System

Abstract

An object of a system according to an embodiment is to provide natural voice guidance based on a user's wording or style.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a destination. The generation unit generates voice guidance based on the language and the style of the user based on the information received by the reception unit. The providing unit provides the voice guidance generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that voice guidance does not adapt to the user's language or writing style, making it difficult to understand the information.

[0005] The system according to the embodiment aims to provide natural voice guidance based on the user's language and writing style. [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 input of a destination. The generating unit generates voice guidance based on the information received by the receiving unit and the user's language and writing style. The providing unit provides the voice guidance generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide natural voice guidance based on the user's language and writing style. [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 using AR glasses for pedestrians and cyclists. The navigation system allows a user to input a destination, and utilizes a large-scale language model (LLM) to generate voice guidance tailored to the user's language and writing style. The voice guidance is then provided through the AR glasses. This allows the user to more easily understand the information and reach the destination. For example, the navigation system allows a user to input a destination in natural language, such as "Tell me the way to the station." This information is input into the system, and the LLM generates voice guidance tailored to the user's language and writing style. The generated voice guidance is provided to the user through the AR glasses. This allows the user to receive natural voice guidance along with visual navigation information. For example, by receiving voice guidance such as "Turn right at the next intersection" along with visual map information, the user can reach their destination without getting lost. This allows the navigation system to provide visual navigation information and natural voice guidance to pedestrians and cyclists, making it easier to understand the information.

[0029] A navigation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit is a component that allows a user to input a destination. For example, the reception unit can input a destination by text input, voice input, selection on a map, or other methods. The reception unit can accept a user's input of a destination in natural language, such as "Tell me the way to the station." The generation unit uses an LLM to generate voice guidance tailored to the user's language and writing style based on the information accepted by the reception unit. For example, the generation unit generates voice guidance using gentle language for children and polite language for business people. The generation unit uses an LLM (large-scale language model) to analyze the user's input and select appropriate language and writing style to generate the voice guidance. The provision unit is a component that provides the voice guidance generated by the generation unit to the user. For example, the provision unit provides voice guidance through AR glasses. By wearing the AR glasses, the provision unit enables the user to receive natural voice guidance along with visual navigation information. As a result, the navigation system according to an embodiment provides natural voice guidance tailored to the user's language and writing style, making it easier to understand information. For example, a navigation system allows a user to input their destination in natural language, such as "Tell me the way to the station," and the LLM analyzes and generates voice guidance, which is then provided through AR glasses. This allows the user to receive natural voice guidance along with visual navigation information, making it easier for the user to understand the information and reach their destination.

[0030] The navigation system includes a collection unit that acquires user profile information. The collection unit is a component that collects the user profile information. For example, the collection unit can collect profile information such as age, gender, hobbies, and past behavioral history. By collecting the user profile information, the collection unit can provide more personalized voice guidance. For example, the collection unit generates voice guidance by selecting appropriate language and writing style based on the user's age and gender. In this way, the navigation system can provide more personalized voice guidance by collecting the user profile information. For example, the collection unit generates voice guidance by selecting appropriate language and writing style based on the user's age and gender. In this way, the navigation system can provide more personalized voice guidance by collecting the user profile information.

[0031] The navigation system includes a display unit that provides visual navigation information. The display unit is a component that displays the visual navigation information. For example, the display unit can provide the visual navigation information by displaying a map, text, icons, or other methods. The display unit allows a user to receive the visual navigation information by wearing AR glasses. By providing the visual navigation information, the navigation system allows the user to reach a destination more easily. For example, the display unit can visually show the user a route to the destination by displaying a map. Furthermore, the display unit can visually show the user guidance to the destination by displaying text. Furthermore, the display unit can visually show the user guidance to the destination by displaying icons. By providing the visual navigation information, the navigation system allows the user to reach a destination more easily.

[0032] The navigation system includes an adjustment unit that changes the volume of the voice guidance. The adjustment unit is a component that adjusts the volume of the voice guidance. For example, the adjustment unit can adjust the volume based on criteria such as a decibel value or a volume relative to environmental sounds. The adjustment unit can appropriately adjust the volume to make the voice guidance easier to hear for the user. For example, the adjustment unit can automatically increase the volume when the user is in a noisy environment. Also, the adjustment unit can automatically decrease the volume when the user is in a quiet environment. In this way, the navigation system can provide voice guidance that is easier for the user to hear by adjusting the volume of the voice guidance. For example, the adjustment unit can automatically increase the volume when the user is in a noisy environment. Also, the adjustment unit can automatically decrease the volume when the user is in a quiet environment. In this way, the navigation system can provide voice guidance that is easier for the user to hear by adjusting the volume of the voice guidance.

[0033] The collection unit can acquire user profile information in cooperation with the reception unit. The collection unit collects user profile information in cooperation with the reception unit. For example, the collection unit can collect profile information such as age, gender, hobbies, and past behavioral history based on the user's input information accepted by the reception unit. This allows the collection unit to collect profile information more efficiently by working with the reception unit. For example, the collection unit can collect profile information such as age, gender, hobbies, and past behavioral history based on the user's input information accepted by the reception unit. This allows the collection unit to collect profile information more efficiently by working with the reception unit.

[0034] The display unit can provide visual navigation information in cooperation with the providing unit. The display unit displays the visual navigation information in cooperation with the providing unit. For example, the display unit can provide the visual navigation information by a method such as map display, text display, or icon display based on the visual navigation information generated by the providing unit. This allows the display unit to more effectively display the visual navigation information in cooperation with the providing unit. For example, the display unit can provide the visual navigation information by a method such as map display, text display, or icon display based on the visual navigation information generated by the providing unit. This allows the display unit to more effectively display the visual navigation information in cooperation with the providing unit.

[0035] The adjustment unit can change the volume of the voice guidance in cooperation with the provision unit. The adjustment unit can adjust the volume of the voice guidance in cooperation with the provision unit. For example, the adjustment unit can adjust the volume based on criteria such as a decibel value or a relative volume to environmental sounds, based on the voice guidance generated by the provision unit. This allows the adjustment unit to more appropriately adjust the volume of the voice guidance in cooperation with the provision unit. For example, the adjustment unit can adjust the volume based on criteria such as a decibel value or a relative volume to environmental sounds, based on the voice guidance generated by the provision unit. This allows the adjustment unit to more appropriately adjust the volume of the voice guidance in cooperation with the provision unit.

[0036] The reception unit can analyze the user's past destination input history and suggest an appropriate input method. The reception unit analyzes the user's past destination input history and suggest the optimal input method. For example, the reception unit automatically displays destinations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destinations to be used during a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the past input history.

[0037] The reception unit can present input candidates based on the user's current location information when inputting a destination. The reception unit presents input candidates taking into account the user's current location information when inputting a destination. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The reception unit can also suggest optimal candidate locations taking into account the distance from the current location when the user inputs a destination. The reception unit can also update the user's current location in real time and reflect it as the departure point when using the app while the user is on the move. This allows the reception unit to present more appropriate input candidates by taking into account the user's current location information.

