system

Abstract

The system according to this embodiment aims to provide users with enjoyment by incorporating entertainment elements into navigation. [Solution] The system according to the embodiment comprises a reception unit, an acquisition unit, a generation unit, a provision unit, and a notification unit. The reception unit receives input from the user. The acquisition unit acquires route and scenery information based on the information received by the reception unit. The generation unit aggregates the information acquired by the acquisition unit and generates a song. The provision unit provides the song generated by the generation unit. The notification unit notifies the user if they are heading in the wrong direction.

Description

Technical Field

[0006] ,

[0003]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, entertainment elements have not been fully incorporated into route guidance, and there is room for improvement.

[0005] [[ID=�9]]The system according to the embodiment aims to incorporate entertainment elements into route guidance and provide enjoyment to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an acquisition unit, a generation unit, a provision unit, and a notification unit. The reception unit receives input from the user. The acquisition unit acquires route and scenery information based on the information received by the reception unit. The generation unit aggregates the information acquired by the acquisition unit and generates a song. The provision unit provides the song generated by the generation unit. The notification unit notifies the user if they are heading in the wrong direction. [Effects of the Invention]

[0007] The system according to this embodiment can incorporate entertainment elements into navigation, providing enjoyment to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An interactive navigation system according to an embodiment of the present invention is a system that generates navigation, lyrics, and music using a generative AI. When a user inputs a destination, the system generates a song and provides navigation based on information such as route, scenery, congestion, obstacles, dangers, local culture, and popular songs. The generated song changes lyrics according to the user's current location, providing information interactively. Furthermore, because it aggregates information from map vendors, local businesses, and municipalities, and is generated based on the style provided by entertainment vendors, it also has a high level of entertainment value. This allows even people who are not good at reading maps or have poor spatial awareness to reach their destination while listening to music. Moreover, if the user proceeds in the wrong direction, the generative AI will provide a fill-in notification, allowing the user to receive navigation with peace of mind. For example, when a user inputs a destination, the system acquires information from map vendors and aggregates route and scenery information. Next, the generative AI generates a song based on this information and provides it to the user. If the user proceeds in the wrong direction, the system notifies them and guides them in the correct direction. This allows the user to reach their destination with peace of mind. This enables interactive navigation by generating and providing directions or songs based on user input.

[0029] The interactive navigation system according to the embodiment comprises a reception unit, an acquisition unit, a generation unit, a provision unit, and a notification unit. The reception unit receives input from the user. User input includes, but is not limited to, voice input, text input, and touch input. The reception unit may, for example, receive voice input from the user using voice recognition technology. The reception unit may also receive touch input from the user using a touchscreen. Furthermore, the reception unit may also be equipped with a keyboard for receiving text input. The acquisition unit acquires route and scenery information based on the information received by the reception unit. Route and scenery information includes, but is not limited to, map data, landmark information, and real-time traffic information. The acquisition unit may, for example, acquire map data from a map vendor. Furthermore, the acquisition unit may cooperate with a traffic information provision service to acquire real-time traffic information. Furthermore, the acquisition unit may also collect local commercial and municipal information to acquire landmark information. The generation unit aggregates the information acquired by the acquisition unit to generate a song. The generation unit may, for example, generate a song using a generation AI. The generation AI generates lyrics using, for example, a text generation AI (e.g., LLM). The generation unit can also generate songs based on styles provided by entertainment vendors. For example, the generation unit generates songs referencing the music genres and artist styles of entertainment vendors. The delivery unit provides the songs generated by the generation unit to the user. The delivery unit plays the songs using, for example, an audio output device. The delivery unit can also stream the songs to the user's device. Furthermore, the delivery unit may be equipped with a display for displaying lyrics. The notification unit notifies the user if they are heading in the wrong direction. The notification unit provides, for example, an audio notification. The notification unit may also provide a vibration notification. Furthermore, the notification unit may be equipped with a display for providing visual notifications. Thus, the interactive navigation system according to the embodiment enables interactive navigation by generating and providing navigation and songs based on user input.

[0030] The reception unit receives input from the user. User input includes, but is not limited to, voice input, text input, and touch input. The reception unit can, for example, receive voice input from the user using speech recognition technology. Specifically, a deep learning-based speech recognition model is used as the speech recognition technology. This model can convert user speech into text with high accuracy by learning from a large amount of voice data. The reception unit can also receive touch input from the user using a touchscreen. The touchscreen uses technologies such as capacitive or resistive touch, and detects input when the user touches the screen. Furthermore, the reception unit can be equipped with a keyboard for receiving text input. The keyboard can have physical keys or be displayed on the screen as a software keyboard. This allows the user to choose the optimal input method according to their preference and situation. The reception unit can comprehensively manage these diverse input methods and accurately grasp the user's intent. For example, it can combine voice input and text input to complete what the user has said. Also, using touch input, the user can point to a specific location, enabling more intuitive operation. This allows the reception area to respond to diverse user needs and achieve smooth interaction.

[0031] The acquisition unit obtains route and scenery information based on the information received by the reception unit. Route and scenery information includes, but is not limited to, map data, landmark information, and real-time traffic information. The acquisition unit can obtain map data from, for example, a map vendor. Specifically, it can obtain the latest map data using the map vendor's API. The acquisition unit can also cooperate with a traffic information service to obtain real-time traffic information. Traffic information services provide real-time information on road congestion and accidents, which allows the user to be guided to the optimal route. Furthermore, the acquisition unit can also collect information on local businesses and municipalities to obtain landmark information. For example, by collecting information on tourist destinations and commercial facilities and providing it to the user, it can provide information that is useful not only for route guidance but also for sightseeing and shopping. The acquisition unit can manage this information comprehensively and provide the user with the optimal route and scenery information. For example, if a user enters a specific landmark as a destination, it can provide detailed information about that landmark and information on nearby tourist spots. It can also calculate the optimal route based on real-time traffic information and guide the user. This allows the data acquisition unit to quickly and accurately provide information tailored to the user's needs, enabling comfortable navigation.

[0032] The generation unit aggregates the information acquired by the acquisition unit to generate a song. The generation unit generates songs using, for example, a generation AI. The generation AI generates lyrics using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI generates relevant lyrics based on information about the destination and directions entered by the user. For example, if the user enters "I want to go to the park," the generation AI can generate lyrics about parks. The generation unit can also generate songs based on the styles provided by entertainment vendors. For example, the generation unit generates songs referencing the music genres and artist styles of entertainment vendors. This allows users to enjoy songs that match their preferences and moods. The generation unit manages this information comprehensively and can generate songs that are optimal for the user. For example, if a user prefers the style of a particular artist, it can generate songs that reference that artist's style. Furthermore, the generation AI can learn the user's past input history and preferences to generate more personalized songs. This enables the generation unit to quickly and accurately generate songs that meet the user's needs and realize interactive navigation.

[0033] The service provider delivers songs generated by the generation unit to the user. The service provider plays the songs using, for example, an audio output device. Specifically, it can let the user listen to the generated songs through speakers or earphones. The service provider can also stream songs to the user's device. For example, it can deliver songs to smartphones or tablets via the internet, allowing users to enjoy songs anytime, anywhere. Furthermore, the service provider can be equipped with a display for displaying lyrics. The lyrics are displayed in real time on the display, allowing users to enjoy the songs while viewing the lyrics. The service provider can comprehensively manage these functions and deliver songs to the user in the most optimal way. For example, if the user is driving a car, it can play songs using an audio output device, allowing them to enjoy songs safely even while driving. Also, if the user is walking, it can stream songs to their smartphone, allowing them to listen to the songs through earphones. In this way, the service provider can deliver songs in the most optimal way according to the user's situation and preferences, realizing interactive navigation.

