System

Abstract

An object of a system according to an embodiment is to provide navigation information according to a language or culture of a region.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a destination from a user. The generation part analyzes the information received by the reception part and generates navigation information on the basis of the language and culture of the area. The providing unit provides the user with the navigation information 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 technologies do not adequately provide navigation information that is appropriate for local languages ​​and cultures, and there is room for improvement.

[0005] The system according to the embodiment aims to provide navigation information according to the local language and culture. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a destination input from a user. The generating unit analyzes the information received by the receiving unit and generates navigation information based on the local language and culture. The providing unit provides the navigation information generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide navigation information according to the local language and culture. [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 provides navigation tailored to the local language and culture. In the navigation system, a user wears a device such as AR glasses or a smartphone and inputs their destination. Next, a large-scale language model (LLM) analyzes the input information based on the local language and culture and generates appropriate navigation information. The generated navigation information is provided to the user through the device. For example, when a user visits a tourist spot in Japan, the LLM understands local idioms and expressions and generates navigation information based on them. If the user inputs "I want to go to the tourist spot," the LLM generates specific guidance, such as "To get to the tourist spot, first go north on the main street, then turn right at the specific intersection." This guidance is displayed to the user through the device. Furthermore, the LLM can translate the guidance into an appropriate language based on the user's language settings. For example, if an English-speaking user visits Japan, the LLM generates guidance such as "Please proceed north on the main street, then turn right at the specific intersection to reach the tourist spot." In this way, the user can receive local-specific navigation in their own language. This allows users to receive navigation adapted to the local language and culture, making travel and sightseeing more comfortable and convenient. It also deepens understanding of local cultures and languages, promoting intercultural exchange.

[0029] A navigation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a destination input from a user. Destination input methods include, but are not limited to, text input, voice input, and multiple-choice input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate navigation information based on the local language and culture. The generation AI analyzes the local language and culture using technologies such as natural language processing, machine learning, and deep learning to generate navigation information. The generation unit understands local idioms and expressions and generates navigation information based on the local idioms and expressions. The generation unit can also translate the navigation information into an appropriate language based on the user's language setting. The provision unit provides the user with the navigation information generated by the generation unit. The provision unit provides the navigation information through a device such as AR glasses or a smartphone. The provision unit can also display the navigation information based on the orientation of the user's device. This allows the navigation system according to an embodiment to provide navigation adapted to the local language and culture of the user. For example, if a user visits a tourist spot in Japan, the AI ​​can understand local idioms and expressions and generate navigation information based on them. When a user inputs, "I want to go to the tourist spot," the AI ​​generates specific directions, such as, "To get to the tourist spot, first go north on the main street, then turn right at the specific intersection." These directions are displayed to the user through the device. Furthermore, the AI ​​can translate the directions into the appropriate language based on the user's language settings. For example, if an English-speaking user visits Japan, the AI ​​can generate directions such as, "Please proceed north on the main street, then turn right at the specific intersection to reach the tourist spot." In this way, users can receive local navigation in their own language.This allows users to receive navigation adapted to the local language and culture, making travel and sightseeing more comfortable and convenient. It also deepens understanding of local cultures and languages, promoting intercultural exchange.

[0030] The generation unit can analyze the language and culture specific to the region using the generation AI and generate navigation information. The generation unit, for example, analyzes the language and culture specific to the region using the generation AI. The generation AI, for example, uses natural language processing technology to understand local idioms and expressions. The generation AI can also analyze the cultural background of the region using a machine learning algorithm. Furthermore, the generation AI can also generate navigation information based on the language and culture specific to the region using deep learning technology. In this way, navigation information based on the language and culture specific to the region can be generated using the generation AI. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input data related to the language and culture of the region into the generation AI and cause the generation AI to generate navigation information.

[0031] The providing unit can translate the navigation information into a language based on the user's language setting based on the user's language setting. The providing unit, for example, acquires the language setting of the user's device and translates the navigation information based on that. The providing unit can acquire the user's language setting from, for example, device settings or application settings. The providing unit can also use a translation algorithm to make the navigation information multilingual. For example, the providing unit can automatically translate the navigation information using a generation AI. This makes it possible to provide navigation information according to the user's language setting. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input the user's language setting to the generation AI and have the generation AI translate the navigation information.

[0032] The providing unit can provide navigation information through AR glasses or a smartphone. The providing unit can provide navigation information through, for example, AR glasses or a smartphone. The providing unit can use, for example, an application compatible with AR glasses or a smartphone of a specific manufacturer or model. The providing unit can also provide a dedicated application for displaying navigation information. This allows users to receive navigation information through various devices. Some or all of the above-described processing in the providing unit is performed using AI. For example, the providing unit can input navigation information to a generation AI and cause the generation AI to execute information to be displayed on the AR glasses or a smartphone.

