their walking speed. The navigation unit can also adjust the navigation display direction in real time if the user changes their direction. The navigation unit can also pause navigation when the user stops and resume it when the user starts walking again. This enables more accurate guidance by providing navigation according to the walking speed and direction. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit can input data on walking speed and direction to a generation AI and have the generation AI adjust the navigation.

[0051] The navigation unit can select an appropriate display method based on the user's device information during navigation. For example, the navigation unit selects an appropriate display method based on the user's device information during navigation. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can provide a simple and highly visible display method. This enables easier navigation by providing a display method that matches the device information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input device information to a generation AI and have the generation AI select a display method.

[0052] The navigation unit can provide navigation that supports multiple languages ​​according to the user's language setting during navigation. The navigation unit, for example, can provide navigation that supports multiple languages ​​according to the user's language setting during navigation. For example, the navigation unit can automatically set the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. The navigation unit can also provide navigation in a specific language when the user selects that language. This allows for multilingual navigation to be provided, thereby accommodating users who speak different languages. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input language setting data to a generation AI and have the generation AI execute language-compatible navigation.

[0053] The navigation unit can analyze the user's social media activity during navigation and provide related information. For example, the navigation unit can analyze the user's social media activity during navigation and provide related information. For example, the navigation unit can provide information about places the user has checked in on social media. The navigation unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The navigation unit can also provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the navigation unit can be performed using, for example, AI, or can be performed without using AI. For example, the navigation unit can input social media activity data into a generation AI and cause the generation AI to provide related information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and calories burned, and suggests an appropriate route based on this data. For example, if the user is tired, the unit may suggest the shortest route. Also, if the user is seeking healthy exercise, the unit may suggest a slightly longer route. Furthermore, if the user is feeling unwell, the unit may suggest a route that includes rest points. This makes it possible to suggest routes that take the user's health condition into consideration, providing a healthier travel experience.

[0056] The navigation system may further include a history analysis unit that analyzes the user's past travel history and suggests an appropriate route. The history analysis unit suggests the optimal route based on the routes and places the user has visited in the past. For example, the history analysis unit may preferentially suggest routes that the user has frequently used in the past. It may also suggest new routes based on places the user has visited in the past. Furthermore, it may suggest routes that avoid congestion based on the user's past travel history. In this way, it is possible to provide the user with the optimal route by referring to the user's past travel history.

[0057] The navigation system may further include a weather response unit that proposes a route based on the user's current weather information. The weather response unit collects real-time weather information and proposes an appropriate route based on the information. For example, when it is raining, the weather response unit may prioritize proposed routes with roofs or underground passages. When it is sunny, the weather response unit may also propose routes with good scenery. Furthermore, when it is snowy, the weather response unit may propose routes that are less slippery. In this way, the current weather information can be taken into consideration to provide the user with the optimal route.

[0058] The navigation system may further include an interest unit that incorporates tourist spots and stores into the route based on the user's interests. The interest unit incorporates tourist spots and stores that may be of interest to the user into the route based on the user's past search history and data on places visited. For example, the interest unit may suggest new tourist spots based on tourist spots that the user has previously visited. It may also incorporate stores that the user frequently visits into the route. It may also provide event information that matches the user's interests. This makes it possible to provide a more enjoyable travel experience by providing a route that matches the user's interests.

[0059] The navigation system may further include a location update unit that instantly updates the user's current location information and performs navigation. The location update unit updates the current location in real time while the user is moving and performs navigation. For example, as the user approaches the destination, the current location can be updated in real time and the optimal route can be suggested. Also, if the user gets lost, the current location can be updated in real time and navigation can be performed again. Furthermore, if the user sets a new destination while moving, the route can be instantly recalculated and navigation can be provided. This allows for more accurate navigation by updating the location information in real time.

[0060] The navigation system may further include a speed and direction detection unit that instantly detects the user's walking speed and direction and adjusts the navigation accordingly. The speed and direction detection unit adjusts the navigation progress speed in real time when the user changes their walking speed. For example, if the user changes direction, the navigation display direction can be adjusted in real time. Also, if the user stops, navigation can be paused and resumed when the user starts walking again. Furthermore, if the user is in a hurry, the shortest route can be suggested. This allows for more accurate guidance by providing navigation according to walking speed and direction.

