system

Abstract

The system according to this embodiment aims to enable users to intuitively understand how to get to their destination. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user. The generation unit creates a video showing how to get from the current location to the destination based on the information received by the reception unit. The navigation unit performs navigation based on the video generated by the generation unit.

Description

Technical Field

[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, and includes 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 that responds 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 conventional technology, there is a problem that it is difficult for a user to intuitively understand how to get to a destination.

[0005] The system according to the embodiment aims to enable a user to intuitively understand how to get to a destination.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit receives an input from a user. The generation unit creates a video on how to get from the current location to the destination based on the information received by the reception unit. The navigation unit performs navigation based on the video generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to intuitively understand how to get to their destination. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) The TalkMate system, according to an embodiment of the present invention, is a system that provides practical and engaging language practice by immersing users in real-life conversations. The TalkMate system provides real-time feedback on pronunciation, grammar, and conversation flow, and is personalized to the needs of each learner. The TalkMate system simulates everyday scenarios (such as airport navigation, business negotiations, and socializing) and provides contextual lessons using interactive dialogues, videos, and current news. For example, the TalkMate system provides an airport simulation for users to practice airport navigation. Users can practice listening to signs and announcements within the airport and taking appropriate actions. The TalkMate system also simulates negotiations in business meetings, providing users with practice to hone their negotiation skills. For example, in a business meeting simulation, users can negotiate with others and learn appropriate language and expressions. Furthermore, the TalkMate system simulates conversations in social settings, providing users with practice to engage in natural conversations. For example, users can acquire the skill to converse naturally with others through simulations of conversations at parties and cafes. The TalkMate system uses gamification to track progress and make learning fun while increasing user motivation. For example, TalkMate displays the goals achieved and points earned by users, allowing them to visually track their learning progress. TalkMate also provides a ranking function that allows users to compete with other learners, further boosting motivation. TalkMate fosters a global learning community, enabling users to practice collaboratively with others. For example, TalkMate provides a function that allows users to practice conversations in pairs with other learners. TalkMate also provides a function that allows users to conduct group discussions, promoting interaction among learners.The TalkMate system offers flexible subscription options and strategic partnership opportunities, aiming to provide an adaptive, dynamic, and efficient learning experience as a next-generation language learning tool. This enables the TalkMate system to deliver a practical and engaging language learning experience to its users.

[0029] The TalkMate system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and touch input. For example, the reception unit receives information entered by the user in text. The reception unit can also receive information entered by the user in voice. Furthermore, the reception unit can also receive information entered by the user via touch operation. The generation unit uses a generation AI to create a video showing how to get from the current location to the destination based on the information received by the reception unit. For example, the generation unit uses a generation AI to calculate the optimal route based on the current location and destination information entered by the user, and generates a video along that route. For example, the generation unit can also use a generation AI to calculate the optimal route based on map data and generate a video along that route. For example, the generation unit can use a generation AI to generate a video in real time based on the current location and destination information entered by the user. The navigation unit performs navigation based on the video generated by the generation unit. The navigation unit, for example, displays the generated video on the user's smartphone and provides navigation until the user reaches their destination. The navigation unit can also display the video according to the orientation of the user's smartphone. The navigation unit can also display the video according to the user's walking speed. The navigation unit can also display the video horizontally if the smartphone is held horizontally. As a result, the TalkMate system according to this embodiment can generate a video and provide navigation based on user input.

[0030] The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and touch input. For example, the reception unit can receive information entered by the user in text. It can also receive information entered by the user in voice. Furthermore, it can receive information entered by the user via touch. Specifically, in the case of text input, the user uses a keyboard or software keyboard to enter information about their current location and destination. In the case of voice input, the user enters their current location and destination by voice through a microphone, and this is converted into text using speech recognition technology. In the case of touch input, the user enters their current location and destination by tapping on the map. This allows the reception unit to support diverse input methods and improve user convenience. Furthermore, the reception unit also plays a role in temporarily storing the entered information and sending it to the generation unit and navigation unit. For example, information entered by the user is stored in a database and reused as needed. This allows the reception unit to efficiently process user input and improve the overall system performance.