[0033] The generation unit can understand idioms and expressions used in tourist destinations or specific regions, and generate navigation information based on that understanding. The generation unit, for example, uses a generation AI to understand idioms and expressions used in tourist destinations or specific regions. The generation AI can, for example, refer to a regional language database and analyze idioms and expressions. The generation AI can also analyze related literature and data to understand the cultural background of the region. Furthermore, the generation AI can generate navigation information based on expressions and phrases unique to the region. This makes it possible to provide navigation information adapted to tourist destinations or specific regions. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input data related to tourist destinations or specific regions into the generation AI and have the generation AI generate navigation information.

[0034] The providing unit can display navigation information based on the orientation of the user's device. The providing unit, for example, acquires the orientation of the user's device and displays the navigation information based on that. The providing unit acquires the orientation of the device using, for example, a gyro sensor, a compass, or a GPS. The providing unit can also display the navigation information in an optimal format depending on the orientation of the device. This enables optimal display depending on the orientation of the user's device. Some or all of the above-described processing in the providing unit can be performed using AI. For example, the providing unit can input device orientation data to a generating AI and cause the generating AI to display the navigation information.

[0035] The reception unit can analyze the user's past destination input history and suggest an input method suitable for the user. For example, the reception unit obtains and analyzes the user's past destination input history from a database, log file, cloud storage, etc. The reception unit can, for example, automatically display destinations that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input past destination input history data into a generation AI and have the generation AI suggest the optimal input method.

[0036] The reception unit can present input candidates based on the user's current location information when the user inputs a destination. The reception unit, for example, acquires the user's current location information using GPS, Wi-Fi location information, cell tower location information, etc. The reception unit, for example, automatically acquires the user's current location when the user opens the app and sets it as the starting point. The reception unit can also suggest optimal candidate locations by taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the current location in real time and reflect it as the starting point. This makes it possible to present optimal input candidates based on the current location information. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input current location information data to the generation AI and cause the generation AI to present input candidates.

[0037] When inputting a destination, the reception unit can select an input means suitable for the user depending on the user's input method. The reception unit, for example, detects the user's input method (voice, text, gesture, etc.) and selects the optimal input means accordingly. For example, the reception unit can automatically set the destination when the user simply inputs "I want to go to a tourist spot" by voice. The reception unit can also allow the user to easily set the destination by performing a specific gesture on the smartphone screen. Furthermore, the reception unit can also allow the user to combine voice input and gesture input to set the destination more intuitively. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0038] When a destination is input, the reception unit can prioritize presenting highly relevant destinations taking into account the user's geographical location information. The reception unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower location information, etc. The reception unit, for example, prioritizes displaying locations close to the user's current location as candidates. The reception unit can also prioritize displaying locations that the user has visited in the past as candidates. Furthermore, the reception unit can also suggest optimal candidate locations taking into account the distance from the user's current location. This makes it possible to present highly relevant destinations based on the geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to present highly relevant destinations.

[0039] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit acquires data such as post content, location information, and friend relationships to analyze the user's social media activity. For example, the reception unit suggests locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the user's social media posts and suggest related locations as candidate locations. Furthermore, the reception unit can also suggest related locations by referring to the activity of the user's friends on social media. This makes it possible to suggest related destinations based on social media activity. Some or all of the above-mentioned processing in the reception unit can be performed 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 destinations.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. The reception unit, for example, acquires and analyzes the user's past feedback from a database, a log file, cloud storage, etc. The reception unit can, for example, suggest the optimal input method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's feedback and customize the input method. This makes it possible to provide the optimal input method based on the past feedback. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the input method.

[0041] When generating navigation information, the generation unit can generate information based on the historical background and cultural elements of the region. For example, the generation unit uses a generation AI to generate navigation information taking into account the historical background and cultural elements of the region. For example, the generation AI can suggest a route that includes historical landmarks in the region. The generation AI can also incorporate information related to cultural events and festivals in the region into the navigation. Furthermore, the generation AI can include information about traditional foods and shops in the navigation. This makes it possible to provide navigation information based on the history and culture of the region. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input data related to the historical background and cultural elements of the region into the generation AI and have the generation AI generate navigation information.

[0042] When generating navigation information, the generation unit can improve the accuracy of the information by referring to the user's past navigation history. The generation unit, for example, acquires and analyzes the user's past navigation history from a database, log file, cloud storage, etc. The generation unit, for example, proposes 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 navigation history. Furthermore, the generation unit can analyze the user's past navigation history and propose the most efficient route. This makes it possible to provide optimal navigation information based on the past navigation history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input past navigation history data into the generation AI and have the generation AI improve the accuracy of the navigation information.

[0043] When generating navigation information, the generation unit can propose an optimal route based on local event information. The generation unit uses a generation AI, for example, to generate navigation information taking local event information into consideration. The generation AI can propose an optimal route based on information about events held in the area, for example. The generation AI can also propose a route that passes through local event locations. Furthermore, the generation AI can propose a route that avoids congestion by taking local event information into consideration. This makes it possible to provide an optimal route based on local event information. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can input local event information data into the generation AI and have the generation AI propose an optimal route.