[0061] The navigation system may further include a device adaptation unit that selects an appropriate display method based on the user's device information. The device adaptation unit collects information about the device the user is using and provides the optimal display method based on this information. For example, if the user is using a smartphone, it may provide a display method that matches the screen size. Also, if the user is using a tablet, it may provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, it may provide a simple and highly visible display method. This allows for easier navigation by providing a display method that matches the device information.

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

[0063] Step 1: The reception unit receives input of the departure point and destination. For example, the user simply inputs the departure point and destination, and the reception unit receives the information. Step 2: The generation unit predicts the position of the sun and the shade of buildings and trees based on the information received by the reception unit, and generates the optimal route. For example, the generation unit uses a generation AI to predict the position of the sun and the shade of buildings and trees, and generates the optimal route. The generation AI calculates the optimal route based on map data and weather data, and generates navigation along that route. Step 3: The navigation unit performs navigation based on the route generated by the generation unit. For example, the navigation unit provides navigation to the user based on the generated route. This allows the user to be provided with an optimal route for walking in the shade, supporting comfortable travel.

[0064] (Example 2) A navigation system according to an embodiment of the present invention accepts input of a departure point and a destination, predicts the position of the sun and the shade of buildings and trees, generates an optimal route, and performs navigation. The navigation system accepts input of a departure point and a destination, predicts the position of the sun and the shade of buildings and trees, and generates an optimal route. For example, the navigation system generates an optimal route by simply inputting a user's departure point and destination. The generated route also offers enjoyment depending on the time of day. For example, the position of the sun is different in the morning and afternoon, so the route also differs. This allows the user to enjoy a different route each time. Furthermore, the navigation system can be switched to Sunny Navigation in winter. This provides a route in the sun during cold seasons. For example, in winter, a route with a lot of sun is generated, taking the position of the sun into consideration. The navigation system also has a thermo function that displays the different surface temperatures for dirt, grass, and asphalt. This allows the user to select a route while checking the surface temperature. For example, if the asphalt temperature is high, the user can select a grass route. Furthermore, the navigation system also provides functions that alert users when it's time to hydrate and indicate the locations of vending machines. This allows users to hydrate at the appropriate time. For example, hydration alerts are displayed at regular intervals, and the locations of vending machines are displayed on a map. In this way, the navigation system provides a navigation function that is useful when users want to walk in the shade as much as possible during dog walks, jogging, commuting, etc. in the summer, and supports users' comfortable travel. In this way, the navigation system can support users' comfortable travel. For example, it can provide users with the optimal route for walking in the shade, supporting a comfortable journey. Users can also select a route while checking the ground surface temperature, allowing them to hydrate at the appropriate time. This allows users to enjoy a comfortable and healthy journey.

[0065] A navigation system according to an embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit receives input of a departure point and a destination. For example, a user simply inputs the departure point and the destination, and the reception unit receives the information. The generation unit predicts the position of the sun and the shade of buildings and trees based on the information received by the reception unit, and generates an optimal route. For example, the generation unit uses a generation AI to predict the position of the sun and the shade of buildings and trees to generate an optimal route. The generation AI calculates an optimal route based on map data and weather data, and generates navigation along that route. For example, the generation AI receives a prompt such as, "Please predict the optimal shade for this route," and predicts the optimal shade for the route. The navigation unit performs navigation based on the route generated by the generation unit. For example, the navigation unit provides navigation to a user based on the generated route. This allows the user to be provided with an optimal route for walking in the shade, supporting a comfortable journey. This allows the navigation system according to an embodiment to support a user's comfortable journey. For example, it provides the user with an optimal route for walking in the shade, supporting a comfortable journey.

[0066] The generation unit can use the generation AI to predict the position of the sun and the shade of buildings or trees, and generate an appropriate route. The generation unit, for example, uses the generation AI to predict the position of the sun and the shade of buildings or trees. For example, the generation AI calculates an optimal route based on map data and weather data, and generates navigation along that route. The generation unit can also use the generation AI to predict the position of the sun and the shade of buildings or trees, and generate an appropriate route. For example, the generation AI receives a prompt such as "Please predict the optimal shade for this route," and predicts the optimal shade for the route. This makes it possible to provide an optimal route that takes shade into consideration by using the generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input map data and weather data into the generation AI and cause the generation AI to generate an optimal route.

