System

Abstract

To provide a system that quickly and inexpensively collects and provides the latest city information.SOLUTION: A system according to the embodiment comprises a collection unit, a generation unit, an extraction unit and a provision unit. The collection unit collects image data from a camera installed in a vehicle. The generation unit analyzes the image data collected by the collection unit, and generates a street view. The extraction unit extracts the latest information about a city based on the street view generated by the generation unit. The provision unit provides the information extracted by the extraction unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to collect the latest city information quickly and inexpensively.

[0005] The system according to the embodiment aims to quickly and inexpensively collect and provide the latest city information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, an extraction unit, and a provision unit. The collection unit collects video data from a camera installed in a vehicle. The generation unit analyzes the video data collected by the collection unit and generates a street view. The extraction unit extracts the latest information about a city based on the street view generated by the generation unit. The provision unit provides the information extracted by the extraction unit. Effect of the Invention

[0007] The system according to the embodiment can quickly and inexpensively collect and provide the latest city information. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The local content generation system according to the embodiment of the present invention is a system in which a camera is installed in the vehicle of a delivery company, and the camera provides video data, and a generation AI analyzes the video, instantly generates a street view, and collects the latest information on the city (opening, closing, gasoline prices, etc.) and converts it into the strongest local content. This local content generation system installs a camera in the vehicle of a delivery company and collects video data from the camera installed in the vehicle. Next, the generation AI analyzes the collected video data and generates a street view. In addition, the generation AI extracts the latest information on the city from the video data and collects information such as opening and closing of stores and gasoline prices. This allows us to obtain local content that is competitive with other major companies at a low cost and quickly. Furthermore, it is expected that the delivery company will obtain funds to resolve the labor shortage by receiving a fee for providing information. For example, a camera is installed in the vehicle of the delivery company. This camera collects video data from the camera installed in the vehicle in real time. For example, when a delivery vehicle drives through the city, the camera takes pictures of the surroundings and collects the data. This video data is sent to the generation AI. Next, the generation AI analyzes the collected video data. The generation AI analyzes the video data and generates a street view. For example, it can analyze the exterior of the city's streets and buildings and display them as a street view. The generation AI also extracts the latest information about the city from the video data. For example, it can collect information such as newly opened and closed stores, and gas station fees. The collected information is provided as the strongest local content. For example, when a user searches for information about a specific area, it can provide information such as the latest street view, opening and closing information, and gasoline prices. This allows users to obtain the latest local information in real time. In addition, delivery companies are expected to obtain funds to resolve labor shortages by receiving compensation for providing information. For example, delivery companies can receive compensation by providing the video data they have collected. This compensation can be used as operating funds for delivery companies and can be used to resolve labor shortages.This system allows us to obtain local content that is competitive with other major companies at low cost and quickly, and the delivery companies can also enjoy economic benefits by receiving payment for providing information. This allows the local content generation system to quickly provide the latest local information.

[0029] The local content generation system according to the embodiment includes a collection unit, a generation unit, an extraction unit, and a provision unit. The collection unit collects video data from a camera installed in a vehicle. For example, the collection unit uses a camera installed in a delivery company's vehicle to collect video data from the camera installed in the vehicle in real time. The collection unit can also collect wide-range video by installing a 360-degree camera on the roof of the vehicle. For example, the collection unit can collect wide-range video data by adjusting the viewing angle and shooting distance of the camera. The generation unit uses a generation AI to analyze the video data collected by the collection unit and generate a street view. The generation unit uses, for example, image recognition technology or machine learning algorithms to analyze the exterior of a building or store. The generation unit can use, for example, object detection algorithms or face recognition technology to analyze the exterior of a building or store. The generation unit can use, for example, machine learning algorithms such as deep learning or support vector machines to analyze the exterior of a building or store. The extraction unit extracts the latest information on the city based on the street view generated by the generation unit. The extraction unit collects, for example, store opening and closing information, gasoline prices, traffic information, and event information. The extraction unit can extract the latest information on the city using, for example, data mining technology and filtering algorithms. The extraction unit can collect store opening and closing information by analyzing, for example, official announcements of stores and posts on SNS. The extraction unit can collect gasoline prices by analyzing, for example, price displays at gas stations and online databases. The provision unit provides the information extracted by the extraction unit. The provision unit provides, for example, the collected information as the strongest local content. The provision unit can provide information such as the latest street view, store opening and closing information, and gasoline prices when a user searches for information on a specific area. The provision unit can provide, for example, the collected information through a website or a mobile application. The provision unit pays, for example, a delivery company for the information provision. The provision unit can receive a reward by providing, for example, the video data collected by the delivery company.As a result, the local content generation system according to the embodiment can quickly provide the latest local information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and provide the information using an algorithm for providing optimal information.

[0030] The collection unit collects video data from a camera installed in the vehicle. The collection unit uses, for example, a camera installed in the delivery company's vehicle to collect video data from the camera installed in the vehicle in real time. Specifically, a high-resolution camera attached to the front or side of the delivery vehicle is used to continuously capture the scenery while driving. This allows the road conditions, surrounding buildings, and the appearance of stores to be recorded in detail. The collection unit can also collect wide-range video by installing, for example, a 360-degree camera on the roof of the vehicle. Since a 360-degree camera can simultaneously capture video in all directions, it is possible to grasp the entire surrounding situation, not limited to a specific direction. This allows information to be collected not only from a specific point, but also from a wide range of areas. The collection unit can collect wide-range video data by, for example, adjusting the viewing angle and shooting distance of the camera. By adjusting the viewing angle, it is possible to focus on shooting a specific area, and by adjusting the shooting distance, it is possible to clearly record distant buildings and signs. This allows the collection unit to efficiently collect high-quality video data and provide information necessary for subsequent analysis and generation. In addition, the collection unit can upload the collected video data to a cloud server in real time and store it in a central database, making the collected data immediately available to the analysis and generation units for rapid processing.

[0031] The generating unit uses the generating AI to analyze the video data collected by the collecting unit and generate a street view. The generating unit uses, for example, image recognition technology and machine learning algorithms to analyze the exterior of buildings and stores. Specifically, the generating AI receives the collected video data as input and identifies the exterior of buildings and stores using a deep learning model. For example, a convolutional neural network (CNN) is used to extract features in the video and analyze the shape, color, texture, and the like of the building. The generating unit can analyze the exterior of buildings and stores using, for example, an object detection algorithm or face recognition technology. The object detection algorithm can identify a specific object in the video and determine its position and size. The face recognition technology can detect the face of a person in the video and identify the individual. This allows the generating unit to extract detailed information from the video data and provide the data necessary for generating a street view. The generating unit can analyze the exterior of buildings and stores using, for example, machine learning algorithms such as deep learning and support vector machines. Deep learning can train a model using a large amount of data and perform highly accurate analysis. The support vector machine is used for data classification and regression analysis, and can accurately identify the characteristics of buildings and stores. This allows the generation unit to generate a highly accurate street view and provide it to the user. Furthermore, the generation unit can update the generated street view in real time to reflect the latest information. For example, when a new building is constructed or a store closes, the street view can be instantly updated based on new video data from the collection unit. This allows the generation unit to always provide the latest information and meet the needs of the user.