[0034] The notification unit alerts the user if they are heading in the wrong direction. For example, it can provide voice notifications. Specifically, if the user deviates from the designated route, it will notify the user by voice, "You have deviated from the route. Recalculating." The notification unit can also provide vibration notifications. For example, the smartphone or smartwatch can vibrate to alert the user. Furthermore, the notification unit can be equipped with a display for visual notifications. The display can show arrows or warning messages so that the user can visually confirm the information. The notification unit manages these functions in an integrated manner to provide notifications to the user in the most optimal way. For example, if the user is driving a car, voice notifications will be prioritized so that they can receive notifications safely even while driving. If the user is walking, vibration and visual notifications will be used in combination to ensure that the user does not miss the notification. In this way, the notification unit can quickly and reliably notify the user even if they are heading in the wrong direction, and help them return to the correct route.

[0035] The acquisition unit can acquire information from map vendors, local businesses, and local governments. For example, the acquisition unit can acquire map data from map vendors. For example, the acquisition unit can acquire map data using the map vendor's API. The acquisition unit can also link with databases of commercial facilities and local governments to acquire information on local businesses and local governments. For example, the acquisition unit can acquire store information from a database of commercial facilities. The acquisition unit can also acquire public service information from a local government database. As a result, the acquisition unit can provide more accurate directions by acquiring information from map vendors and local areas. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input map data acquired using the map vendor's API into a generating AI and have the generating AI perform analysis of the map data.

[0036] The generation unit can generate songs based on the styles provided by the entertainment vendor. For example, the generation unit can generate songs by referencing the music genres and artist styles of the entertainment vendor. For example, the generation unit can generate songs based on the music genres provided by the entertainment vendor (e.g., pop, rock, jazz, etc.). The generation unit can also generate songs by referencing the artist styles of the entertainment vendor (e.g., the singing style or song characteristics of a specific artist). Furthermore, the generation unit can generate songs based on the entertainment content provided by the entertainment vendor (e.g., movies, television programs, games, etc.). This allows the generation unit to provide highly entertaining guidance by generating songs based on the styles of the entertainment vendor. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the music genres and artist styles provided by the entertainment vendor as prompts to the generation AI and have the generation AI perform song generation.

[0037] The service provider can provide the generated song to the user. The service provider can, for example, play the song using an audio output device. For example, the service provider can provide the generated song to the user using a speaker. The service provider can also stream the song to the user's device. For example, the service provider can stream the song to the user's smartphone or tablet via the internet. Furthermore, the service provider may be equipped with a display for displaying lyrics. For example, the service provider can display lyrics on the display of a car navigation system. This enables interactive navigation by providing the generated song to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI determine the optimal timing for streaming the generated song to the user's device.

[0038] The notification unit can provide fill-in notifications if the user is heading in the wrong direction. For example, the notification unit can provide voice notifications. For example, if the user is heading in the wrong direction, the notification unit can provide a voice message saying, "You are heading in the wrong direction." The notification unit can also provide vibration notifications. For example, the notification unit can vibrate the user's smartphone to indicate that they are heading in the wrong direction. Furthermore, the notification unit may be equipped with a display for providing visual notifications. For example, the notification unit can display a warning message on the car navigation system's display to indicate that they are heading in the wrong direction. This allows the user to receive directions with peace of mind by notifying them if they are heading in the wrong direction. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can monitor the user's direction of travel in real time, and the AI ​​can automatically provide notifications if the direction of travel is wrong.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. For example, the reception desk can analyze the user's past destination history and prioritize displaying the most frequently entered destinations. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. For example, if the reception desk has frequently entered a particular destination during a specific time period, it will prioritize suggesting that destination during that time period. In this way, the reception desk can suggest the optimal input method to the user by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0040] The reception desk can filter input content based on the user's current situation. For example, during rainy weather, the reception desk may prioritize displaying indoor destinations. For instance, the reception desk may obtain current weather information and prioritize displaying indoor destinations during rainy weather. The reception desk may also suggest destinations with safe routes at night. For example, the reception desk may consider the current time of day and prioritize suggesting destinations with safe routes at night. Furthermore, the reception desk may prioritize displaying tourist attractions and leisure facilities on holidays. For example, the reception desk may obtain current date information and prioritize displaying tourist attractions and leisure facilities on holidays. In this way, the reception desk can provide more appropriate information by filtering input content based on the current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input current weather information and time of day information into a generating AI and have the generating AI perform the filtering of the input content.

[0041] The reception unit can present highly relevant input candidates by considering the user's geographical location. For example, the reception unit can prioritize displaying destinations close to the user's current location. For example, the reception unit can obtain the user's current location information and prioritize displaying destinations close to the current location. The reception unit can also suggest destinations related to a specific region if the user is in that region. For example, if the reception unit is in a specific region, it can suggest tourist attractions or shops related to that region. Furthermore, if the user is on the move, the reception unit can suggest the optimal destination based on the user's current location. For example, if the user is on the move, the reception unit can suggest the optimal route based on the user's current location. In this way, the reception unit can present highly relevant input candidates by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of presenting highly relevant input candidates.

[0042] The reception desk can analyze a user's social media activity and suggest relevant inputs. For example, the reception desk can suggest relevant destinations based on locations where the user has checked in on social media. For example, the reception desk can obtain information on locations where the user has checked in on social media and suggest destinations related to those locations. The reception desk can also suggest destinations based on events and places where the user has shown interest on social media. For example, the reception desk can obtain information on events and places where the user has shown interest on social media and suggest destinations related to them. Furthermore, the reception desk can analyze the content of a user's social media posts and suggest destinations related to those posts. For example, the reception desk can analyze the content of a user's social media posts and suggest destinations related to those posts. In this way, the reception desk can suggest relevant inputs by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant inputs.

[0043] The acquisition unit can analyze past acquisition history and select the optimal information acquisition method. For example, the acquisition unit can select the optimal information acquisition method based on information that the user has frequently acquired in the past. For example, the acquisition unit can analyze the history of information that the user has frequently acquired in the past and select the most efficient information acquisition method. The acquisition unit can also predict and acquire information needed at a specific time period based on the user's past acquisition history. For example, if the acquisition unit has frequently acquired specific information at a specific time period, it will prioritize acquiring that information at that time period. Furthermore, the acquisition unit can analyze the user's past acquisition history and select the most efficient information acquisition method. For example, the acquisition unit can analyze the user's past acquisition history and select the most efficient information acquisition method. In this way, the acquisition unit can select the optimal information acquisition method by analyzing past acquisition history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past acquisition history data into a generating AI and have the generating AI select the optimal information acquisition method.

[0044] The information acquisition unit can acquire current traffic conditions and weather information in real time and reflect it in the route. For example, the information acquisition unit can propose the optimal route based on real-time traffic congestion information. For example, the information acquisition unit can acquire real-time traffic congestion information from a traffic information service and propose the optimal route. The information acquisition unit can also propose the optimal route based on real-time weather information. For example, the information acquisition unit can acquire real-time weather information from a weather information service and propose the optimal route. Furthermore, the information acquisition unit can also propose the optimal route based on the real-time operating status of public transportation. For example, the information acquisition unit can acquire the operating status of public transportation in real time and propose the optimal route. As a result, the information acquisition unit can provide more accurate directions by acquiring real-time information. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without AI. For example, the information acquisition unit can input real-time traffic congestion information and weather information into a generating AI and have the generating AI propose the optimal route.

