System

Abstract

An object of a system according to an embodiment is to effectively convey the atmosphere of a restaurant or a cafe and the appeal of food.SOLUTION: A system according to an embodiment includes a moving image posting unit and a moving image browsing unit. The moving image posting unit captures, as a moving image, a state of a meal at a restaurant visited by a consumer, an atmosphere of a cafe, or details of a dish, and posts the moving image on a service. The moving image browsing unit browses the moving image posted by the moving image posting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the challenge of making it difficult to fully convey the atmosphere of a restaurant or cafe or the appeal of its food.

[0005] The system according to the embodiment aims to effectively convey the atmosphere of a restaurant or cafe and the appeal of its food. [Means for solving the problem]

[0006] The system according to the embodiment includes a video posting unit and a video viewing unit. The video posting unit takes videos of the food served at restaurants visited by consumers, or of the atmosphere of the cafe and details of the food, and posts the videos on the service. The video viewing unit views the videos posted by the video posting unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively convey the atmosphere of a restaurant or cafe and the appeal of its food. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The video simulation system according to an embodiment of the present invention is a system that allows consumers to film videos of their meals at restaurants they visit, the atmosphere of the cafe, and details of the food, and then post the videos on the service. This allows consumers to check the atmosphere of the restaurant and the quality of the food in advance, and also allows restaurants to effectively promote the appeal of their own establishments.

[0029] The video simulation system according to the embodiment includes a video posting unit and a video viewing unit. The video posting unit shoots videos of the meal at a restaurant visited by a consumer, the atmosphere of the cafe, and details of the food, and posts the videos on the service. For example, the video posts videos of the consumer enjoying a meal with friends. The video posting unit can also record videos of the cafe's interior and the moment the food is served. The video posting unit can also record details of the food in the video. For example, the video viewing unit can record videos of the ingredients and cooking methods of the food. The video viewing unit views videos posted by the video posting unit. For example, a consumer can view videos posted by other consumers and have a simulated experience of actually visiting the restaurant. The video viewing unit can check the atmosphere of the cafe and the appearance of the food through the videos. The video viewing unit can check the quality of the service provided through the videos. For example, the video viewing unit can check the atmosphere of the cafe and get a feel for the atmosphere before visiting. In this way, the video simulation system according to the embodiment allows consumers to check the atmosphere of a restaurant and the quality of the food in advance. For example, consumers can watch a video and think, "I want to go to this cafe." Restaurants can also effectively promote the appeal of their establishments. For example, restaurants can communicate the appeal of their establishments through videos.

[0030] The video posting unit automatically adds subtitles to videos, making it accessible to those with hearing impairments. For example, when a video is posted, the video posting unit uses a generation AI to automatically analyze the audio and generate subtitles. For example, what the poster is saying is converted into text in real time and displayed as subtitles on the video. The video posting unit also uses a generation AI to analyze the content of the video and add subtitles to important parts. For example, subtitles are added to descriptions of dishes or parts related to the atmosphere of the cafe. The video posting unit also uses a generation AI to generate subtitles in multiple languages. For example, subtitles are added in the viewer's language, such as English or Spanish. This makes it accessible to those with hearing impairments and allows it to be used by a wider range of users.

[0031] The video posting unit allows the generation AI to automatically provide additional information such as recipes and the history of the cafe for posted videos. For example, when a video is posted, the generation AI in the video posting unit analyzes the content of the video and automatically generates related recipes. For example, the recipe for the dish introduced by the poster is displayed below the video. The video posting unit also allows the generation AI to automatically generate information about the history of the cafe. For example, the year the cafe was founded and information about past events are displayed below the video. The video posting unit also allows the generation AI to provide additional information about the ingredients and cooking methods of the dishes. For example, a list of ingredients and cooking steps for the dish are displayed below the video. This allows viewers to deepen their understanding by providing additional information.

[0032] The video posting unit can add effects that visually express the aroma and taste of food. For example, when posting a video, the video posting unit uses a generation AI to automatically generate effects that visually express the aroma and taste of food. For example, an aroma effect is added to the video to express the aroma of the food. The video posting unit also uses a generation AI to automatically generate effects that visually express the taste of the food. For example, a taste effect is added to the video to express the sweetness or sourness of the food. The video posting unit also uses a generation AI to automatically generate effects that visually express the texture of the food. For example, a texture effect is added to the video to express the creaminess or crunchiness of the food. This visually expresses the aroma and taste of the food, thereby attracting the interest of viewers.