[0032] The extraction unit extracts the latest information on the town based on the street view generated by the generation unit. The extraction unit collects, for example, store opening / closing information, gasoline prices, traffic information, and event information. Specifically, the extraction unit analyzes the generated street view and automatically extracts text information, signs, price displays, and the like in the video. For example, optical character recognition (OCR) technology can be used to read store signs and gas station price displays to obtain the latest information. The extraction unit can extract the latest information on the town using, for example, data mining technology and filtering algorithms. Data mining technology is a method of extracting useful information from large amounts of data, and filtering algorithms are a method of selecting data based on specific conditions. This allows the extraction unit to efficiently extract necessary information and provide it to the user. The extraction unit can collect opening / closing information by, for example, analyzing official store announcements and SNS posts. By analyzing SNS posts, the latest information posted by users can be obtained and updated in real time. The extraction unit can collect gasoline prices by, for example, analyzing gas station price displays and online databases. By utilizing the online database, a wide range of information can be collected quickly and the latest gasoline prices can be provided. This allows the extraction unit to quickly and accurately extract the latest information on the city and provide it to the user. Furthermore, the extraction unit can centrally manage the extracted information and link it with other systems and departments as necessary. For example, the extracted information can be stored in a cloud server and made accessible to the provision unit. This allows the extraction unit to extract information efficiently and effectively and improve the performance of the entire system.

[0033] The providing unit provides the information extracted by the extracting unit. The providing unit provides, for example, the collected information as the strongest local content. Specifically, when a user searches for information on a specific area, the providing unit can provide information such as the latest street view, opening and closing information, and gasoline prices. The providing unit can provide, for example, the collected information through a website or a mobile application. The website or mobile application provides an interface that is easily accessible to the user, allowing the user to quickly search for the necessary information. The providing unit, for example, pays a delivery company for providing the information. The delivery company can receive a reward by providing the collected video data. This allows the delivery company to obtain an incentive to continue the collection activity. The providing unit can, for example, input the collected information into an AI model and provide the information using an algorithm for providing optimal information. Specifically, the providing unit inputs the collected information into an AI model and provides optimal information based on the user's search history and location information. For example, when a user searches for gasoline prices in a specific area, the providing unit can provide the latest gasoline price information and further provide related information such as the location and business hours of nearby gas stations. This enables the providing unit to provide users with fast and accurate information, improving convenience. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information to be provided. For example, based on user evaluations and comments, the providing unit can review the content and display method of the information to be provided, and provide a more user-friendly service. This enables the providing unit to provide high-quality local content that meets the needs of users, improving the reliability and satisfaction of the entire system.

[0034] The collection unit can install a 360-degree camera on the roof of the car and collect wide-range video. The collection unit can install a 360-degree camera on the roof of the car, for example, and collect wide-range video. The collection unit can collect wide-range video data, for example, by adjusting the viewing angle of the camera. The collection unit can collect wide-range video data, for example, by adjusting the shooting distance of the camera. As a result, wide-range video data can be collected by using the 360-degree camera. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input video data acquired by the 360-degree camera to the generation AI and cause the generation AI to perform analysis of the wide-range video data.

[0035] The generation unit can analyze the exterior of a building or store using image recognition technology or a machine learning algorithm. The generation unit can analyze the exterior of a building or store using, for example, image recognition technology. The generation unit can analyze the exterior of a building or store using, for example, an object detection algorithm. The generation unit can analyze the exterior of a building or store using, for example, face recognition technology. The generation unit can analyze the exterior of a building or store using, for example, a machine learning algorithm. The generation unit can analyze the exterior of a building or store using, for example, deep learning. The generation unit can analyze the exterior of a building or store using, for example, a support vector machine. This makes it possible to accurately analyze the exterior of a building or store by using image recognition technology or a machine learning algorithm. A part or all of the above-mentioned 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 exterior data of a building or store to the generation AI and cause the generation AI to execute an analysis of the exterior.

[0036] The extraction unit can collect at least one of opening and closing information, gasoline prices, traffic information, and event information. The extraction unit, for example, collects opening and closing information. The extraction unit, for example, can analyze official announcements of stores and posts on SNS to collect opening and closing information. The extraction unit, for example, collects gasoline prices. The extraction unit, for example, can analyze price displays at gas stations and online databases to collect gasoline prices. The extraction unit, for example, collects traffic information. The extraction unit, for example, can analyze traffic sensors and GPS data to collect traffic information. The extraction unit, for example, collects event information. The extraction unit, for example, can analyze official websites and posts on SNS of event organizers to collect event information. By collecting various information, it is possible to provide the latest local content to users. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the collected information to the generation AI and cause the generation AI to execute the extraction of information.

[0037] The providing unit can provide the collected information as the most valuable local content. The providing unit, for example, provides the collected information as the strongest local content. The providing unit can provide information such as the latest street view, opening and closing information, and gasoline prices when a user searches for information on a specific area. The providing unit can provide the collected information through a website or a mobile application, for example. The providing unit can provide valuable information based on, for example, user feedback and the number of accesses. This makes it possible to provide valuable information to a user by providing the collected information as the strongest local content. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and provide the information using an algorithm for providing optimal information.

[0038] The providing unit can pay the delivery company a fee for providing the information. The providing unit, for example, pays the delivery company a fee for providing the information. The providing unit can receive a reward, for example, by providing the video data collected by the delivery company. The providing unit can pay a fee, for example, in the form of money, points, service usage rights, etc. This makes it possible to increase an incentive for providing the information by paying a fee to the delivery company. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and pay the fee using an algorithm for optimizing the payment of the fee.

[0039] The collection unit can automatically adjust the camera settings according to the weather or time of day at the time of collection. The collection unit, for example, automatically adjusts the camera settings according to the weather and time of day at the time of collection. For example, at night, the collection unit increases the sensitivity of the camera to collect bright images. For example, in rainy weather, the collection unit can automatically deploy a waterproof cover to protect the camera lens. For example, under strong sunlight during the day, the collection unit can adjust the exposure of the camera to collect images with appropriate brightness. This makes it possible to collect optimal image data by automatically adjusting the camera settings according to the weather and time of day. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on weather and time of day to the generation AI and cause the generation AI to automatically adjust the camera settings.

[0040] The collection unit can collect information on specific events, festivals, etc. preferentially at the time of collection. For example, the collection unit obtains information on local festivals and events in advance and preferentially collects images of the area. For example, the collection unit can collect information on newly opened stores and facilities and preferentially collect images of the surrounding area. For example, the collection unit can collect information on traffic congestion and accidents and preferentially collect images of the area. As a result, valuable information can be provided to users by preferentially collecting information on specific events and festivals. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on events and festivals to the generation AI and cause the generation AI to perform preferential collection.

[0041] The collection unit can select an optimal collection point based on the speed or route of the vehicle at the time of collection. For example, when the vehicle is traveling at a low speed, the collection unit selects a stop point to collect detailed video. For example, when the vehicle is traveling at a high speed, the collection unit can select a major intersection or landmark to collect a wide range of video. For example, the collection unit can preferentially collect video of a specific area based on the route of the vehicle. This allows efficient collection of video data by selecting an optimal collection point based on the speed and route of the vehicle. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the speed and route of the vehicle to the generation AI and cause the generation AI to select an optimal collection point.

[0042] The collection unit can collect other sensor data in combination at the time of collection. The collection unit can use, for example, a temperature sensor to preferentially collect images within a specific temperature range. The collection unit can use, for example, a humidity sensor to preferentially collect images of areas with high humidity. The collection unit can use, for example, a noise level sensor to preferentially collect images of quiet areas and busy areas. This allows more multifaceted information to be collected by using other sensor data in combination. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input other sensor data to the generation AI and cause the generation AI to execute the combination of the sensor data.