[0031] The generation unit uses a generation AI to create a video showing how to get from the user's current location to their destination, based on information received by the reception unit. For example, the generation unit can use the generation AI to calculate the optimal route based on the user's entered current location and destination information, and generate a video along that route. The generation unit can also use the generation AI to calculate the optimal route based on map data and generate a video along that route. For example, the generation unit can use the generation AI to generate a video in real time based on the user's entered current location and destination information. Specifically, the generation AI obtains the coordinates of the current location and destination from a map database and calculates the optimal route. Next, the generation AI generates a video that includes scenery and landmarks along that route. The generated video is based on the user's perspective as they would actually walk the route, and is designed to ensure the user reaches their destination without getting lost. Furthermore, because the generation unit generates videos in real time, it can quickly respond to changes in the user's route or the addition of new information. For example, if the user changes their destination midway, the generation unit calculates a new route and immediately generates a new video. The generation unit can also provide the optimal route by considering real-time information such as weather and traffic conditions. This allows the generation unit to always provide users with navigation based on the latest information, supporting a comfortable travel experience.

[0032] The navigation unit provides navigation based on videos generated by the generation unit. For example, the navigation unit displays the generated video on the user's smartphone and provides navigation until the user reaches their destination. The navigation unit can also display the video according to the orientation of the user's smartphone. For example, the navigation unit can display the video according to the user's walking speed. For example, if the smartphone is held horizontally, the navigation unit can also display the video horizontally. Specifically, the navigation unit uses the smartphone's accelerometer and gyroscope to detect the user's movements and the device's orientation. This allows the navigation unit to optimize the video display according to how the user is holding the smartphone. For example, if the user is holding the smartphone vertically, the video will be displayed vertically, and if the user is holding the smartphone horizontally, the video will be displayed horizontally. The navigation unit can also detect the user's walking speed and adjust the video playback speed accordingly. This allows the user to walk at a natural pace while watching the video, and the navigation will be smooth and uninterrupted. Furthermore, the navigation unit is equipped with a voice guidance function and can provide voice navigation in conjunction with the video. This allows users to receive navigation using both sight and hearing, providing more intuitive and easy-to-understand directions. As a result, the navigation system can support the user throughout the entire process of reaching their destination, providing a safe and secure environment for travel.

[0033] The generation unit can generate videos using a generation AI. For example, the generation unit can use a generation AI to calculate the optimal route based on the user's inputted current location and destination information, and generate a video along that route. The generation unit can also use a generation AI to calculate the optimal route based on map data, and generate a video along that route. For example, the generation unit can use a generation AI to generate videos in real time based on the user's inputted current location and destination information. This improves the accuracy of video generation by using a generation AI. The generation AI includes, but is not limited to, deep learning algorithms and training data. Some or all of the above-described processes in the generation unit are performed using a generation AI.

[0034] The generation unit can use generation AI to calculate the optimal route based on map data and generate a video along that route. For example, the generation unit can use generation AI to calculate the optimal route based on map data and generate a video along that route. The generation unit can also use generation AI to generate a video in real time based on the user's input of their current location and destination. By calculating the optimal route based on map data and generating a video along that route, the accuracy of navigation is improved. Some or all of the above-described processes in the generation unit are performed using generation AI.

[0035] The navigation unit can provide navigation according to the orientation of the user's smartphone. For example, the navigation unit can detect the orientation of the user's smartphone and display a video accordingly. For example, the navigation unit can detect the orientation of the smartphone using a gyroscope or accelerometer and display a video accordingly. For example, if the user is holding the smartphone vertically, the navigation unit will display the video vertically. Also, if the user is holding the smartphone horizontally, the navigation unit can display the video horizontally. This improves user convenience by providing navigation according to the orientation of the user's smartphone. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0036] The navigation unit can display videos in accordance with the user's walking speed. For example, the navigation unit can detect the user's walking speed and display videos accordingly. For example, the navigation unit can detect the user's walking speed using a pedometer or GPS data and display videos accordingly. For example, if the user is walking fast, the navigation unit can increase the video playback speed. Conversely, if the user is walking slowly, the navigation unit can decrease the video playback speed. This improves the accuracy of navigation by displaying videos in accordance with the user's walking speed. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0037] The navigation unit can display videos horizontally when the smartphone is held horizontally. The navigation unit can, for example, detect the horizontal state of the smartphone and display the video horizontally accordingly. The navigation unit can, for example, use a gyroscope or accelerometer to detect the horizontal state of the smartphone and display the video horizontally accordingly. The navigation unit can, for example, display videos horizontally when the user is holding the smartphone horizontally. The navigation unit can also display videos vertically when the user is holding the smartphone vertically. This improves user convenience by displaying videos horizontally when the smartphone is held horizontally. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0038] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can customize input methods by referring to content that the user has entered in the past. This improves user convenience by analyzing the user's past input history and selecting the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not.