[0038] The reception unit can select an appropriate input means depending on the user's input method when inputting a destination. The reception unit selects the optimal input means depending on the user's input method (voice, text, gesture, etc.) when inputting a destination. For example, the reception unit can automatically set the destination when the user simply inputs "to the station" by voice. The reception unit can also enable the user to easily set the destination by performing a specific gesture on the smartphone screen. The reception unit can also enable the user to set the destination more intuitively by combining voice input and gesture input. This allows the reception unit to select the optimal input means depending on the user's input method, enabling more intuitive input.

[0039] The reception unit can present relevant destinations based on the user's geographical location information when the user inputs a destination. The reception unit preferentially presents highly relevant destinations in consideration of the user's geographical location information when the user inputs a destination. For example, the reception unit preferentially displays locations close to the user's current location as candidates. The reception unit can also preferentially display locations that the user has visited in the past as candidates. The reception unit can also suggest optimal candidate locations based on the distance from the user's current location. In this way, the reception unit can preferentially present highly relevant destinations by considering the geographical location information.

[0040] The reception unit can suggest destinations based on the user's social media activity when the user inputs a destination. When the destination is input, the reception unit analyzes the user's social media activity and suggests related destinations. 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 content of the user's social media posts and suggest related locations as candidate locations. The reception unit can also suggest related locations by referring to the activity of the user's friends on social media. In this way, the reception unit can suggest related destinations by analyzing social media activity.

[0041] The reception unit can change the input method based on the user's past feedback when inputting a destination. The reception unit customizes the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also suggest the optimal input means based on the user's past feedback. The reception unit can also customize the interface by reflecting the user's past feedback. In this way, the reception unit can provide an input method that is easier to use by reflecting the user's past feedback.

[0042] The generation unit can change the language and writing style based on the user's profile information when generating voice guidance. The generation unit customizes the language and writing style based on the user's profile information when generating voice guidance. For example, the generation unit can generate voice guidance using gentle language for children. The generation unit can also generate voice guidance using polite language for business people. The generation unit can also generate voice guidance at a slower pace for elderly people. In this way, the generation unit can provide more personalized guidance by customizing the language and writing style based on the profile information.

[0043] The generation unit can apply different guidance algorithms based on the type of destination when generating voice guidance. The generation unit applies different guidance algorithms based on the type of destination when generating voice guidance. For example, in the case of a tourist destination, the generation unit generates voice guidance including information about tourist spots. In addition, in the case of a commercial facility, the generation unit can also generate voice guidance including store information. In addition, in the case of a public facility, the generation unit can also generate voice guidance including how to use the facility and important points to note. In this way, the generation unit can provide more appropriate guidance by applying different guidance algorithms based on the type of destination.

[0044] When generating voice guidance, the generation unit can improve the accuracy of the guidance based on the user's past guidance history. When generating voice guidance, the generation unit improves the accuracy of the guidance by referring to the user's past guidance history. For example, the generation unit generates optimal voice guidance based on routes the user has used in the past. The generation unit can also suggest routes that avoid congestion based on the user's past guidance history. The generation unit can also analyze the user's past guidance history and suggest the most efficient route. In this way, the generation unit can improve the accuracy of the guidance by referring to the past guidance history.

[0045] When generating voice guidance, the generation unit can determine the order of guidance based on the time of submission of the destination. When generating voice guidance, the generation unit determines the priority of guidance based on the time of submission of the destination. For example, the generation unit generates guidance with the highest priority for an urgent destination. Furthermore, the generation unit can quickly generate guidance when an appointment is approaching. Furthermore, the generation unit can also generate detailed guidance when there is time to spare. In this way, the generation unit determines the priority of guidance based on the time of submission of the destination, making it possible to provide guidance according to the level of urgency.

[0046] The generation unit can change the order of guidance based on the relevance of destinations when generating voice guidance. The generation unit adjusts the order of guidance based on the relevance of destinations when generating voice guidance. For example, the generation unit provides guidance to important destinations first. The generation unit can also provide guidance to highly relevant destinations with priority. The generation unit can also adjust the order of guidance based on the user's interests. This allows the generation unit to adjust the order of guidance based on the relevance of destinations, thereby enabling more efficient guidance.

[0047] When generating voice guidance, the generation unit can change the use of technical terminology in the guidance based on the user's level of expertise. When generating voice guidance, the generation unit adjusts the use of technical terminology in the guidance according to the user's level of expertise. For example, the generation unit generates guidance including technical terminology for a user with specialized knowledge. The generation unit can also generate guidance in simple language for a user without specialized knowledge. The generation unit can also adjust the use of optimal technical terminology based on the user's past guidance history. In this way, the generation unit can adjust the use of technical terminology according to the user's level of expertise, thereby enabling guidance that is easier to understand.

[0048] The providing unit can change the method of providing voice guidance based on the user's current situation when providing voice guidance. The providing unit customizes the method of providing voice guidance according to the user's current situation (walking, riding a bicycle, etc.). For example, when the user is walking, the providing unit can provide voice guidance that matches the user's walking speed. Furthermore, when the user is riding a bicycle, the providing unit can provide voice guidance that matches the user's bicycle speed. Furthermore, when the user is standing still, the providing unit can also provide voice guidance that includes detailed explanations. This allows the providing unit to customize the method of providing voice guidance according to the user's current situation, thereby enabling more appropriate guidance.

[0049] When providing voice guidance, the providing unit can improve the accuracy of the provision based on the user's past guidance history. When providing voice guidance, the providing unit improves the accuracy of the provision by referring to the user's past guidance history. For example, the providing unit provides optimal voice guidance based on routes the user has used in the past. The providing unit can also suggest routes that avoid congestion based on the user's past guidance history. The providing unit can also analyze the user's past guidance history and suggest the most efficient route. In this way, the providing unit can improve the accuracy of the provision by referring to the past guidance history.

[0050] The providing unit can change the method of providing voice guidance based on user feedback when providing voice guidance. The providing unit improves the method of providing voice guidance by reflecting user feedback when providing voice guidance. For example, the providing unit improves the method of providing voice guidance based on feedback provided by the user on voice guidance provided in the past. The providing unit can also provide optimal voice guidance by reflecting user feedback. The providing unit can also adjust the content and timing of the voice guidance based on user feedback. In this way, the providing unit can improve the method of providing voice guidance by reflecting user feedback, thereby enabling more appropriate guidance.

[0051] The providing unit can select an appropriate provision method based on the user's geographical location information when providing voice guidance. The providing unit selects the optimal provision method by taking the user's geographical location information into consideration when providing voice guidance. For example, the providing unit can prioritize guidance to places close to the user's current location. The providing unit can also prioritize guidance to places the user has visited in the past. The providing unit can also suggest the optimal guidance method based on the distance from the user's current location. In this way, the providing unit can select the optimal provision method by taking the geographical location information into consideration.

[0052] The providing unit can change the method of providing voice guidance based on the user's social media activity when providing voice guidance. The providing unit analyzes the user's social media activity and customizes the method of providing voice guidance. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, the providing unit can customize the method of providing voice guidance by analyzing social media activity.