[0043] The generation unit can highlight changes when generating the image data by comparing it with past image data. The generation unit can highlight, for example, newly opened stores and closed stores. The generation unit can highlight changes such as road construction and the construction of new buildings. The generation unit can highlight, for example, seasonal changes and event status. This makes it possible to provide the user with the latest information by highlighting changes compared with past image data. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past image data and current image data to the generation AI and cause the generation AI to execute highlighting of changes.

[0044] The generation unit can automatically tag specific buildings and landmarks at the time of generation. The generation unit can automatically tag, for example, famous tourist spots and landmarks. The generation unit can automatically tag, for example, major commercial facilities and stores. The generation unit can automatically tag, for example, public facilities and transportation stations. In this way, by automatically tagging specific buildings and landmarks, it is possible to provide easy-to-understand information to users. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input data of buildings and landmarks to the generation AI and have the generation AI execute tagging.

[0045] The generation unit can integrate and display images from different viewpoints and angles at the time of generation. The generation unit, for example, integrates and displays images taken at the same location under different time periods or weather conditions. The generation unit can, for example, integrate images acquired from multiple cameras to generate a 360-degree panoramic view. The generation unit can, for example, integrate images taken by a drone or from a high place to generate a three-dimensional street view. This makes it possible to provide a more three-dimensional street view by integrating and displaying images from different viewpoints and angles. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input image data from different viewpoints and angles to the generation AI and cause the generation AI to execute image integration.

[0046] The generation unit can correct images at night or in bad weather at the time of generation and display them. The generation unit can, for example, correct images at night to make them brighter, thereby improving visibility. The generation unit can, for example, correct images in rainy weather to make them clearer, thereby improving visibility. The generation unit can, for example, correct images on foggy or snowy days to improve visibility. As a result, images at night or in bad weather are corrected and displayed, thereby improving visibility. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input image data at night or in bad weather to the generation AI and have the generation AI execute image correction.

[0047] The extraction unit can detect abnormal values ​​by comparing with past data at the time of extraction. For example, if gasoline prices fluctuate suddenly, the extraction unit extracts the information. For example, if a specific store suddenly closes, the extraction unit can extract the information. For example, if traffic volume is higher than normal, the extraction unit can extract the information. This makes it possible to quickly grasp abnormal changes by detecting abnormal values ​​by comparing with past data. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input past data and current data to the generation AI and cause the generation AI to detect abnormal values.

[0048] The extraction unit can preferentially extract information of a specific category at the time of extraction. For example, the extraction unit preferentially extracts information on new openings and closings of restaurants. For example, the extraction unit can preferentially extract price information of gas stations. For example, the extraction unit can preferentially extract traffic information and event information. In this way, valuable information can be provided to users by preferentially extracting information of a specific category. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input information of a specific category to the generation AI and cause the generation AI to perform preferential extraction.

[0049] The extraction unit can complement information in cooperation with other data sources at the time of extraction. The extraction unit can, for example, analyze posts on SNS and extract real-time event information. The extraction unit can, for example, analyze information on a news site and extract the latest opening and closing information. The extraction unit can, for example, complement gasoline prices and traffic information in cooperation with other data sources. This makes it possible to provide more multifaceted information by linking with other data sources. Some or all of the above-mentioned processing in the extraction unit can be performed, for example, using AI or without using AI. For example, the extraction unit can input information from other data sources to the generation AI and have the generation AI execute the complementation of the information.

[0050] The extraction unit can customize information based on the characteristics of each region at the time of extraction. For example, in a tourist destination, the extraction unit can preferentially extract tourist spots and event information. For example, in a residential area, the extraction unit can preferentially extract information on nearby stores and facilities. For example, in a commercial area, the extraction unit can preferentially extract newly opened stores and sale information. This allows more appropriate information to be provided by customizing information based on the characteristics of each region. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input characteristic data for each region to the generation AI and cause the generation AI to customize the information.

[0051] The providing unit can provide optimal information by referring to the user's past search history at the time of providing. For example, the providing unit can preferentially provide the latest information on an area where the user has searched in the past. For example, the providing unit can preferentially provide information on a category (e.g., restaurants, gas stations) where the user has searched in the past. For example, the providing unit can automatically suggest related information from the user's past search history. This makes it possible to provide more appropriate information by referring to the user's past search history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's search history data to the generating AI and cause the generating AI to provide optimal information.

[0052] The providing unit can automatically adjust the display format according to the user's device when providing. For example, when the user is using a smartphone, the providing unit provides a display format that matches the screen size. For example, when the user is using a tablet, the providing unit can provide a display format optimized for a large screen. For example, when the user is using a desktop, the providing unit can provide a display format including detailed information. This makes it possible to provide more appropriate information by automatically adjusting the display format according to the user's device. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generating AI and cause the generating AI to automatically adjust the display format.

[0053] The providing unit can provide optimal information by considering the geographical location information of the user at the time of providing. For example, the providing unit preferentially provides the latest information on the area where the user is currently located. For example, when the user is moving, the providing unit can provide information related to the destination. For example, when the user approaches a specific area, the providing unit can provide information on the area. This makes it possible to provide more appropriate information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide optimal information.

[0054] The providing unit can provide related information by analyzing the user's social media activity at the time of providing. The providing unit can provide, for example, information on places and events in which the user has shown interest on social media. The providing unit can provide related information based on, for example, the content of posts of accounts the user follows. The providing unit can provide, for example, the latest information on places where the user has checked in on social media. This makes it possible to provide more appropriate information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to provide related information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection unit can analyze the vehicle's driving data in real time and automatically select the optimal video collection route. For example, the collection unit can analyze information on traffic congestion and road construction and select a detour route. For example, the collection unit can analyze traffic volume data in a specific time period and select the most efficient collection route. For example, the collection unit can analyze weather data and select a safe route in bad weather. This allows the collection unit to use real-time driving data to efficiently and safely collect video data.

[0057] The generation unit can analyze the user's past search history and preferentially generate street views of areas that are likely to interest the user. For example, the generation unit can analyze data of areas previously searched by the user and generate the latest street views of the areas. For example, the generation unit can analyze data of places frequently visited by the user and generate detailed street views of the places. For example, the generation unit can analyze data of a specific category (e.g., restaurants, tourist spots) in which the user has shown interest and generate street views related to the category. In this way, the generation unit can provide a more personalized street view by utilizing the user's past behavior data.

[0058] The collection unit can collect other sensor data in combination. For example, a temperature sensor is used to preferentially collect images within a specific temperature range. The collection unit can use a humidity sensor to preferentially collect images of areas with high humidity. The collection unit can use a noise level sensor to preferentially collect images of quiet areas and busy areas. This allows more multifaceted information to be collected by using other sensor data in combination. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input other sensor data to the generation AI and cause the generation AI to perform the combination of the sensor data.

[0059] The generation unit can highlight changes when generating the image data by comparing it with past image data. For example, it highlights newly opened stores and closed stores. The generation unit can highlight changes such as road construction and the construction of new buildings. The generation unit can highlight seasonal changes and event statuses, for example. This makes it possible to provide the user with the latest information by highlighting changes compared with past image data. Some or all of the above-mentioned 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 past image data and current image data to the generation AI and cause the generation AI to execute highlighting of changes.

[0060] The extraction unit can complement information in cooperation with other data sources at the time of extraction. For example, it can analyze posts on SNS and extract real-time event information. The extraction unit can analyze information on news sites and extract the latest opening and closing information. The extraction unit can complement gasoline prices and traffic information in cooperation with other data sources. This makes it possible to provide more multifaceted information by linking with other data sources. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input information from other data sources to the generation AI and have the generation AI execute the complementation of the information.