[0039] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can display only relevant information according to the user's current situation. For example, the reception unit can filter input content based on the user's areas of interest and provide appropriate information. For example, the reception unit can prioritize input content based on the user's current situation and areas of interest. This improves user convenience by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0040] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, the reception unit can prioritize receiving relevant information based on the user's current location. For example, the reception unit can suggest the optimal input method by considering the user's geographical location. For example, the reception unit can filter input content based on the user's current location. This improves user convenience by prioritizing the receipt of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or it may be performed without using AI.

[0041] The reception unit can analyze the user's social media activity and receive relevant information when receiving input. For example, the reception unit can analyze the user's social media activity and prioritize receiving relevant information. For example, the reception unit can filter the input content based on the user's social media activity. For example, the reception unit can suggest the optimal input method by referring to the user's social media activity. This improves user convenience by analyzing the user's social media activity and receiving relevant information. Some or all of the above processing in the reception unit may be performed using AI or not.

[0042] The generation unit can adjust the level of detail in a video based on the importance of the destination during video generation. For example, the generation unit can generate a video with a detailed explanation for important destinations. For example, the generation unit can generate a video with a concise explanation for general destinations. The generation unit can adjust the level of detail in a video according to the importance of the user's destination. This improves user convenience by adjusting the level of detail in the video based on the importance of the destination. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0043] The generation unit can apply different generation algorithms depending on the destination category when generating videos. For example, in the case of a tourist destination, the generation unit can generate a video that includes tourist information. For example, in the case of a business destination, the generation unit can generate a video that includes business information. The generation unit can apply the optimal generation algorithm depending on the user's destination category. This improves user convenience by applying different generation algorithms depending on the destination category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0044] The generation unit can determine the priority of videos based on the submission deadlines for each destination when generating videos. For example, the generation unit will prioritize generating videos for destinations with approaching deadlines. For example, the generation unit can postpone generating videos for destinations with distant submission deadlines. The generation unit can determine the priority of videos according to the submission deadlines for each user's destination. This improves user convenience by prioritizing videos based on the submission deadlines for each destination. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0045] The generation unit can adjust the order of videos based on the relevance of destinations during video generation. For example, the generation unit can prioritize generating videos for highly relevant destinations. For example, the generation unit can postpone generating videos for less relevant destinations. The generation unit can adjust the order of videos according to the relevance of the user's destinations. This improves user convenience by adjusting the order of videos based on the relevance of destinations. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0046] The navigation unit can select the optimal navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest the optimal navigation method based on routes previously used by the user. For example, the navigation unit can suggest a navigation method that avoids congestion based on the user's past travel history. For example, the navigation unit can analyze the user's past travel history and suggest the most efficient navigation method. This improves user convenience by selecting the optimal navigation method by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0047] The navigation unit can customize the navigation method based on the user's current lifestyle during navigation. For example, if the user is busy, the navigation unit may suggest the shortest route. For example, if the user is relaxed, the navigation unit may suggest a scenic route. For example, the navigation unit may suggest the optimal navigation method according to the user's lifestyle. This improves user convenience by customizing the navigation method based on the user's current lifestyle. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0048] The navigation unit can select the optimal navigation method during navigation, taking into account the user's geographical location information. For example, the navigation unit can suggest the optimal navigation method based on the user's current location. For example, the navigation unit can suggest the optimal route, taking into account the user's geographical location information. For example, the navigation unit can customize the means of navigation based on the user's current location. This improves user convenience by selecting the optimal navigation method, taking into account the user's geographical location information. Some or all of the above-described processes in the navigation unit may be performed using AI, or they may not be performed using AI.

[0049] The navigation unit can analyze the user's social media activity during navigation and suggest navigation methods. For example, the navigation unit can analyze the user's social media activity and suggest relevant navigation methods. For example, the navigation unit can suggest the optimal route based on the user's social media activity. For example, the navigation unit can customize navigation methods by referring to the user's social media activity. This improves user convenience by analyzing the user's social media activity and suggesting navigation methods. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The TalkMate system can analyze a user's past learning history and propose an optimal learning plan. For example, it can suggest a plan that focuses on areas the user has struggled with in the past. It can also suggest a plan that further develops areas the user excels at. Furthermore, it can adjust the plan to match the user's learning pace. By analyzing the user's past learning history and proposing the optimal learning plan, the system can improve learning efficiency.

[0052] The TalkMate system can provide learning content based on the user's geographical location, taking into account local culture and customs. For example, if the user is in Japan, it can provide content related to Japanese culture and customs. If the user is in France, it can provide content related to French culture and customs. Furthermore, if the user is traveling, it can provide content related to the culture and customs of their travel destination. By providing learning content that takes the user's geographical location into consideration, the practicality of learning can be improved.

