system

Abstract

The system according to the embodiment aims to enable inbound customers to obtain visually easy-to-understand information in real time. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, a reception unit, and an answering unit. The analysis unit analyzes images acquired from a smartphone camera. The generation unit generates AR based on the information analyzed by the analysis unit. The reception unit receives voice questions from a user. The answering unit quickly answers the questions received by the reception unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for inbound customers to obtain visually easy-to-understand information in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to enable inbound customers to obtain visually easy-to-understand information in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a reception unit, and an answering unit. The analysis unit analyzes an image acquired from a smartphone camera. The generation unit generates AR based on the information analyzed by the analysis unit. The reception unit receives a voice question from a user. The answering unit quickly answers the question received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable inbound customers to obtain visually easy-to-understand information in real time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A navigation system according to an embodiment of the present invention analyzes images captured by a smartphone camera, generates AR, and instantly answers voice questions. The navigation system analyzes images captured by the smartphone camera, understands the surrounding environment, and generates AR based on the analyzed information. Furthermore, the navigation system accepts voice questions from users and instantly answers them. For example, when a user holds a smartphone over a tourist spot, AI provides real-time information about the location's history and tourist attractions. This allows for visually easy-to-understand information to be displayed via AR. Furthermore, when a user voice-questions such as "I want to go to X," the AI ​​instantly responds. For example, in response to questions such as "What are the recommended places? Where are the good restaurants? What are the local specialties?", the AI ​​responds in a foreign language and provides easy-to-understand navigation using AR and voice. This allows inbound tourists to enjoy sightseeing comfortably without experiencing language barriers. Furthermore, navigation systems can be used not only in tourist spots but also in shopping malls, airports, and other locations. For example, when a user holds a smartphone over a shopping mall, AI provides store information and sale information, allowing the user to enjoy shopping efficiently. At airports, it also provides real-time information about boarding gates and facilities, supporting smooth travel. By combining image recognition and AR navigation, the navigation system provides inbound tourists with visually easy-to-understand information in real time, helping to ensure a comfortable sightseeing experience. For example, when used at tourist spots, users can obtain detailed information about a location simply by holding their smartphone over the screen. At shopping malls and airports, users can also obtain information efficiently, supporting smooth travel and shopping.

[0029] A navigation system according to an embodiment includes an analysis unit, a generation unit, a reception unit, and a response unit. The analysis unit analyzes an image acquired from a smartphone camera. For example, the analysis unit analyzes the image using an image recognition algorithm to understand the surrounding environment. The analysis unit can also identify objects in the image using object recognition technology. The analysis unit can also understand scenes in the image using scene analysis technology. For example, the analysis unit can identify buildings and landmarks in the image and understand the surrounding environment based on that information. The generation unit generates AR based on the information analyzed by the analysis unit. For example, the generation unit generates a 3D model and displays it as AR. The generation unit can also use overlay technology to display information over a real image. The generation unit can also generate AR in real time and provide it to the user. For example, the generation unit generates information about tourist spots as a 3D model and displays it on the user's smartphone. The reception unit receives a voice question from the user. For example, the reception unit converts the user's voice into text using voice recognition technology. The reception unit can also remove noise from the voice data using noise filtering technology. Furthermore, the reception unit can support multiple languages ​​and accept voice questions in different languages. For example, the reception unit accepts voice questions in English, Japanese, Chinese, and the like. The answering unit quickly answers the questions accepted by the reception unit. The answering unit can generate optimal answers to the questions using, for example, AI. The answering unit can also provide relevant information based on the content of the question. Furthermore, the answering unit can generate answers in real time and provide them to the user. For example, in response to a question about a tourist destination, the answering unit can provide information about the history of the location and tourist attractions. This allows the navigation system according to the embodiment to analyze images acquired from a smartphone camera, generate AR, and instantly answer voice questions. This allows inbound tourists to receive visually easy-to-understand information in real time.

[0030] The analysis unit can analyze images acquired from the smartphone camera and understand the surrounding environment. The analysis unit can analyze images using, for example, an image recognition algorithm to understand the surrounding environment. For example, the analysis unit can identify objects in the image using object recognition technology. The analysis unit can also understand scenes in the image using scene analysis technology. For example, the analysis unit can identify buildings and landmarks in the image and understand the surrounding environment based on that information. In this way, by analyzing images acquired from the smartphone camera and understanding the surrounding environment, appropriate information can be provided to the user. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input images acquired from the smartphone camera to a generation AI and have the generation AI perform image analysis.

[0031] The generation unit can generate AR based on the information analyzed by the analysis unit. The generation unit, for example, generates a 3D model and displays it as AR. For example, the generation unit generates information about tourist spots as a 3D model and displays it on the user's smartphone. The generation unit can also use overlay technology to display information over a real image. For example, the generation unit displays information about tourist spots overlaid on a real image. Furthermore, the generation unit can generate AR in real time and provide it to the user. For example, the generation unit generates information about tourist spots in real time and displays it on the user's smartphone. By generating AR based on the analyzed information, it is possible to provide the user with visually easy-to-understand information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the information analyzed by the analysis unit into a generation AI and have the generation AI generate AR.

[0032] The reception unit can accept a user's voice question. The reception unit can convert the user's voice into text using, for example, voice recognition technology. For example, the reception unit can convert the user's voice into text in real time using voice recognition software. The reception unit can also remove noise from the voice data using noise filtering technology. For example, the reception unit can remove background noise from the voice data using noise canceling technology. The reception unit can also support multiple languages ​​and accept voice questions in different languages. For example, the reception unit can accept voice questions in English, Japanese, Chinese, etc. This allows the user to easily ask a question by accepting the user's voice question. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's voice data into a generation AI and have the generation AI perform voice recognition.

[0033] The answering unit can quickly answer questions received by the receiving unit. The answering unit generates optimal answers to questions, for example, using AI. For example, the answering unit generates answers to user questions using text generation AI (e.g., LLM). The answering unit can also provide related information based on the content of the question. For example, in response to a question about a tourist destination, the answering unit provides information about the history of the location and tourist attractions. Furthermore, the answering unit can generate answers in real time and provide them to the user. For example, the answering unit can instantly generate answers to user questions and display them on the user's smartphone. This allows for the user to be provided with information quickly by instantly answering questions received by the receiving unit. Some or all of the above-described processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input questions received by the receiving unit into a generation AI and have the generation AI generate answers.

[0034] The generation unit can provide information on tourist attractions, shopping malls, and airports in real time. The generation unit, for example, provides information on tourist attractions in real time. For example, the generation unit collects the latest information on tourist attractions and displays it on the user's smartphone. The generation unit can also provide information on shopping malls in real time. For example, the generation unit collects information on stores and sales in shopping malls and provides it to the user. The generation unit can also provide information on airports in real time. For example, the generation unit collects information on boarding gates and facilities in airports and provides it to the user. This allows the user to efficiently obtain information by providing information on tourist attractions, shopping malls, airports, etc. in real time. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input information on tourist attractions, shopping malls, and airports into the generation AI and cause the generation AI to provide the information.

[0035] When analyzing an image, the analysis unit can adjust the analysis algorithm by referring to past analysis data. For example, the analysis unit optimizes the analysis algorithm by referring to data previously analyzed for the same location. For example, the analysis unit retrieves past analysis data from a database and reflects it in the current analysis. The analysis unit can also adjust the analysis algorithm based on the user's past behavior history. For example, the analysis unit analyzes the user's past behavior patterns and optimizes the analysis algorithm. Furthermore, the analysis unit can compare past analysis results with current analysis results and improve the algorithm. For example, the analysis unit compares past analysis results with current analysis results and makes adjustments to improve accuracy. By optimizing the analysis algorithm by referring to past analysis data, analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0036] During image analysis, the analysis unit can correct the analysis results based on the user's current location information. The analysis unit corrects the analysis results based on, for example, the user's current location information. For example, the analysis unit uses GPS data to identify the user's current location and corrects the analysis results based on that location. Furthermore, when the user is moving, the analysis unit can update the location information in real time and correct the analysis results. For example, the analysis unit acquires the user's location information in real time and corrects the analysis results as the user moves. Furthermore, when the user is in a specific location, the analysis unit can prioritize analyzing information related to that location. For example, when the user is in a tourist spot, the analysis unit prioritizes analyzing information related to that location. This allows for correcting the analysis results taking the user's current location information into consideration, thereby providing more accurate information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's location information into the generation AI and have the generation AI correct the analysis results.