[0061] The providing unit can provide optimal information by referring to the user's past search history at the time of providing. For example, the providing unit can provide the latest information on an area where the user has searched in the past with priority. For example, the providing unit can provide information on a category (e.g., restaurants, gas stations) where the user has searched in the past with priority. For example, the providing unit can automatically suggest related information from the user's past search history. This makes it possible to provide more appropriate information by referring to the user's past search history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's search history data to the generating AI and cause the generating AI to provide optimal information.

[0062] The collection unit can automatically adjust the camera settings according to the weather or time of day when collecting. For example, the collection unit increases the sensitivity of the camera at night to collect bright images. For example, the collection unit can automatically deploy a waterproof cover to protect the camera lens during rainy weather. For example, the collection unit can adjust the exposure of the camera to collect images with appropriate brightness under strong sunlight during the day. This allows optimal image data to be collected by automatically adjusting the camera settings according to the weather and time of day. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the weather and time of day to the generation AI and cause the generation AI to automatically adjust the camera settings.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The collection unit collects video data from a camera installed in a vehicle. For example, the collection unit uses a camera installed in a delivery company's vehicle to collect video data from the camera installed in the vehicle in real time. For example, the collection unit can also collect wide-range video by installing a 360-degree camera on the roof of the vehicle. For example, the collection unit can collect wide-range video data by adjusting the viewing angle and shooting distance of the camera. Step 2: The generation unit uses a generation AI to analyze the video data collected by the collection unit and generate a street view. The generation unit analyzes the exterior of a building or store using, for example, image recognition technology or machine learning algorithms. The generation unit can analyze the exterior of a building or store using, for example, object detection algorithms or face recognition technology. The generation unit can analyze the exterior of a building or store using, for example, machine learning algorithms such as deep learning or support vector machines. Step 3: The extraction unit extracts the latest information about the city based on the street view generated by the generation unit. The extraction unit collects, for example, store opening / closing information, gasoline prices, traffic information, and event information. The extraction unit can extract the latest information about the city using, for example, data mining techniques and filtering algorithms. The extraction unit can collect store opening / closing information by analyzing, for example, official store announcements and SNS posts. The extraction unit can collect gasoline prices by analyzing, for example, price displays at gas stations and online databases. Step 4: The providing unit provides the information extracted by the extracting unit. The providing unit provides the collected information as the strongest local content, for example. The providing unit can provide information such as the latest street view, opening and closing information, and gasoline prices when a user searches for information on a specific area. The providing unit can provide the collected information through a website or a mobile application, for example. The providing unit pays a fee for providing the information to a delivery company, for example. The providing unit can receive a reward by providing video data collected by the delivery company, for example. This allows the local content generation system according to the embodiment to quickly provide the latest local information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and provide the information using an algorithm for providing optimal information.

[0065] (Example 2) The local content generation system according to the embodiment of the present invention is a system in which a camera is installed in the vehicle of a delivery company, and the camera provides video data, and a generation AI analyzes the video, instantly generates a street view, and collects the latest information on the city (opening, closing, gasoline prices, etc.) and converts it into the strongest local content. This local content generation system installs a camera in the vehicle of a delivery company and collects video data from the camera installed in the vehicle. Next, the generation AI analyzes the collected video data and generates a street view. In addition, the generation AI extracts the latest information on the city from the video data and collects information such as opening and closing of stores and gasoline prices. This allows us to obtain local content that is competitive with other major companies at a low cost and quickly. Furthermore, it is expected that the delivery company will obtain funds to resolve the labor shortage by receiving a fee for providing information. For example, a camera is installed in the vehicle of the delivery company. This camera collects video data from the camera installed in the vehicle in real time. For example, when a delivery vehicle drives through the city, the camera takes pictures of the surroundings and collects the data. This video data is sent to the generation AI. Next, the generation AI analyzes the collected video data. The generation AI analyzes the video data and generates a street view. For example, it can analyze the exterior of the city's streets and buildings and display them as a street view. The generation AI also extracts the latest information about the city from the video data. For example, it can collect information such as newly opened and closed stores, and gas station fees. The collected information is provided as the strongest local content. For example, when a user searches for information about a specific area, it can provide information such as the latest street view, opening and closing information, and gasoline prices. This allows users to obtain the latest local information in real time. In addition, delivery companies are expected to obtain funds to resolve labor shortages by receiving compensation for providing information. For example, delivery companies can receive compensation by providing the video data they have collected. This compensation can be used as operating funds for delivery companies and can be used to resolve labor shortages.This system allows us to obtain local content that is competitive with other major companies at low cost and quickly, and the delivery companies can also enjoy economic benefits by receiving payment for providing information. This allows the local content generation system to quickly provide the latest local information.

[0066] The local content generation system according to the embodiment includes a collection unit, a generation unit, an extraction unit, and a provision unit. The collection unit collects video data from a camera installed in a vehicle. For example, the collection unit uses a camera installed in a delivery company's vehicle to collect video data from the camera installed in the vehicle in real time. The collection unit can also collect wide-range video by installing a 360-degree camera on the roof of the vehicle. For example, the collection unit can collect wide-range video data by adjusting the viewing angle and shooting distance of the camera. The generation unit uses a generation AI to analyze the video data collected by the collection unit and generate a street view. The generation unit uses, for example, image recognition technology or machine learning algorithms to analyze the exterior of a building or store. The generation unit can use, for example, object detection algorithms or face recognition technology to analyze the exterior of a building or store. The generation unit can use, for example, machine learning algorithms such as deep learning or support vector machines to analyze the exterior of a building or store. The extraction unit extracts the latest information on the city based on the street view generated by the generation unit. The extraction unit collects, for example, store opening and closing information, gasoline prices, traffic information, and event information. The extraction unit can extract the latest information on the city using, for example, data mining technology and filtering algorithms. The extraction unit can collect store opening and closing information by analyzing, for example, official announcements of stores and posts on SNS. The extraction unit can collect gasoline prices by analyzing, for example, price displays at gas stations and online databases. The provision unit provides the information extracted by the extraction unit. The provision unit provides, for example, the collected information as the strongest local content. The provision unit can provide information such as the latest street view, store opening and closing information, and gasoline prices when a user searches for information on a specific area. The provision unit can provide, for example, the collected information through a website or a mobile application. The provision unit pays, for example, a delivery company for the information provision. The provision unit can receive a reward by providing, for example, the video data collected by the delivery company.As a result, the local content generation system according to the embodiment can quickly provide the latest local information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and provide the information using an algorithm for providing optimal information.

[0067] The collection unit collects video data from a camera installed in the vehicle. The collection unit uses, for example, a camera installed in the delivery company's vehicle to collect video data from the camera installed in the vehicle in real time. Specifically, a high-resolution camera attached to the front or side of the delivery vehicle is used to continuously capture the scenery while driving. This allows the road conditions, surrounding buildings, and the appearance of stores to be recorded in detail. The collection unit can also collect wide-range video by installing, for example, a 360-degree camera on the roof of the vehicle. Since a 360-degree camera can simultaneously capture video in all directions, it is possible to grasp the entire surrounding situation, not limited to a specific direction. This allows information to be collected not only from a specific point, but also from a wide range of areas. The collection unit can collect wide-range video data by, for example, adjusting the viewing angle and shooting distance of the camera. By adjusting the viewing angle, it is possible to focus on shooting a specific area, and by adjusting the shooting distance, it is possible to clearly record distant buildings and signs. This allows the collection unit to efficiently collect high-quality video data and provide information necessary for subsequent analysis and generation. In addition, the collection unit can upload the collected video data to a cloud server in real time and store it in a central database, making the collected data immediately available to the analysis and generation units for rapid processing.