[0053] The TalkMate system can analyze a user's social media activity and provide relevant learning content. For example, if a user frequently posts about a particular topic on social media, it can provide learning content related to that topic. It can also provide learning content related to a specific language if the user uses it. Furthermore, it can customize learning content based on the user's social media activity. This allows the system to stimulate learning interest by analyzing the user's social media activity and providing relevant learning content.

[0054] The TalkMate system can customize learning content based on the user's learning style. For example, it can provide more visual content to visual learners, more audio content to auditory learners, and more interactive content to tactile learners. By customizing learning content based on the user's learning style, the system can maximize the effectiveness of learning.

[0055] The TalkMate system can personalize learning content based on the user's learning goals. For example, a user who wants to learn business English will be provided with content based on business scenarios. Similarly, a user who wants to learn travel English will be provided with content based on travel scenarios. Furthermore, a user who wants to learn everyday conversation will be provided with content based on everyday scenarios. By personalizing learning content based on the user's learning goals, the effectiveness of learning can be maximized.

[0056] The TalkMate system can adjust learning content based on the user's learning environment. For example, it can provide content that enhances concentration for users in quiet environments, and simpler content for users in noisy environments. Furthermore, it can provide content that can be learned in a short amount of time for users on the go. By adjusting learning content based on the user's learning environment, the system can maximize the effectiveness of learning.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and touch input. For example, it can accept information entered by the user as text, voice, and touch. Step 2: The generation unit uses generation AI to create a video showing how to get from the current location to the destination, based on the information received by the reception unit. For example, it calculates the optimal route based on the current location and destination information entered by the user and generates a video along that route. It can also calculate the optimal route based on map data and generate a video along that route. It can also generate videos in real time. Step 3: The navigation unit provides navigation based on the video generated by the generation unit. For example, it displays the generated video on the user's smartphone and provides navigation until the user reaches their destination. The video can also be displayed according to the orientation of the user's smartphone. The video can also be displayed according to the user's walking speed. If the smartphone is held horizontally, the video can also be displayed horizontally.

[0059] (Example of form 2) The TalkMate system, according to an embodiment of the present invention, is a system that provides practical and engaging language practice by immersing users in real-life conversations. The TalkMate system provides real-time feedback on pronunciation, grammar, and conversation flow, and is personalized to the needs of each learner. The TalkMate system simulates everyday scenarios (such as airport navigation, business negotiations, and socializing) and provides contextual lessons using interactive dialogues, videos, and current news. For example, the TalkMate system provides an airport simulation for users to practice airport navigation. Users can practice listening to signs and announcements within the airport and taking appropriate actions. The TalkMate system also simulates negotiations in business meetings, providing users with practice to hone their negotiation skills. For example, in a business meeting simulation, users can negotiate with others and learn appropriate language and expressions. Furthermore, the TalkMate system simulates conversations in social settings, providing users with practice to engage in natural conversations. For example, users can acquire the skill to converse naturally with others through simulations of conversations at parties and cafes. The TalkMate system uses gamification to track progress and make learning fun while increasing user motivation. For example, TalkMate displays the goals achieved and points earned by users, allowing them to visually track their learning progress. TalkMate also provides a ranking function that allows users to compete with other learners, further boosting motivation. TalkMate fosters a global learning community, enabling users to practice collaboratively with others. For example, TalkMate provides a function that allows users to practice conversations in pairs with other learners. TalkMate also provides a function that allows users to conduct group discussions, promoting interaction among learners.The TalkMate system offers flexible subscription options and strategic partnership opportunities, aiming to provide an adaptive, dynamic, and efficient learning experience as a next-generation language learning tool. This enables the TalkMate system to deliver a practical and engaging language learning experience to its users.

[0060] The TalkMate system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and touch input. For example, the reception unit receives information entered by the user in text. The reception unit can also receive information entered by the user in voice. Furthermore, the reception unit can also receive information entered by the user via touch operation. The generation unit uses a generation AI to create a video showing how to get from the current location to the destination based on the information received by the reception unit. For example, the generation unit uses a generation AI to calculate the optimal route based on the current location and destination information entered by the user, and generates a video along that route. For example, the generation unit can also use a generation AI to calculate the optimal route based on map data and generate a video along that route. For example, the generation unit can use a generation AI to generate a video in real time based on the current location and destination information entered by the user. The navigation unit performs navigation based on the video generated by the generation unit. The navigation unit, for example, displays the generated video on the user's smartphone and provides navigation until the user reaches their destination. The navigation unit can also display the video according to the orientation of the user's smartphone. The navigation unit can also display the video according to the user's walking speed. The navigation unit can also display the video horizontally if the smartphone is held horizontally. As a result, the TalkMate system according to this embodiment can generate a video and provide navigation based on user input.