[0053] The providing unit can change the method of providing voice guidance based on the user's past feedback when providing voice guidance. The providing unit customizes the method of providing voice guidance by reflecting the user's past feedback when providing voice guidance. For example, the providing unit provides a style of voice guidance that the user has preferred in the past with priority. The providing unit can also provide optimal voice guidance based on the user's past feedback. The providing unit can also adjust the content and timing of the voice guidance by reflecting the user's past feedback. In this way, the providing unit can customize the method of providing voice guidance by reflecting the user's past feedback.

[0054] When collecting profile information, the collection unit can select an appropriate collection method based on the user's past behavioral history. When collecting profile information, the collection unit analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit suggests the optimal profile information collection method based on information previously entered by the user. The collection unit can also automatically collect necessary information from the user's past behavioral history. The collection unit can also analyze the user's past behavioral history and suggest the most efficient profile information collection method. This allows the collection unit to select the optimal collection method by analyzing the past behavioral history.

[0055] The collection unit can change the collection means based on the user's current situation when collecting profile information. The collection unit customizes the collection means based on the user's current situation when collecting profile information. For example, when the user is walking, the collection unit can collect profile information in the form of simple questions. When the user is riding a bicycle, the collection unit can collect profile information using voice input. When the user is standing still, the collection unit can also collect detailed profile information. In this way, the collection unit can collect more appropriate information by customizing the collection means based on the current situation.

[0056] When collecting profile information, the collection unit can collect relevant information based on the user's geographical location information. When collecting profile information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about places close to the user's current location. The collection unit can also prioritize collecting information about places the user has visited in the past. The collection unit can also collect optimal information based on the distance from the user's current location. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information.

[0057] When collecting profile information, the collection unit can change the collection method based on the user's social media activity. When collecting profile information, the collection unit analyzes the user's social media activity to customize the collection method. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit can customize the collection method by analyzing social media activity.

[0058] The display unit can select an appropriate display method based on the user's past display history when displaying visual navigation information. The display unit selects the optimal display method by referring to the user's past display history when displaying visual navigation information. For example, the display unit preferentially provides display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past display history. The display unit can also analyze the user's past display history and suggest the most efficient display method. In this way, the display unit can select the optimal display method by referring to the past display history.

[0059] The display unit can change the display content based on the user's current task when displaying visual navigation information. The display unit customizes the display content according to the user's current task when displaying visual navigation information. For example, when the user is walking, the display unit can provide display content suitable for walking. Furthermore, when the user is riding a bicycle, the display unit can provide display content suitable for cycling. Furthermore, when the user is standing still, the display unit can also provide detailed display content. In this way, the display unit can provide more appropriate information by customizing the display content according to the current task.

[0060] The display unit can select an appropriate display method based on the user's device information when displaying visual navigation information. The display unit selects the optimal display method by taking the user's device information into consideration when displaying visual navigation information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the display unit to select the optimal display method by taking the device information into consideration.

[0061] The display unit can make the display content multilingual based on the user's language setting when displaying visual navigation information. The display unit can make the display content multilingual based on the user's language setting when displaying visual navigation information. For example, the display unit automatically sets the navigation language based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide navigation in a specific language when the user selects that language. This allows the display unit to accommodate a wider range of users by providing multilingual support based on the language setting.

[0062] The display unit can change the display method based on the user's past feedback when displaying visual navigation information. The display unit customizes the display method by reflecting the user's past feedback when displaying visual navigation information. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also provide an optimal display method based on the user's past feedback. The display unit can also adjust the display content and timing by reflecting the user's past feedback. In this way, the display unit can customize the display method by reflecting the user's past feedback.

[0063] When adjusting the volume of the voice guidance, the adjustment unit can select an appropriate volume based on the user's past volume setting history. When adjusting the volume of the voice guidance, the adjustment unit selects the optimal volume by referring to the user's past volume setting history. For example, the adjustment unit automatically sets the optimal volume based on the volume previously set by the user. The adjustment unit can also analyze the user's past volume setting history and suggest the most appropriate volume. The adjustment unit can also select the optimal volume based on the volume setting used by the user in a specific environment. In this way, the adjustment unit can select the optimal volume by referring to the past volume setting history.

[0064] When adjusting the volume of the voice guidance, the adjustment unit can change the volume based on the user's current environmental sound. When adjusting the volume of the voice guidance, the adjustment unit analyzes the user's current environmental sound and customizes the volume. For example, the adjustment unit can automatically increase the volume when the user is in a noisy environment. Also, the adjustment unit can automatically decrease the volume when the user is in a quiet environment. The adjustment unit can also analyze the environmental sound around the user in real time and set an optimal volume. In this way, the adjustment unit can provide an optimal volume by analyzing the current environmental sound.

[0065] When adjusting the volume of the voice guidance, the adjustment unit can select an appropriate volume based on the user's geographical location information. When adjusting the volume of the voice guidance, the adjustment unit selects the optimal volume by taking into account the user's geographical location information. For example, the adjustment unit can automatically increase the volume when the user is in an urban area. Also, the adjustment unit can automatically decrease the volume when the user is in a suburban area. The adjustment unit can also set the optimal volume based on the user's current location. In this way, the adjustment unit can provide the optimal volume by taking into account the geographical location information.

[0066] The adjustment unit can change the volume based on the user's social media activity when adjusting the volume of the voice guidance. The adjustment unit analyzes the user's social media activity to customize the volume when adjusting the volume of the voice guidance. For example, the adjustment unit adjusts the volume based on the location where the user checked in on social media. The adjustment unit can also analyze the content of the user's social media posts to set an optimal volume. The adjustment unit can also customize the volume based on the activity of the user's friends on social media. In this way, the adjustment unit can provide an optimal volume by analyzing social media activity.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The navigation system may further include a preference reflecting unit that customizes routes based on the user's preferences. The preference reflecting unit analyzes data on routes selected by the user in the past and places visited by the user, and suggests routes that suit the user's preferences. For example, if the user likes nature, it may preferentially suggest routes with abundant greenery. Also, if the user likes shopping, it may suggest routes that pass through commercial facilities. Furthermore, the preference reflecting unit may customize the content of the voice guidance based on the user's preferences. This allows the navigation system to provide optimal navigation that suits the user's preferences.

[0069] The navigation system may further include a traffic information acquisition unit that acquires real-time traffic information for the user. The traffic information acquisition unit analyzes real-time traffic conditions and proposes an optimal route. For example, if a traffic jam occurs, the traffic information acquisition unit can propose a route that avoids the jam. Also, if a traffic accident occurs, the traffic information acquisition unit can propose a detour route. Furthermore, the traffic information acquisition unit can propose a route taking into account the operating status of public transportation. This allows the navigation system to provide optimal navigation based on real-time traffic information.

[0070] The navigation system may further include a history analysis unit that analyzes the user's past navigation history and suggests an optimal route. The history analysis unit analyzes data on routes selected by the user in the past and places visited by the user to suggest an optimal route. For example, the history analysis unit may suggest an optimal route based on routes the user has used in the past. It may also suggest related places taking into account places the user has visited in the past. Furthermore, the history analysis unit may suggest a route that avoids crowded areas based on the user's past navigation history. This allows the navigation system to provide optimal navigation based on the user's past navigation history.