[0067] The generation unit can generate different routes depending on the time of day. The generation unit, for example, generates different routes depending on the time of day. For example, the route differs because the position of the sun is different in the morning and afternoon. The generation unit can also generate different routes depending on the time of day. For example, the generation unit generates an optimal route depending on the time of day. This improves the user's travel experience by providing an optimal route depending on the time of day. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input time of day information into the generation AI and cause the generation AI to generate an optimal route depending on the time of day.

[0068] The generation unit can generate a route that can be switched to sunshine navigation in winter. The generation unit, for example, generates a route that can be switched to sunshine navigation in winter. For example, a route with a lot of sunshine is generated in winter, taking into account the position of the sun. The generation unit can also generate a route that can be switched to sunshine navigation in winter. For example, the generation unit generates a route with a lot of sunshine based on winter information. This supports comfortable travel even in cold seasons by providing a route that walks in sunny areas in winter. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input winter information into the generation AI and cause the generation AI to generate a route that can be switched to sunshine navigation.

[0069] The navigation unit may be equipped with a thermo function that displays the ground surface temperature for each of soil, grass, and asphalt. The navigation unit may be equipped with a thermo function that displays the ground surface temperature for each of soil, grass, and asphalt. For example, the navigation unit may select a route while checking the ground surface temperature. The navigation unit may also be equipped with a thermo function that displays the ground surface temperature for each of soil, grass, and asphalt. For example, the navigation unit may select a grass route if the asphalt temperature is high. This allows for a more comfortable journey by selecting a route while checking the ground surface temperature. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit may input ground surface temperature data into a generation AI and have the generation AI display the ground surface temperature.

[0070] The navigation unit may have a function for notifying the user when it is time to hydrate. The navigation unit may have, for example, a function for notifying the user when it is time to hydrate. For example, the navigation unit may display a hydration alert at regular intervals. The navigation unit may also have a function for notifying the user when it is time to hydrate. For example, the navigation unit may display a hydration alert at a timing based on the temperature or the amount of exercise. This allows the user to hydrate at the appropriate time, supporting their health. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit may input the timing for hydration into the generation AI and cause the generation AI to execute a hydration notification.

[0071] The navigation unit may have a function to display the installation location of a beverage vending machine. The navigation unit may have a function to display the installation location of a beverage vending machine. For example, the navigation unit may display the installation location of the vending machine on a map. The navigation unit may also have a function to display the installation location of the beverage vending machine. For example, the navigation unit may update and display the installation location of the vending machine in real time. This allows a user to hydrate when needed by checking the installation location of the vending machine. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit may input data on the installation location of the vending machine into a generation AI and cause the generation AI to display the installation location.

[0072] The reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated emotions. For example, the reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, 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. This provides an input method that matches the user's emotions, thereby realizing a more comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the input method based on the emotion.

[0073] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an appropriate input method. For example, the reception unit automatically displays departure points and destinations that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest departure points and destinations to be used during a specific time period based on the user's past input history. This makes it possible to provide the user with the optimal input method by referring to the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data to a generation AI and have the generation AI suggest the optimal input method.

[0074] The reception unit can simplify the input by automatically acquiring the user's current location information when inputting the departure point and destination. For example, the reception unit can simplify the input by automatically acquiring the user's current location information when inputting the departure point and destination. For example, when the user opens the app, the reception unit can automatically acquire the user's current location and set it as the departure point. Furthermore, when the user inputs a destination, the reception unit can also suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the departure point. This simplifies the input work by automatically acquiring the current location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the current location information to the generation AI and cause the generation AI to simplify the input.

[0075] The reception unit can support voice input or gesture input when inputting the starting point and destination. The reception unit supports voice input or gesture input when inputting the starting point and destination, for example. For example, the reception unit can automatically set the starting point and destination by the user simply inputting "from home to the park" by voice. The reception unit can also allow the user to easily set the starting point and destination by performing specific gestures on the smartphone screen. The reception unit can also combine voice input and gesture input to set the starting point and destination more intuitively. In this way, supporting voice input and gesture input enables intuitive input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data or gesture data to a generation AI and have the generation AI perform input support.