[0061] The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and touch input. For example, the reception unit can receive information entered by the user in text. It can also receive information entered by the user in voice. Furthermore, it can receive information entered by the user via touch. Specifically, in the case of text input, the user uses a keyboard or software keyboard to enter information about their current location and destination. In the case of voice input, the user enters their current location and destination by voice through a microphone, and this is converted into text using speech recognition technology. In the case of touch input, the user enters their current location and destination by tapping on the map. This allows the reception unit to support diverse input methods and improve user convenience. Furthermore, the reception unit also plays a role in temporarily storing the entered information and sending it to the generation unit and navigation unit. For example, information entered by the user is stored in a database and reused as needed. This allows the reception unit to efficiently process user input and improve the overall system performance.

[0062] The generation unit uses a generation AI to create a video showing how to get from the user's current location to their destination, based on information received by the reception unit. For example, the generation unit can use the generation AI to calculate the optimal route based on the user's entered current location and destination information, and generate a video along that route. The generation unit can also use the generation AI to calculate the optimal route based on map data and generate a video along that route. For example, the generation unit can use the generation AI to generate a video in real time based on the user's entered current location and destination information. Specifically, the generation AI obtains the coordinates of the current location and destination from a map database and calculates the optimal route. Next, the generation AI generates a video that includes scenery and landmarks along that route. The generated video is based on the user's perspective as they would actually walk the route, and is designed to ensure the user reaches their destination without getting lost. Furthermore, because the generation unit generates videos in real time, it can quickly respond to changes in the user's route or the addition of new information. For example, if the user changes their destination midway, the generation unit calculates a new route and immediately generates a new video. The generation unit can also provide the optimal route by considering real-time information such as weather and traffic conditions. This allows the generation unit to always provide users with navigation based on the latest information, supporting a comfortable travel experience.

[0063] The navigation unit provides navigation based on videos generated by the generation unit. For example, the navigation unit displays the generated video on the user's smartphone and provides navigation until the user reaches their destination. The navigation unit can also display the video according to the orientation of the user's smartphone. For example, the navigation unit can display the video according to the user's walking speed. For example, if the smartphone is held horizontally, the navigation unit can also display the video horizontally. Specifically, the navigation unit uses the smartphone's accelerometer and gyroscope to detect the user's movements and the device's orientation. This allows the navigation unit to optimize the video display according to how the user is holding the smartphone. For example, if the user is holding the smartphone vertically, the video will be displayed vertically, and if the user is holding the smartphone horizontally, the video will be displayed horizontally. The navigation unit can also detect the user's walking speed and adjust the video playback speed accordingly. This allows the user to walk at a natural pace while watching the video, and the navigation will be smooth and uninterrupted. Furthermore, the navigation unit is equipped with a voice guidance function and can provide voice navigation in conjunction with the video. This allows users to receive navigation using both sight and hearing, providing more intuitive and easy-to-understand directions. As a result, the navigation system can support the user throughout the entire process of reaching their destination, providing a safe and secure environment for travel.

[0064] The generation unit can generate videos using a generation AI. For example, the generation unit can use a generation AI to calculate the optimal route based on the user's inputted current location and destination information, and generate a video along that route. The generation unit can also use a generation AI to calculate the optimal route based on map data, and generate a video along that route. For example, the generation unit can use a generation AI to generate videos in real time based on the user's inputted current location and destination information. This improves the accuracy of video generation by using a generation AI. The generation AI includes, but is not limited to, deep learning algorithms and training data. Some or all of the above-described processes in the generation unit are performed using a generation AI.

[0065] The generation unit can use generation AI to calculate the optimal route based on map data and generate a video along that route. For example, the generation unit can use generation AI to calculate the optimal route based on map data and generate a video along that route. The generation unit can also use generation AI to generate a video in real time based on the user's input of their current location and destination. By calculating the optimal route based on map data and generating a video along that route, the accuracy of navigation is improved. Some or all of the above-described processes in the generation unit are performed using generation AI.