[0045] The acquisition unit can prioritize the acquisition of highly relevant information by considering geographical location information. For example, the acquisition unit can prioritize the acquisition of information that is close to the user's current location. For example, the acquisition unit acquires the user's current location information and prioritizes the acquisition of information that is close to the current location. The acquisition unit can also acquire information related to a specific region if the user is in that region. For example, if the user is in a specific region, the acquisition unit can acquire information about tourist attractions and shops related to that region. Furthermore, if the user is on the move, the acquisition unit can acquire optimal information based on the user's current location. For example, if the user is on the move, the acquisition unit can acquire optimal route information based on the user's current location. In this way, the acquisition unit can prioritize the acquisition of highly relevant information by considering geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0046] The acquisition unit can analyze social media activity and obtain relevant information. For example, the acquisition unit can obtain relevant information based on locations where the user has checked in on social media. For example, the acquisition unit can obtain information about locations where the user has checked in on social media and obtain information related to those locations. The acquisition unit can also obtain information based on events and places that the user has shown interest in on social media. For example, the acquisition unit can obtain information about events and places that the user has shown interest in on social media and obtain information related to them. Furthermore, the acquisition unit can analyze the content of the user's social media posts and obtain relevant information. For example, the acquisition unit can analyze the content of the user's social media posts and obtain information related to the content of the posts. In this way, the acquisition unit can obtain relevant information by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0047] The generation unit can analyze past generation history and select the optimal generation algorithm. For example, the generation unit can select the optimal generation algorithm based on the lyric style that the user has preferred in the past. For example, the generation unit can analyze the lyric style that the user has preferred in the past and select the most suitable generation algorithm. The generation unit can also generate lyrics suitable for a specific time period from the user's past generation history. For example, the generation unit can analyze the lyric style that the user preferred at a specific time period and generate lyrics suitable for that time period. Furthermore, the generation unit can analyze the user's past generation history and select the most efficient generation algorithm. For example, the generation unit can analyze the user's past generation history and select the most efficient generation algorithm. In this way, the generation unit can select the optimal generation algorithm by analyzing past generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past generation history data into a generation AI and have the generation AI select the optimal generation algorithm.

[0048] The generation unit can adjust the content of the lyrics based on the current situation. For example, when it is raining, the generation unit generates lyrics related to rain. For example, the generation unit obtains current weather information and generates lyrics related to rain when it is raining. The generation unit can also generate lyrics related to nighttime when it is nighttime. For example, the generation unit takes the current time of day into consideration and generates lyrics related to nighttime when it is nighttime. Furthermore, the generation unit can also generate lyrics related to holidays when it is a holiday. For example, the generation unit obtains current date information and generates lyrics related to holidays when it is a holiday. In this way, the generation unit can generate more appropriate lyrics by adjusting the content of the lyrics based on the current situation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input current weather information and time of day information into the generation AI and have the generation AI perform the adjustment of the content of the lyrics.

[0049] The generation unit can generate highly relevant lyrics by considering the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate lyrics related to that region. The generation unit can also generate lyrics based on the user's current location if the user is on the move. Furthermore, if the user is in a tourist destination, the generation unit can generate lyrics related to that tourist destination. In this way, the generation unit can generate highly relevant lyrics by considering geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant lyrics.

[0050] The generation unit can analyze social media activity and generate relevant lyrics. For example, the generation unit can generate relevant lyrics based on locations where a user has checked in on social media. For example, the generation unit can obtain information about locations where a user has checked in on social media and generate lyrics related to those locations. The generation unit can also generate lyrics based on events and places where a user has shown interest on social media. For example, the generation unit can obtain information about events and places where a user has shown interest on social media and generate lyrics related to them. Furthermore, the generation unit can analyze the content of a user's social media posts and generate relevant lyrics. For example, the generation unit can analyze the content of a user's social media posts and generate lyrics related to the content of the posts. In this way, the generation unit can generate relevant lyrics by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant lyrics.

[0051] The service provider can analyze past service history and select the optimal service method. For example, the service provider can select the optimal service method based on the service method the user has preferred in the past. For example, the service provider can analyze the service method the user has preferred in the past and select the most suitable service method. The service provider can also select a service method suitable for a specific time period based on the user's past service history. For example, the service provider can analyze the service method the user preferred during a specific time period and select a service method suitable for that time period. Furthermore, the service provider can analyze the user's past service history and select the most efficient service method. For example, the service provider can analyze the user's past service history and select the most efficient service method. In this way, the service provider can select the optimal service method by analyzing past service history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past service history data into a generating AI and have the generating AI select the optimal service method.

[0052] The service provider can adjust the content offered based on the current situation. For example, when it is raining, the service provider can offer songs related to rain. For example, the service provider can obtain current weather information and offer songs related to rain when it is raining. The service provider can also offer songs related to nighttime when it is nighttime. For example, the service provider can consider the current time of day and offer songs related to nighttime when it is nighttime. Furthermore, the service provider can offer songs related to holidays when it is a holiday. For example, the service provider can obtain current date information and offer songs related to holidays when it is a holiday. In this way, the service provider can provide more appropriate content by adjusting the content offered based on the current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input current weather information and time of day information into a generating AI and have the generating AI perform the adjustment of the content offered.

[0053] The service provider can prioritize providing highly relevant content by considering geographical location information. For example, the service provider can prioritize providing information that is close to the user's current location. For example, the service provider can obtain the user's current location information and prioritize providing information that is close to that location. The service provider can also provide information related to a specific region if the user is in that region. For example, if the service provider is in a specific region, it can provide information about tourist attractions and shops related to that region. Furthermore, if the service provider is on the move, it can provide optimal information based on the user's current location. For example, if the service provider is on the move, it can provide optimal route information based on the user's current location. In this way, the service provider can prioritize providing highly relevant content by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant content.

[0054] The service provider can analyze social media activity and provide relevant content. For example, the service provider can provide relevant information based on locations where users have checked in on social media. For example, the service provider can obtain information on locations where users have checked in on social media and provide information related to those locations. The service provider can also provide information based on events and places where users have shown interest on social media. For example, the service provider can obtain information on events and places where users have shown interest on social media and provide information related to them. Furthermore, the service provider can analyze the content of users' social media posts and provide relevant information. For example, the service provider can analyze the content of users' social media posts and provide information related to those posts. In this way, the service provider can provide relevant content by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI perform the provision of relevant content.

[0055] The notification unit can analyze past notification history and select the optimal notification method. For example, the notification unit can select the optimal notification method based on the notification method the user has preferred in the past. For example, the notification unit can analyze the notification method the user has preferred in the past and select the most suitable notification method. The notification unit can also select a notification method suitable for a specific time period from the user's past notification history. For example, the notification unit can analyze the notification method the user preferred during a specific time period and select a notification method suitable for that time period. Furthermore, the notification unit can analyze the user's past notification history and select the most efficient notification method. For example, the notification unit can analyze the user's past notification history and select the most efficient notification method. In this way, the notification unit can select the optimal notification method by analyzing past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0056] The notification unit can adjust the notification content based on the current situation. For example, when it is raining, the notification unit will send notifications related to rain. For example, the notification unit will obtain current weather information and send notifications related to rain when it is raining. The notification unit can also send notifications related to nighttime when it is nighttime. For example, the notification unit will consider the current time of day and send notifications related to nighttime when it is nighttime. Furthermore, the notification unit can send notifications related to holidays when it is a holiday. For example, the notification unit will obtain current date information and send notifications related to holidays when it is a holiday. In this way, the notification unit can send more appropriate notifications by adjusting the notification content based on the current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input current weather information and time of day information into a generating AI and have the generating AI perform the adjustment of the notification content.

[0057] The notification unit can prioritize notifying users of highly relevant content by considering geographical location information. For example, the notification unit can prioritize notifying users of information close to their current location. For example, the notification unit can obtain the user's current location information and prioritize notifying users of information close to their current location. The notification unit can also notify users of information related to a specific region if the user is in that region. For example, if the user is in a specific region, the notification unit can notify users of information about tourist attractions and shops related to that region. Furthermore, if the user is on the move, the notification unit can notify users of the most suitable information based on their current location. For example, if the user is on the move, the notification unit can notify users of the most suitable route information based on their current location. In this way, the notification unit can prioritize notifying users of highly relevant content by considering geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI execute notifications of highly relevant content.