[0068] The generating unit uses the generating AI to analyze the video data collected by the collecting unit and generate a street view. The generating unit uses, for example, image recognition technology and machine learning algorithms to analyze the exterior of buildings and stores. Specifically, the generating AI receives the collected video data as input and identifies the exterior of buildings and stores using a deep learning model. For example, a convolutional neural network (CNN) is used to extract features in the video and analyze the shape, color, texture, and the like of the building. The generating unit can analyze the exterior of buildings and stores using, for example, an object detection algorithm or face recognition technology. The object detection algorithm can identify a specific object in the video and determine its position and size. The face recognition technology can detect the face of a person in the video and identify the individual. This allows the generating unit to extract detailed information from the video data and provide the data necessary for generating a street view. The generating unit can analyze the exterior of buildings and stores using, for example, machine learning algorithms such as deep learning and support vector machines. Deep learning can train a model using a large amount of data and perform highly accurate analysis. The support vector machine is used for data classification and regression analysis, and can accurately identify the characteristics of buildings and stores. This allows the generation unit to generate a highly accurate street view and provide it to the user. Furthermore, the generation unit can update the generated street view in real time to reflect the latest information. For example, when a new building is constructed or a store closes, the street view can be instantly updated based on new video data from the collection unit. This allows the generation unit to always provide the latest information and meet the needs of the user.

[0069] The extraction unit extracts the latest information on the town based on the street view generated by the generation unit. The extraction unit collects, for example, store opening / closing information, gasoline prices, traffic information, and event information. Specifically, the extraction unit analyzes the generated street view and automatically extracts text information, signs, price displays, and the like in the video. For example, optical character recognition (OCR) technology can be used to read store signs and gas station price displays to obtain the latest information. The extraction unit can extract the latest information on the town using, for example, data mining technology and filtering algorithms. Data mining technology is a method of extracting useful information from large amounts of data, and filtering algorithms are a method of selecting data based on specific conditions. This allows the extraction unit to efficiently extract necessary information and provide it to the user. The extraction unit can collect opening / closing information by, for example, analyzing official store announcements and SNS posts. By analyzing SNS posts, the latest information posted by users can be obtained and updated in real time. The extraction unit can collect gasoline prices by, for example, analyzing gas station price displays and online databases. By utilizing the online database, a wide range of information can be collected quickly and the latest gasoline prices can be provided. This allows the extraction unit to quickly and accurately extract the latest information on the city and provide it to the user. Furthermore, the extraction unit can centrally manage the extracted information and link it with other systems and departments as necessary. For example, the extracted information can be stored in a cloud server and made accessible to the provision unit. This allows the extraction unit to extract information efficiently and effectively and improve the performance of the entire system.

[0070] The providing unit provides the information extracted by the extracting unit. The providing unit provides, for example, the collected information as the strongest local content. Specifically, when a user searches for information on a specific area, the providing unit can provide information such as the latest street view, opening and closing information, and gasoline prices. The providing unit can provide, for example, the collected information through a website or a mobile application. The website or mobile application provides an interface that is easily accessible to the user, allowing the user to quickly search for the necessary information. The providing unit, for example, pays a delivery company for providing the information. The delivery company can receive a reward by providing the collected video data. This allows the delivery company to obtain an incentive to continue the collection activity. The providing unit can, for example, input the collected information into an AI model and provide the information using an algorithm for providing optimal information. Specifically, the providing unit inputs the collected information into an AI model and provides optimal information based on the user's search history and location information. For example, when a user searches for gasoline prices in a specific area, the providing unit can provide the latest gasoline price information and further provide related information such as the location and business hours of nearby gas stations. This enables the providing unit to provide users with fast and accurate information, improving convenience. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information to be provided. For example, based on user evaluations and comments, the providing unit can review the content and display method of the information to be provided, and provide a more user-friendly service. This enables the providing unit to provide high-quality local content that meets the needs of users, improving the reliability and satisfaction of the entire system.

[0071] The collection unit can install a 360-degree camera on the roof of the car and collect wide-range video. The collection unit can install a 360-degree camera on the roof of the car, for example, and collect wide-range video. The collection unit can collect wide-range video data, for example, by adjusting the viewing angle of the camera. The collection unit can collect wide-range video data, for example, by adjusting the shooting distance of the camera. As a result, wide-range video data can be collected by using the 360-degree camera. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input video data acquired by the 360-degree camera to the generation AI and cause the generation AI to perform analysis of the wide-range video data.

[0072] The generation unit can analyze the exterior of a building or store using image recognition technology or a machine learning algorithm. The generation unit can analyze the exterior of a building or store using, for example, image recognition technology. The generation unit can analyze the exterior of a building or store using, for example, an object detection algorithm. The generation unit can analyze the exterior of a building or store using, for example, face recognition technology. The generation unit can analyze the exterior of a building or store using, for example, a machine learning algorithm. The generation unit can analyze the exterior of a building or store using, for example, deep learning. The generation unit can analyze the exterior of a building or store using, for example, a support vector machine. This makes it possible to accurately analyze the exterior of a building or store by using image recognition technology or a machine learning algorithm. A part or all of the above-mentioned 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 exterior data of a building or store to the generation AI and cause the generation AI to execute an analysis of the exterior.

[0073] The extraction unit can collect at least one of opening and closing information, gasoline prices, traffic information, and event information. The extraction unit, for example, collects opening and closing information. The extraction unit, for example, can analyze official announcements of stores and posts on SNS to collect opening and closing information. The extraction unit, for example, collects gasoline prices. The extraction unit, for example, can analyze price displays at gas stations and online databases to collect gasoline prices. The extraction unit, for example, collects traffic information. The extraction unit, for example, can analyze traffic sensors and GPS data to collect traffic information. The extraction unit, for example, collects event information. The extraction unit, for example, can analyze official websites and posts on SNS of event organizers to collect event information. By collecting various information, it is possible to provide the latest local content to users. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the collected information to the generation AI and cause the generation AI to execute the extraction of information.

[0074] The providing unit can provide the collected information as the most valuable local content. The providing unit, for example, provides the collected information as the strongest local content. The providing unit can provide information such as the latest street view, opening and closing information, and gasoline prices when a user searches for information on a specific area. The providing unit can provide the collected information through a website or a mobile application, for example. The providing unit can provide valuable information based on, for example, user feedback and the number of accesses. This makes it possible to provide valuable information to a user by providing the collected information as the strongest local content. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and provide the information using an algorithm for providing optimal information.

[0075] The providing unit can pay the delivery company a fee for providing the information. The providing unit, for example, pays the delivery company a fee for providing the information. The providing unit can receive a reward, for example, by providing the video data collected by the delivery company. The providing unit can pay a fee, for example, in the form of money, points, service usage rights, etc. This makes it possible to increase an incentive for providing the information by paying a fee to the delivery company. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and pay the fee using an algorithm for optimizing the payment of the fee.

[0076] The collection unit can estimate the user's emotion and adjust the collection timing of the video data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the collection timing of the video data based on the estimated user's emotion. For example, when the user is excited, the collection unit preferentially collects video of a specific event or festival. For example, when the user is relaxed, the collection unit can collect video of a quiet residential area or a park. For example, when the user is in a hurry, the collection unit can preferentially collect video of a major traffic route or a shopping area. This allows more appropriate video data to be collected by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing based on the emotion.