[0066] The navigation unit can provide navigation according to the orientation of the user's smartphone. For example, the navigation unit can detect the orientation of the user's smartphone and display a video accordingly. For example, the navigation unit can detect the orientation of the smartphone using a gyroscope or accelerometer and display a video accordingly. For example, if the user is holding the smartphone vertically, the navigation unit will display the video vertically. Also, if the user is holding the smartphone horizontally, the navigation unit can display the video horizontally. This improves user convenience by providing navigation according to the orientation of the user's smartphone. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0067] The navigation unit can display videos in accordance with the user's walking speed. For example, the navigation unit can detect the user's walking speed and display videos accordingly. For example, the navigation unit can detect the user's walking speed using a pedometer or GPS data and display videos accordingly. For example, if the user is walking fast, the navigation unit can increase the video playback speed. Conversely, if the user is walking slowly, the navigation unit can decrease the video playback speed. This improves the accuracy of navigation by displaying videos in accordance with the user's walking speed. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0068] The navigation unit can display videos horizontally when the smartphone is held horizontally. The navigation unit can, for example, detect the horizontal state of the smartphone and display the video horizontally accordingly. The navigation unit can, for example, use a gyroscope or accelerometer to detect the horizontal state of the smartphone and display the video horizontally accordingly. The navigation unit can, for example, display videos horizontally when the user is holding the smartphone horizontally. The navigation unit can also display videos vertically when the user is holding the smartphone vertically. This improves user convenience by displaying videos horizontally when the smartphone is held horizontally. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0069] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of input acceptance to give the user time to relax. For example, if the user is focused, the reception unit can accept input immediately to facilitate smooth operation. For example, if the user is tired, the reception unit can adjust the timing of input acceptance and suggest a break. This improves user convenience by adjusting the timing of input acceptance based on 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, 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 unit may be performed using AI or not using AI.

[0070] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can customize input methods by referring to content that the user has entered in the past. This improves user convenience by analyzing the user's past input history and selecting the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not.

[0071] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can display only relevant information according to the user's current situation. For example, the reception unit can filter input content based on the user's areas of interest and provide appropriate information. For example, the reception unit can prioritize input content based on the user's current situation and areas of interest. This improves user convenience by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0072] The reception unit can estimate the user's emotions and determine the priority of input to be received based on the estimated emotions. For example, if the user is nervous, the reception unit may prioritize important inputs. For example, if the user is relaxed, the reception unit may prioritize detailed inputs. For example, if the user is in a hurry, the reception unit may prioritize quick inputs. This improves user convenience by determining the priority of inputs to be received based on 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, 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 unit may be performed using AI or not using AI.

[0073] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, the reception unit can prioritize receiving relevant information based on the user's current location. For example, the reception unit can suggest the optimal input method by considering the user's geographical location. For example, the reception unit can filter input content based on the user's current location. This improves user convenience by prioritizing the receipt of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or it may be performed without using AI.

[0074] The reception unit can analyze the user's social media activity and receive relevant information when receiving input. For example, the reception unit can analyze the user's social media activity and prioritize receiving relevant information. For example, the reception unit can filter the input content based on the user's social media activity. For example, the reception unit can suggest the optimal input method by referring to the user's social media activity. This improves user convenience by analyzing the user's social media activity and receiving relevant information. Some or all of the above processing in the reception unit may be performed using AI or not.

[0075] The generation unit can estimate the user's emotions and adjust the video's presentation based on those emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a video that emphasizes the shortest route. If the user is excited, the generation unit can generate a video with visually stimulating effects. This improves user convenience by adjusting the video's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, 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 generation unit are performed using a generation AI.

[0076] The generation unit can adjust the level of detail in a video based on the importance of the destination during video generation. For example, the generation unit can generate a video with a detailed explanation for important destinations. For example, the generation unit can generate a video with a concise explanation for general destinations. The generation unit can adjust the level of detail in a video according to the importance of the user's destination. This improves user convenience by adjusting the level of detail in the video based on the importance of the destination. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0077] The generation unit can apply different generation algorithms depending on the destination category when generating videos. For example, in the case of a tourist destination, the generation unit can generate a video that includes tourist information. For example, in the case of a business destination, the generation unit can generate a video that includes business information. The generation unit can apply the optimal generation algorithm depending on the user's destination category. This improves user convenience by applying different generation algorithms depending on the destination category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0078] The generation unit can estimate the user's emotions and adjust the video length based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise video. For example, if the user is relaxed, the generation unit can generate a longer video with detailed explanations. For example, if the user is excited, the generation unit can generate a video with visually stimulating effects. This improves user convenience by adjusting the video length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit is performed using a generation AI.