[0037] The analysis unit can perform correction processing to accommodate different light and weather conditions during image analysis. For example, when light is strong, the analysis unit performs analysis by adjusting the brightness of the image. For example, the analysis unit adjusts the brightness of the image using exposure correction technology. The analysis unit can also adjust the contrast of the image when it is raining. For example, the analysis unit adjusts the contrast of the image when it is raining using contrast correction technology. The analysis unit can also remove noise from the image when it is nighttime when performing analysis. For example, the analysis unit removes noise from the image when it is nighttime using noise removal technology. This improves the accuracy of the analysis by performing correction processing to accommodate different light and weather conditions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to perform correction processing according to the light and weather conditions.

[0038] When analyzing images, the analysis unit can customize the analysis results by referring to the user's past behavioral history. The analysis unit customizes the analysis results, for example, based on data on places the user has visited in the past. For example, the analysis unit retrieves the user's past behavioral history from a database and reflects it in the current analysis. The analysis unit can also analyze the user's past behavioral patterns and adjust the analysis results. For example, the analysis unit analyzes the user's past behavioral patterns and reflects it in the current analysis. Furthermore, the analysis unit can prioritize analysis of information in which the user has shown interest in the past. For example, the analysis unit prioritizes analysis of information about tourist spots in which the user has shown interest in the past. This allows the analysis results to be customized by referring to the user's past behavioral history, thereby providing the user with appropriate information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past behavioral history into a generation AI and have the generation AI customize the analysis results.

[0039] When analyzing images, the analysis unit can select the optimal analysis method by taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit selects the optimal analysis method for the smartphone. For example, the analysis unit selects the optimal analysis method based on the smartphone's performance and screen size. Furthermore, if the user is using a tablet, the analysis unit can also select the optimal analysis method for the tablet. For example, the analysis unit selects the optimal analysis method based on the tablet's performance and screen size. Furthermore, if the user is using a smartwatch, the analysis unit can also select the optimal analysis method for the smartwatch. For example, the analysis unit selects the optimal analysis method based on the smartwatch's performance and screen size. This improves analysis accuracy by selecting the optimal analysis method by taking into account the user's device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's device information into the generation AI and cause the generation AI to select the optimal analysis method.

[0040] The analysis unit can analyze the user's social media activity during image analysis and provide related information. The analysis unit can provide, for example, information about places where the user has checked in on social media. For example, the analysis unit can obtain the user's social media check-in data and provide information about the places. The analysis unit can also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the analysis unit can analyze the user's posts and provide information about related tourist spots and stores. Furthermore, the analysis unit can provide information about related places and events based on the activities of the user's friends on social media. For example, the analysis unit can analyze the check-in data and posts of the user's friends and provide information about related places and events. By analyzing the user's social media activity and providing related information, appropriate information can be provided to the user. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's social media data into a generation AI and cause the generation AI to provide related information.

[0041] When generating the AR image, the generation unit can customize the display content based on the user's current interests. The generation unit, for example, incorporates tourist spots that the user is interested in into the AR display. For example, the generation unit displays information about tourist spots based on the user's interests. The generation unit can also incorporate stores that the user frequently visits into the AR display. For example, the generation unit displays store information based on the user's visit history. Furthermore, the generation unit can incorporate places that the user may be interested in into the AR display based on the user's past search history. For example, the generation unit analyzes the user's search history and displays information about related places. This allows the display content to be customized based on the user's current interests, thereby providing the user with appropriate information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input the user's interest data into the generation AI and have the generation AI customize the display content.

[0042] When generating the AR, the generation unit can optimize the display content by referring to the user's past usage history. The generation unit optimizes the AR display content, for example, based on data on places the user has visited in the past. For example, the generation unit retrieves the user's past usage history from a database and reflects it in the current display content. The generation unit can also analyze the user's past behavioral patterns and adjust the AR display content. For example, the generation unit analyzes the user's past behavioral patterns and reflects it in the current display content. Furthermore, the generation unit can prioritize incorporating information that the user has shown interest in in the AR display. For example, the generation unit prioritizes displaying information about tourist spots that the user has shown interest in in the past. This allows the display content to be optimized by referring to the user's past usage history, thereby providing the user with appropriate information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past usage history into the generation AI and cause the generation AI to optimize the display content.

[0043] The generation unit can make adjustments to support different devices and platforms when generating AR. The generation unit makes adjustments to support different devices, such as smartphones, tablets, and smartwatches. For example, the generation unit provides optimal display content based on the device's performance and screen size. The generation unit can also make adjustments to support different platforms, such as iOS and Android. For example, the generation unit optimizes the display content for each platform and provides appropriate display content to the user. The generation unit can also adjust the AR display content according to the device's screen size and resolution. For example, the generation unit optimizes the display content based on the device's screen size. This makes it possible to provide appropriate information to the user by making adjustments to support different devices and platforms. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input device and platform information into the generation AI and have the generation AI perform the adjustments.

[0044] When generating AR, the generation unit can display highly relevant information based on the user's geographical location information. The generation unit, for example, prioritizes displaying information about tourist spots close to the user's current location. For example, the generation unit can use GPS data to identify the user's current location and display information about tourist spots nearby. The generation unit can also prioritize displaying information about stores close to the user's current location. For example, the generation unit can display information about nearby stores based on the user's current location. The generation unit can also prioritize displaying information about events close to the user's current location. For example, the generation unit can display information about nearby events based on the user's current location. This allows the user to be provided with appropriate information by displaying highly relevant information taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to display related information.

[0045] The generation unit can analyze the user's social media activity and display related information when generating AR. The generation unit, for example, displays information about places where the user has checked in on social media. For example, the generation unit obtains the user's social media check-in data and displays information about the places. The generation unit can also analyze the user's social media posts and display information about related tourist spots and stores. For example, the generation unit analyzes the user's posts and displays information about related tourist spots and stores. The generation unit can also display information about related places and events based on the activities of the user's friends on social media. For example, the generation unit analyzes the check-in data and posts of the user's friends and displays information about related places and events. This makes it possible to provide the user with appropriate information by analyzing the user's social media activity and displaying related information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's social media data into the generation AI and cause the generation AI to display related information.

[0046] When generating AR, the generation unit can customize the display content by reflecting the user's past feedback. The generation unit customizes the AR display content based on, for example, feedback provided by the user in the past. For example, the generation unit retrieves the user's past feedback from a database and reflects it in the current display content. The generation unit can also prioritize displaying information preferred by the user based on the user's past feedback. For example, the generation unit analyzes the user's past feedback and prioritizes displaying preferred information. The generation unit can also analyze the user's past feedback and optimize the display content. For example, the generation unit optimizes the display content based on the user's past feedback. In this way, appropriate information can be provided to the user by customizing the display content by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the display content.

[0047] When receiving a voice question, the reception unit can select the optimal reception method by referring to the user's past question history. The reception unit selects the optimal reception method, for example, based on questions frequently asked by the user in the past. For example, the reception unit retrieves the user's past question history from a database and reflects the retrieved question history in the current reception method. The reception unit can also prioritize reception of related questions from the user's past question history. For example, the reception unit analyzes the user's past question history and prioritizes reception of related questions. The reception unit can also analyze the user's past question history and suggest the optimal reception method. For example, the reception unit suggests the optimal reception method based on the user's past question history. This allows the user to receive an appropriate question by selecting the optimal reception method by referring to the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past question history into a generation AI and have the generation AI select the optimal reception method.