[0044] When generating the navigation information, the generation unit can make the information multilingual based on the user's language setting. The generation unit, for example, acquires the language setting of the user's device and makes the navigation information multilingual based on that. The generation unit, for example, acquires the user's language setting from device settings or application settings. The generation unit can also use a translation algorithm to make the navigation information multilingual. For example, the generation unit can automatically translate the navigation information using a generation AI. This makes it possible to provide multilingual navigation information according to the user's language setting. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input the user's language setting data into the generation AI and have the generation AI make the navigation information multilingual.

[0045] When generating navigation information, the generation unit can select the optimal display format based on the user's device information. The generation unit, for example, acquires the user's device information and selects the display format of the navigation information based on that information. The generation unit acquires information such as the device type, OS version, and hardware specifications. The generation unit can also select the optimal display format based on the device's screen size and resolution. This makes it possible to provide a display format optimized for the user's device. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input device information data to the generation AI and cause the generation AI to select the optimal display format.

[0046] When generating navigation information, the generation unit can adjust the use of technical terminology in the information based on the user's level of expertise. For example, the generation unit acquires data such as the user's occupation, educational background, and past usage history to evaluate the user's level of expertise. For example, if the user has technical expertise, the generation unit provides navigation information using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can provide concise and easy-to-understand navigation information. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past navigation history. This allows the provision of optimal navigation information according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0047] When providing navigation information, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, acquires and analyzes the user's past operation history from a database, log file, cloud storage, etc. The providing unit, for example, proposes the optimal display method based on the display method used by the user in the past. The providing unit can also propose a display method with high visibility based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and propose the most efficient display method. This makes it possible to provide the optimal display method based on the past operation history. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input past operation history data to a generation AI and cause the generation AI to select the optimal display method.

[0048] The providing unit can customize the display content according to the user's current task when providing navigation information. The providing unit, for example, acquires the user's current task and customizes the display content based on that. The providing unit acquires data such as the user's current activity and application usage status. The providing unit can also prioritize displaying information about tourist spots when the user is sightseeing. The providing unit can also prioritize displaying store information when the user is shopping. This makes it possible to provide optimal display content according to the user's current task. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input current task data to a generating AI and have the generating AI customize the display content.

[0049] When providing navigation information, the providing unit can adjust the display method based on the remaining battery level of the user's device. For example, the providing unit acquires the remaining battery level of the user's device and adjusts the display method based on that. The providing unit acquires data such as the battery percentage and available time. The providing unit can also display the information in low power mode when the remaining battery level of the user's device is low. Furthermore, the providing unit can provide a display method including detailed information when the remaining battery level of the user's device is sufficient. This makes it possible to provide an optimal display method according to the remaining battery level of the device. Some or all of the above-mentioned processing in the providing unit can be performed using AI. For example, the providing unit can input remaining battery level data to the generating AI and cause the generating AI to adjust the display method.

[0050] When providing navigation information, the providing unit can adjust the display content based on the orientation of the user's device. The providing unit, for example, acquires the orientation of the user's device and adjusts the display content based on that. The providing unit acquires the orientation of the device using, for example, a gyro sensor, a compass, or a GPS. The providing unit can also display the navigation information in an optimal format depending on the orientation of the device. This makes it possible to provide optimal display content depending on the orientation of the device. Some or all of the above-described processing in the providing unit is performed using AI. For example, the providing unit can input device orientation data to a generating AI and cause the generating AI to adjust the display content.

[0051] The providing unit can optimize the display format according to the screen size of the user's device when providing navigation information. The providing unit, for example, acquires the screen size of the user's device and optimizes the display format based on that. The providing unit acquires data such as the number of inches, resolution, and aspect ratio of the device. The providing unit can also provide the optimal display format according to the screen size of the device. This makes it possible to provide the optimal display format according to the screen size of the device. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input screen size data to the generating AI and cause the generating AI to optimize the display format.

[0052] When providing navigation information, the providing unit can translate the information into an appropriate language based on the user's language setting. The providing unit, for example, acquires the language setting of the user's device and translates the navigation information based on that. The providing unit, for example, acquires the user's language setting from device settings or application settings. The providing unit can also use a translation algorithm to make the navigation information multilingual. For example, the providing unit can automatically translate the navigation information using a generation AI. This makes it possible to provide optimal navigation information according to the user's language setting. Some or all of the above-mentioned processing in the providing unit can be performed using AI. For example, the providing unit can input the user's language setting data into the generation AI and have the generation AI translate the navigation information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the user's past navigation history and automatically display locations frequently visited by the user as candidates. For example, it can prioritize displaying cafes and restaurants that the user has visited many times in the past. The reception unit can also predict locations that the user will visit on specific days of the week or during specific times of the day, and suggest candidate locations suitable for those times. Furthermore, the reception unit can suggest locations related to specific events or seasons based on the user's past navigation history. This makes it possible to provide optimal candidate locations based on the user's past behavioral patterns.