[0071] The navigation system may further include an activity detection unit that detects the user's current activity status. The activity detection unit detects whether the user is walking, riding a bicycle, driving a vehicle, or the like, and provides navigation accordingly. For example, if the user is walking, voice guidance tailored to the user's walking speed may be provided. Also, if the user is riding a bicycle, voice guidance tailored to the user's bicycle speed may be provided. Furthermore, if the user is driving a vehicle, concise voice guidance may be provided to allow the user to concentrate on driving. This allows the navigation system to provide optimal navigation according to the user's activity status.

[0072] The navigation system may further include a device information acquisition unit that acquires device information of the user. The device information acquisition unit acquires the type and settings of the device used by the user and provides navigation accordingly. For example, if the user is using a smartphone, a display method tailored to the screen size may be provided. Also, if the user is using a tablet, a display method optimized for a large screen may be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible may be provided. This allows the navigation system to provide optimal navigation according to the user's device information.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The reception unit is a section where the user inputs a destination. For example, the reception unit can input a destination by text input, voice input, selection on a map, or other methods. The reception unit can accept the user input of a destination in natural language, such as "Tell me the way to the station." Step 2: The generator uses the LLM to generate voice guidance that matches the user's language and writing style based on the information received by the receiver. For example, the generator generates voice guidance using gentle language for children and polite language for business people. The generator uses the LLM (large-scale language model) to analyze the user's input, select appropriate language and writing style, and generate voice guidance. Step 3: The providing unit is a component that provides the audio guidance generated by the generating unit to the user. For example, the providing unit provides the audio guidance through AR glasses. The providing unit enables the user to receive natural audio guidance along with visual navigation information by wearing the AR glasses.

[0075] (Example 2) A navigation system according to an embodiment of the present invention provides navigation using AR glasses for pedestrians and cyclists. The navigation system allows a user to input a destination, and utilizes a large-scale language model (LLM) to generate voice guidance tailored to the user's language and writing style. The voice guidance is then provided through the AR glasses. This allows the user to more easily understand the information and reach the destination. For example, the navigation system allows a user to input a destination in natural language, such as "Tell me the way to the station." This information is input into the system, and the LLM generates voice guidance tailored to the user's language and writing style. The generated voice guidance is provided to the user through the AR glasses. This allows the user to receive natural voice guidance along with visual navigation information. For example, by receiving voice guidance such as "Turn right at the next intersection" along with visual map information, the user can reach their destination without getting lost. This allows the navigation system to provide visual navigation information and natural voice guidance to pedestrians and cyclists, making it easier to understand the information.

[0076] A navigation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit is a component that allows a user to input a destination. For example, the reception unit can input a destination by text input, voice input, selection on a map, or other methods. The reception unit can accept a user's input of a destination in natural language, such as "Tell me the way to the station." The generation unit uses an LLM to generate voice guidance tailored to the user's language and writing style based on the information accepted by the reception unit. For example, the generation unit generates voice guidance using gentle language for children and polite language for business people. The generation unit uses an LLM (large-scale language model) to analyze the user's input and select appropriate language and writing style to generate the voice guidance. The provision unit is a component that provides the voice guidance generated by the generation unit to the user. For example, the provision unit provides voice guidance through AR glasses. By wearing the AR glasses, the provision unit enables the user to receive natural voice guidance along with visual navigation information. As a result, the navigation system according to an embodiment provides natural voice guidance tailored to the user's language and writing style, making it easier to understand information. For example, a navigation system allows a user to input their destination in natural language, such as "Tell me the way to the station," and the LLM analyzes and generates voice guidance, which is then provided through AR glasses. This allows the user to receive natural voice guidance along with visual navigation information, making it easier for the user to understand the information and reach their destination.

[0077] The navigation system includes a collection unit that acquires user profile information. The collection unit is a component that collects the user profile information. For example, the collection unit can collect profile information such as age, gender, hobbies, and past behavioral history. By collecting the user profile information, the collection unit can provide more personalized voice guidance. For example, the collection unit generates voice guidance by selecting appropriate language and writing style based on the user's age and gender. In this way, the navigation system can provide more personalized voice guidance by collecting the user profile information. For example, the collection unit generates voice guidance by selecting appropriate language and writing style based on the user's age and gender. In this way, the navigation system can provide more personalized voice guidance by collecting the user profile information.

[0078] The navigation system includes a display unit that provides visual navigation information. The display unit is a component that displays the visual navigation information. For example, the display unit can provide the visual navigation information by displaying a map, text, icons, or other methods. The display unit allows a user to receive the visual navigation information by wearing AR glasses. By providing the visual navigation information, the navigation system allows the user to reach a destination more easily. For example, the display unit can visually show the user a route to the destination by displaying a map. Furthermore, the display unit can visually show the user guidance to the destination by displaying text. Furthermore, the display unit can visually show the user guidance to the destination by displaying icons. By providing the visual navigation information, the navigation system allows the user to reach a destination more easily.

[0079] The navigation system includes an adjustment unit that changes the volume of the voice guidance. The adjustment unit is a component that adjusts the volume of the voice guidance. For example, the adjustment unit can adjust the volume based on criteria such as a decibel value or a volume relative to environmental sounds. The adjustment unit can appropriately adjust the volume to make the voice guidance easier to hear for the user. For example, the adjustment unit can automatically increase the volume when the user is in a noisy environment. Also, the adjustment unit can automatically decrease the volume when the user is in a quiet environment. In this way, the navigation system can provide voice guidance that is easier for the user to hear by adjusting the volume of the voice guidance. For example, the adjustment unit can automatically increase the volume when the user is in a noisy environment. Also, the adjustment unit can automatically decrease the volume when the user is in a quiet environment. In this way, the navigation system can provide voice guidance that is easier for the user to hear by adjusting the volume of the voice guidance.

[0080] The collection unit can acquire user profile information in cooperation with the reception unit. The collection unit collects user profile information in cooperation with the reception unit. For example, the collection unit can collect profile information such as age, gender, hobbies, and past behavioral history based on the user's input information accepted by the reception unit. This allows the collection unit to collect profile information more efficiently by working with the reception unit. For example, the collection unit can collect profile information such as age, gender, hobbies, and past behavioral history based on the user's input information accepted by the reception unit. This allows the collection unit to collect profile information more efficiently by working with the reception unit.

[0081] The display unit can provide visual navigation information in cooperation with the providing unit. The display unit displays the visual navigation information in cooperation with the providing unit. For example, the display unit can provide the visual navigation information by a method such as map display, text display, or icon display based on the visual navigation information generated by the providing unit. This allows the display unit to more effectively display the visual navigation information in cooperation with the providing unit. For example, the display unit can provide the visual navigation information by a method such as map display, text display, or icon display based on the visual navigation information generated by the providing unit. This allows the display unit to more effectively display the visual navigation information in cooperation with the providing unit.

[0082] The adjustment unit can change the volume of the voice guidance in cooperation with the provision unit. The adjustment unit can adjust the volume of the voice guidance in cooperation with the provision unit. For example, the adjustment unit can adjust the volume based on criteria such as a decibel value or a relative volume to environmental sounds, based on the voice guidance generated by the provision unit. This allows the adjustment unit to more appropriately adjust the volume of the voice guidance in cooperation with the provision unit. For example, the adjustment unit can adjust the volume based on criteria such as a decibel value or a relative volume to environmental sounds, based on the voice guidance generated by the provision unit. This allows the adjustment unit to more appropriately adjust the volume of the voice guidance in cooperation with the provision unit.