[0048] When receiving a voice question, the reception unit can customize the reception method based on the user's current situation and environment. For example, when the user is in a noisy environment, the reception unit receives the voice question using a noise canceling function. For example, the reception unit uses noise canceling technology to remove background noise from the voice data. The reception unit can also provide a normal voice question reception method when the user is in a quiet environment. For example, the reception unit receives a voice question using normal voice recognition technology in a quiet environment. Furthermore, the reception unit can also provide a simple voice question reception method when the user is on the move. For example, the reception unit provides an interface for receiving simple voice questions while the user is on the move. This allows the reception method to be customized based on the user's current situation and environment, thereby providing the user with an appropriate question reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's current situation and environmental data into a generation AI and have the generation AI customize the reception method.

[0049] The reception unit can adjust to accommodate different languages ​​or dialects when receiving a voice question. For example, when a user asks a question in a different language, the reception unit provides a reception method corresponding to that language. For example, the reception unit uses a language model to recognize a voice question in a different language and provides a corresponding reception method. Furthermore, when a user asks a question in a specific dialect, the reception unit can also provide a reception method corresponding to that dialect. For example, the reception unit uses dialect recognition technology to recognize a voice question in a specific dialect and provide a corresponding reception method. Furthermore, the reception unit can provide a language switching function when a user speaks multiple languages. For example, the reception unit provides a function to automatically switch languages ​​when a user asks a question in multiple languages. This makes it possible to adjust to accommodate different languages ​​or dialects and provide an appropriate question reception service for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data in different languages ​​or dialects to a generation AI and have the generation AI adjust the corresponding reception method.

[0050] When receiving a voice question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a tourist spot, the reception unit prioritizes receiving questions related to that location. For example, the reception unit uses GPS data to identify the user's current location and prioritizes receiving questions related to that location. Furthermore, if the user is in a shopping mall, the reception unit can prioritize receiving questions about stores. For example, the reception unit prioritizes receiving questions about stores in the shopping mall based on the user's current location. Furthermore, if the user is in an airport, the reception unit can prioritize receiving questions about boarding gates and facilities. For example, the reception unit prioritizes receiving questions about boarding gates and facilities in the airport based on the user's current location. This prioritizes receiving highly relevant questions by taking into account the user's geographical location information, thereby providing the user with appropriate question reception. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant questions.

[0051] When receiving a voice question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit prioritizes receiving questions related to places where the user has checked in on social media. For example, the reception unit acquires the user's social media check-in data and prioritizes receiving questions related to those places. The reception unit can also analyze the user's social media posts and receive related questions. For example, the reception unit analyzes the user's posts and receives related questions. Furthermore, the reception unit can receive related questions based on the activities of the user's friends on social media. For example, the reception unit analyzes the check-in data and posts of the user's friends and receives related questions. In this way, by analyzing the user's social media activity and receiving related questions, it is possible to provide the user with appropriate question reception. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related questions.

[0052] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a voice question. The reception unit customizes the reception method for voice questions, for example, based on feedback provided by the user in the past. For example, the reception unit retrieves the user's past feedback from a database and reflects the feedback in the current reception method. The reception unit can also prioritize providing a reception method preferred by the user based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and prioritizes providing a preferred reception method. The reception unit can also analyze the user's past feedback and optimize the reception method. For example, the reception unit optimizes the reception method based on the user's past feedback. In this way, the reception method can be customized by reflecting the user's past feedback, thereby providing an appropriate question reception method for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the reception method.

[0053] When answering a question, the answering unit can provide an optimal answer by referring to the user's past question history. The answering unit provides an optimal answer, for example, based on questions the user has previously asked. For example, the answering unit retrieves the user's past question history from a database and reflects it in the current answer. The answering unit can also prioritize providing related information from the user's past question history. For example, the answering unit analyzes the user's past question history and prioritizes providing related information. The answering unit can also analyze the user's past question history and suggest an optimal answer. For example, the answering unit suggests an optimal answer based on the user's past question history. This makes it possible to provide an appropriate answer to the user by referring to the user's past question history. Some or all of the above-described processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input the user's past question history into a generation AI and cause the generation AI to provide an optimal answer.

[0054] When answering a question, the answer unit can customize the answer based on the user's current situation and environment. For example, if the user is at a tourist spot, the answer unit can provide information related to the location. For example, the answer unit can use GPS data to identify the user's current location and provide information related to the location. Furthermore, if the user is at a shopping mall, the answer unit can provide information about stores. For example, the answer unit can provide information about stores in the shopping mall based on the user's current location. Furthermore, if the user is at an airport, the answer unit can provide information about boarding gates and facilities. For example, the answer unit can provide information about boarding gates and facilities in the airport based on the user's current location. This allows the answer to be customized based on the user's current situation and environment, thereby providing an appropriate answer to the user. Some or all of the above-described processing in the answer unit may be performed using, or without, AI. For example, the answer unit can input the user's current situation and environmental data into a generation AI and have the generation AI customize the answer.

[0055] The answering unit can adjust the answer to accommodate different languages ​​or dialects. For example, when a user asks a question in a different language, the answering unit provides an answer corresponding to that language. For example, the answering unit uses a language model to recognize questions in different languages ​​and provide a corresponding answer. Furthermore, when a user asks a question in a specific dialect, the answering unit can also provide an answer corresponding to that dialect. For example, the answering unit uses dialect recognition technology to recognize questions in a specific dialect and provide a corresponding answer. Furthermore, the answering unit can provide a language switching function when a user uses multiple languages. For example, the answering unit provides a function to automatically switch languages ​​when a user asks a question in multiple languages. This makes it possible to provide an appropriate answer to the user by adjusting the answer to accommodate different languages ​​or dialects. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without AI. For example, the answering unit can input question data in different languages ​​or dialects to a generation AI and have the generation AI adjust the corresponding answer.

[0056] When providing an answer, the answering unit can provide a highly relevant answer by taking into account the user's geographical location information. For example, if the user is in a tourist spot, the answering unit can prioritize providing information related to that location. For example, the answering unit can use GPS data to identify the user's current location and provide information related to that location. Furthermore, if the user is in a shopping mall, the answering unit can prioritize providing information about stores. For example, the answering unit can provide information about stores in the shopping mall based on the user's current location. Furthermore, if the user is in an airport, the answering unit can prioritize providing information about boarding gates and facilities. For example, the answering unit can provide information about boarding gates and facilities in the airport based on the user's current location. This allows the user to be provided with an appropriate answer by taking into account the user's geographical location information. Some or all of the above-described processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input the user's geographical location information into a generation AI and cause the generation AI to provide a highly relevant answer.

[0057] The answering unit can analyze the user's social media activity and provide related information when answering a question. The answering unit can provide, for example, information about places where the user has checked in on social media. For example, the answering unit can obtain the user's social media check-in data and provide information related to the places. The answering unit can also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the answering unit can analyze the user's posts and provide information about related tourist spots and stores. Furthermore, the answering unit can provide information about related places and events based on the activities of the user's friends on social media. For example, the answering unit can analyze the check-in data and posts of the user's friends and provide information about related places and events. This allows the user's social media activity to be analyzed and related information to be provided, thereby providing an appropriate answer to the user. Some or all of the above-described processing in the answering unit can be performed using, or without, AI. For example, the answering unit can input the user's social media data into a generation AI and have the generation AI provide related information.

[0058] When answering a question, the answer unit can customize the answer content by reflecting the user's past feedback. The answer unit customizes the answer content based on, for example, feedback provided by the user in the past. For example, the answer unit retrieves the user's past feedback from a database and reflects it in the current answer. The answer unit can also prioritize providing information that is preferred based on the user's past feedback. For example, the answer unit analyzes the user's past feedback and prioritizes providing the preferred information. The answer unit can also analyze the user's past feedback and optimize the answer content. For example, the answer unit optimizes the answer content based on the user's past feedback. This allows the answer content to be customized to reflect the user's past feedback, thereby providing an appropriate answer to the user. Some or all of the above-described processing in the answer unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer unit can input the user's past feedback to a generation AI and have the generation AI customize the answer content.