[0055] The providing unit can adjust the display method of the navigation information based on the remaining battery level of the user's device. For example, when the remaining battery level is low, the display is in low-power mode and detailed information is omitted. When the remaining battery level is sufficient, detailed navigation information can be provided. Furthermore, when the remaining battery level is very low, only the most important information can be displayed to minimize battery consumption. This makes it possible to provide an optimal display method according to the remaining battery level of the device.

[0056] The reception unit can analyze the user's social media activity and suggest related destinations. For example, it can suggest places where the user has checked in on social media as candidate destinations. It can also suggest related places based on places and events visited by the user's friends. It can also analyze the content of the user's social media posts and suggest places that the user might be interested in. This makes it possible to suggest related destinations based on social media activity.

[0057] The generation unit can generate navigation information taking into account the historical background and cultural elements of the region. For example, it can suggest routes that include historical landmarks in the region. It can also incorporate information related to local cultural events and festivals into the navigation. Furthermore, it can include information about traditional foods and shops in the region. This makes it possible to provide navigation information based on the history and culture of the region.

[0058] The receiving unit can customize the input method by reflecting the user's past feedback. For example, the receiving unit can suggest the optimal input method based on the user's past feedback. It can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, it can analyze the user's feedback and customize the input method. This makes it possible to provide the optimal input method based on the user's past feedback.

[0059] The providing unit can customize the display content according to the user's current task. For example, if the user is sightseeing, information about tourist spots can be displayed with priority. Also, if the user is shopping, store information can be displayed with priority. Furthermore, if the user is heading to a business meeting, the shortest route and traffic information can be displayed with priority. This makes it possible to provide the optimal display content according to the user's current task.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives a destination input from the user. The destination input method includes text input, voice input, multiple choice input, and the like. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate navigation information based on the local language and culture. The generation AI uses technologies such as natural language processing, machine learning, and deep learning to analyze the local language and culture and generate navigation information. It can also translate the navigation information into an appropriate language based on the user's language settings. Step 3: The providing unit provides the navigation information generated by the generating unit to the user. The providing unit provides the navigation information through a device such as AR glasses or a smartphone, and can also display the navigation information based on the orientation of the user's device.

[0062] (Example 2) A navigation system according to an embodiment of the present invention provides navigation tailored to the local language and culture. In the navigation system, a user wears a device such as AR glasses or a smartphone and inputs their destination. Next, a large-scale language model (LLM) analyzes the input information based on the local language and culture and generates appropriate navigation information. The generated navigation information is provided to the user through the device. For example, when a user visits a tourist spot in Japan, the LLM understands local idioms and expressions and generates navigation information based on them. If the user inputs "I want to go to the tourist spot," the LLM generates specific guidance, such as "To get to the tourist spot, first go north on the main street, then turn right at the specific intersection." This guidance is displayed to the user through the device. Furthermore, the LLM can translate the guidance into an appropriate language based on the user's language settings. For example, if an English-speaking user visits Japan, the LLM generates guidance such as "Please proceed north on the main street, then turn right at the specific intersection to reach the tourist spot." In this way, the user can receive local-specific navigation in their own language. This allows users to receive navigation adapted to the local language and culture, making travel and sightseeing more comfortable and convenient. It also deepens understanding of local cultures and languages, promoting intercultural exchange.

[0063] A navigation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a destination input from a user. Destination input methods include, but are not limited to, text input, voice input, and multiple-choice input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate navigation information based on the local language and culture. The generation AI analyzes the local language and culture using technologies such as natural language processing, machine learning, and deep learning to generate navigation information. The generation unit understands local idioms and expressions and generates navigation information based on the local idioms and expressions. The generation unit can also translate the navigation information into an appropriate language based on the user's language setting. The provision unit provides the user with the navigation information generated by the generation unit. The provision unit provides the navigation information through a device such as AR glasses or a smartphone. The provision unit can also display the navigation information based on the orientation of the user's device. This allows the navigation system according to an embodiment to provide navigation adapted to the local language and culture of the user. For example, if a user visits a tourist spot in Japan, the AI ​​can understand local idioms and expressions and generate navigation information based on them. When a user inputs, "I want to go to the tourist spot," the AI ​​generates specific directions, such as, "To get to the tourist spot, first go north on the main street, then turn right at the specific intersection." These directions are displayed to the user through the device. Furthermore, the AI ​​can translate the directions into the appropriate language based on the user's language settings. For example, if an English-speaking user visits Japan, the AI ​​can generate directions such as, "Please proceed north on the main street, then turn right at the specific intersection to reach the tourist spot." In this way, users can receive local navigation in their own language.This allows users to receive navigation adapted to the local language and culture, making travel and sightseeing more comfortable and convenient. It also deepens understanding of local cultures and languages, promoting intercultural exchange.