[0083] The reception unit can estimate the user's emotion and change the destination input interface based on the estimated user emotion. The reception unit can estimate the user's emotion and customize the destination input interface based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Alternatively, if the user is in a hurry, the reception unit can prioritize voice input to enable quick destination input. This allows the reception unit to customize the interface according to the user's emotion, thereby providing a more user-friendly system. Emotion estimation is achieved using an emotion estimation function, such as 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. This allows the reception unit to customize the interface according to the user's emotion, thereby providing a more user-friendly system.

[0084] The reception unit can analyze the user's past destination input history and suggest an appropriate input method. The reception unit analyzes the user's past destination input history and suggest the optimal input method. For example, the reception unit automatically displays destinations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destinations to be used during a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the past input history.

[0085] The reception unit can present input candidates based on the user's current location information when inputting a destination. The reception unit presents input candidates taking into account the user's current location information when inputting a destination. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The reception unit can also suggest optimal candidate locations taking into account the distance from the current location when the user inputs a destination. The reception unit can also update the user's current location in real time and reflect it as the departure point when using the app while the user is on the move. This allows the reception unit to present more appropriate input candidates by taking into account the user's current location information.

[0086] The reception unit can select an appropriate input means depending on the user's input method when inputting a destination. The reception unit selects the optimal input means depending on the user's input method (voice, text, gesture, etc.) when inputting a destination. For example, the reception unit can automatically set the destination when the user simply inputs "to the station" by voice. The reception unit can also enable the user to easily set the destination by performing a specific gesture on the smartphone screen. The reception unit can also enable the user to set the destination more intuitively by combining voice input and gesture input. This allows the reception unit to select the optimal input means depending on the user's input method, enabling more intuitive input.

[0087] The reception unit can estimate the user's emotion and determine the order of input candidates based on the estimated user's emotion. The reception unit can estimate the user's emotion and determine the priority of the input candidates based on the estimated user's emotion. For example, if the user is nervous, the reception unit can prioritize simple and highly visible candidates. Also, if the user is relaxed, the reception unit can prioritize detailed candidates. Also, if the user is in a hurry, the reception unit can prioritize the closest candidates. In this way, the reception unit can prioritize the input candidates according to the user's emotion and present more appropriate candidates. 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. In this way, the reception unit can prioritize the input candidates according to the user's emotion and present more appropriate candidates.

[0088] The reception unit can present relevant destinations based on the user's geographical location information when the user inputs a destination. The reception unit preferentially presents highly relevant destinations in consideration of the user's geographical location information when the user inputs a destination. For example, the reception unit preferentially displays locations close to the user's current location as candidates. The reception unit can also preferentially display locations that the user has visited in the past as candidates. The reception unit can also suggest optimal candidate locations based on the distance from the user's current location. In this way, the reception unit can preferentially present highly relevant destinations by considering the geographical location information.

[0089] The reception unit can suggest destinations based on the user's social media activity when the user inputs a destination. When the destination is input, the reception unit analyzes the user's social media activity and suggests related destinations. 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 content of the user's social media posts and suggest related locations as candidate locations. The reception unit can also suggest related locations by referring to the activity of the user's friends on social media. In this way, the reception unit can suggest related destinations by analyzing social media activity.

[0090] The reception unit can change the input method based on the user's past feedback when inputting a destination. The reception unit customizes the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also suggest the optimal input means based on the user's past feedback. The reception unit can also customize the interface by reflecting the user's past feedback. In this way, the reception unit can provide an input method that is easier to use by reflecting the user's past feedback.

[0091] The generation unit can estimate the user's emotion and change the way the voice guidance is presented based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the way the voice guidance is presented based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate voice guidance that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate quick and concise voice guidance. If the user is excited, the generation unit can generate voice guidance that adds visually stimulating effects. This allows the generation unit to provide more appropriate guidance by adjusting the way the voice guidance is presented based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the generation unit to provide more appropriate guidance by adjusting the way the voice guidance is presented based on the user's emotion.

[0092] The generation unit can change the language and writing style based on the user's profile information when generating voice guidance. The generation unit customizes the language and writing style based on the user's profile information when generating voice guidance. For example, the generation unit can generate voice guidance using gentle language for children. The generation unit can also generate voice guidance using polite language for business people. The generation unit can also generate voice guidance at a slower pace for elderly people. In this way, the generation unit can provide more personalized guidance by customizing the language and writing style based on the profile information.

[0093] The generation unit can apply different guidance algorithms based on the type of destination when generating voice guidance. The generation unit applies different guidance algorithms based on the type of destination when generating voice guidance. For example, in the case of a tourist destination, the generation unit generates voice guidance including information about tourist spots. In addition, in the case of a commercial facility, the generation unit can also generate voice guidance including store information. In addition, in the case of a public facility, the generation unit can also generate voice guidance including how to use the facility and important points to note. In this way, the generation unit can provide more appropriate guidance by applying different guidance algorithms based on the type of destination.

[0094] When generating voice guidance, the generation unit can improve the accuracy of the guidance based on the user's past guidance history. When generating voice guidance, the generation unit improves the accuracy of the guidance by referring to the user's past guidance history. For example, the generation unit generates optimal voice guidance based on routes the user has used in the past. The generation unit can also suggest routes that avoid congestion based on the user's past guidance history. The generation unit can also analyze the user's past guidance history and suggest the most efficient route. In this way, the generation unit can improve the accuracy of the guidance by referring to the past guidance history.

[0095] The generation unit can estimate the user's emotion and change the length of the audio guidance based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the length of the audio guidance based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can generate short, to-the-point audio guidance. If the user is relaxed, the generation unit can generate longer audio guidance with detailed explanations. If the user is excited, the generation unit can generate audio guidance with visually stimulating effects. This allows the generation unit to adjust the length of the audio guidance according to the user's emotion and provide more appropriate guidance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the generation unit to adjust the length of the audio guidance according to the user's emotion and provide more appropriate guidance.

[0096] When generating voice guidance, the generation unit can determine the order of guidance based on the time of submission of the destination. When generating voice guidance, the generation unit determines the priority of guidance based on the time of submission of the destination. For example, the generation unit generates guidance with the highest priority for an urgent destination. Furthermore, the generation unit can quickly generate guidance when an appointment is approaching. Furthermore, the generation unit can also generate detailed guidance when there is time to spare. In this way, the generation unit determines the priority of guidance based on the time of submission of the destination, making it possible to provide guidance according to the level of urgency.

[0097] The generation unit can change the order of guidance based on the relevance of destinations when generating voice guidance. The generation unit adjusts the order of guidance based on the relevance of destinations when generating voice guidance. For example, the generation unit provides guidance to important destinations first. The generation unit can also provide guidance to highly relevant destinations with priority. The generation unit can also adjust the order of guidance based on the user's interests. This allows the generation unit to adjust the order of guidance based on the relevance of destinations, thereby enabling more efficient guidance.