[0058] The notification unit can analyze social media activity and notify relevant content. For example, the notification unit can notify relevant information based on locations where the user has checked in on social media. For example, the notification unit can obtain information on locations where the user has checked in on social media and notify information related to those locations. The notification unit can also notify information based on events and places that the user has shown interest in on social media. For example, the notification unit can obtain information on events and places that the user has shown interest in on social media and notify information related to them. Furthermore, the notification unit can analyze the content of the user's social media posts and notify relevant information. For example, the notification unit can analyze the content of the user's social media posts and notify information related to the content of the posts. In this way, the notification unit can notify relevant content by analyzing social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI execute notifications of relevant content.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can suggest a navigation style tailored to the user's preferences based on the user's input. For example, it can analyze the navigation styles the user has previously selected (e.g., voice guidance, visual guidance, text guidance, etc.) and suggest the optimal style. It can also suggest an appropriate navigation style based on the user's current situation (e.g., driving, walking, etc.). Furthermore, it can suggest the optimal navigation style based on the type of device the user is using (e.g., smartphone, tablet, car navigation system, etc.). This allows the reception desk to provide a more user-friendly system by suggesting navigation styles that match the user's preferences and circumstances.

[0061] The data acquisition unit can prioritize the acquisition of relevant information based on the user's current activity. For example, if the user is exercising, the unit will prioritize acquiring information about nearby parks and jogging courses. Similarly, if the user is shopping, the unit can prioritize acquiring information about nearby shopping malls and stores. Furthermore, if the user is sightseeing, the unit can prioritize acquiring information about nearby tourist attractions and restaurants. This allows the data acquisition unit to provide more relevant information by prioritizing the acquisition of information according to the user's current activity.

[0062] The service provider can select the optimal delivery method by considering the battery level of the user's device. For example, if the user's device battery level is low, the service provider will prioritize providing voice guidance. Furthermore, if the user's device battery level is sufficient, the service provider can also provide visual or video guidance. In addition, the service provider can adjust the amount and frequency of information provided according to the user's device battery level. This allows the service provider to select a more appropriate delivery method by considering the user's device battery level.

[0063] The notification unit can select the most appropriate notification method based on the user's current activity. For example, if the user is driving, the notification unit will prioritize voice notifications. It can also provide vibration or visual notifications if the user is walking. Furthermore, if the user is in a meeting, the notification unit can select a quieter notification method (e.g., vibration or screen display). This allows the notification unit to provide more appropriate notifications by selecting the most suitable method according to the user's current activity.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives input from the user. User input includes voice input, text input, and touch input. The reception desk can receive voice input from the user using speech recognition technology, and can also receive touch input from the user using a touchscreen. It can also be equipped with a keyboard for receiving text input. Step 2: The acquisition unit acquires route and scenery information based on the information received by the reception unit. Route and scenery information includes map data, landmark information, and real-time traffic information. The acquisition unit can acquire map data from map vendors and can link with traffic information services to obtain real-time traffic information. It can also collect local commercial and municipal information to acquire landmark information. Step 3: The generation unit aggregates the information acquired by the acquisition unit to generate a song. The generation unit can generate songs using generation AI, for example, by using text generation AI (LLM) to generate lyrics. It can also generate songs based on styles provided by entertainment vendors, generating songs by referencing music genres and artist styles. Step 4: The providing unit provides the user with the song generated by the generating unit. The providing unit can play the song using an audio output device and can also stream the song to the user's device. It may also be equipped with a display for displaying lyrics. Step 5: The notification unit will alert you if you are heading in the wrong direction. The notification unit can provide voice notifications, vibration notifications, and visual notifications. It may also be equipped with a display for visual notifications.

[0066] (Example of form 2) An interactive navigation system according to an embodiment of the present invention is a system that generates navigation, lyrics, and music using a generative AI. When a user inputs a destination, the system generates a song and provides navigation based on information such as route, scenery, congestion, obstacles, dangers, local culture, and popular songs. The generated song changes lyrics according to the user's current location, providing information interactively. Furthermore, because it aggregates information from map vendors, local businesses, and municipalities, and is generated based on the style provided by entertainment vendors, it also has a high level of entertainment value. This allows even people who are not good at reading maps or have poor spatial awareness to reach their destination while listening to music. Moreover, if the user proceeds in the wrong direction, the generative AI will provide a fill-in notification, allowing the user to receive navigation with peace of mind. For example, when a user inputs a destination, the system acquires information from map vendors and aggregates route and scenery information. Next, the generative AI generates a song based on this information and provides it to the user. If the user proceeds in the wrong direction, the system notifies them and guides them in the correct direction. This allows the user to reach their destination with peace of mind. This enables interactive navigation by generating and providing directions or songs based on user input.

[0067] The interactive navigation system according to the embodiment comprises a reception unit, an acquisition unit, a generation unit, a provision unit, and a notification unit. The reception unit receives input from the user. User input includes, but is not limited to, voice input, text input, and touch input. The reception unit may, for example, receive voice input from the user using voice recognition technology. The reception unit may also receive touch input from the user using a touchscreen. Furthermore, the reception unit may also be equipped with a keyboard for receiving text input. The acquisition unit acquires route and scenery information based on the information received by the reception unit. Route and scenery information includes, but is not limited to, map data, landmark information, and real-time traffic information. The acquisition unit may, for example, acquire map data from a map vendor. Furthermore, the acquisition unit may cooperate with a traffic information provision service to acquire real-time traffic information. Furthermore, the acquisition unit may also collect local commercial and municipal information to acquire landmark information. The generation unit aggregates the information acquired by the acquisition unit to generate a song. The generation unit may, for example, generate a song using a generation AI. The generation AI generates lyrics using, for example, a text generation AI (e.g., LLM). The generation unit can also generate songs based on styles provided by entertainment vendors. For example, the generation unit generates songs referencing the music genres and artist styles of entertainment vendors. The delivery unit provides the songs generated by the generation unit to the user. The delivery unit plays the songs using, for example, an audio output device. The delivery unit can also stream the songs to the user's device. Furthermore, the delivery unit may be equipped with a display for displaying lyrics. The notification unit notifies the user if they are heading in the wrong direction. The notification unit provides, for example, an audio notification. The notification unit may also provide a vibration notification. Furthermore, the notification unit may be equipped with a display for providing visual notifications. Thus, the interactive navigation system according to the embodiment enables interactive navigation by generating and providing navigation and songs based on user input.

[0068] The reception unit receives input from the user. User input includes, but is not limited to, voice input, text input, and touch input. The reception unit can, for example, receive voice input from the user using speech recognition technology. Specifically, a deep learning-based speech recognition model is used as the speech recognition technology. This model can convert user speech into text with high accuracy by learning from a large amount of voice data. The reception unit can also receive touch input from the user using a touchscreen. The touchscreen uses technologies such as capacitive or resistive touch, and detects input when the user touches the screen. Furthermore, the reception unit can be equipped with a keyboard for receiving text input. The keyboard can have physical keys or be displayed on the screen as a software keyboard. This allows the user to choose the optimal input method according to their preference and situation. The reception unit can comprehensively manage these diverse input methods and accurately grasp the user's intent. For example, it can combine voice input and text input to complete what the user has said. Also, using touch input, the user can point to a specific location, enabling more intuitive operation. This allows the reception area to respond to diverse user needs and achieve smooth interaction.

[0069] The acquisition unit obtains route and scenery information based on the information received by the reception unit. Route and scenery information includes, but is not limited to, map data, landmark information, and real-time traffic information. The acquisition unit can obtain map data from, for example, a map vendor. Specifically, it can obtain the latest map data using the map vendor's API. The acquisition unit can also cooperate with a traffic information service to obtain real-time traffic information. Traffic information services provide real-time information on road congestion and accidents, which allows the user to be guided to the optimal route. Furthermore, the acquisition unit can also collect information on local businesses and municipalities to obtain landmark information. For example, by collecting information on tourist destinations and commercial facilities and providing it to the user, it can provide information that is useful not only for route guidance but also for sightseeing and shopping. The acquisition unit can manage this information comprehensively and provide the user with the optimal route and scenery information. For example, if a user enters a specific landmark as a destination, it can provide detailed information about that landmark and information on nearby tourist spots. It can also calculate the optimal route based on real-time traffic information and guide the user. This allows the data acquisition unit to quickly and accurately provide information tailored to the user's needs, enabling comfortable navigation.