[0064] The generation unit can analyze the language and culture specific to the region using the generation AI and generate navigation information. The generation unit, for example, analyzes the language and culture specific to the region using the generation AI. The generation AI, for example, uses natural language processing technology to understand local idioms and expressions. The generation AI can also analyze the cultural background of the region using a machine learning algorithm. Furthermore, the generation AI can also generate navigation information based on the language and culture specific to the region using deep learning technology. In this way, navigation information based on the language and culture specific to the region can be generated using the generation AI. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input data related to the language and culture of the region into the generation AI and cause the generation AI to generate navigation information.

[0065] The providing unit can translate the navigation information into a language based on the user's language setting based on the user's language setting. The providing unit, for example, acquires the language setting of the user's device and translates the navigation information based on that. The providing unit can acquire the user's language setting from, for example, device settings or application settings. The providing unit can also use a translation algorithm to make the navigation information multilingual. For example, the providing unit can automatically translate the navigation information using a generation AI. This makes it possible to provide navigation information according to the user's language setting. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input the user's language setting to the generation AI and have the generation AI translate the navigation information.

[0066] The providing unit can provide navigation information through AR glasses or a smartphone. The providing unit can provide navigation information through, for example, AR glasses or a smartphone. The providing unit can use, for example, an application compatible with AR glasses or a smartphone of a specific manufacturer or model. The providing unit can also provide a dedicated application for displaying navigation information. This allows users to receive navigation information through various devices. Some or all of the above-described processing in the providing unit is performed using AI. For example, the providing unit can input navigation information to a generation AI and cause the generation AI to execute information to be displayed on the AR glasses or a smartphone.

[0067] The generation unit can understand idioms and expressions used in tourist destinations or specific regions, and generate navigation information based on that understanding. The generation unit, for example, uses a generation AI to understand idioms and expressions used in tourist destinations or specific regions. The generation AI can, for example, refer to a regional language database and analyze idioms and expressions. The generation AI can also analyze related literature and data to understand the cultural background of the region. Furthermore, the generation AI can generate navigation information based on expressions and phrases unique to the region. This makes it possible to provide navigation information adapted to tourist destinations or specific regions. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input data related to tourist destinations or specific regions into the generation AI and have the generation AI generate navigation information.

[0068] The providing unit can display navigation information based on the orientation of the user's device. The providing unit, for example, acquires the orientation of the user's device and displays the navigation information based on that. The providing unit acquires the orientation of the device using, for example, a gyro sensor, a compass, or a GPS. The providing unit can also display the navigation information in an optimal format depending on the orientation of the device. This enables optimal display depending on the orientation of the user's device. Some or all of the above-described processing in the providing unit can be performed using AI. For example, the providing unit can input device orientation data to a generating AI and cause the generating AI to display the navigation information.

[0069] The reception unit can estimate the user's emotions and adjust the destination input method based on the estimated user emotions. The reception unit, for example, uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's 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 quick destination input. This allows the optimal input method to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the destination input method.

[0070] The reception unit can analyze the user's past destination input history and suggest an input method suitable for the user. For example, the reception unit obtains and analyzes the user's past destination input history from a database, log file, cloud storage, etc. The reception unit can, for example, automatically display destinations that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input past destination input history data into a generation AI and have the generation AI suggest the optimal input method.

[0071] The reception unit can present input candidates based on the user's current location information when the user inputs a destination. The reception unit, for example, acquires the user's current location information using GPS, Wi-Fi location information, cell tower location information, etc. The reception unit, for example, automatically acquires the user's current location when the user opens the app and sets it as the starting point. The reception unit can also suggest optimal candidate locations by taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the current location in real time and reflect it as the starting point. This makes it possible to present optimal input candidates based on the current location information. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input current location information data to the generation AI and cause the generation AI to present input candidates.

[0072] When inputting a destination, the reception unit can select an input means suitable for the user depending on the user's input method. The reception unit, for example, detects the user's input method (voice, text, gesture, etc.) and selects the optimal input means accordingly. For example, the reception unit can automatically set the destination when the user simply inputs "I want to go to a tourist spot" by voice. The reception unit can also allow the user to easily set the destination by performing a specific gesture on the smartphone screen. Furthermore, the reception unit can also allow the user to combine voice input and gesture input to set the destination more intuitively. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0073] The reception unit can estimate the user's emotions and prioritize input candidates based on the estimated user emotions. The reception unit, for example, uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. For example, if the user is nervous, the reception unit can prioritize the simplest and most intuitive input candidates. Furthermore, if the user is relaxed, the reception unit can provide detailed input candidates and suggest customizable options. Furthermore, if the user is in a hurry, the reception unit can prioritize input candidates that the user has used frequently in the past. This allows the provision of optimal input candidates according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the input candidates.