[0098] When generating voice guidance, the generation unit can change the use of technical terminology in the guidance based on the user's level of expertise. When generating voice guidance, the generation unit adjusts the use of technical terminology in the guidance according to the user's level of expertise. For example, the generation unit generates guidance including technical terminology for a user with specialized knowledge. The generation unit can also generate guidance in simple language for a user without specialized knowledge. The generation unit can also adjust the use of optimal technical terminology based on the user's past guidance history. In this way, the generation unit can adjust the use of technical terminology according to the user's level of expertise, thereby enabling guidance that is easier to understand.

[0099] The providing unit can estimate the user's emotion and change the method of providing voice guidance based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the method of providing voice guidance based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide guidance in a calm voice. If the user is relaxed, the providing unit can provide guidance in a cheerful voice. If the user is in a hurry, the providing unit can also provide quick and concise voice guidance. This allows the providing unit to adjust the method of providing voice guidance according to the user's emotion, thereby enabling more appropriate guidance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the providing unit to adjust the method of providing voice guidance according to the user's emotion, thereby enabling more appropriate guidance.

[0100] The providing unit can change the method of providing voice guidance based on the user's current situation when providing voice guidance. The providing unit customizes the method of providing voice guidance according to the user's current situation (walking, riding a bicycle, etc.). For example, when the user is walking, the providing unit can provide voice guidance that matches the user's walking speed. Furthermore, when the user is riding a bicycle, the providing unit can provide voice guidance that matches the user's bicycle speed. Furthermore, when the user is standing still, the providing unit can also provide voice guidance that includes detailed explanations. This allows the providing unit to customize the method of providing voice guidance according to the user's current situation, thereby enabling more appropriate guidance.

[0101] When providing voice guidance, the providing unit can improve the accuracy of the provision based on the user's past guidance history. When providing voice guidance, the providing unit improves the accuracy of the provision by referring to the user's past guidance history. For example, the providing unit provides optimal voice guidance based on routes the user has used in the past. The providing unit can also suggest routes that avoid congestion based on the user's past guidance history. The providing unit can also analyze the user's past guidance history and suggest the most efficient route. In this way, the providing unit can improve the accuracy of the provision by referring to the past guidance history.

[0102] The providing unit can change the method of providing voice guidance based on user feedback when providing voice guidance. The providing unit improves the method of providing voice guidance by reflecting user feedback when providing voice guidance. For example, the providing unit improves the method of providing voice guidance based on feedback provided by the user on voice guidance provided in the past. The providing unit can also provide optimal voice guidance by reflecting user feedback. The providing unit can also adjust the content and timing of the voice guidance based on user feedback. In this way, the providing unit can improve the method of providing voice guidance by reflecting user feedback, thereby enabling more appropriate guidance.

[0103] The providing unit can estimate the user's emotion and change the timing of providing the voice guidance based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the timing of providing the voice guidance based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide voice guidance at a calm timing. Furthermore, if the user is relaxed, the providing unit can provide voice guidance at appropriate intervals. Furthermore, if the user is in a hurry, the providing unit can provide voice guidance quickly. This allows the providing unit to adjust the timing of provision according to the user's emotion, thereby enabling more appropriate guidance. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the providing unit to adjust the timing of provision according to the user's emotion, thereby enabling more appropriate guidance.

[0104] The providing unit can select an appropriate provision method based on the user's geographical location information when providing voice guidance. The providing unit selects the optimal provision method by taking the user's geographical location information into consideration when providing voice guidance. For example, the providing unit can prioritize guidance to places close to the user's current location. The providing unit can also prioritize guidance to places the user has visited in the past. The providing unit can also suggest the optimal guidance method based on the distance from the user's current location. In this way, the providing unit can select the optimal provision method by taking the geographical location information into consideration.

[0105] The providing unit can change the method of providing voice guidance based on the user's social media activity when providing voice guidance. The providing unit analyzes the user's social media activity and customizes the method of providing voice guidance. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, the providing unit can customize the method of providing voice guidance by analyzing social media activity.

[0106] The providing unit can change the method of providing voice guidance based on the user's past feedback when providing voice guidance. The providing unit customizes the method of providing voice guidance by reflecting the user's past feedback when providing voice guidance. For example, the providing unit provides a style of voice guidance that the user has preferred in the past with priority. The providing unit can also provide optimal voice guidance based on the user's past feedback. The providing unit can also adjust the content and timing of the voice guidance by reflecting the user's past feedback. In this way, the providing unit can customize the method of providing voice guidance by reflecting the user's past feedback.

[0107] The collection unit can estimate the user's emotions and change the profile information collection method based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the profile information collection method based on the estimated user emotions. For example, the collection unit can collect detailed profile information when the user is relaxed. Alternatively, the collection unit can collect the minimum amount of profile information when the user is in a hurry. Alternatively, the collection unit can provide a profile information collection screen with a visually stimulating effect when the user is excited. This allows the collection unit to collect more appropriate information by adjusting the profile information collection method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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. This allows the collection unit to collect more appropriate information by adjusting the profile information collection method according to the user's emotions.

[0108] When collecting profile information, the collection unit can select an appropriate collection method based on the user's past behavioral history. When collecting profile information, the collection unit analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit suggests the optimal profile information collection method based on information previously entered by the user. The collection unit can also automatically collect necessary information from the user's past behavioral history. The collection unit can also analyze the user's past behavioral history and suggest the most efficient profile information collection method. This allows the collection unit to select the optimal collection method by analyzing the past behavioral history.

[0109] The collection unit can change the collection means based on the user's current situation when collecting profile information. The collection unit customizes the collection means based on the user's current situation when collecting profile information. For example, when the user is walking, the collection unit can collect profile information in the form of simple questions. When the user is riding a bicycle, the collection unit can collect profile information using voice input. When the user is standing still, the collection unit can also collect detailed profile information. In this way, the collection unit can collect more appropriate information by customizing the collection means based on the current situation.

[0110] The collection unit can estimate the user's emotions and determine the order of profile information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of profile information to be collected based on the estimated user emotions. For example, when the user is nervous, the collection unit can prioritize collecting the minimum amount of information necessary. Also, when the user is relaxed, the collection unit can prioritize collecting detailed information. Also, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This allows the collection unit to prioritize the information to be collected based on the user's emotions, thereby enabling more efficient information collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the collection unit to prioritize the information to be collected based on the user's emotions, thereby enabling more efficient information collection.

[0111] When collecting profile information, the collection unit can collect relevant information based on the user's geographical location information. When collecting profile information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about places close to the user's current location. The collection unit can also prioritize collecting information about places the user has visited in the past. The collection unit can also collect optimal information based on the distance from the user's current location. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information.

[0112] When collecting profile information, the collection unit can change the collection method based on the user's social media activity. When collecting profile information, the collection unit analyzes the user's social media activity to customize the collection method. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit can customize the collection method by analyzing social media activity.