[0077] The collection unit can automatically adjust the camera settings according to the weather or time of day at the time of collection. The collection unit, for example, automatically adjusts the camera settings according to the weather and time of day at the time of collection. For example, at night, the collection unit increases the sensitivity of the camera to collect bright images. For example, in rainy weather, the collection unit can automatically deploy a waterproof cover to protect the camera lens. For example, under strong sunlight during the day, the collection unit can adjust the exposure of the camera to collect images with appropriate brightness. This makes it possible to collect optimal image data by automatically adjusting the camera settings according to the weather and time of day. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on weather and time of day to the generation AI and cause the generation AI to automatically adjust the camera settings.

[0078] The collection unit can collect information on specific events, festivals, etc. preferentially at the time of collection. For example, the collection unit obtains information on local festivals and events in advance and preferentially collects images of the area. For example, the collection unit can collect information on newly opened stores and facilities and preferentially collect images of the surrounding area. For example, the collection unit can collect information on traffic congestion and accidents and preferentially collect images of the area. As a result, valuable information can be provided to users by preferentially collecting information on specific events and festivals. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information on events and festivals to the generation AI and cause the generation AI to perform preferential collection.

[0079] The collection unit can estimate the user's emotion and determine the priority of the video data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the video data to be collected based on the estimated user's emotion. For example, when the user is excited, the collection unit preferentially collects images of bustling shopping streets and tourist spots. For example, when the user is relaxed, the collection unit can preferentially collect images of natural landscapes and parks. For example, when the user is in a hurry, the collection unit can preferentially collect images of major traffic routes and shopping areas. This allows more appropriate video data to be collected by determining the priority of the video data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data into the generation AI and cause the generation AI to determine priorities based on emotions.

[0080] The collection unit can select an optimal collection point based on the speed or route of the vehicle at the time of collection. For example, when the vehicle is traveling at a low speed, the collection unit selects a stop point to collect detailed video. For example, when the vehicle is traveling at a high speed, the collection unit can select a major intersection or landmark to collect a wide range of video. For example, the collection unit can preferentially collect video of a specific area based on the route of the vehicle. This allows efficient collection of video data by selecting an optimal collection point based on the speed and route of the vehicle. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the speed and route of the vehicle to the generation AI and cause the generation AI to select an optimal collection point.

[0081] The collection unit can collect other sensor data in combination at the time of collection. The collection unit can use, for example, a temperature sensor to preferentially collect images within a specific temperature range. The collection unit can use, for example, a humidity sensor to preferentially collect images of areas with high humidity. The collection unit can use, for example, a noise level sensor to preferentially collect images of quiet areas and busy areas. This allows more multifaceted information to be collected by using other sensor data in combination. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input other sensor data to the generation AI and cause the generation AI to execute the combination of the sensor data.

[0082] The generation unit can estimate the user's emotion and adjust the display method of the street view based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the display method of the street view based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a street view that proceeds at a leisurely pace. For example, when the user is in a hurry, the generation unit can generate a street view that emphasizes the shortest route. For example, when the user is excited, the generation unit can generate a street view that adds a visually stimulating effect. This allows for more appropriate display by adjusting the display method of the street view according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0083] The generation unit can highlight changes when generating the image data by comparing it with past image data. The generation unit can highlight, for example, newly opened stores and closed stores. The generation unit can highlight changes such as road construction and the construction of new buildings. The generation unit can highlight, for example, seasonal changes and event status. This makes it possible to provide the user with the latest information by highlighting changes compared with past image data. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past image data and current image data to the generation AI and cause the generation AI to execute highlighting of changes.

[0084] The generation unit can automatically tag specific buildings and landmarks at the time of generation. The generation unit can automatically tag, for example, famous tourist spots and landmarks. The generation unit can automatically tag, for example, major commercial facilities and stores. The generation unit can automatically tag, for example, public facilities and transportation stations. In this way, by automatically tagging specific buildings and landmarks, it is possible to provide easy-to-understand information to users. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input data of buildings and landmarks to the generation AI and have the generation AI execute tagging.

[0085] The generation unit can estimate the user's emotion and adjust the level of detail of the street view based on the estimated user's emotion. The generation unit can, for example, estimate the user's emotion and adjust the level of detail of the street view based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a street view including detailed information. For example, when the user is in a hurry, the generation unit can generate a concise street view that focuses on the main points. For example, when the user is excited, the generation unit can generate a street view with a visually stimulating effect. This allows more appropriate information to be provided by adjusting the level of detail of the street view according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the level of detail based on the emotion.

[0086] The generation unit can integrate and display images from different viewpoints and angles at the time of generation. The generation unit, for example, integrates and displays images taken at the same location under different time periods or weather conditions. The generation unit can, for example, integrate images acquired from multiple cameras to generate a 360-degree panoramic view. The generation unit can, for example, integrate images taken by a drone or from a high place to generate a three-dimensional street view. This makes it possible to provide a more three-dimensional street view by integrating and displaying images from different viewpoints and angles. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input image data from different viewpoints and angles to the generation AI and cause the generation AI to execute image integration.

[0087] The generation unit can correct images at night or in bad weather at the time of generation and display them. The generation unit can, for example, correct images at night to make them brighter, thereby improving visibility. The generation unit can, for example, correct images in rainy weather to make them clearer, thereby improving visibility. The generation unit can, for example, correct images on foggy or snowy days to improve visibility. As a result, images at night or in bad weather are corrected and displayed, thereby improving visibility. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input image data at night or in bad weather to the generation AI and have the generation AI execute image correction.

[0088] The extraction unit can estimate the user's emotion and determine the priority of information to be extracted based on the estimated user's emotion. The extraction unit, for example, estimates the user's emotion and determines the priority of information to be extracted based on the estimated user's emotion. For example, when the user is excited, the extraction unit preferentially extracts event information and newly opened store information. For example, when the user is relaxed, the extraction unit can preferentially extract information about parks and natural scenery. For example, when the user is in a hurry, the extraction unit can preferentially extract traffic information and information about major facilities. This allows more appropriate information to be provided by determining the priority of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information based on the emotion.

[0089] The extraction unit can detect abnormal values ​​by comparing with past data at the time of extraction. For example, if gasoline prices fluctuate suddenly, the extraction unit extracts the information. For example, if a specific store suddenly closes, the extraction unit can extract the information. For example, if traffic volume is higher than normal, the extraction unit can extract the information. This makes it possible to quickly grasp abnormal changes by detecting abnormal values ​​by comparing with past data. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input past data and current data to the generation AI and cause the generation AI to detect abnormal values.

[0090] The extraction unit can preferentially extract information of a specific category at the time of extraction. For example, the extraction unit preferentially extracts information on new openings and closings of restaurants. For example, the extraction unit can preferentially extract price information of gas stations. For example, the extraction unit can preferentially extract traffic information and event information. In this way, valuable information can be provided to users by preferentially extracting information of a specific category. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input information of a specific category to the generation AI and cause the generation AI to perform preferential extraction.