[0070] The generation unit aggregates the information acquired by the acquisition unit to generate a song. The generation unit generates songs using, for example, a generation AI. The generation AI generates lyrics using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI generates relevant lyrics based on information about the destination and directions entered by the user. For example, if the user enters "I want to go to the park," the generation AI can generate lyrics about parks. The generation unit can also generate songs based on the styles provided by entertainment vendors. For example, the generation unit generates songs referencing the music genres and artist styles of entertainment vendors. This allows users to enjoy songs that match their preferences and moods. The generation unit manages this information comprehensively and can generate songs that are optimal for the user. For example, if a user prefers the style of a particular artist, it can generate songs that reference that artist's style. Furthermore, the generation AI can learn the user's past input history and preferences to generate more personalized songs. This enables the generation unit to quickly and accurately generate songs that meet the user's needs and realize interactive navigation.

[0071] The service provider delivers songs generated by the generation unit to the user. The service provider plays the songs using, for example, an audio output device. Specifically, it can let the user listen to the generated songs through speakers or earphones. The service provider can also stream songs to the user's device. For example, it can deliver songs to smartphones or tablets via the internet, allowing users to enjoy songs anytime, anywhere. Furthermore, the service provider can be equipped with a display for displaying lyrics. The lyrics are displayed in real time on the display, allowing users to enjoy the songs while viewing the lyrics. The service provider can comprehensively manage these functions and deliver songs to the user in the most optimal way. For example, if the user is driving a car, it can play songs using an audio output device, allowing them to enjoy songs safely even while driving. Also, if the user is walking, it can stream songs to their smartphone, allowing them to listen to the songs through earphones. In this way, the service provider can deliver songs in the most optimal way according to the user's situation and preferences, realizing interactive navigation.

[0072] The notification unit alerts the user if they are heading in the wrong direction. For example, it can provide voice notifications. Specifically, if the user deviates from the designated route, it will notify the user by voice, "You have deviated from the route. Recalculating." The notification unit can also provide vibration notifications. For example, the smartphone or smartwatch can vibrate to alert the user. Furthermore, the notification unit can be equipped with a display for visual notifications. The display can show arrows or warning messages so that the user can visually confirm the information. The notification unit manages these functions in an integrated manner to provide notifications to the user in the most optimal way. For example, if the user is driving a car, voice notifications will be prioritized so that they can receive notifications safely even while driving. If the user is walking, vibration and visual notifications will be used in combination to ensure that the user does not miss the notification. In this way, the notification unit can quickly and reliably notify the user even if they are heading in the wrong direction, and help them return to the correct route.

[0073] The acquisition unit can acquire information from map vendors, local businesses, and local governments. For example, the acquisition unit can acquire map data from map vendors. For example, the acquisition unit can acquire map data using the map vendor's API. The acquisition unit can also link with databases of commercial facilities and local governments to acquire information on local businesses and local governments. For example, the acquisition unit can acquire store information from a database of commercial facilities. The acquisition unit can also acquire public service information from a local government database. As a result, the acquisition unit can provide more accurate directions by acquiring information from map vendors and local areas. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input map data acquired using the map vendor's API into a generating AI and have the generating AI perform analysis of the map data.

[0074] The generation unit can generate songs based on the styles provided by the entertainment vendor. For example, the generation unit can generate songs by referencing the music genres and artist styles of the entertainment vendor. For example, the generation unit can generate songs based on the music genres provided by the entertainment vendor (e.g., pop, rock, jazz, etc.). The generation unit can also generate songs by referencing the artist styles of the entertainment vendor (e.g., the singing style or song characteristics of a specific artist). Furthermore, the generation unit can generate songs based on the entertainment content provided by the entertainment vendor (e.g., movies, television programs, games, etc.). This allows the generation unit to provide highly entertaining guidance by generating songs based on the styles of the entertainment vendor. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the music genres and artist styles provided by the entertainment vendor as prompts to the generation AI and have the generation AI perform song generation.

[0075] The service provider can provide the generated song to the user. The service provider can, for example, play the song using an audio output device. For example, the service provider can provide the generated song to the user using a speaker. The service provider can also stream the song to the user's device. For example, the service provider can stream the song to the user's smartphone or tablet via the internet. Furthermore, the service provider may be equipped with a display for displaying lyrics. For example, the service provider can display lyrics on the display of a car navigation system. This enables interactive navigation by providing the generated song to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI determine the optimal timing for streaming the generated song to the user's device.

[0076] The notification unit can provide fill-in notifications if the user is heading in the wrong direction. For example, the notification unit can provide voice notifications. For example, if the user is heading in the wrong direction, the notification unit can provide a voice message saying, "You are heading in the wrong direction." The notification unit can also provide vibration notifications. For example, the notification unit can vibrate the user's smartphone to indicate that they are heading in the wrong direction. Furthermore, the notification unit may be equipped with a display for providing visual notifications. For example, the notification unit can display a warning message on the car navigation system's display to indicate that they are heading in the wrong direction. This allows the user to receive directions with peace of mind by notifying them if they are heading in the wrong direction. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can monitor the user's direction of travel in real time, and the AI ​​can automatically provide notifications if the direction of travel is wrong.

[0077] The reception system can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. For instance, if the user is stressed, the reception system can reduce the number of input fields and allow the user to enter their destination with simple operations. Furthermore, if the user is relaxed, the reception system can provide detailed input options and suggest customizable input methods. For example, if the user is relaxed, the reception system can offer multiple input methods (voice, text, touch, etc.) and allow the user to choose freely. Additionally, if the user is in a hurry, the reception system can prioritize voice input using speech recognition technology to allow for quick destination entry. This allows the reception system to provide a more user-friendly system by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0078] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently entered in the past as suggestions. For example, the reception desk can analyze the user's past destination history and prioritize displaying the most frequently entered destinations. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest destinations to be used during specific time periods based on the user's past input history. For example, if the reception desk has frequently entered a particular destination during a specific time period, it will prioritize suggesting that destination during that time period. In this way, the reception desk can suggest the optimal input method to the user by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0079] The reception desk can filter input content based on the user's current situation. For example, during rainy weather, the reception desk may prioritize displaying indoor destinations. For instance, the reception desk may obtain current weather information and prioritize displaying indoor destinations during rainy weather. The reception desk may also suggest destinations with safe routes at night. For example, the reception desk may consider the current time of day and prioritize suggesting destinations with safe routes at night. Furthermore, the reception desk may prioritize displaying tourist attractions and leisure facilities on holidays. For example, the reception desk may obtain current date information and prioritize displaying tourist attractions and leisure facilities on holidays. In this way, the reception desk can provide more appropriate information by filtering input content based on the current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input current weather information and time of day information into a generating AI and have the generating AI perform the filtering of the input content.

[0080] The reception desk can estimate the user's emotions and determine input priorities based on the estimated emotions. For example, if the user is in a hurry, the reception desk will prioritize displaying the nearest destination. For example, if the user is in a hurry, the reception desk will prioritize displaying the destination closest to the user's current location. The reception desk can also prioritize displaying tourist attractions and leisure facilities if the user is relaxed. For example, if the reception desk is relaxed, the reception desk will prioritize displaying tourist attractions and leisure facilities. Furthermore, if the user is stressed, the reception desk can prioritize displaying simple input options. For example, if the user is stressed, the reception desk will reduce the number of input fields and allow the user to enter their destination with simple operations. In this way, the reception desk can provide more appropriate information by prioritizing inputs according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0081] The reception unit can present highly relevant input candidates by considering the user's geographical location. For example, the reception unit can prioritize displaying destinations close to the user's current location. For example, the reception unit can obtain the user's current location information and prioritize displaying destinations close to the current location. The reception unit can also suggest destinations related to a specific region if the user is in that region. For example, if the reception unit is in a specific region, it can suggest tourist attractions or shops related to that region. Furthermore, if the user is on the move, the reception unit can suggest the optimal destination based on the user's current location. For example, if the user is on the move, the reception unit can suggest the optimal route based on the user's current location. In this way, the reception unit can present highly relevant input candidates by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of presenting highly relevant input candidates.