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

[0060] The generation unit can customize the AR display content by referring to the user's past purchase history. For example, the generation unit can prioritize displaying information related to products the user has previously purchased. The generation unit can also analyze the user's purchase history and display promotion information for related products. Furthermore, the generation unit can prioritize displaying information about products in which the user has shown interest in the past. In this way, by customizing the AR display content by referring to the user's past purchase history, it is possible to provide the user with appropriate information.

[0061] The analysis unit can track the user's gaze and adjust the accuracy of image analysis based on the gaze movement. For example, if the user is gazing at a specific object, the analysis unit will prioritize analyzing information about that object. Also, if the user's gaze moves frequently, the analysis unit can analyze a wide range of information. Furthermore, if the user's gaze is focused on a specific area, the analysis unit can analyze information related to that area in detail. This allows the accuracy of image analysis to be adjusted based on the user's gaze movement, making it possible to provide the user with appropriate information.

[0062] The generation unit can adjust the AR display content by referring to the user's current weather information. For example, the generation unit can prioritize displaying information about indoor tourist spots and stores when it is raining. Also, the generation unit can prioritize displaying information about outdoor tourist spots and events when it is sunny. Furthermore, on snowy days, the generation unit can display information about snow-related activities and events. By adjusting the AR display content by referring to the user's current weather information, it is possible to provide the user with appropriate information.

[0063] The analysis unit can analyze the user's past behavioral patterns and optimize the analysis algorithm. For example, the analysis unit can reflect data on places the user has visited in the past in the current analysis. It can also analyze the user's past behavioral patterns and adjust the analysis algorithm. It can also compare past analysis results with current analysis results and improve the algorithm. In this way, the analysis accuracy can be improved by optimizing the analysis algorithm with reference to the user's past behavioral patterns.

[0064] The generation unit can customize the AR display content based on the user's current location information. For example, if the user is at a tourist spot, information related to that location can be displayed with priority. If the user is at a shopping mall, information about stores can be displayed with priority. Furthermore, if the user is at an airport, information about boarding gates and facilities can be displayed with priority. In this way, by customizing the AR display content taking into account the user's current location information, it is possible to provide the user with appropriate information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The analysis unit analyzes the image captured by the smartphone camera. The analysis unit uses an image recognition algorithm to analyze the image and understand the surrounding environment. It also uses object recognition technology to identify objects in the image and scene analysis technology to understand the scene in the image. For example, it identifies buildings and landmarks in the image and uses that information to understand the surrounding environment. Step 2: The generation unit generates AR based on the information analyzed by the analysis unit. The generation unit generates a 3D model and displays it as AR. It also uses overlay technology to overlay information on real-world images, generating AR in real time and providing it to the user. For example, it generates information about tourist spots as a 3D model and displays it on the user's smartphone. Step 3: The reception unit receives a voice query from the user. The reception unit converts the user's voice into text using voice recognition technology and removes noise from the voice data using noise filtering technology. Furthermore, the reception unit supports multiple languages ​​and can receive voice queries in different languages. For example, voice queries in English, Japanese, Chinese, etc. can be received. Step 4: The answering unit quickly answers the questions received by the reception unit. The answering unit uses AI to generate optimal answers to questions and provide relevant information based on the content of the question. Furthermore, the answering unit generates answers in real time and provides them to the user. For example, in response to a question about a tourist destination, it provides information about the history of the place and its tourist attractions.

[0067] (Example 2) A navigation system according to an embodiment of the present invention analyzes images captured by a smartphone camera, generates AR, and instantly answers voice questions. The navigation system analyzes images captured by the smartphone camera, understands the surrounding environment, and generates AR based on the analyzed information. Furthermore, the navigation system accepts voice questions from users and instantly answers them. For example, when a user holds a smartphone over a tourist spot, AI provides real-time information about the location's history and tourist attractions. This allows for visually easy-to-understand information to be displayed via AR. Furthermore, when a user voice-questions such as "I want to go to X," the AI ​​instantly responds. For example, in response to questions such as "What are the recommended places? Where are the good restaurants? What are the local specialties?", the AI ​​responds in a foreign language and provides easy-to-understand navigation using AR and voice. This allows inbound tourists to enjoy sightseeing comfortably without experiencing language barriers. Furthermore, navigation systems can be used not only in tourist spots but also in shopping malls, airports, and other locations. For example, when a user holds a smartphone over a shopping mall, AI provides store information and sale information, allowing the user to enjoy shopping efficiently. At airports, it also provides real-time information about boarding gates and facilities, supporting smooth travel. By combining image recognition and AR navigation, the navigation system provides inbound tourists with visually easy-to-understand information in real time, helping to ensure a comfortable sightseeing experience. For example, when used at tourist spots, users can obtain detailed information about a location simply by holding their smartphone over the screen. At shopping malls and airports, users can also obtain information efficiently, supporting smooth travel and shopping.

[0068] A navigation system according to an embodiment includes an analysis unit, a generation unit, a reception unit, and a response unit. The analysis unit analyzes an image acquired from a smartphone camera. For example, the analysis unit analyzes the image using an image recognition algorithm to understand the surrounding environment. The analysis unit can also identify objects in the image using object recognition technology. The analysis unit can also understand scenes in the image using scene analysis technology. For example, the analysis unit can identify buildings and landmarks in the image and understand the surrounding environment based on that information. The generation unit generates AR based on the information analyzed by the analysis unit. For example, the generation unit generates a 3D model and displays it as AR. The generation unit can also use overlay technology to display information over a real image. The generation unit can also generate AR in real time and provide it to the user. For example, the generation unit generates information about tourist spots as a 3D model and displays it on the user's smartphone. The reception unit receives a voice question from the user. For example, the reception unit converts the user's voice into text using voice recognition technology. The reception unit can also remove noise from the voice data using noise filtering technology. Furthermore, the reception unit can support multiple languages ​​and accept voice questions in different languages. For example, the reception unit accepts voice questions in English, Japanese, Chinese, and the like. The answering unit quickly answers the questions accepted by the reception unit. The answering unit can generate optimal answers to the questions using, for example, AI. The answering unit can also provide relevant information based on the content of the question. Furthermore, the answering unit can generate answers in real time and provide them to the user. For example, in response to a question about a tourist destination, the answering unit can provide information about the history of the location and tourist attractions. This allows the navigation system according to the embodiment to analyze images acquired from a smartphone camera, generate AR, and instantly answer voice questions. This allows inbound tourists to receive visually easy-to-understand information in real time.

[0069] The analysis unit can analyze images acquired from the smartphone camera and understand the surrounding environment. The analysis unit can analyze images using, for example, an image recognition algorithm to understand the surrounding environment. For example, the analysis unit can identify objects in the image using object recognition technology. The analysis unit can also understand scenes in the image using scene analysis technology. For example, the analysis unit can identify buildings and landmarks in the image and understand the surrounding environment based on that information. In this way, by analyzing images acquired from the smartphone camera and understanding the surrounding environment, appropriate information can be provided to the user. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input images acquired from the smartphone camera to a generation AI and have the generation AI perform image analysis.

[0070] The generation unit can generate AR based on the information analyzed by the analysis unit. The generation unit, for example, generates a 3D model and displays it as AR. For example, the generation unit generates information about tourist spots as a 3D model and displays it on the user's smartphone. The generation unit can also use overlay technology to display information over a real image. For example, the generation unit displays information about tourist spots overlaid on a real image. Furthermore, the generation unit can generate AR in real time and provide it to the user. For example, the generation unit generates information about tourist spots in real time and displays it on the user's smartphone. By generating AR based on the analyzed information, it is possible to provide the user with visually easy-to-understand information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the information analyzed by the analysis unit into a generation AI and have the generation AI generate AR.