[0076] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, the reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This provides an interface that corresponds to the user's emotions, thereby realizing a more comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the interface design.

[0077] The reception unit can automatically suggest candidate locations by referring to the user's past travel history when the departure point and destination are input. For example, the reception unit can automatically suggest candidate locations by referring to the user's past travel history when the departure point and destination are input. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. The reception unit can also analyze the user's past travel patterns and suggest optimal candidate locations. In this way, the optimal candidate locations can be suggested to the user by referring to the past travel history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past travel history data into a generation AI and cause the generation AI to suggest candidate locations.

[0078] The reception unit can make schedule-based suggestions based on the user's calendar information when the departure point and destination are input. For example, the reception unit can make schedule-based suggestions based on the user's calendar information when the departure point and destination are input. For example, the reception unit can refer to plans registered in the user's calendar and automatically set the departure point and destination. The reception unit can also suggest locations related to specific events as candidate locations based on the user's calendar information. The reception unit can also suggest an optimal route based on the schedule based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the calendar information. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input calendar information to a generation AI and cause the generation AI to execute schedule-based suggestions.

[0079] The reception unit can analyze the user's social media activity and suggest related places when the departure point and destination are input. For example, when the departure point and destination are input, the reception unit analyzes the user's social media activity and suggests related places. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. In this way, related places can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data into a generation AI and cause the generation AI to suggest related places.

[0080] The generation unit can estimate the user's emotions and adjust the route generation method by the generation AI based on the estimated emotions. For example, the generation unit estimates the user's emotions and adjusts the route generation method by the generation AI based on the estimated emotions. For example, if the user is relaxed, the generation unit causes the generation AI to generate a route that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can cause the generation AI to generate a route that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can cause the generation AI to generate a route that adds visually stimulating effects. This provides a route generation method that corresponds to the user's emotions, thereby realizing a more comfortable travel experience. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the route generation method.

[0081] The generation unit can propose an appropriate route by referring to the user's past travel history when generating a route. For example, the generation unit can propose an appropriate route by referring to the user's past travel history when generating a route. For example, the generation unit can propose an optimal route based on routes the user has used in the past. The generation unit can also propose a route that avoids congestion based on the user's past travel history. The generation unit can also analyze the user's past travel history and propose the most efficient route. In this way, the optimal route can be proposed to the user by referring to the past travel history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past travel history data into the generation AI and cause the generation AI to propose an optimal route.

[0082] The generation unit can optimize the route based on real-time traffic information when generating the route. For example, the generation unit optimizes the route based on real-time traffic information when generating the route. For example, the generation unit proposes an optimal route based on real-time traffic congestion information. The generation unit can also propose an optimal route taking into account the real-time operation status of public transportation. The generation unit can also propose a detour route based on real-time road construction information. In this way, the optimal route can be provided by taking into account real-time traffic information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input real-time traffic information to the generation AI and cause the generation AI to optimize the route.

[0083] The generation unit can generate routes from different perspectives or styles according to the user's preferences when generating a route. For example, the generation unit generates routes from different perspectives or styles according to the user's preferences when generating a route. For example, the generation unit selects a viewpoint (bird's-eye view, first-person perspective, etc.) preferred by the user and generates the route. The generation unit can also select a style (animation, realistic footage, etc.) preferred by the user and generate the route. The generation unit can also suggest optimal viewpoints and styles based on the user's past selection history. This provides a more enjoyable travel experience by providing a route according to the user's preferences. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user preference data into the generation AI and cause the generation AI to adjust the viewpoint and style.

[0084] The generation unit estimates the user's emotions, and the generation AI adjusts the length and level of detail of the route based on the estimated emotions. For example, the generation unit estimates the user's emotions, and the generation AI adjusts the length and level of detail of the route based on the estimated emotions. For example, if the user is in a hurry, the generation AI generates a short, concise route. If the user is relaxed, the generation AI can generate a longer route with detailed explanations. If the user is excited, the generation AI can generate a route with visually stimulating effects. This provides a more comfortable travel experience by providing the length and level of detail of the route according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the length and level of detail of the route.