[0079] The generation unit can determine the priority of videos based on the submission deadlines for each destination when generating videos. For example, the generation unit will prioritize generating videos for destinations with approaching deadlines. For example, the generation unit can postpone generating videos for destinations with distant submission deadlines. The generation unit can determine the priority of videos according to the submission deadlines for each user's destination. This improves user convenience by prioritizing videos based on the submission deadlines for each destination. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0080] The generation unit can adjust the order of videos based on the relevance of destinations during video generation. For example, the generation unit can prioritize generating videos for highly relevant destinations. For example, the generation unit can postpone generating videos for less relevant destinations. The generation unit can adjust the order of videos according to the relevance of the user's destinations. This improves user convenience by adjusting the order of videos based on the relevance of destinations. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0081] The navigation unit can estimate the user's emotions and adjust the display method of the navigation based on the estimated user emotions. For example, if the user is tense, the navigation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the navigation unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the navigation unit can provide a display method that gets straight to the point. By adjusting the display method of the navigation based on the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 navigation unit may be performed using AI or not using AI.

[0082] The navigation unit can select the optimal navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest the optimal navigation method based on routes previously used by the user. For example, the navigation unit can suggest a navigation method that avoids congestion based on the user's past travel history. For example, the navigation unit can analyze the user's past travel history and suggest the most efficient navigation method. This improves user convenience by selecting the optimal navigation method by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0083] The navigation unit can customize the navigation method based on the user's current lifestyle during navigation. For example, if the user is busy, the navigation unit may suggest the shortest route. For example, if the user is relaxed, the navigation unit may suggest a scenic route. For example, the navigation unit may suggest the optimal navigation method according to the user's lifestyle. This improves user convenience by customizing the navigation method based on the user's current lifestyle. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0084] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated emotions. For example, if the user is stressed, the navigation unit can prioritize important navigation. For example, if the user is relaxed, the navigation unit can provide detailed navigation. For example, if the user is in a hurry, the navigation unit can provide fast navigation. This improves user convenience by prioritizing navigation based on 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not using AI.

[0085] The navigation unit can select the optimal navigation method during navigation, taking into account the user's geographical location information. For example, the navigation unit can suggest the optimal navigation method based on the user's current location. For example, the navigation unit can suggest the optimal route, taking into account the user's geographical location information. For example, the navigation unit can customize the means of navigation based on the user's current location. This improves user convenience by selecting the optimal navigation method, taking into account the user's geographical location information. Some or all of the above-described processes in the navigation unit may be performed using AI, or they may not be performed using AI.

[0086] The navigation unit can analyze the user's social media activity during navigation and suggest navigation methods. For example, the navigation unit can analyze the user's social media activity and suggest relevant navigation methods. For example, the navigation unit can suggest the optimal route based on the user's social media activity. For example, the navigation unit can customize navigation methods by referring to the user's social media activity. This improves user convenience by analyzing the user's social media activity and suggesting navigation methods. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The TalkMate system can estimate a user's emotions and adjust learning content based on those emotions. For example, if a user is stressed, it can provide relaxing content. If a user is excited, it can provide more challenging content. Furthermore, if a user is tired, it can suggest a break. By adjusting learning content based on the user's emotions, the system can maximize the effectiveness of learning.

[0089] The TalkMate system can analyze a user's past learning history and propose an optimal learning plan. For example, it can suggest a plan that focuses on areas the user has struggled with in the past. It can also suggest a plan that further develops areas the user excels at. Furthermore, it can adjust the plan to match the user's learning pace. By analyzing the user's past learning history and proposing the optimal learning plan, the system can improve learning efficiency.

[0090] The TalkMate system can provide learning content based on the user's geographical location, taking into account local culture and customs. For example, if the user is in Japan, it can provide content related to Japanese culture and customs. If the user is in France, it can provide content related to French culture and customs. Furthermore, if the user is traveling, it can provide content related to the culture and customs of their travel destination. By providing learning content that takes the user's geographical location into consideration, the practicality of learning can be improved.

[0091] The TalkMate system can analyze a user's social media activity and provide relevant learning content. For example, if a user frequently posts about a particular topic on social media, it can provide learning content related to that topic. It can also provide learning content related to a specific language if the user uses it. Furthermore, it can customize learning content based on the user's social media activity. This allows the system to stimulate learning interest by analyzing the user's social media activity and providing relevant learning content.

[0092] The TalkMate system can estimate the user's emotions and adjust the learning progress based on those emotions. For example, if the user is stressed, the progress can be slowed down to give them time to relax. Conversely, if the user is focused, the progress can be sped up to facilitate smooth learning. Furthermore, if the user is tired, the progress can be adjusted and a break can be suggested. In this way, the learning effect can be maximized by adjusting the learning progress based on the user's emotions.