[0071] The reception unit can accept a user's voice question. The reception unit can convert the user's voice into text using, for example, voice recognition technology. For example, the reception unit can convert the user's voice into text in real time using voice recognition software. The reception unit can also remove noise from the voice data using noise filtering technology. For example, the reception unit can remove background noise from the voice data using noise canceling technology. The reception unit can also support multiple languages ​​and accept voice questions in different languages. For example, the reception unit can accept voice questions in English, Japanese, Chinese, etc. This allows the user to easily ask a question by accepting the user's voice question. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's voice data into a generation AI and have the generation AI perform voice recognition.

[0072] The answering unit can quickly answer questions received by the receiving unit. The answering unit generates optimal answers to questions, for example, using AI. For example, the answering unit generates answers to user questions using text generation AI (e.g., LLM). The answering unit can also provide related information based on the content of the question. For example, in response to a question about a tourist destination, the answering unit provides information about the history of the location and tourist attractions. Furthermore, the answering unit can generate answers in real time and provide them to the user. For example, the answering unit can instantly generate answers to user questions and display them on the user's smartphone. This allows for the user to be provided with information quickly by instantly answering questions received by the receiving unit. Some or all of the above-described processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input questions received by the receiving unit into a generation AI and have the generation AI generate answers.

[0073] The generation unit can provide information on tourist attractions, shopping malls, and airports in real time. The generation unit, for example, provides information on tourist attractions in real time. For example, the generation unit collects the latest information on tourist attractions and displays it on the user's smartphone. The generation unit can also provide information on shopping malls in real time. For example, the generation unit collects information on stores and sales in shopping malls and provides it to the user. The generation unit can also provide information on airports in real time. For example, the generation unit collects information on boarding gates and facilities in airports and provides it to the user. This allows the user to efficiently obtain information by providing information on tourist attractions, shopping malls, airports, etc. in real time. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input information on tourist attractions, shopping malls, and airports into the generation AI and cause the generation AI to provide the information.

[0074] The analysis unit can estimate the user's emotions and adjust the accuracy of image analysis based on the estimated user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit records the user's voice and estimates the emotions using a voice analysis algorithm. The analysis unit can also adjust the accuracy of image analysis based on the estimated user's emotions. For example, if the user is excited, the accuracy of image analysis can be increased to provide detailed information. If the user is relaxed, the accuracy of image analysis can be kept normal and the minimum necessary information can be provided. If the user is tired, the accuracy of image analysis can be reduced to provide concise information. By adjusting the accuracy of image analysis based on the user's emotions, appropriate information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the accuracy of image analysis.

[0075] When analyzing an image, the analysis unit can adjust the analysis algorithm by referring to past analysis data. For example, the analysis unit optimizes the analysis algorithm by referring to data previously analyzed for the same location. For example, the analysis unit retrieves past analysis data from a database and reflects it in the current analysis. The analysis unit can also adjust the analysis algorithm based on the user's past behavior history. For example, the analysis unit analyzes the user's past behavior patterns and optimizes the analysis algorithm. Furthermore, the analysis unit can compare past analysis results with current analysis results and improve the algorithm. For example, the analysis unit compares past analysis results with current analysis results and makes adjustments to improve accuracy. By optimizing the analysis algorithm by referring to past analysis data, analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0076] During image analysis, the analysis unit can correct the analysis results based on the user's current location information. The analysis unit corrects the analysis results based on, for example, the user's current location information. For example, the analysis unit uses GPS data to identify the user's current location and corrects the analysis results based on that location. Furthermore, when the user is moving, the analysis unit can update the location information in real time and correct the analysis results. For example, the analysis unit acquires the user's location information in real time and corrects the analysis results as the user moves. Furthermore, when the user is in a specific location, the analysis unit can prioritize analyzing information related to that location. For example, when the user is in a tourist spot, the analysis unit prioritizes analyzing information related to that location. This allows for correcting the analysis results taking the user's current location information into consideration, thereby providing more accurate information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's location information into the generation AI and have the generation AI correct the analysis results.

[0077] The analysis unit can perform correction processing to accommodate different light and weather conditions during image analysis. For example, when light is strong, the analysis unit performs analysis by adjusting the brightness of the image. For example, the analysis unit adjusts the brightness of the image using exposure correction technology. The analysis unit can also adjust the contrast of the image when it is raining. For example, the analysis unit adjusts the contrast of the image when it is raining using contrast correction technology. The analysis unit can also remove noise from the image when it is nighttime when performing analysis. For example, the analysis unit removes noise from the image when it is nighttime using noise removal technology. This improves the accuracy of the analysis by performing correction processing to accommodate different light and weather conditions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to perform correction processing according to the light and weather conditions.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit records the user's voice and estimates the emotions using a voice analysis algorithm. The analysis unit can also adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, detailed analysis results can be displayed. If the user is relaxed, concise analysis results can be displayed. If the user is tired, analysis results that focus on the main points can be displayed. In this way, by adjusting the display method of the analysis results based on the user's emotions, appropriate information can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI adjust the display method of the analysis results.

[0079] When analyzing images, the analysis unit can customize the analysis results by referring to the user's past behavioral history. The analysis unit customizes the analysis results, for example, based on data on places the user has visited in the past. For example, the analysis unit retrieves the user's past behavioral history from a database and reflects it in the current analysis. The analysis unit can also analyze the user's past behavioral patterns and adjust the analysis results. For example, the analysis unit analyzes the user's past behavioral patterns and reflects it in the current analysis. Furthermore, the analysis unit can prioritize analysis of information in which the user has shown interest in the past. For example, the analysis unit prioritizes analysis of information about tourist spots in which the user has shown interest in the past. This allows the analysis results to be customized by referring to the user's past behavioral history, thereby providing the user with appropriate information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past behavioral history into a generation AI and have the generation AI customize the analysis results.

[0080] When analyzing images, the analysis unit can select the optimal analysis method by taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit selects the optimal analysis method for the smartphone. For example, the analysis unit selects the optimal analysis method based on the smartphone's performance and screen size. Furthermore, if the user is using a tablet, the analysis unit can also select the optimal analysis method for the tablet. For example, the analysis unit selects the optimal analysis method based on the tablet's performance and screen size. Furthermore, if the user is using a smartwatch, the analysis unit can also select the optimal analysis method for the smartwatch. For example, the analysis unit selects the optimal analysis method based on the smartwatch's performance and screen size. This improves analysis accuracy by selecting the optimal analysis method by taking into account the user's device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's device information into the generation AI and cause the generation AI to select the optimal analysis method.

[0081] The analysis unit can analyze the user's social media activity during image analysis and provide related information. The analysis unit can provide, for example, information about places where the user has checked in on social media. For example, the analysis unit can obtain the user's social media check-in data and provide information about the places. The analysis unit can also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the analysis unit can analyze the user's posts and provide information about related tourist spots and stores. Furthermore, the analysis unit can provide information about related places and events based on the activities of the user's friends on social media. For example, the analysis unit can analyze the check-in data and posts of the user's friends and provide information about related places and events. By analyzing the user's social media activity and providing related information, appropriate information can be provided to the user. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's social media data into a generation AI and cause the generation AI to provide related information.

[0082] The generation unit can estimate the user's emotions and adjust the content of the AR display based on the estimated user emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit records the user's voice and estimates the emotions using a voice analysis algorithm. The generation unit can also adjust the content of the AR display based on the estimated user emotions. For example, if the user is excited, a visually stimulating AR display can be provided. If the user is relaxed, a calm AR display can be provided. If the user is tired, a simple and highly visible AR display can be provided. This allows appropriate information to be provided to the user by adjusting the content of the AR display based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data to the generation AI and cause the generation AI to adjust the content of the AR display.

[0083] When generating the AR image, the generation unit can customize the display content based on the user's current interests. The generation unit, for example, incorporates tourist spots that the user is interested in into the AR display. For example, the generation unit displays information about tourist spots based on the user's interests. The generation unit can also incorporate stores that the user frequently visits into the AR display. For example, the generation unit displays store information based on the user's visit history. Furthermore, the generation unit can incorporate places that the user may be interested in into the AR display based on the user's past search history. For example, the generation unit analyzes the user's search history and displays information about related places. This allows the display content to be customized based on the user's current interests, thereby providing the user with appropriate information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input the user's interest data into the generation AI and have the generation AI customize the display content.