[0091] The extraction unit can estimate the user's emotion and adjust the display method of the information to be extracted based on the estimated user's emotion. The extraction unit, for example, estimates the user's emotion and adjusts the display method of the information to be extracted based on the estimated user's emotion. The extraction unit, for example, when the user is nervous, provides a display method that is simple and highly visible. The extraction unit, for example, when the user is relaxed, can provide a display method that includes detailed information. The extraction unit, for example, when the user is in a hurry, can provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0092] The extraction unit can complement information in cooperation with other data sources at the time of extraction. The extraction unit can, for example, analyze posts on SNS and extract real-time event information. The extraction unit can, for example, analyze information on a news site and extract the latest opening and closing information. The extraction unit can, for example, complement gasoline prices and traffic information in cooperation with other data sources. This makes it possible to provide more multifaceted information by linking with other data sources. Some or all of the above-mentioned processing in the extraction unit can be performed, for example, using AI or without using AI. For example, the extraction unit can input information from other data sources to the generation AI and have the generation AI execute the complementation of the information.

[0093] The extraction unit can customize information based on the characteristics of each region at the time of extraction. For example, in a tourist destination, the extraction unit can preferentially extract tourist spots and event information. For example, in a residential area, the extraction unit can preferentially extract information on nearby stores and facilities. For example, in a commercial area, the extraction unit can preferentially extract newly opened stores and sale information. This allows more appropriate information to be provided by customizing information based on the characteristics of each region. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input characteristic data for each region to the generation AI and cause the generation AI to customize the information.

[0094] The providing unit can estimate the user's emotion and adjust the information providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the information providing method based on the estimated user's emotion. The providing unit provides, for example, a simple and highly visible information providing method when the user is nervous. The providing unit can provide, for example, a providing method including detailed information when the user is relaxed. The providing unit can provide, for example, an information providing method that focuses on the main points when the user is in a hurry. This allows more appropriate information to be provided by adjusting the information providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the providing method based on the emotion.

[0095] The providing unit can provide optimal information by referring to the user's past search history at the time of providing. For example, the providing unit can preferentially provide the latest information on an area where the user has searched in the past. For example, the providing unit can preferentially provide information on a category (e.g., restaurants, gas stations) where the user has searched in the past. For example, the providing unit can automatically suggest related information from the user's past search history. This makes it possible to provide more appropriate information by referring to the user's past search history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's search history data to the generating AI and cause the generating AI to provide optimal information.

[0096] The providing unit can automatically adjust the display format according to the user's device when providing. For example, when the user is using a smartphone, the providing unit provides a display format that matches the screen size. For example, when the user is using a tablet, the providing unit can provide a display format optimized for a large screen. For example, when the user is using a desktop, the providing unit can provide a display format including detailed information. This makes it possible to provide more appropriate information by automatically adjusting the display format according to the user's device. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generating AI and cause the generating AI to automatically adjust the display format.

[0097] The providing unit can estimate the user's emotion and adjust the frequency of providing information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the frequency of providing information based on the estimated user's emotion. The providing unit, for example, provides information frequently when the user is excited. The providing unit, for example, can provide information at a moderate frequency when the user is relaxed. The providing unit, for example, can provide the minimum necessary information when the user is in a hurry. This allows more appropriate information to be provided by adjusting the frequency of providing information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the provision frequency based on the emotion.

[0098] The providing unit can provide optimal information by considering the geographical location information of the user at the time of providing. For example, the providing unit preferentially provides the latest information on the area where the user is currently located. For example, when the user is moving, the providing unit can provide information related to the destination. For example, when the user approaches a specific area, the providing unit can provide information on the area. This makes it possible to provide more appropriate information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide optimal information.

[0099] The providing unit can provide related information by analyzing the user's social media activity at the time of providing. The providing unit can provide, for example, information on places and events in which the user has shown interest on social media. The providing unit can provide related information based on, for example, the content of posts of accounts the user follows. The providing unit can provide, for example, the latest information on places where the user has checked in on social media. This makes it possible to provide more appropriate information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to provide related information.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The collection unit can analyze the vehicle's driving data in real time and automatically select the optimal video collection route. For example, the collection unit can analyze information on traffic congestion and road construction and select a detour route. For example, the collection unit can analyze traffic volume data in a specific time period and select the most efficient collection route. For example, the collection unit can analyze weather data and select a safe route in bad weather. This allows the collection unit to use real-time driving data to efficiently and safely collect video data.

[0102] The generation unit can analyze the user's past search history and preferentially generate street views of areas that are likely to interest the user. For example, the generation unit can analyze data of areas previously searched by the user and generate the latest street views of the areas. For example, the generation unit can analyze data of places frequently visited by the user and generate detailed street views of the places. For example, the generation unit can analyze data of a specific category (e.g., restaurants, tourist spots) in which the user has shown interest and generate street views related to the category. In this way, the generation unit can provide a more personalized street view by utilizing the user's past behavior data.

[0103] The extraction unit can estimate the user's emotion and adjust the display method of the extracted information based on the estimated user's emotion. For example, when the user is nervous, the extraction unit provides a simple and highly visible display method. When the user is relaxed, the extraction unit can provide a display method including detailed information. When the user is in a hurry, the extraction unit can provide a display method that focuses on the main points. This allows more appropriate information to be provided by adjusting the display method of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0104] The providing unit can estimate the user's emotion and adjust the information providing method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible information providing method. When the user is relaxed, the providing unit can provide a detailed information providing method. When the user is in a hurry, the providing unit can provide an information providing method that focuses on the main points. This allows more appropriate information to be provided by adjusting the information providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the providing method based on the emotion.

[0105] The collection unit can collect other sensor data in combination. For example, a temperature sensor is used to preferentially collect images within a specific temperature range. The collection unit can use a humidity sensor to preferentially collect images of areas with high humidity. The collection unit can use a noise level sensor to preferentially collect images of quiet areas and busy areas. This allows more multifaceted information to be collected by using other sensor data in combination. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input other sensor data to the generation AI and cause the generation AI to perform the combination of the sensor data.

[0106] The generation unit can highlight changes when generating the image data by comparing it with past image data. For example, it highlights newly opened stores and closed stores. The generation unit can highlight changes such as road construction and the construction of new buildings. The generation unit can highlight seasonal changes and event statuses, for example. This makes it possible to provide the user with the latest information by highlighting changes compared with past image data. Some or all of the above-mentioned 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 past image data and current image data to the generation AI and cause the generation AI to execute highlighting of changes.

[0107] The extraction unit can complement information in cooperation with other data sources at the time of extraction. For example, it can analyze posts on SNS and extract real-time event information. The extraction unit can analyze information on news sites and extract the latest opening and closing information. The extraction unit can complement gasoline prices and traffic information in cooperation with other data sources. This makes it possible to provide more multifaceted information by linking with other data sources. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input information from other data sources to the generation AI and have the generation AI execute the complementation of the information.

[0108] The providing unit can provide optimal information by referring to the user's past search history at the time of providing. For example, the providing unit can provide the latest information on an area where the user has searched in the past with priority. For example, the providing unit can provide information on a category (e.g., restaurants, gas stations) where the user has searched in the past with priority. For example, the providing unit can automatically suggest related information from the user's past search history. This makes it possible to provide more appropriate information by referring to the user's past search history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's search history data to the generating AI and cause the generating AI to provide optimal information.

[0109] The collection unit can automatically adjust the camera settings according to the weather or time of day when collecting. For example, the collection unit increases the sensitivity of the camera at night to collect bright images. For example, the collection unit can automatically deploy a waterproof cover to protect the camera lens during rainy weather. For example, the collection unit can adjust the exposure of the camera to collect images with appropriate brightness under strong sunlight during the day. This allows optimal image data to be collected by automatically adjusting the camera settings according to the weather and time of day. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the weather and time of day to the generation AI and cause the generation AI to automatically adjust the camera settings.