[0085] The generation unit can propose a route based on the user's current weather information when generating a route. For example, the generation unit proposes a route based on the user's current weather information when generating a route. For example, the generation unit prioritizes proposed covered routes and underground passages when it is raining. The generation unit can also propose scenic routes when it is sunny. The generation unit can also propose routes that are less slippery on snowy days. In this way, the optimal route can be provided to the user by taking current weather information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input current weather information into the generation AI and cause the generation AI to propose a route.

[0086] The generation unit can propose an appropriate walking route based on the user's health condition when generating a route. For example, the generation unit can propose an appropriate walking route based on the user's health condition when generating a route. For example, the generation unit can propose the shortest route if the user is tired. The generation unit can also propose a slightly longer route if the user is seeking healthy exercise. The generation unit can also propose a route that includes rest points if the user is not feeling well. This allows for a healthier travel experience by taking the user's health condition into consideration. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's health data into the generation AI and have the generation AI suggest a walking route.

[0087] The generation unit can incorporate tourist spots or stores into the route based on the user's interests when generating the route. For example, the generation unit incorporates tourist spots or stores into the route based on the user's interests when generating the route. For example, the generation unit incorporates tourist spots that the user is interested in into the route. The generation unit can also incorporate stores that the user frequently visits into the route. The generation unit can also incorporate places that the user is likely to be interested in into the route based on the user's past search history. This provides a route that matches the user's interests, thereby realizing a more enjoyable travel experience. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to incorporate tourist spots and stores.

[0088] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated emotions. For example, the navigation unit estimates the user's emotions and adjusts the navigation display method based on the estimated emotions. For example, if the user is nervous, the navigation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the navigation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a display method that focuses on the main points. This provides a navigation display that corresponds to the user's emotions, thereby realizing a more comfortable travel experience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0089] The navigation unit can suggest an appropriate navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest an appropriate navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest an optimal navigation method based on routes the user has used in the past. The navigation unit can also suggest a navigation method that avoids congestion based on the user's past travel history. The navigation unit can also analyze the user's past travel history and suggest the most efficient navigation method. In this way, by referring to the past travel history, the optimal navigation method can be provided to the user. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input past travel history data into a generation AI and have the generation AI execute a navigation method suggestion.

[0090] The navigation unit can instantly update the user's current location information during navigation to perform navigation. For example, the navigation unit instantly updates the user's current location information during navigation to perform navigation. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the user's current location in real time as the user approaches the destination and suggest an optimal route. If the user gets lost, the navigation unit can update the user's current location in real time and perform navigation again. This enables more accurate navigation by updating the location information in real time. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the current location information to the generation AI and have the generation AI perform navigation updates.

[0091] The navigation unit can instantly detect the user's walking speed or direction during navigation and adjust the navigation accordingly. For example, the navigation unit instantly detects the user's walking speed or direction during navigation and adjusts the navigation accordingly. For example, the navigation unit adjusts the navigation progress speed in real time if the user changes their walking speed. The navigation unit can also adjust the navigation display direction in real time if the user changes their direction. The navigation unit can also pause navigation when the user stops and resume it when the user starts walking again. This enables more accurate guidance by providing navigation according to the walking speed and direction. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit can input data on walking speed and direction to a generation AI and have the generation AI adjust the navigation.

[0092] The navigation unit can estimate the user's emotions and adjust the navigation voice guidance based on the estimated emotions. For example, the navigation unit estimates the user's emotions and adjusts the navigation voice guidance based on the estimated emotions. For example, if the user is nervous, the navigation unit can provide guidance in a calm voice. Also, if the user is relaxed, the navigation unit can provide guidance in a cheerful voice. Also, if the user is in a hurry, the navigation unit can provide quick and concise voice guidance. This provides voice guidance according to the user's emotions, thereby realizing a more comfortable travel experience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the voice guidance.

[0093] The navigation unit can select an appropriate display method based on the user's device information during navigation. For example, the navigation unit selects an appropriate display method based on the user's device information during navigation. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can provide a simple and highly visible display method. This enables easier navigation by providing a display method that matches the device information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input device information to a generation AI and have the generation AI select a display method.