[0084] When generating the AR, the generation unit can optimize the display content by referring to the user's past usage history. The generation unit optimizes the AR display content, for example, based on data on places the user has visited in the past. For example, the generation unit retrieves the user's past usage history from a database and reflects it in the current display content. The generation unit can also analyze the user's past behavioral patterns and adjust the AR display content. For example, the generation unit analyzes the user's past behavioral patterns and reflects it in the current display content. Furthermore, the generation unit can prioritize incorporating information that the user has shown interest in in the AR display. For example, the generation unit prioritizes displaying information about tourist spots that the user has shown interest in in the past. This allows the display content to be optimized by referring to the user's past usage history, thereby providing the user with appropriate information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past usage history into the generation AI and cause the generation AI to optimize the display content.

[0085] The generation unit can make adjustments to support different devices and platforms when generating AR. The generation unit makes adjustments to support different devices, such as smartphones, tablets, and smartwatches. For example, the generation unit provides optimal display content based on the device's performance and screen size. The generation unit can also make adjustments to support different platforms, such as iOS and Android. For example, the generation unit optimizes the display content for each platform and provides appropriate display content to the user. The generation unit can also adjust the AR display content according to the device's screen size and resolution. For example, the generation unit optimizes the display content based on the device's screen size. This makes it possible to provide appropriate information to the user by making adjustments to support different devices and platforms. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input device and platform information into the generation AI and have the generation AI perform the adjustments.

[0086] The generation unit can estimate the user's emotions and determine the priority of AR display based on the estimated user emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit records the user's voice and estimates the emotions using a voice analysis algorithm. The generation unit can also determine the priority of AR display based on the estimated user emotions. For example, if the user is excited, visually stimulating information can be displayed preferentially. If the user is relaxed, calming information can be displayed preferentially. If the user is tired, concise and highly visible information can be displayed preferentially. In this way, by determining the priority of AR display based on the user's emotions, appropriate information can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data to the generation AI and have the generation AI determine the priority of AR display.

[0087] When generating AR, the generation unit can display highly relevant information based on the user's geographical location information. The generation unit, for example, prioritizes displaying information about tourist spots close to the user's current location. For example, the generation unit can use GPS data to identify the user's current location and display information about tourist spots nearby. The generation unit can also prioritize displaying information about stores close to the user's current location. For example, the generation unit can display information about nearby stores based on the user's current location. The generation unit can also prioritize displaying information about events close to the user's current location. For example, the generation unit can display information about nearby events based on the user's current location. This allows the user to be provided with appropriate information by displaying highly relevant information taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to display related information.

[0088] The generation unit can analyze the user's social media activity and display related information when generating AR. The generation unit, for example, displays information about places where the user has checked in on social media. For example, the generation unit obtains the user's social media check-in data and displays information about the places. The generation unit can also analyze the user's social media posts and display information about related tourist spots and stores. For example, the generation unit analyzes the user's posts and displays information about related tourist spots and stores. The generation unit can also display information about related places and events based on the activities of the user's friends on social media. For example, the generation unit analyzes the check-in data and posts of the user's friends and displays information about related places and events. This makes it possible to provide the user with appropriate information by analyzing the user's social media activity and displaying related information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's social media data into the generation AI and cause the generation AI to display related information.

[0089] When generating AR, the generation unit can customize the display content by reflecting the user's past feedback. The generation unit customizes the AR display content based on, for example, feedback provided by the user in the past. For example, the generation unit retrieves the user's past feedback from a database and reflects it in the current display content. The generation unit can also prioritize displaying information preferred by the user based on the user's past feedback. For example, the generation unit analyzes the user's past feedback and prioritizes displaying preferred information. The generation unit can also analyze the user's past feedback and optimize the display content. For example, the generation unit optimizes the display content based on the user's past feedback. In this way, appropriate information can be provided to the user by customizing the display content by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the display content.

[0090] The reception unit can estimate the user's emotion and adjust the method of receiving voice questions based on the estimated user's emotion. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis algorithm. The reception unit can also adjust the method of receiving voice questions based on the estimated user's emotion. For example, if the user is excited, a method of receiving a concise voice question can be provided. If the user is relaxed, a method of receiving a detailed voice question can be provided. If the user is tired, a method of receiving a simple voice question can be provided. By adjusting the method of receiving voice questions based on the user's emotion, it is possible to provide the user with an appropriate question reception. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to adjust the method of receiving voice questions.

[0091] When receiving a voice question, the reception unit can select the optimal reception method by referring to the user's past question history. The reception unit selects the optimal reception method, for example, based on questions frequently asked by the user in the past. For example, the reception unit retrieves the user's past question history from a database and reflects the retrieved question history in the current reception method. The reception unit can also prioritize reception of related questions from the user's past question history. For example, the reception unit analyzes the user's past question history and prioritizes reception of related questions. The reception unit can also analyze the user's past question history and suggest the optimal reception method. For example, the reception unit suggests the optimal reception method based on the user's past question history. This allows the user to receive an appropriate question by selecting the optimal reception method by referring to the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past question history into a generation AI and have the generation AI select the optimal reception method.

[0092] When receiving a voice question, the reception unit can customize the reception method based on the user's current situation and environment. For example, when the user is in a noisy environment, the reception unit receives the voice question using a noise canceling function. For example, the reception unit uses noise canceling technology to remove background noise from the voice data. The reception unit can also provide a normal voice question reception method when the user is in a quiet environment. For example, the reception unit receives a voice question using normal voice recognition technology in a quiet environment. Furthermore, the reception unit can also provide a simple voice question reception method when the user is on the move. For example, the reception unit provides an interface for receiving simple voice questions while the user is on the move. This allows the reception method to be customized based on the user's current situation and environment, thereby providing the user with an appropriate question reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's current situation and environmental data into a generation AI and have the generation AI customize the reception method.

[0093] The reception unit can adjust to accommodate different languages ​​or dialects when receiving a voice question. For example, when a user asks a question in a different language, the reception unit provides a reception method corresponding to that language. For example, the reception unit uses a language model to recognize a voice question in a different language and provides a corresponding reception method. Furthermore, when a user asks a question in a specific dialect, the reception unit can also provide a reception method corresponding to that dialect. For example, the reception unit uses dialect recognition technology to recognize a voice question in a specific dialect and provide a corresponding reception method. Furthermore, the reception unit can provide a language switching function when a user speaks multiple languages. For example, the reception unit provides a function to automatically switch languages ​​when a user asks a question in multiple languages. This makes it possible to adjust to accommodate different languages ​​or dialects and provide an appropriate question reception service for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data in different languages ​​or dialects to a generation AI and have the generation AI adjust the corresponding reception method.

[0094] The reception unit can estimate the user's emotions and prioritize voice questions based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit records the user's voice and estimates the emotions using a voice analysis algorithm. The reception unit can also prioritize voice questions based on the estimated user emotions. For example, if the user is excited, it can prioritize urgent questions. If the user is relaxed, it can prioritize general questions. If the user is tired, it can prioritize simple questions. By prioritizing voice questions based on the user's emotions, it is possible to provide the user with appropriate question reception. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and have the generation AI determine the priority of voice questions.

[0095] When receiving a voice question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a tourist spot, the reception unit prioritizes receiving questions related to that location. For example, the reception unit uses GPS data to identify the user's current location and prioritizes receiving questions related to that location. Furthermore, if the user is in a shopping mall, the reception unit can prioritize receiving questions about stores. For example, the reception unit prioritizes receiving questions about stores in the shopping mall based on the user's current location. Furthermore, if the user is in an airport, the reception unit can prioritize receiving questions about boarding gates and facilities. For example, the reception unit prioritizes receiving questions about boarding gates and facilities in the airport based on the user's current location. This prioritizes receiving highly relevant questions by taking into account the user's geographical location information, thereby providing the user with appropriate question reception. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant questions.