[0082] The reception desk can analyze a user's social media activity and suggest relevant inputs. For example, the reception desk can suggest relevant destinations based on locations where the user has checked in on social media. For example, the reception desk can obtain information on locations where the user has checked in on social media and suggest destinations related to those locations. The reception desk can also suggest destinations based on events and places where the user has shown interest on social media. For example, the reception desk can obtain information on events and places where the user has shown interest on social media and suggest destinations related to them. Furthermore, the reception desk can analyze the content of a user's social media posts and suggest destinations related to those posts. For example, the reception desk can analyze the content of a user's social media posts and suggest destinations related to those posts. In this way, the reception desk can suggest relevant inputs by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant inputs.

[0083] The information acquisition unit can estimate the user's emotions and adjust the type of information it acquires based on the estimated emotions. For example, if the user is relaxed, the acquisition unit will prioritize acquiring information about tourist attractions and leisure facilities. The acquisition unit can also prioritize acquiring the shortest route and traffic information if the user is in a hurry. Furthermore, if the user is stressed, the acquisition unit can prioritize acquiring information about places where they can relax. In this way, the acquisition unit can provide more appropriate information by adjusting the type of information it acquires according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0084] The acquisition unit can analyze past acquisition history and select the optimal information acquisition method. For example, the acquisition unit can select the optimal information acquisition method based on information that the user has frequently acquired in the past. For example, the acquisition unit can analyze the history of information that the user has frequently acquired in the past and select the most efficient information acquisition method. The acquisition unit can also predict and acquire information needed at a specific time period based on the user's past acquisition history. For example, if the acquisition unit has frequently acquired specific information at a specific time period, it will prioritize acquiring that information at that time period. Furthermore, the acquisition unit can analyze the user's past acquisition history and select the most efficient information acquisition method. For example, the acquisition unit can analyze the user's past acquisition history and select the most efficient information acquisition method. In this way, the acquisition unit can select the optimal information acquisition method by analyzing past acquisition history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past acquisition history data into a generating AI and have the generating AI select the optimal information acquisition method.

[0085] The information acquisition unit can acquire current traffic conditions and weather information in real time and reflect it in the route. For example, the information acquisition unit can propose the optimal route based on real-time traffic congestion information. For example, the information acquisition unit can acquire real-time traffic congestion information from a traffic information service and propose the optimal route. The information acquisition unit can also propose the optimal route based on real-time weather information. For example, the information acquisition unit can acquire real-time weather information from a weather information service and propose the optimal route. Furthermore, the information acquisition unit can also propose the optimal route based on the real-time operating status of public transportation. For example, the information acquisition unit can acquire the operating status of public transportation in real time and propose the optimal route. As a result, the information acquisition unit can provide more accurate directions by acquiring real-time information. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without AI. For example, the information acquisition unit can input real-time traffic congestion information and weather information into a generating AI and have the generating AI propose the optimal route.

[0086] The information acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is in a hurry, the information acquisition unit will prioritize acquiring the shortest route and traffic information. For example, if the user is relaxed, the information acquisition unit can prioritize acquiring information about tourist attractions and leisure facilities. For example, if the user is relaxed, the information acquisition unit can prioritize acquiring information about tourist attractions and leisure facilities. For example, if the user is stressed, the information acquisition unit can prioritize acquiring information about places where the user can relax. For example, if the user is stressed, the information acquisition unit can prioritize acquiring information about places where the user can relax. In this way, the information acquisition unit can provide more appropriate information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The acquisition unit can prioritize the acquisition of highly relevant information by considering geographical location information. For example, the acquisition unit can prioritize the acquisition of information that is close to the user's current location. For example, the acquisition unit acquires the user's current location information and prioritizes the acquisition of information that is close to the current location. The acquisition unit can also acquire information related to a specific region if the user is in that region. For example, if the user is in a specific region, the acquisition unit can acquire information about tourist attractions and shops related to that region. Furthermore, if the user is on the move, the acquisition unit can acquire optimal information based on the user's current location. For example, if the user is on the move, the acquisition unit can acquire optimal route information based on the user's current location. In this way, the acquisition unit can prioritize the acquisition of highly relevant information by considering geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0088] The acquisition unit can analyze social media activity and obtain relevant information. For example, the acquisition unit can obtain relevant information based on locations where the user has checked in on social media. For example, the acquisition unit can obtain information about locations where the user has checked in on social media and obtain information related to those locations. The acquisition unit can also obtain information based on events and places that the user has shown interest in on social media. For example, the acquisition unit can obtain information about events and places that the user has shown interest in on social media and obtain information related to them. Furthermore, the acquisition unit can analyze the content of the user's social media posts and obtain relevant information. For example, the acquisition unit can analyze the content of the user's social media posts and obtain information related to the content of the posts. In this way, the acquisition unit can obtain relevant information by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0089] The generation unit can estimate the user's emotions and adjust the way the lyrics are expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate relaxed lyrics. For example, if the user is relaxed, the generation unit can generate lyrics with a relaxed tempo. The generation unit can also generate fast-paced lyrics if the user is in a hurry. For example, if the user is in a hurry, the generation unit can generate fast-paced lyrics. Furthermore, if the user is excited, the generation unit can generate energetic lyrics. For example, if the user is excited, the generation unit can generate energetic lyrics. In this way, the generation unit can generate more appropriate lyrics by adjusting the way the lyrics are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0090] The generation unit can analyze past generation history and select the optimal generation algorithm. For example, the generation unit can select the optimal generation algorithm based on the lyric style that the user has preferred in the past. For example, the generation unit can analyze the lyric style that the user has preferred in the past and select the most suitable generation algorithm. The generation unit can also generate lyrics suitable for a specific time period from the user's past generation history. For example, the generation unit can analyze the lyric style that the user preferred at a specific time period and generate lyrics suitable for that time period. Furthermore, the generation unit can analyze the user's past generation history and select the most efficient generation algorithm. For example, the generation unit can analyze the user's past generation history and select the most efficient generation algorithm. In this way, the generation unit can select the optimal generation algorithm by analyzing past generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past generation history data into a generation AI and have the generation AI select the optimal generation algorithm.

[0091] The generation unit can adjust the content of the lyrics based on the current situation. For example, when it is raining, the generation unit generates lyrics related to rain. For example, the generation unit obtains current weather information and generates lyrics related to rain when it is raining. The generation unit can also generate lyrics related to nighttime when it is nighttime. For example, the generation unit takes the current time of day into consideration and generates lyrics related to nighttime when it is nighttime. Furthermore, the generation unit can also generate lyrics related to holidays when it is a holiday. For example, the generation unit obtains current date information and generates lyrics related to holidays when it is a holiday. In this way, the generation unit can generate more appropriate lyrics by adjusting the content of the lyrics based on the current situation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input current weather information and time of day information into the generation AI and have the generation AI perform the adjustment of the content of the lyrics.

[0092] The generation unit can estimate the user's emotions and adjust the length of the lyrics based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short lyrics. The generation unit can also generate long lyrics if the user is relaxed. Furthermore, if the user is excited, the generation unit can generate energetic lyrics. In this way, the generation unit can generate more appropriate lyrics by adjusting the length of the lyrics according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0093] The generation unit can generate highly relevant lyrics by considering the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate lyrics related to that region. The generation unit can also generate lyrics based on the user's current location if the user is on the move. Furthermore, if the user is in a tourist destination, the generation unit can generate lyrics related to that tourist destination. In this way, the generation unit can generate highly relevant lyrics by considering geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant lyrics.

[0094] The generation unit can analyze social media activity and generate relevant lyrics. For example, the generation unit can generate relevant lyrics based on locations where a user has checked in on social media. For example, the generation unit can obtain information about locations where a user has checked in on social media and generate lyrics related to those locations. The generation unit can also generate lyrics based on events and places where a user has shown interest on social media. For example, the generation unit can obtain information about events and places where a user has shown interest on social media and generate lyrics related to them. Furthermore, the generation unit can analyze the content of a user's social media posts and generate relevant lyrics. For example, the generation unit can analyze the content of a user's social media posts and generate lyrics related to the content of the posts. In this way, the generation unit can generate relevant lyrics by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant lyrics.