[0094] The navigation unit can provide navigation that supports multiple languages ​​according to the user's language setting during navigation. The navigation unit, for example, can provide navigation that supports multiple languages ​​according to the user's language setting during navigation. For example, the navigation unit can automatically set the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. The navigation unit can also provide navigation in a specific language when the user selects that language. This allows for multilingual navigation to be provided, thereby accommodating users who speak different languages. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input language setting data to a generation AI and have the generation AI execute language-compatible navigation.

[0095] The navigation unit can analyze the user's social media activity during navigation and provide related information. For example, the navigation unit can analyze the user's social media activity during navigation and provide related information. For example, the navigation unit can provide information about places the user has checked in on social media. The navigation unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The navigation unit can also provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the navigation unit can be performed using, for example, AI, or can be performed without using AI. For example, the navigation unit can input social media activity data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and navigation 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 is realized by the reception device 38 of the smart device 14, and allows the user to input a starting point and a destination. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the position of the sun and the shade of buildings and trees, and generates an optimal route. The navigation unit is realized by the control unit 46A of the smart device 14, and provides navigation to the user based on the generated route. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and navigation 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 is realized by the microphone 238 of the smart glasses 214, allowing the user to input a starting point and destination by voice. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the position of the sun and the shade of buildings and trees to generate an optimal route. The navigation unit is realized by the control unit 46A of the smart glasses 214, and provides navigation to the user based on the generated route. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and navigation 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 is realized by the microphone 238 of the headset type terminal 314, allowing the user to input the departure point and destination by voice. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the position of the sun and the shade of buildings and tree-lined streets to generate an optimal route. The navigation unit is realized by the control unit 46A of the headset type terminal 314, and provides navigation to the user based on the generated route. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and navigation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input the starting point and destination by voice. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the position of the sun and the shade of buildings and tree-lined streets to generate an optimal route. The navigation unit is realized by the control unit 46A of the robot 414, and provides navigation to the user based on the generated route.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and calories burned, and suggests an appropriate route based on this data. For example, if the user is tired, the unit may suggest the shortest route. Also, if the user is seeking healthy exercise, the unit may suggest a slightly longer route. Furthermore, if the user is feeling unwell, the unit may suggest a route that includes rest points. This makes it possible to suggest routes that take the user's health condition into consideration, providing a healthier travel experience.

[0098] The navigation system may further include an entertainment unit that plays music according to the user's preferences. The entertainment unit selects and plays appropriate music based on the user's musical preferences and current emotions. For example, if the user is relaxed, it may play calm music. If the user is in a hurry, it may play fast-paced music. Furthermore, if the user is excited, it may play energetic music. This allows the user to enjoy a more comfortable travel experience by providing music that matches their emotions.

[0099] The navigation system may further include a history analysis unit that analyzes the user's past travel history and suggests an appropriate route. The history analysis unit suggests the optimal route based on the routes and places the user has visited in the past. For example, the history analysis unit may preferentially suggest routes that the user has frequently used in the past. It may also suggest new routes based on places the user has visited in the past. Furthermore, it may suggest routes that avoid congestion based on the user's past travel history. In this way, it is possible to provide the user with the optimal route by referring to the user's past travel history.

[0100] The navigation system may further include a weather response unit that proposes a route based on the user's current weather information. The weather response unit collects real-time weather information and proposes an appropriate route based on the information. For example, when it is raining, the weather response unit may prioritize proposed routes with roofs or underground passages. When it is sunny, the weather response unit may also propose routes with good scenery. Furthermore, when it is snowy, the weather response unit may propose routes that are less slippery. In this way, the current weather information can be taken into consideration to provide the user with the optimal route.

[0101] The navigation system may further include an interest unit that incorporates tourist spots and stores into the route based on the user's interests. The interest unit incorporates tourist spots and stores that may be of interest to the user into the route based on the user's past search history and data on places visited. For example, the interest unit may suggest new tourist spots based on tourist spots that the user has previously visited. It may also incorporate stores that the user frequently visits into the route. It may also provide event information that matches the user's interests. This makes it possible to provide a more enjoyable travel experience by providing a route that matches the user's interests.