[0096] When receiving a voice question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit prioritizes receiving questions related to places where the user has checked in on social media. For example, the reception unit acquires the user's social media check-in data and prioritizes receiving questions related to those places. The reception unit can also analyze the user's social media posts and receive related questions. For example, the reception unit analyzes the user's posts and receives related questions. Furthermore, the reception unit can receive related questions based on the activities of the user's friends on social media. For example, the reception unit analyzes the check-in data and posts of the user's friends and receives related questions. In this way, by analyzing the user's social media activity and receiving related questions, it is possible to provide the user with appropriate question reception. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related questions.

[0097] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a voice question. The reception unit customizes the reception method for voice questions, for example, based on feedback provided by the user in the past. For example, the reception unit retrieves the user's past feedback from a database and reflects the feedback in the current reception method. The reception unit can also prioritize providing a reception method preferred by the user based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and prioritizes providing a preferred reception method. The reception unit can also analyze the user's past feedback and optimize the reception method. For example, the reception unit optimizes the reception method based on the user's past feedback. In this way, the reception method can be customized by reflecting the user's past feedback, thereby providing an appropriate question reception method for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the reception method.

[0098] The answering unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. The answering unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the answering unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The answering unit can also estimate the user's emotions using voice analysis technology. For example, the answering unit records the user's voice and estimates the emotions using a voice analysis algorithm. Furthermore, the answering unit can adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is excited, the answering unit can respond in a visually stimulating way. If the user is relaxed, the answering unit can respond in a calm way. If the user is tired, the answering unit can respond in a concise and highly visible way. In this way, by adjusting the way the answer is expressed based on the user's emotions, it is possible to provide an appropriate answer to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering section may be performed using, for example, AI, or may be performed without using AI. For example, the answering section may input user emotion data into the generating AI and have the generating AI adjust the way the answer is expressed.

[0099] When answering a question, the answering unit can provide an optimal answer by referring to the user's past question history. The answering unit provides an optimal answer, for example, based on questions the user has previously asked. For example, the answering unit retrieves the user's past question history from a database and reflects it in the current answer. The answering unit can also prioritize providing related information from the user's past question history. For example, the answering unit analyzes the user's past question history and prioritizes providing related information. The answering unit can also analyze the user's past question history and suggest an optimal answer. For example, the answering unit suggests an optimal answer based on the user's past question history. This makes it possible to provide an appropriate answer to the user by referring to the user's past question history. Some or all of the above-described processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input the user's past question history into a generation AI and cause the generation AI to provide an optimal answer.

[0100] When answering a question, the answer unit can customize the answer based on the user's current situation and environment. For example, if the user is at a tourist spot, the answer unit can provide information related to the location. For example, the answer unit can use GPS data to identify the user's current location and provide information related to the location. Furthermore, if the user is at a shopping mall, the answer unit can provide information about stores. For example, the answer unit can provide information about stores in the shopping mall based on the user's current location. Furthermore, if the user is at an airport, the answer unit can provide information about boarding gates and facilities. For example, the answer unit can provide information about boarding gates and facilities in the airport based on the user's current location. This allows the answer to be customized based on the user's current situation and environment, thereby providing an appropriate answer to the user. Some or all of the above-described processing in the answer unit may be performed using, or without, AI. For example, the answer unit can input the user's current situation and environmental data into a generation AI and have the generation AI customize the answer.

[0101] The answering unit can adjust the answer to accommodate different languages ​​or dialects. For example, when a user asks a question in a different language, the answering unit provides an answer corresponding to that language. For example, the answering unit uses a language model to recognize questions in different languages ​​and provide a corresponding answer. Furthermore, when a user asks a question in a specific dialect, the answering unit can also provide an answer corresponding to that dialect. For example, the answering unit uses dialect recognition technology to recognize questions in a specific dialect and provide a corresponding answer. Furthermore, the answering unit can provide a language switching function when a user uses multiple languages. For example, the answering unit provides a function to automatically switch languages ​​when a user asks a question in multiple languages. This makes it possible to provide an appropriate answer to the user by adjusting the answer to accommodate different languages ​​or dialects. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without AI. For example, the answering unit can input question data in different languages ​​or dialects to a generation AI and have the generation AI adjust the corresponding answer.

[0102] The answering unit can estimate the user's emotions and prioritize answers based on the estimated user's emotions. The answering unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the answering unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The answering unit can also estimate the user's emotions using voice analysis technology. For example, the answering unit can record the user's voice and estimate the emotions using a voice analysis algorithm. The answering unit can also prioritize answers based on the estimated user's emotions. For example, if the user is excited, answers to urgent questions can be provided preferentially. If the user is relaxed, answers to general questions can be provided preferentially. If the user is tired, answers to simple questions can be provided preferentially. By prioritizing answers based on the user's emotions, appropriate answers can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit may input user emotion data into the generating AI and have the generating AI determine the priority of answers.

[0103] When providing an answer, the answering unit can provide a highly relevant answer by taking into account the user's geographical location information. For example, if the user is in a tourist spot, the answering unit can prioritize providing information related to that location. For example, the answering unit can use GPS data to identify the user's current location and provide information related to that location. Furthermore, if the user is in a shopping mall, the answering unit can prioritize providing information about stores. For example, the answering unit can provide information about stores in the shopping mall based on the user's current location. Furthermore, if the user is in an airport, the answering unit can prioritize providing information about boarding gates and facilities. For example, the answering unit can provide information about boarding gates and facilities in the airport based on the user's current location. This allows the user to be provided with an appropriate answer by taking into account the user's geographical location information. Some or all of the above-described processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input the user's geographical location information into a generation AI and cause the generation AI to provide a highly relevant answer.

[0104] The answering unit can analyze the user's social media activity and provide related information when answering a question. The answering unit can provide, for example, information about places where the user has checked in on social media. For example, the answering unit can obtain the user's social media check-in data and provide information related to the places. The answering unit can also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the answering unit can analyze the user's posts and provide information about related tourist spots and stores. Furthermore, the answering unit can provide information about related places and events based on the activities of the user's friends on social media. For example, the answering unit can analyze the check-in data and posts of the user's friends and provide information about related places and events. This allows the user's social media activity to be analyzed and related information to be provided, thereby providing an appropriate answer to the user. Some or all of the above-described processing in the answering unit can be performed using, or without, AI. For example, the answering unit can input the user's social media data into a generation AI and have the generation AI provide related information.