[0074] When a destination is input, the reception unit can prioritize presenting highly relevant destinations taking into account the user's geographical location information. The reception unit acquires the user's geographical location information using, for example, GPS, Wi-Fi location information, cell tower location information, etc. The reception unit, for example, prioritizes displaying locations close to the user's current location as candidates. The reception unit can also prioritize displaying locations that the user has visited in the past as candidates. Furthermore, the reception unit can also suggest optimal candidate locations taking into account the distance from the user's current location. This makes it possible to present highly relevant destinations based on the geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to present highly relevant destinations.

[0075] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit acquires data such as post content, location information, and friend relationships to analyze the user's social media activity. For example, the reception unit suggests locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the user's social media posts and suggest related locations as candidate locations. Furthermore, the reception unit can also suggest related locations by referring to the activity of the user's friends on social media. This makes it possible to suggest related destinations based on social media activity. Some or all of the above-mentioned processing in the reception unit can be performed 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 destinations.

[0076] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. The reception unit, for example, acquires and analyzes the user's past feedback from a database, a log file, cloud storage, etc. The reception unit can, for example, suggest the optimal input method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's feedback and customize the input method. This makes it possible to provide the optimal input method based on the past feedback. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the input method.

[0077] The generation unit can estimate the user's emotions and adjust the presentation method of the navigation information based on the estimated user emotions. The generation unit uses, for example, technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. For example, if the user is relaxed, the generation unit generates navigation information that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate navigation information that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can also generate navigation information that adds visually stimulating effects. This allows optimal navigation information to be provided according to the user's emotions. The 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 generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the navigation information.

[0078] When generating navigation information, the generation unit can generate information based on the historical background and cultural elements of the region. For example, the generation unit uses a generation AI to generate navigation information taking into account the historical background and cultural elements of the region. For example, the generation AI can suggest a route that includes historical landmarks in the region. The generation AI can also incorporate information related to cultural events and festivals in the region into the navigation. Furthermore, the generation AI can include information about traditional foods and shops in the navigation. This makes it possible to provide navigation information based on the history and culture of the region. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input data related to the historical background and cultural elements of the region into the generation AI and have the generation AI generate navigation information.

[0079] When generating navigation information, the generation unit can improve the accuracy of the information by referring to the user's past navigation history. The generation unit, for example, acquires and analyzes the user's past navigation history from a database, log file, cloud storage, etc. The generation unit, for example, proposes 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 navigation history. Furthermore, the generation unit can analyze the user's past navigation history and propose the most efficient route. This makes it possible to provide optimal navigation information based on the past navigation history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input past navigation history data into the generation AI and have the generation AI improve the accuracy of the navigation information.

[0080] When generating navigation information, the generation unit can propose an optimal route based on local event information. The generation unit uses a generation AI, for example, to generate navigation information taking local event information into consideration. The generation AI can propose an optimal route based on information about events held in the area, for example. The generation AI can also propose a route that passes through local event locations. Furthermore, the generation AI can propose a route that avoids congestion by taking local event information into consideration. This makes it possible to provide an optimal route based on local event information. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can input local event information data into the generation AI and have the generation AI propose an optimal route.

[0081] The generation unit can estimate the user's emotions and adjust the level of detail of the navigation information based on the estimated user's emotions. The generation unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. For example, the generation unit can provide detailed navigation information when the user is relaxed. Furthermore, the generation unit can provide concise navigation information that focuses on the main points when the user is in a hurry. Furthermore, the generation unit can provide navigation information with visually stimulating effects when the user is excited. This allows navigation information with an optimal level of detail to be provided according to the user's emotions. The 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 generation unit can be performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the navigation information.

[0082] When generating the navigation information, the generation unit can make the information multilingual based on the user's language setting. The generation unit, for example, acquires the language setting of the user's device and makes the navigation information multilingual based on that. The generation unit, for example, acquires the user's language setting from device settings or application settings. The generation unit can also use a translation algorithm to make the navigation information multilingual. For example, the generation unit can automatically translate the navigation information using a generation AI. This makes it possible to provide multilingual navigation information according to the user's language setting. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input the user's language setting data into the generation AI and have the generation AI make the navigation information multilingual.

[0083] When generating navigation information, the generation unit can select the optimal display format based on the user's device information. The generation unit, for example, acquires the user's device information and selects the display format of the navigation information based on that information. The generation unit acquires information such as the device type, OS version, and hardware specifications. The generation unit can also select the optimal display format based on the device's screen size and resolution. This makes it possible to provide a display format optimized for the user's device. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input device information data to the generation AI and cause the generation AI to select the optimal display format.