[0102] The navigation system may further include a display adjustment unit that estimates the user's emotions and adjusts the navigation display method based on the estimated emotions. The display adjustment unit estimates the user's emotions and adjusts the navigation display method based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method may be provided. If the user is relaxed, a display method including detailed information may be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points may be provided. This makes it possible to provide a navigation display that corresponds to the user's emotions, thereby achieving a more comfortable travel experience.

[0103] The navigation system may further include a location update unit that instantly updates the user's current location information and performs navigation. The location update unit updates the current location in real time while the user is moving and performs navigation. For example, as the user approaches the destination, the current location can be updated in real time and the optimal route can be suggested. Also, if the user gets lost, the current location can be updated in real time and navigation can be performed again. Furthermore, if the user sets a new destination while moving, the route can be instantly recalculated and navigation can be provided. This allows for more accurate navigation by updating the location information in real time.

[0104] The navigation system may further include a speed and direction detection unit that instantly detects the user's walking speed and direction and adjusts the navigation accordingly. The speed and direction detection unit adjusts the navigation progress speed in real time when the user changes their walking speed. For example, if the user changes direction, the navigation display direction can be adjusted in real time. Also, if the user stops, navigation can be paused and resumed when the user starts walking again. Furthermore, if the user is in a hurry, the shortest route can be suggested. This allows for more accurate guidance by providing navigation according to walking speed and direction.

[0105] The navigation system may further include a voice guidance adjustment unit that estimates the user's emotions and adjusts the voice guidance of the navigation based on the estimated emotions. The voice guidance adjustment unit estimates the user's emotions and adjusts the voice guidance of the navigation based on the estimated emotions. For example, if the user is nervous, the voice guidance adjustment unit may provide guidance in a calm voice. If the user is relaxed, the voice guidance may be provided in a cheerful voice. Furthermore, if the user is in a hurry, the voice guidance may be provided in a quick and concise voice. This makes it possible to provide voice guidance that corresponds to the user's emotions, thereby realizing a more comfortable travel experience.

[0106] The navigation system may further include a device adaptation unit that selects an appropriate display method based on the user's device information. The device adaptation unit collects information about the device the user is using and provides the optimal display method based on this information. For example, if the user is using a smartphone, it may provide a display method that matches the screen size. Also, if the user is using a tablet, it may provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, it may provide a simple and highly visible display method. This allows for easier navigation by providing a display method that matches the device information.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit receives input of the departure point and destination. For example, the user simply inputs the departure point and destination, and the reception unit receives the information. Step 2: The generation unit predicts the position of the sun and the shade of buildings and trees based on the information received by the reception unit, and generates the optimal route. For example, the generation unit uses a generation AI to predict the position of the sun and the shade of buildings and trees, and generates the optimal route. The generation AI calculates the optimal route based on map data and weather data, and generates navigation along that route. Step 3: The navigation unit performs navigation based on the route generated by the generation unit. For example, the navigation unit provides navigation to the user based on the generated route. This allows the user to be provided with an optimal route for walking in the shade, supporting comfortable travel.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

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

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0132] The 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.

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

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

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

[0136] Fig. 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.

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

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

[0139] In the 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.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0143] 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.

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

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0148] The 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.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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; a generation unit that predicts the position of the sun and the shadow of buildings or trees based on the information received by the reception unit and generates an appropriate route; a navigation unit that performs navigation based on the route generated by the generation unit. A system characterized by:

2. The generation unit Generative AI predicts the position of the sun and the shadow of buildings or trees, and generates an appropriate route.

2. The system of claim 1.

3. The generation unit Generate different routes depending on the time of day 2. The system of claim 1.

4. The generation unit Generates routes that can be switched to daylight navigation in winter 2. The system of claim 1.

5. The navigation unit Equipped with a thermo function that displays the surface temperature of soil, grass, and asphalt 2. The system of claim 1.

6. The navigation unit Equipped with a hydration time notification function 2. The system of claim 1.

7. The navigation unit Equipped with a function to display the location of beverage vending machines 2. The system of claim 1.

8. The reception unit Inferring user emotions and adjusting the way origins and destinations are entered based on the inferred emotions 2. The system of claim 1.

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