[0105] When answering a question, the answer unit can customize the answer content by reflecting the user's past feedback. The answer unit customizes the answer content based on, for example, feedback provided by the user in the past. For example, the answer unit retrieves the user's past feedback from a database and reflects it in the current answer. The answer unit can also prioritize providing information that is preferred based on the user's past feedback. For example, the answer unit analyzes the user's past feedback and prioritizes providing the preferred information. The answer unit can also analyze the user's past feedback and optimize the answer content. For example, the answer unit optimizes the answer content based on the user's past feedback. This allows the answer content to be customized to reflect the user's past feedback, thereby providing an appropriate answer to the user. Some or all of the above-described processing in the answer unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer unit can input the user's past feedback to a generation AI and have the generation AI customize the answer content. === Hard Collateral 1-1 === Each of the multiple elements including the above-described analysis unit, generation unit, reception unit, and answering unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit acquires an image using the camera 42 of the smart device 14 and analyzes the image using the specific processing unit 290 of the data processing device 12. For example, the generation unit generates AR based on the information analyzed by the specific processing unit 290 of the data processing device 12 and displays it on the display 40A of the smart device 14. The reception unit receives a voice question from the user using the microphone 38B of the smart device 14 and analyzes the voice using the specific processing unit 290 of the data processing device 12. For example, the answering unit generates an optimal answer to the question using the specific processing unit 290 of the data processing device 12 and provides it to the user through the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, and answering unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit acquires an image using the camera 42 of the smart glasses 214 and analyzes the image using the specific processing unit 290 of the data processing device 12. For example, the generation unit generates AR based on information analyzed by the specific processing unit 290 of the data processing device 12 and displays it on the display of the smart glasses 214. The reception unit receives a voice question from the user using the microphone 238 of the smart glasses 214 and analyzes the voice using the specific processing unit 290 of the data processing device 12. For example, the answering unit generates an optimal answer to the question using the specific processing unit 290 of the data processing device 12 and provides it to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, and answering unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit acquires an image using the camera 42 of the headset type terminal 314 and analyzes the image using the specific processing unit 290 of the data processing device 12. For example, the generation unit generates AR based on the information analyzed by the specific processing unit 290 of the data processing device 12 and displays it on the display 343 of the headset type terminal 314. The reception unit receives a voice question from the user using the microphone 238 of the headset type terminal 314 and analyzes the voice using the specific processing unit 290 of the data processing device 12. For example, the answering unit generates an optimal answer to the question using the specific processing unit 290 of the data processing device 12 and provides it to the user through the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, reception unit, and answering unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit acquires an image using the camera 42 of the robot 414 and analyzes the image using the specific processing unit 290 of the data processing device 12. For example, the generation unit generates AR based on the information analyzed by the specific processing unit 290 of the data processing device 12 and displays it on the display of the robot 414. The reception unit receives a voice question from the user using the microphone 238 of the robot 414 and analyzes the voice using the specific processing unit 290 of the data processing device 12. For example, the answering unit generates an optimal answer to the question using the specific processing unit 290 of the data processing device 12 and provides it to the user through the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's current health condition and adjust the accuracy of image analysis based on the estimated health condition. For example, the analysis unit acquires vital data such as the user's heart rate and body temperature to estimate the health condition. Furthermore, if the user's health condition is poor, the analysis unit can increase the accuracy of image analysis and provide more detailed information. Furthermore, if the user's health condition is good, the analysis unit can maintain the accuracy of image analysis at normal levels and provide the minimum necessary information. In this way, by adjusting the accuracy of image analysis based on the user's health condition, appropriate information can be provided to the user.

[0108] The generation unit can customize the AR display content by referring to the user's past purchase history. For example, the generation unit can prioritize displaying information related to products the user has previously purchased. The generation unit can also analyze the user's purchase history and display promotion information for related products. Furthermore, the generation unit can prioritize displaying information about products in which the user has shown interest in the past. In this way, by customizing the AR display content by referring to the user's past purchase history, it is possible to provide the user with appropriate information.

[0109] When receiving a voice question from a user, the reception unit can analyze the tone and speed of the user's voice and estimate the urgency of the question. For example, if the user's voice is spoken in a high tone and at a fast speed, the reception unit can determine that the urgency is high and respond quickly. Alternatively, if the user's voice is spoken in a low tone and at a slow speed, the reception unit can determine that the urgency is low and respond normally. Furthermore, the priority of the question can be determined based on the tone and speed of the user's voice. In this way, by estimating the urgency of the question based on the tone and speed of the user's voice, it is possible to provide the user with an appropriate response.

[0110] The answering unit can estimate the user's current activity level and adjust the content of the answer based on the estimated activity level. For example, if the user is exercising, the answering unit can provide a concise and to-the-point answer. If the user is resting, the answering unit can provide an answer with detailed information. Furthermore, if the user is traveling, the answering unit can provide an answer that can be understood in a short time. In this way, by adjusting the content of the answer based on the user's current activity level, it is possible to provide the user with appropriate information.

[0111] The analysis unit can track the user's gaze and adjust the accuracy of image analysis based on the gaze movement. For example, if the user is gazing at a specific object, the analysis unit will prioritize analyzing information about that object. Also, if the user's gaze moves frequently, the analysis unit can analyze a wide range of information. Furthermore, if the user's gaze is focused on a specific area, the analysis unit can analyze information related to that area in detail. This allows the accuracy of image analysis to be adjusted based on the user's gaze movement, making it possible to provide the user with appropriate information.

[0112] The generation unit can adjust the AR display content by referring to the user's current weather information. For example, the generation unit can prioritize displaying information about indoor tourist spots and stores when it is raining. Also, the generation unit can prioritize displaying information about outdoor tourist spots and events when it is sunny. Furthermore, on snowy days, the generation unit can display information about snow-related activities and events. By adjusting the AR display content by referring to the user's current weather information, it is possible to provide the user with appropriate information.

[0113] The reception unit can estimate the user's emotions and adjust the method of receiving voice questions based on the estimated user's emotions. For example, if the user is excited, a simple method of receiving voice questions can be provided. If the user is relaxed, a detailed method of receiving voice questions can be provided. If the user is tired, a simple method of receiving voice questions can be provided. In this way, by adjusting the method of receiving voice questions based on the user's emotions, it is possible to provide the user with an appropriate question reception.

[0114] The analysis unit can analyze the user's past behavioral patterns and optimize the analysis algorithm. For example, the analysis unit can reflect data on places the user has visited in the past in the current analysis. It can also analyze the user's past behavioral patterns and adjust the analysis algorithm. It can also compare past analysis results with current analysis results and improve the algorithm. In this way, the analysis accuracy can be improved by optimizing the analysis algorithm with reference to the user's past behavioral patterns.

[0115] The generation unit can customize the AR display content based on the user's current location information. For example, if the user is at a tourist spot, information related to that location can be displayed with priority. If the user is at a shopping mall, information about stores can be displayed with priority. Furthermore, if the user is at an airport, information about boarding gates and facilities can be displayed with priority. In this way, by customizing the AR display content taking into account the user's current location information, it is possible to provide the user with appropriate information.

[0116] The answering unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is excited, the answering unit can respond in a visually stimulating way. If the user is relaxed, the answering unit can respond in a calm way. If the user is tired, the answering unit can respond in a concise and highly visible way. In this way, by adjusting the way the answer is expressed based on the user's emotions, it is possible to provide the user with an appropriate answer.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The analysis unit analyzes the image captured by the smartphone camera. The analysis unit uses an image recognition algorithm to analyze the image and understand the surrounding environment. It also uses object recognition technology to identify objects in the image and scene analysis technology to understand the scene in the image. For example, it identifies buildings and landmarks in the image and uses that information to understand the surrounding environment. Step 2: The generation unit generates AR based on the information analyzed by the analysis unit. The generation unit generates a 3D model and displays it as AR. It also uses overlay technology to overlay information on real-world images, generating AR in real time and providing it to the user. For example, it generates information about tourist spots as a 3D model and displays it on the user's smartphone. Step 3: The reception unit receives a voice query from the user. The reception unit converts the user's voice into text using voice recognition technology and removes noise from the voice data using noise filtering technology. Furthermore, the reception unit supports multiple languages ​​and can receive voice queries in different languages. For example, voice queries in English, Japanese, Chinese, etc. can be received. Step 4: The answering unit quickly answers the questions received by the reception unit. The answering unit uses AI to generate optimal answers to questions and provide relevant information based on the content of the question. Furthermore, the answering unit generates answers in real time and provides them to the user. For example, in response to a question about a tourist destination, it provides information about the history of the place and its tourist attractions.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes images acquired from the smartphone camera; a generation unit that generates AR based on the information analyzed by the analysis unit; a reception unit that receives voice questions from a user; a replying unit that promptly replies to the questions received by the receiving unit; Equipped with A system characterized by:

2. The analysis unit Analyzes images captured by a smartphone camera to understand the surrounding environment 2. The system of claim 1.

3. The generation unit Generate AR based on the information analyzed by the analysis unit 2. The system of claim 1.

4. The reception unit Accepts user voice questions 2. The system of claim 1.

5. The answering section To quickly respond to questions received by the reception unit 2. The system of claim 1.

6. The generation unit Providing real-time information on tourist spots, shopping malls, and airports 2. The system of claim 1.

7. The analysis unit Estimate the user's emotions and adjust the accuracy of image analysis based on the estimated user emotions.

2. The system of claim 1.

8. The analysis unit When analyzing images, refer to past analysis data to adjust the analysis algorithm.

2. The system of claim 1.

Images