[0113] The display unit can estimate the user's emotion and change the display method of the visual navigation information based on the estimated user's emotion. The display unit can estimate the user's emotion and adjust the display method of the visual navigation information based on the estimated user's emotion. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can provide a display method including detailed information. If the user is in a hurry, the display unit can provide a display method that focuses on the main points. In this way, the display unit can adjust the display method according to the user's emotion to provide more appropriate visual navigation information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. In this way, the display unit can adjust the display method according to the user's emotion to provide more appropriate visual navigation information.

[0114] The display unit can select an appropriate display method based on the user's past display history when displaying visual navigation information. The display unit selects the optimal display method by referring to the user's past display history when displaying visual navigation information. For example, the display unit preferentially provides display methods that the user has previously preferred. The display unit can also suggest the optimal display method based on the user's past display history. The display unit can also analyze the user's past display history and suggest the most efficient display method. In this way, the display unit can select the optimal display method by referring to the past display history.

[0115] The display unit can change the display content based on the user's current task when displaying visual navigation information. The display unit customizes the display content according to the user's current task when displaying visual navigation information. For example, when the user is walking, the display unit can provide display content suitable for walking. Furthermore, when the user is riding a bicycle, the display unit can provide display content suitable for cycling. Furthermore, when the user is standing still, the display unit can also provide detailed display content. In this way, the display unit can provide more appropriate information by customizing the display content according to the current task.

[0116] The display unit can estimate the user's emotion and change the display timing of the visual navigation information based on the estimated user's emotion. The display unit can estimate the user's emotion and adjust the display timing of the visual navigation information based on the estimated user's emotion. For example, if the user is nervous, the display unit can provide the visual navigation information at a calm timing. If the user is relaxed, the display unit can provide the visual navigation information at appropriate intervals. If the user is in a hurry, the display unit can also provide the visual navigation information quickly. This allows the display unit to provide more appropriate information by adjusting the display timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the display unit to provide more appropriate information by adjusting the display timing according to the user's emotion.

[0117] The display unit can select an appropriate display method based on the user's device information when displaying visual navigation information. The display unit selects the optimal display method by taking the user's device information into consideration when displaying visual navigation information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the display unit to select the optimal display method by taking the device information into consideration.

[0118] The display unit can make the display content multilingual based on the user's language setting when displaying visual navigation information. The display unit can make the display content multilingual based on the user's language setting when displaying visual navigation information. For example, the display unit automatically sets the navigation language based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide navigation in a specific language when the user selects that language. This allows the display unit to accommodate a wider range of users by providing multilingual support based on the language setting.

[0119] The display unit can change the display method based on the user's past feedback when displaying visual navigation information. The display unit customizes the display method by reflecting the user's past feedback when displaying visual navigation information. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also provide an optimal display method based on the user's past feedback. The display unit can also adjust the display content and timing by reflecting the user's past feedback. In this way, the display unit can customize the display method by reflecting the user's past feedback.

[0120] The adjustment unit can estimate the user's emotion and change the volume of the voice guidance based on the estimated user's emotion. The adjustment unit can estimate the user's emotion and adjust the volume of the voice guidance based on the estimated user's emotion. For example, if the user is nervous, the adjustment unit can provide voice guidance at a calm volume. Also, if the user is relaxed, the adjustment unit can provide voice guidance at an appropriate volume. Also, if the user is in a hurry, the adjustment unit can provide voice guidance at a volume that is easy to hear quickly. In this way, the adjustment unit can adjust the volume according to the user's emotion to provide more appropriate voice guidance. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. In this way, the adjustment unit can adjust the volume according to the user's emotion to provide more appropriate voice guidance.

[0121] When adjusting the volume of the voice guidance, the adjustment unit can select an appropriate volume based on the user's past volume setting history. When adjusting the volume of the voice guidance, the adjustment unit selects the optimal volume by referring to the user's past volume setting history. For example, the adjustment unit automatically sets the optimal volume based on the volume previously set by the user. The adjustment unit can also analyze the user's past volume setting history and suggest the most appropriate volume. The adjustment unit can also select the optimal volume based on the volume setting used by the user in a specific environment. In this way, the adjustment unit can select the optimal volume by referring to the past volume setting history.

[0122] When adjusting the volume of the voice guidance, the adjustment unit can change the volume based on the user's current environmental sound. When adjusting the volume of the voice guidance, the adjustment unit analyzes the user's current environmental sound and customizes the volume. For example, the adjustment unit can automatically increase the volume when the user is in a noisy environment. Also, the adjustment unit can automatically decrease the volume when the user is in a quiet environment. The adjustment unit can also analyze the environmental sound around the user in real time and set an optimal volume. In this way, the adjustment unit can provide an optimal volume by analyzing the current environmental sound.

[0123] The adjustment unit can estimate the user's emotion and determine the order of volume adjustment of the voice guidance based on the estimated user's emotion. The adjustment unit can estimate the user's emotion and determine the priority of volume adjustment of the voice guidance based on the estimated user's emotion. For example, if the user is nervous, the adjustment unit can adjust the volume as a top priority. If the user is relaxed, the adjustment unit can adjust the volume moderately. If the user is in a hurry, the adjustment unit can also adjust the volume quickly. In this way, the adjustment unit can provide a more appropriate volume by determining the priority of volume adjustment according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. In this way, the adjustment unit can provide a more appropriate volume by determining the priority of volume adjustment according to the user's emotion.

[0124] When adjusting the volume of the voice guidance, the adjustment unit can select an appropriate volume based on the user's geographical location information. When adjusting the volume of the voice guidance, the adjustment unit selects the optimal volume by taking into account the user's geographical location information. For example, the adjustment unit can automatically increase the volume when the user is in an urban area. Also, the adjustment unit can automatically decrease the volume when the user is in a suburban area. The adjustment unit can also set the optimal volume based on the user's current location. In this way, the adjustment unit can provide the optimal volume by taking into account the geographical location information.

[0125] The adjustment unit can change the volume based on the user's social media activity when adjusting the volume of the voice guidance. The adjustment unit analyzes the user's social media activity to customize the volume when adjusting the volume of the voice guidance. For example, the adjustment unit adjusts the volume based on the location where the user checked in on social media. The adjustment unit can also analyze the content of the user's social media posts to set an optimal volume. The adjustment unit can also customize the volume based on the activity of the user's friends on social media. In this way, the adjustment unit can provide an optimal volume by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, collection unit, display unit, and adjustment 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 accepts a user's destination input using a touch panel 38A or a microphone 38B of the smart device 14. The generation unit generates voice guidance using LLM by the specific processing unit 290 of the data processing device 12. The provision unit provides voice guidance through the speaker 40B of the smart device 14. The collection unit collects user profile information by the specific processing unit 290 of the data processing device 12. The display unit displays visual navigation information using the display 40A of the smart device 14. The adjustment unit adjusts the volume of the voice guidance by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, collection unit, display unit, and adjustment unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a destination input from the user using the microphone 238 of the smart glasses 214. The generation unit generates voice guidance using LLM by the specific processing unit 290 of the data processing device 12. The provision unit provides voice guidance through the speaker 240 of the smart glasses 214. The collection unit collects user profile information by the specific processing unit 290 of the data processing device 12. The display unit displays visual navigation information using the display of the smart glasses 214. The adjustment unit adjusts the volume of the voice guidance by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, collection unit, display unit, and adjustment 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 accepts a destination input from the user using the microphone 238 of the headset type terminal 314. The generation unit generates voice guidance using LLM by the specific processing unit 290 of the data processing device 12. The provision unit provides voice guidance through the speaker 240 of the headset type terminal 314. The collection unit collects user profile information by the specific processing unit 290 of the data processing device 12. The display unit displays visual navigation information using the display 343 of the headset type terminal 314. The adjustment unit adjusts the volume of the voice guidance by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, collection unit, display unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a destination input from the user using the microphone 238 of the robot 414. The generation unit generates voice guidance using LLM by the specific processing unit 290 of the data processing device 12. The provision unit provides voice guidance through the speaker 240 of the robot 414. The collection unit collects user profile information by the specific processing unit 290 of the data processing device 12. The display unit displays visual navigation information using the display of the robot 414. The adjustment unit adjusts the volume of the voice guidance by the control unit 46A of the robot 414.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and stress level, and adjusts the navigation method. For example, if the user indicates a high stress level, the health management unit may suggest a relaxing route. Also, if the user is tired, the health management unit may suggest a route that includes rest spots. Furthermore, the health management unit may adjust the tone and pace of the voice guidance based on the user's health condition. This allows the navigation system to provide optimal navigation according to the user's health condition.