[0084] When generating navigation information, the generation unit can adjust the use of technical terminology in the information based on the user's level of expertise. For example, the generation unit acquires data such as the user's occupation, educational background, and past usage history to evaluate the user's level of expertise. For example, if the user has technical expertise, the generation unit provides navigation information using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can provide concise and easy-to-understand navigation information. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past navigation history. This allows the provision of optimal navigation information according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0085] The providing unit can estimate the user's emotions and adjust the display method of the navigation information based on the estimated user's emotions. The providing unit, for example, uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This allows the optimal display method to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using 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 providing unit can be performed using AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the navigation information.

[0086] When providing navigation information, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, acquires and analyzes the user's past operation history from a database, log file, cloud storage, etc. The providing unit, for example, proposes the optimal display method based on the display method used by the user in the past. The providing unit can also propose a display method with high visibility based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and propose the most efficient display method. This makes it possible to provide the optimal display method based on the past operation history. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input past operation history data to a generation AI and cause the generation AI to select the optimal display method.

[0087] The providing unit can customize the display content according to the user's current task when providing navigation information. The providing unit, for example, acquires the user's current task and customizes the display content based on that. The providing unit acquires data such as the user's current activity and application usage status. The providing unit can also prioritize displaying information about tourist spots when the user is sightseeing. The providing unit can also prioritize displaying store information when the user is shopping. This makes it possible to provide optimal display content according to the user's current task. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input current task data to a generating AI and have the generating AI customize the display content.

[0088] When providing navigation information, the providing unit can adjust the display method based on the remaining battery level of the user's device. For example, the providing unit acquires the remaining battery level of the user's device and adjusts the display method based on that. The providing unit acquires data such as the battery percentage and available time. The providing unit can also display the information in low power mode when the remaining battery level of the user's device is low. Furthermore, the providing unit can provide a display method including detailed information when the remaining battery level of the user's device is sufficient. This makes it possible to provide an optimal display method according to the remaining battery level of the device. Some or all of the above-mentioned processing in the providing unit can be performed using AI. For example, the providing unit can input remaining battery level data to the generating AI and cause the generating AI to adjust the display method.

[0089] The providing unit can estimate the user's emotions and adjust the display order of the navigation information based on the estimated user emotions. The providing unit, for example, uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. For example, when the user is nervous, the providing unit prioritizes displaying the most important information. Furthermore, when the user is relaxed, the providing unit can sequentially display detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize displaying information that highlights the main points. This allows for providing an optimal display order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit is performed using AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of the navigation information.

[0090] When providing navigation information, the providing unit can adjust the display content based on the orientation of the user's device. The providing unit, for example, acquires the orientation of the user's device and adjusts the display content based on that. The providing unit acquires the orientation of the device using, for example, a gyro sensor, a compass, or a GPS. The providing unit can also display the navigation information in an optimal format depending on the orientation of the device. This makes it possible to provide optimal display content depending on the orientation of the device. Some or all of the above-described processing in the providing unit is performed using AI. For example, the providing unit can input device orientation data to a generating AI and cause the generating AI to adjust the display content.

[0091] The providing unit can optimize the display format according to the screen size of the user's device when providing navigation information. The providing unit, for example, acquires the screen size of the user's device and optimizes the display format based on that. The providing unit acquires data such as the number of inches, resolution, and aspect ratio of the device. The providing unit can also provide the optimal display format according to the screen size of the device. This makes it possible to provide the optimal display format according to the screen size of the device. Some or all of the above-mentioned processing in the providing unit is performed using AI. For example, the providing unit can input screen size data to the generating AI and cause the generating AI to optimize the display format.

[0092] When providing navigation information, the providing unit can translate the information into an appropriate language based on the user's language setting. The providing unit, for example, acquires the language setting of the user's device and translates the navigation information based on that. The providing unit, for example, acquires the user's language setting from device settings or application settings. The providing unit can also use a translation algorithm to make the navigation information multilingual. For example, the providing unit can automatically translate the navigation information using a generation AI. This makes it possible to provide optimal navigation information according to the user's language setting. Some or all of the above-mentioned processing in the providing unit can be performed using AI. For example, the providing unit can input the user's language setting data into the generation AI and have the generation AI translate the navigation information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive a destination input from a user using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates navigation information based on the local language and culture using a generation AI. The provision unit can provide the generated navigation information to a user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a destination input from a user using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates navigation information based on the local language and culture using a generation AI. The provision unit can provide the generated navigation information to a user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive input of a destination from a user using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates navigation information based on the language and culture of the region using a generation AI. The provision unit can provide the generated navigation information to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input of a destination from a user using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates navigation information based on the language and culture of the region using a generation AI. The provision unit can provide the generated navigation information to the user using the speaker 240 of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can analyze the user's past navigation history and automatically display locations frequently visited by the user as candidates. For example, it can prioritize displaying cafes and restaurants that the user has visited many times in the past. The reception unit can also predict locations that the user will visit on specific days of the week or during specific times of the day, and suggest candidate locations suitable for those times. Furthermore, the reception unit can suggest locations related to specific events or seasons based on the user's past navigation history. This makes it possible to provide optimal candidate locations based on the user's past behavioral patterns.