[0095] The service provider can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver a song at a relaxed pace. For example, if the user is relaxed, the service provider can deliver a song at a relaxed pace. The service provider can also deliver a song at a fast tempo if the user is in a hurry. For example, if the user is excited, the service provider can deliver an energetic song. For example, if the user is excited, the service provider can deliver an energetic song. In this way, the service provider can deliver more appropriate content by adjusting the delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0096] The service provider can analyze past service history and select the optimal service method. For example, the service provider can select the optimal service method based on the service method the user has preferred in the past. For example, the service provider can analyze the service method the user has preferred in the past and select the most suitable service method. The service provider can also select a service method suitable for a specific time period based on the user's past service history. For example, the service provider can analyze the service method the user preferred during a specific time period and select a service method suitable for that time period. Furthermore, the service provider can analyze the user's past service history and select the most efficient service method. For example, the service provider can analyze the user's past service history and select the most efficient service method. In this way, the service provider can select the optimal service method by analyzing past service history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past service history data into a generating AI and have the generating AI select the optimal service method.

[0097] The service provider can adjust the content offered based on the current situation. For example, when it is raining, the service provider can offer songs related to rain. For example, the service provider can obtain current weather information and offer songs related to rain when it is raining. The service provider can also offer songs related to nighttime when it is nighttime. For example, the service provider can consider the current time of day and offer songs related to nighttime when it is nighttime. Furthermore, the service provider can offer songs related to holidays when it is a holiday. For example, the service provider can obtain current date information and offer songs related to holidays when it is a holiday. In this way, the service provider can provide more appropriate content by adjusting the content offered based on the current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input current weather information and time of day information into a generating AI and have the generating AI perform the adjustment of the content offered.

[0098] The service provider can estimate the user's emotions and determine the priority of its offerings based on those emotions. For example, if the user is in a hurry, the service provider will prioritize providing the most important information. Similarly, if the user is relaxed, the service provider can prioritize providing entertaining information. Furthermore, if the user is stressed, the service provider can prioritize providing relaxing information. This allows the service provider to provide more appropriate content by prioritizing offerings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0099] The service provider can prioritize providing highly relevant content by considering geographical location information. For example, the service provider can prioritize providing information that is close to the user's current location. For example, the service provider can obtain the user's current location information and prioritize providing information that is close to that location. The service provider can also provide information related to a specific region if the user is in that region. For example, if the service provider is in a specific region, it can provide information about tourist attractions and shops related to that region. Furthermore, if the service provider is on the move, it can provide optimal information based on the user's current location. For example, if the service provider is on the move, it can provide optimal route information based on the user's current location. In this way, the service provider can prioritize providing highly relevant content by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant content.

[0100] The service provider can analyze social media activity and provide relevant content. For example, the service provider can provide relevant information based on locations where users have checked in on social media. For example, the service provider can obtain information on locations where users have checked in on social media and provide information related to those locations. The service provider can also provide information based on events and places where users have shown interest on social media. For example, the service provider can obtain information on events and places where users have shown interest on social media and provide information related to them. Furthermore, the service provider can analyze the content of users' social media posts and provide relevant information. For example, the service provider can analyze the content of users' social media posts and provide information related to those posts. In this way, the service provider can provide relevant content by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI perform the provision of relevant content.

[0101] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is nervous, the notification unit will notify in a calm voice. For example, if the user is nervous, the notification unit will notify in a calm voice. The notification unit can also notify in a cheerful voice if the user is relaxed. For example, if the user is relaxed, the notification unit will notify in a cheerful voice. Furthermore, if the user is in a hurry, the notification unit can notify quickly and concisely. For example, if the user is in a hurry, the notification unit will notify quickly and concisely. In this way, the notification unit can provide more appropriate notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0102] The notification unit can analyze past notification history and select the optimal notification method. For example, the notification unit can select the optimal notification method based on the notification method the user has preferred in the past. For example, the notification unit can analyze the notification method the user has preferred in the past and select the most suitable notification method. The notification unit can also select a notification method suitable for a specific time period from the user's past notification history. For example, the notification unit can analyze the notification method the user preferred during a specific time period and select a notification method suitable for that time period. Furthermore, the notification unit can analyze the user's past notification history and select the most efficient notification method. For example, the notification unit can analyze the user's past notification history and select the most efficient notification method. In this way, the notification unit can select the optimal notification method by analyzing past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0103] The notification unit can adjust the notification content based on the current situation. For example, when it is raining, the notification unit will send notifications related to rain. For example, the notification unit will obtain current weather information and send notifications related to rain when it is raining. The notification unit can also send notifications related to nighttime when it is nighttime. For example, the notification unit will consider the current time of day and send notifications related to nighttime when it is nighttime. Furthermore, the notification unit can send notifications related to holidays when it is a holiday. For example, the notification unit will obtain current date information and send notifications related to holidays when it is a holiday. In this way, the notification unit can send more appropriate notifications by adjusting the notification content based on the current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input current weather information and time of day information into a generating AI and have the generating AI perform the adjustment of the notification content.

[0104] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is in a hurry, the notification unit will prioritize the most important notifications. For example, if the user is in a hurry, the notification unit will prioritize the most important notifications. The notification unit can also prioritize entertaining notifications if the user is relaxed. For example, if the user is relaxed, the notification unit will prioritize entertaining notifications. Furthermore, if the user is stressed, the notification unit can prioritize relaxing notifications. For example, if the user is stressed, the notification unit will prioritize relaxing notifications. In this way, the notification unit can provide more appropriate notifications by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The notification unit can prioritize notifying users of highly relevant content by considering geographical location information. For example, the notification unit can prioritize notifying users of information close to their current location. For example, the notification unit can obtain the user's current location information and prioritize notifying users of information close to their current location. The notification unit can also notify users of information related to a specific region if the user is in that region. For example, if the user is in a specific region, the notification unit can notify users of information about tourist attractions and shops related to that region. Furthermore, if the user is on the move, the notification unit can notify users of the most suitable information based on their current location. For example, if the user is on the move, the notification unit can notify users of the most suitable route information based on their current location. In this way, the notification unit can prioritize notifying users of highly relevant content by considering geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI execute notifications of highly relevant content.

[0106] The notification unit can analyze social media activity and notify relevant content. For example, the notification unit can notify relevant information based on locations where the user has checked in on social media. For example, the notification unit can obtain information on locations where the user has checked in on social media and notify information related to those locations. The notification unit can also notify information based on events and places that the user has shown interest in on social media. For example, the notification unit can obtain information on events and places that the user has shown interest in on social media and notify information related to them. Furthermore, the notification unit can analyze the content of the user's social media posts and notify relevant information. For example, the notification unit can analyze the content of the user's social media posts and notify information related to the content of the posts. In this way, the notification unit can notify relevant content by analyzing social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI execute notifications of relevant content.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception desk can suggest a navigation style tailored to the user's preferences based on the user's input. For example, it can analyze the navigation styles the user has previously selected (e.g., voice guidance, visual guidance, text guidance, etc.) and suggest the optimal style. It can also suggest an appropriate navigation style based on the user's current situation (e.g., driving, walking, etc.). Furthermore, it can suggest the optimal navigation style based on the type of device the user is using (e.g., smartphone, tablet, car navigation system, etc.). This allows the reception desk to provide a more user-friendly system by suggesting navigation styles that match the user's preferences and circumstances.

[0109] The data acquisition unit can prioritize the acquisition of relevant information based on the user's current activity. For example, if the user is exercising, the unit will prioritize acquiring information about nearby parks and jogging courses. Similarly, if the user is shopping, the unit can prioritize acquiring information about nearby shopping malls and stores. Furthermore, if the user is sightseeing, the unit can prioritize acquiring information about nearby tourist attractions and restaurants. This allows the data acquisition unit to provide more relevant information by prioritizing the acquisition of information according to the user's current activity.