[0128] The navigation system may further include a preference reflecting unit that customizes routes based on the user's preferences. The preference reflecting unit analyzes data on routes selected by the user in the past and places visited by the user, and suggests routes that suit the user's preferences. For example, if the user likes nature, it may preferentially suggest routes with abundant greenery. Also, if the user likes shopping, it may suggest routes that pass through commercial facilities. Furthermore, the preference reflecting unit may customize the content of the voice guidance based on the user's preferences. This allows the navigation system to provide optimal navigation that suits the user's preferences.

[0129] The navigation system may further include a traffic information acquisition unit that acquires real-time traffic information for the user. The traffic information acquisition unit analyzes real-time traffic conditions and proposes an optimal route. For example, if a traffic jam occurs, the traffic information acquisition unit can propose a route that avoids the jam. Also, if a traffic accident occurs, the traffic information acquisition unit can propose a detour route. Furthermore, the traffic information acquisition unit can propose a route taking into account the operating status of public transportation. This allows the navigation system to provide optimal navigation based on real-time traffic information.

[0130] The navigation system can further estimate the user's emotions and change the content of the voice guidance based on the estimated user's emotions. For example, if the user is feeling anxious, the emotion estimation function can be used to provide voice guidance in a gentle tone that gives a sense of security. If the user is excited, the navigation system can provide voice guidance in a calm tone that encourages the user to stay calm. Furthermore, if the user is tired, the navigation system can provide voice guidance that includes words of encouragement. This allows the navigation system to provide optimal voice guidance according to the user's emotions.

[0131] The navigation system may further include a history analysis unit that analyzes the user's past navigation history and suggests an optimal route. The history analysis unit analyzes data on routes selected by the user in the past and places visited by the user to suggest an optimal route. For example, the history analysis unit may suggest an optimal route based on routes the user has used in the past. It may also suggest related places taking into account places the user has visited in the past. Furthermore, the history analysis unit may suggest a route that avoids crowded areas based on the user's past navigation history. This allows the navigation system to provide optimal navigation based on the user's past navigation history.

[0132] The navigation system can further estimate the user's emotions and change the way visual navigation information is displayed based on the estimated user emotions. For example, if the user is nervous, the emotion estimation function can be used to provide a simple, highly visible display method. 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. This allows the navigation system to provide optimal visual navigation information according to the user's emotions.

[0133] The navigation system may further include an activity detection unit that detects the user's current activity status. The activity detection unit detects whether the user is walking, riding a bicycle, driving a vehicle, or the like, and provides navigation accordingly. For example, if the user is walking, voice guidance tailored to the user's walking speed may be provided. Also, if the user is riding a bicycle, voice guidance tailored to the user's bicycle speed may be provided. Furthermore, if the user is driving a vehicle, concise voice guidance may be provided to allow the user to concentrate on driving. This allows the navigation system to provide optimal navigation according to the user's activity status.

[0134] The navigation system can further estimate the user's emotions and change the timing of providing navigation based on the estimated user emotions. For example, if the user is nervous, the emotion estimation function can be used to provide voice guidance at a calm timing. If the user is relaxed, voice guidance can be provided at appropriate intervals. Furthermore, if the user is in a hurry, voice guidance can be provided quickly. This allows the navigation system to provide navigation at the optimal timing according to the user's emotions.

[0135] The navigation system may further include a device information acquisition unit that acquires device information of the user. The device information acquisition unit acquires the type and settings of the device used by the user and provides navigation accordingly. For example, if the user is using a smartphone, a display method tailored to the screen size may be provided. Also, if the user is using a tablet, a display method optimized for a large screen may be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible may be provided. This allows the navigation system to provide optimal navigation according to the user's device information.

[0136] The navigation system can further estimate the user's emotions and change the length of the voice guidance based on the estimated user's emotions. For example, if the user is in a hurry, the emotion estimation function can be used to provide short, to-the-point voice guidance. If the user is relaxed, longer voice guidance with detailed explanations can be provided. Furthermore, if the user is excited, voice guidance with visually stimulating effects can be provided. This allows the navigation system to provide the optimal length of voice guidance according to the user's emotions.

[0137] The processing flow of the second embodiment will be briefly explained below.

[0138] Step 1: The reception unit is a section where the user inputs a destination. For example, the reception unit can input a destination by text input, voice input, selection on a map, or other methods. The reception unit can accept the user input of a destination in natural language, such as "Tell me the way to the station." Step 2: The generator uses the LLM to generate voice guidance that matches the user's language and writing style based on the information received by the receiver. For example, the generator generates voice guidance using gentle language for children and polite language for business people. The generator uses the LLM (large-scale language model) to analyze the user's input, select appropriate language and writing style, and generate voice guidance. Step 3: The providing unit is a component that provides the audio guidance generated by the generating unit to the user. For example, the providing unit provides the audio guidance through AR glasses. The providing unit enables the user to receive natural audio guidance along with visual navigation information by wearing the AR glasses.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[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 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.

[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 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.

[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 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.

[0157] 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.

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0190] 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.

[0191] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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).

[0196] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Explanation of symbols]

[0211] 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; a generating unit that generates voice guidance based on the user's language and writing style based on the information received by the receiving unit; a providing unit that provides the voice guidance generated by the generating unit. A system characterized by:

2. A collection unit for acquiring user profile information is provided.

2. The system of claim 1.

3. Equipped with a display that provides visual navigation information 2. The system of claim 1.

4. Equipped with an adjustment unit to change the volume of voice guidance 2. The system of claim 1.

5. The collecting unit Acquires user profile information in cooperation with the reception unit 3. The system of claim 2.

6. The display unit Providing visual navigation information in cooperation with the providing unit 4. The system of claim 3.

7. The adjustment unit Changing the volume of the voice guidance in cooperation with the providing unit 5. The system of claim 4.

8. The reception unit Estimate the user's emotions and change the destination input interface based on the estimated user emotions.

2. The system of claim 1.

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

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