[0093] The TalkMate system can customize learning content based on the user's learning style. For example, it can provide more visual content to visual learners, more audio content to auditory learners, and more interactive content to tactile learners. By customizing learning content based on the user's learning style, the system can maximize the effectiveness of learning.

[0094] The TalkMate system can estimate a user's emotions and adjust the content of feedback based on those emotions. For example, if a user is feeling down, it can provide encouraging feedback. If a user is confident, it can provide challenging feedback. Furthermore, if a user is feeling anxious, it can provide reassuring feedback. By adjusting the content of feedback based on the user's emotions, it can increase learning motivation.

[0095] The TalkMate system can personalize learning content based on the user's learning goals. For example, a user who wants to learn business English will be provided with content based on business scenarios. Similarly, a user who wants to learn travel English will be provided with content based on travel scenarios. Furthermore, a user who wants to learn everyday conversation will be provided with content based on everyday scenarios. By personalizing learning content based on the user's learning goals, the effectiveness of learning can be maximized.

[0096] The TalkMate system can estimate the user's emotions and adjust the timing of learning based on those emotions. For example, if the user is relaxed, it can suggest a good time to start learning. If the user is stressed, it can suggest a time to delay learning. Furthermore, if the user is focused, it can suggest a time to continue learning. By adjusting the timing of learning based on the user's emotions, the system can maximize the effectiveness of learning.

[0097] The TalkMate system can adjust learning content based on the user's learning environment. For example, it can provide content that enhances concentration for users in quiet environments, and simpler content for users in noisy environments. Furthermore, it can provide content that can be learned in a short amount of time for users on the go. By adjusting learning content based on the user's learning environment, the system can maximize the effectiveness of learning.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and touch input. For example, it can accept information entered by the user as text, voice, and touch. Step 2: The generation unit uses generation AI to create a video showing how to get from the current location to the destination, based on the information received by the reception unit. For example, it calculates the optimal route based on the current location and destination information entered by the user and generates a video along that route. It can also calculate the optimal route based on map data and generate a video along that route. It can also generate videos in real time. Step 3: The navigation unit provides navigation based on the video generated by the generation unit. For example, it displays the generated video on the user's smartphone and provides navigation until the user reaches their destination. The video can also be displayed according to the orientation of the user's smartphone. The video can also be displayed according to the user's walking speed. If the smartphone is held horizontally, the video can also be displayed horizontally.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts text input, voice input, and touch input from the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and calculates the optimal route using generation AI and generates a video along that route. The navigation unit is implemented, for example, by the output device 40 of the smart device 14 and displays the generated video on the user's smartphone and provides navigation. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the optimal route using generation AI and generates a video along that route. The navigation unit is implemented by the speaker 240 of the smart glasses 214 and displays the generated video on the user's smart glasses and provides navigation. 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.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the optimal route using generation AI and generates a video along that route. The navigation unit is implemented by the display 343 of the headset terminal 314 and displays the generated video on the user's headset to provide navigation. 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.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the optimal route using a generation AI and generates a video along that route. The navigation unit is implemented by the display and speaker 240 of the robot 414 and displays the generated video to the user and provides navigation. 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A reception area that receives input from users, Based on the information received by the reception unit, a generation unit creates a video showing how to get from the current location to the destination, The system includes a navigation unit that performs navigation based on the video generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate videos using AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The generation AI calculates the optimal route based on map data and generates a video along that route. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is Navigate the user's smartphone according to its orientation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned navigation unit is The video is displayed in sync with the user's walking speed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned navigation unit is If your smartphone is held horizontally, the video will also be displayed horizontally. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the video's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a video, adjust the level of detail based on the importance of the destination. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating videos, different generation algorithms are applied depending on the destination category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating videos, prioritize them based on the submission deadline for the destination. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating videos, the order of the videos is adjusted based on the relevance of their destinations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned navigation unit is It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is During navigation, the navigation method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and suggests navigation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 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 reception unit, a generation unit creates a video showing how to get from the current location to the destination, The system includes a navigation unit that performs navigation based on the video generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate videos using AI. The system according to feature 1.

3. The generating unit is The AI ​​generates the optimal route based on map data and creates a video that follows that route. The system according to feature 1.

4. The aforementioned navigation unit is Navigate the user's smartphone according to its orientation. The system according to feature 1.

5. The aforementioned navigation unit is The video is displayed in sync with the user's walking speed. The system according to feature 1.

6. The aforementioned navigation unit is If your smartphone is held horizontally, the video will also be displayed horizontally. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

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