[0110] The generation unit can analyze a user's past behavioral history and generate lyrics tailored to the user's preferences. For example, it can analyze the music genres and artist styles the user has liked in the past and generate lyrics based on that. It can also generate lyrics related to places and events the user has visited in the past. Furthermore, it can analyze the user's past emotional states and adjust the tone and content of the lyrics based on that. As a result, the generation unit can generate more personalized lyrics based on the user's preferences and past behavioral history.

[0111] The service provider can select the optimal delivery method by considering the battery level of the user's device. For example, if the user's device battery level is low, the service provider will prioritize providing voice guidance. Furthermore, if the user's device battery level is sufficient, the service provider can also provide visual or video guidance. In addition, the service provider can adjust the amount and frequency of information provided according to the user's device battery level. This allows the service provider to select a more appropriate delivery method by considering the user's device battery level.

[0112] The notification unit can select the most appropriate notification method based on the user's current activity. For example, if the user is driving, the notification unit will prioritize voice notifications. It can also provide vibration or visual notifications if the user is walking. Furthermore, if the user is in a meeting, the notification unit can select a quieter notification method (e.g., vibration or screen display). This allows the notification unit to provide more appropriate notifications by selecting the most suitable method according to the user's current activity.

[0113] The reception desk can estimate the user's emotions and adjust how it confirms input based on those emotions. For example, if the user is nervous, the reception desk can display a more detailed confirmation message. If the user is relaxed, it can display a more concise confirmation message. Furthermore, if the user is in a hurry, the reception desk can omit the confirmation message altogether. In this way, the reception desk can provide a more user-friendly system by adjusting how it confirms input according to the user's emotions.

[0114] The information retrieval unit can estimate the user's emotions and adjust the level of detail of the information it retrieves based on those emotions. For example, if the user is relaxed, the unit will retrieve detailed tourist information. If the user is in a hurry, the unit can also retrieve concise directions. Furthermore, if the user is stressed, the unit can prioritize retrieving information about places where they can relax. In this way, the information retrieval unit can provide more appropriate information by adjusting the level of detail of the information it retrieves according to the user's emotions.

[0115] The generation unit can estimate the user's emotions and adjust the lyrics based on those emotions. For example, if the user is relaxed, the generation unit will generate calm lyrics. It can also generate energetic lyrics if the user is excited. Furthermore, if the user is sad, it can generate encouraging lyrics. In this way, the generation unit can produce more appropriate lyrics by adjusting the content according to the user's emotions.

[0116] The information provider can estimate the user's emotions and adjust the amount of information provided based on those estimates. For example, if the user is relaxed, the provider can provide detailed information. If the user is in a hurry, the provider can provide concise information. Furthermore, if the user is stressed, the provider can prioritize providing relaxing information. In this way, the provider can provide more appropriate information by adjusting the amount of information provided according to the user's emotions.

[0117] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is relaxed, the notification unit can reduce the frequency of notifications. Conversely, if the user is in a hurry, the notification unit can increase the frequency of notifications. Furthermore, if the user is stressed, the notification unit can adjust the timing of notifications to alleviate stress. In this way, the notification unit can provide more appropriate notifications by adjusting the timing of notifications according to the user's emotions.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk receives input from the user. User input includes voice input, text input, and touch input. The reception desk can receive voice input from the user using speech recognition technology, and can also receive touch input from the user using a touchscreen. It can also be equipped with a keyboard for receiving text input. Step 2: The acquisition unit acquires route and scenery information based on the information received by the reception unit. Route and scenery information includes map data, landmark information, and real-time traffic information. The acquisition unit can acquire map data from map vendors and can link with traffic information services to obtain real-time traffic information. It can also collect local commercial and municipal information to acquire landmark information. Step 3: The generation unit aggregates the information acquired by the acquisition unit to generate a song. The generation unit can generate songs using generation AI, for example, by using text generation AI (LLM) to generate lyrics. It can also generate songs based on styles provided by entertainment vendors, generating songs by referencing music genres and artist styles. Step 4: The providing unit provides the user with the song generated by the generating unit. The providing unit can play the song using an audio output device and can also stream the song to the user's device. It may also be equipped with a display for displaying lyrics. Step 5: The notification unit will alert you if you are heading in the wrong direction. The notification unit can provide voice notifications, vibration notifications, and visual notifications. It may also be equipped with a display for visual notifications.

[0120] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0123] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, provision unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user input using the touch panel 38A and microphone 38B of the smart device 14. The acquisition unit acquires map data and real-time traffic information from a map vendor using the specific processing unit 290 of the data processing unit 12. The generation unit generates a song using a generation AI using the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated song to the user using the speaker 40B and display 40A of the smart device 14. The notification unit notifies the user if they are heading in the wrong direction using the speaker 40B and vibration function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 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.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, provision unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the smart glasses 214. The acquisition unit acquires map data and real-time traffic information from a map vendor using the identification processing unit 290 of the data processing unit 12. The generation unit generates a song using a generation AI using the identification processing unit 290 of the data processing unit 12. The provision unit provides the generated song to the user using the speaker 240 of the smart glasses 214. The notification unit notifies the user if they are heading in the wrong direction using the speaker 240 or display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, provision unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives the user's voice input using the microphone 238 of the headset terminal 314. The acquisition unit acquires map data and real-time traffic information from a map vendor using the specific processing unit 290 of the data processing unit 12. The generation unit generates a song using a generation AI using the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated song to the user using the speaker 240 and display 343 of the headset terminal 314. The notification unit notifies the user if they are heading in the wrong direction using the speaker 240 and vibration function of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, provision unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The acquisition unit acquires map data and real-time traffic information from a map vendor using the specific processing unit 290 of the data processing unit 12. The generation unit generates a song using a generation AI using the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated song to the user using the speaker 240 and display of the robot 414. The notification unit notifies the user if the robot 414 is moving in the wrong direction using the speaker 240 and vibration function of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] 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.

[0183] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) A reception area that receives input from users, Based on the information received by the aforementioned reception unit, an acquisition unit obtains information on routes and scenery, A generation unit that aggregates the information acquired by the acquisition unit and generates a song, A providing unit that provides the song generated by the generation unit, It includes a notification unit that alerts the user if they proceed in the wrong direction. A system characterized by the following features. (Note 2) The acquisition unit is, Obtain information on map vendors, local businesses, and municipalities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate songs based on the styles provided by entertainment vendors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated song to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, If you go in the wrong direction, use a fill-in to notify. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Filter input based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system considers the user's geographical location to suggest highly relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, It estimates the user's emotions and adjusts the type of information it retrieves based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, Analyze past acquisition history and select the optimal information acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, The system obtains real-time traffic and weather information and incorporates it into the route. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, Prioritize the acquisition of highly relevant information, taking geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, Analyze social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the way the lyrics are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Analyze past generation history and select the optimal generation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The lyrics will be adjusted based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the lyrics based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating lyrics, the system takes the user's geographical location into consideration to generate highly relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Analyze social media activity and generate relevant lyrics. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, Analyze past delivery history and select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We will adjust the services offered based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We prioritize providing highly relevant content, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, Analyze social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, Analyze past notification history to select the optimal notification method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, We will adjust the notification content based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, We prioritize notifications of highly relevant content, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, Analyze social media activity and notify relevant content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, Based on the information received by the aforementioned reception unit, an acquisition unit obtains information on routes and scenery, A generation unit that aggregates the information acquired by the acquisition unit and generates a song, A providing unit that provides the song generated by the generation unit, It includes a notification unit that alerts the user if they proceed in the wrong direction. A system characterized by the following features.

2. The acquisition unit is, Obtain information on map vendors, local businesses, and municipalities. The system according to feature 1.

3. The generating unit is Generate songs based on the styles provided by entertainment vendors. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated song to the user. The system according to feature 1.

5. The aforementioned notification unit, If you go in the wrong direction, use a fill-in to notify. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is Filter input based on the user's current status. The system according to feature 1.

Images