[0095] The generator can customize navigation information based on the user's current mood and emotions. For example, if the user is feeling relaxed, it can suggest relaxing places such as tourist spots and parks. If the user is feeling stressed, it can suggest quiet cafes and relaxation spots. Furthermore, if the user is excited, it can suggest places with many activities and events. This makes it possible to provide optimal navigation information according to the user's emotions.

[0096] The providing unit can adjust the display method of the navigation information based on the remaining battery level of the user's device. For example, when the remaining battery level is low, the display is in low-power mode and detailed information is omitted. When the remaining battery level is sufficient, detailed navigation information can be provided. Furthermore, when the remaining battery level is very low, only the most important information can be displayed to minimize battery consumption. This makes it possible to provide an optimal display method according to the remaining battery level of the device.

[0097] The reception unit can analyze the user's social media activity and suggest related destinations. For example, it can suggest places where the user has checked in on social media as candidate destinations. It can also suggest related places based on places and events visited by the user's friends. It can also analyze the content of the user's social media posts and suggest places that the user might be interested in. This makes it possible to suggest related destinations based on social media activity.

[0098] The generation unit can generate navigation information taking into account the historical background and cultural elements of the region. For example, it can suggest routes that include historical landmarks in the region. It can also incorporate information related to local cultural events and festivals into the navigation. Furthermore, it can include information about traditional foods and shops in the region. This makes it possible to provide navigation information based on the history and culture of the region.

[0099] The providing unit can estimate the user's emotions and adjust the display method of the navigation information based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, it is possible to provide the optimal display method according to the user's emotions.

[0100] The receiving unit can customize the input method by reflecting the user's past feedback. For example, the receiving unit can suggest the optimal input method based on the user's past feedback. It can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, it can analyze the user's feedback and customize the input method. This makes it possible to provide the optimal input method based on the user's past feedback.

[0101] The generation unit can estimate the user's emotions and adjust the presentation method of the navigation information based on the estimated user's emotions. For example, if the user is relaxed, navigation information that progresses at a leisurely pace can be generated. If the user is in a hurry, navigation information that emphasizes the shortest route can be generated. Furthermore, if the user is excited, navigation information that adds visually stimulating effects can be generated. This makes it possible to provide optimal navigation information according to the user's emotions.

[0102] The providing unit can customize the display content according to the user's current task. For example, if the user is sightseeing, information about tourist spots can be displayed with priority. Also, if the user is shopping, store information can be displayed with priority. Furthermore, if the user is heading to a business meeting, the shortest route and traffic information can be displayed with priority. This makes it possible to provide the optimal display content according to the user's current task.

[0103] The providing unit can estimate the user's emotions and adjust the display order of navigation information based on the estimated user's emotions. For example, if the user is nervous, the most important information can be displayed preferentially. If the user is relaxed, detailed information can be displayed sequentially. Furthermore, if the user is in a hurry, information that focuses on the main points can be displayed preferentially. This makes it possible to provide an optimal display order according to the user's emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives a destination input from the user. The destination input method includes text input, voice input, multiple choice input, and the like. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate navigation information based on the local language and culture. The generation AI uses technologies such as natural language processing, machine learning, and deep learning to analyze the local language and culture and generate navigation information. It can also translate the navigation information into an appropriate language based on the user's language settings. Step 3: The providing unit provides the navigation information generated by the generating unit to the user. The providing unit provides the navigation information through a device such as AR glasses or a smartphone, and can also display the navigation information based on the orientation of the user's device.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 AI 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 AI 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 AI 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 an input of a destination from a user; a generating unit that analyzes the information received by the receiving unit and generates navigation information based on the language and culture of the region; a providing unit that provides the navigation information generated by the generating unit to a user. A system characterized by:

2. The generation unit Generative AI analyzes local languages ​​and cultures to generate navigation information 2. The system of claim 1.

3. The providing unit Translate navigation information based on the user's language preference into a language based on the user's language preference 2. The system of claim 1.

4. The providing unit Providing navigation information through AR glasses or smartphones 2. The system of claim 1.

5. The generation unit Understanding idioms and expressions in tourist spots and specific regions, and generating navigation information based on them 2. The system of claim 1.

6. The providing unit Display navigation information based on the user's device orientation 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the destination input method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past destination input history and suggests the most suitable input method for the user 2. The system of claim 1.

9. The reception unit When entering a destination, suggestions are provided based on the user's current location.

2. The system of claim 1.

10. The reception unit When inputting a destination, select an input method appropriate for the user depending on the user's input method.

2. The system of claim 1.

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