[0110] The generation unit can estimate the user's emotion and adjust the display method of the street view based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a street view that proceeds at a leisurely pace. When the user is in a hurry, the generation unit can generate a street view that emphasizes the shortest route. When the user is excited, the generation unit can generate a street view that adds a visually stimulating effect. This allows for a more appropriate display by adjusting the display method of the street view according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects video data from a camera installed in a vehicle. For example, the collection unit uses a camera installed in a delivery company's vehicle to collect video data from the camera installed in the vehicle in real time. For example, the collection unit can also collect wide-range video by installing a 360-degree camera on the roof of the vehicle. For example, the collection unit can collect wide-range video data by adjusting the viewing angle and shooting distance of the camera. Step 2: The generation unit uses a generation AI to analyze the video data collected by the collection unit and generate a street view. The generation unit analyzes the exterior of a building or store using, for example, image recognition technology or machine learning algorithms. The generation unit can analyze the exterior of a building or store using, for example, object detection algorithms or face recognition technology. The generation unit can analyze the exterior of a building or store using, for example, machine learning algorithms such as deep learning or support vector machines. Step 3: The extraction unit extracts the latest information about the city based on the street view generated by the generation unit. The extraction unit collects, for example, store opening / closing information, gasoline prices, traffic information, and event information. The extraction unit can extract the latest information about the city using, for example, data mining techniques and filtering algorithms. The extraction unit can collect store opening / closing information by analyzing, for example, official store announcements and SNS posts. The extraction unit can collect gasoline prices by analyzing, for example, price displays at gas stations and online databases. Step 4: The providing unit provides the information extracted by the extracting unit. The providing unit provides the collected information as the strongest local content, for example. The providing unit can provide information such as the latest street view, opening and closing information, and gasoline prices when a user searches for information on a specific area. The providing unit can provide the collected information through a website or a mobile application, for example. The providing unit pays a fee for providing the information to a delivery company, for example. The providing unit can receive a reward by providing video data collected by the delivery company, for example. This allows the local content generation system according to the embodiment to quickly provide the latest local information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected information into an AI model and provide the information using an algorithm for providing optimal information.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0114] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] For example, the collection unit is realized by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected video data to generate a street view. The extraction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and extracts the latest information on the city from the generated street view. The provision unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides the extracted information to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] Fig. 4 shows an example of 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.

[0125] 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.

[0126] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] For example, the collection unit is realized by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected video data to generate a street view. The extraction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and extracts the latest information on the city from the generated street view. The provision unit is realized by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides the extracted information to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] 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.

[0141] 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.

[0142] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the collection unit is realized by the camera 42 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected video data to generate a street view. The extraction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and extracts the latest information on the city from the generated street view. The provision unit is realized by, for example, the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the extracted information to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] 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 performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0157] 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.

[0158] 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.

[0159] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 a voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0165] For example, the collection unit is realized by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing device 12. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected video data to generate a street view. The extraction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and extracts the latest information on the city from the generated street view. The provision unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides the extracted information to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] 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 Universal Serial Bus (USB) 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.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0179] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit that collects video data from a camera installed in the vehicle; A generation unit that analyzes the video data collected by the collection unit and generates a street view; an extraction unit that extracts the latest information on a city based on the street view generated by the generation unit; a providing unit that provides the information extracted by the extracting unit. A system characterized by: (Appendix 2) The collecting unit includes: A 360-degree camera is installed on the roof of the car to collect wide-area footage. 2. The system of claim 1, (Appendix 3) The generation unit is Use image recognition technology or machine learning algorithms to analyze the exterior appearance of buildings and stores 2. The system of claim 1, (Appendix 4) The extraction unit is Collect at least one of the following information: store opening / closing information, gasoline prices, traffic information, and event information 2. The system of claim 1, (Appendix 5) The providing unit is Providing collected information as the most valuable local content 2. The system of claim 1, (Appendix 6) The providing unit is Pay the delivery company for providing the information 2. The system of claim 1, (Appendix 7) The collecting unit includes: A method for estimating a user's emotions and adjusting the timing of video data collection based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Automatically adjust camera settings depending on weather or time of day during collection 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting, prioritize collecting information on specific events, festivals, etc. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize video data to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: At collection time, choose the best collection point based on the vehicle's speed or route 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting, collect other sensor data in combination. 2. The system of claim 1, (Appendix 13) The generation unit is Infer user sentiment and adjust Street View display based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The generation unit is When generating, it compares with past video data to highlight changes 2. The system of claim 1, (Appendix 15) The generation unit is Automatically tagging certain buildings and landmarks as they are generated 2. The system of claim 1, (Appendix 16) The generation unit is Infer user sentiment and adjust Street View detail based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The generation unit is When generating the image, images from different viewpoints and angles are integrated and displayed. 2. The system of claim 1, (Appendix 18) The generation unit is When generating the video, the video is corrected to display images taken at night or in bad weather. 2. The system of claim 1, (Appendix 19) The extraction unit is Estimate the user's emotions and determine the priority of information to be extracted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The extraction unit is Upon extraction, compare with past data to detect outliers 2. The system of claim 1, (Appendix 21) The extraction unit is During extraction, prioritize extraction of information from specific categories 2. The system of claim 1, (Appendix 22) The extraction unit is Estimate user's emotions and adjust the display method of extracted information based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The extraction unit is During extraction, integrate with other data sources to supplement information 2. The system of claim 1, (Appendix 24) The extraction unit is Customize information based on local characteristics during extraction 2. The system of claim 1, (Appendix 25) The providing unit is To estimate a user's emotion and adjust a method of providing information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, the most appropriate information is provided by referring to the user's past search history. 2. The system of claim 1, (Appendix 27) The providing unit is When provided, the display format is automatically adjusted according to the user's device 2. The system of claim 1, (Appendix 28) The providing unit is To estimate a user's emotion and adjust the frequency of providing information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, the most appropriate information is provided taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The providing unit is When provided, analyze your social media activity to provide you with relevant information 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects video data from a camera installed in the vehicle; A generation unit that analyzes the video data collected by the collection unit and generates a street view; an extraction unit that extracts the latest information on a city based on the street view generated by the generation unit; a providing unit that provides the information extracted by the extracting unit. A system characterized by:

2. The collecting unit includes: A 360-degree camera is installed on the roof of the car to collect wide-area footage.

2. The system of claim 1 .

3. The generation unit is Use image recognition technology or machine learning algorithms to analyze the exterior appearance of buildings and stores 2. The system of claim 1 .

4. The extraction unit is Collect at least one of the following information: store opening / closing information, gasoline prices, traffic information, and event information.

2. The system of claim 1 .

5. The providing unit is Providing collected information as valuable local content 2. The system of claim 1 .

6. The providing unit is Pay the delivery company for providing the information 2. The system of claim 1 .

7. The collecting unit includes: A method for estimating a user's emotions and adjusting the timing of video data collection based on the estimated user's emotions 2. The system of claim 1 .

8. The collecting unit includes: Automatically adjust camera settings depending on weather or time of day during collection 2. The system of claim 1 .

9. The collecting unit includes: When collecting, prioritize collecting information on specific events, festivals, etc.

2. The system of claim 1 .

10. The collecting unit includes: Estimate user emotions and prioritize video data to be collected based on the estimated user emotions.

2. The system of claim 1 .

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