[0033] The video posting unit can add a tutorial mode in which the poster explains how to make a dish. For example, when posting a video, the video posting unit has the generation AI automatically analyze the poster's cooking method and add the tutorial mode. For example, when the poster explains the steps of a dish, the steps are displayed in the video. The video posting unit also has the generation AI automatically generate a list of ingredients for the dish and add it to the tutorial mode. For example, the ingredients used by the poster are displayed in the video. The video posting unit also has the generation AI automatically calculate the cooking time of the dish and add it to the tutorial mode. For example, the cooking time for each step is displayed in the video. In this way, adding the tutorial mode makes it easier for viewers to learn how to make the dish.

[0034] The video viewing unit can automatically adjust the playback speed of the video and display important scenes in slow motion. For example, when a video is viewed, the generation AI in the video viewing unit analyzes the content of the video and automatically detects important scenes. For example, the moment when a dish is served is displayed in slow motion. The generation AI in the video viewing unit also analyzes the viewer's reaction and displays important scenes in slow motion. For example, scenes that the viewer is interested in are displayed in slow motion. The generation AI in the video viewing unit also automatically adjusts the playback speed of the video and displays important scenes in slow motion. For example, the cooking process of a dish is displayed in slow motion. This allows the viewer to deepen their understanding by displaying important scenes in slow motion.

[0035] The video viewing unit can add an interactive function that displays detailed information when the viewer clicks on a dish or drink introduced in the video. For example, when the video is viewed, the generation AI analyzes the content of the video and automatically tags the introduced dishes and drinks. For example, when the viewer clicks on a dish, detailed information is displayed. The video viewing unit also analyzes the viewer's click history and displays related detailed information. For example, it displays detailed information about dishes that the viewer is interested in. The video viewing unit also displays detailed information that matches the viewer's preferences. For example, it displays detailed information about dishes that the viewer has clicked on in the past. This allows the viewer to easily check detailed information about dishes and drinks that interest them.

[0036] The video viewing unit can provide a link that allows a viewer to order food on the spot. For example, when a video is viewed, the generation AI of the video viewing unit analyzes the content of the video and automatically generates a link that allows the viewer to order the food featured in the video. For example, when a viewer clicks on a food, they are taken to an order page. The video viewing unit also analyzes the viewer's order history and provides a link that allows the viewer to order related food. For example, it provides a link that allows the viewer to order the same food that they have ordered in the past. The video viewing unit also provides a link that allows the generation AI to order food that suits the viewer's preferences. For example, it provides a link that allows the viewer to order related food based on the genre of food that the viewer has ordered in the past. This allows the viewer to order food that they are interested in on the spot.

[0037] The video viewing unit can suggest customized restaurant tours to the viewer. For example, the generation AI in the video viewing unit analyzes the viewer's preferences when the viewer is viewing a video and suggests a customized restaurant tour. For example, the video viewing unit suggests a tour based on the viewer's favorite food or cafe. The generation AI in the video viewing unit also analyzes the viewer's viewing history and suggests a customized restaurant tour. For example, the video viewing unit suggests a tour based on the genre of videos the viewer has previously watched. The generation AI in the video viewing unit also analyzes the viewer's emotions and suggests a customized restaurant tour. For example, the video viewing unit suggests a tour that includes restaurants in the same genre as a video that impressed the viewer. This makes it possible to attract the viewer's interest by suggesting restaurant tours that match the viewer's preferences.

[0038] The video viewing unit can display real-time congestion status and waiting times. For example, the video viewing unit adds a function to store information in which the generation AI displays real-time congestion status and waiting times. For example, the current congestion level is displayed in color and waiting times are displayed in minutes. The video viewing unit also adds a function to display real-time congestion status and waiting times in which the generation AI displays real-time congestion status and waiting times. For example, the congestion level is displayed in a graph and waiting times are displayed numerically. The video viewing unit also adds a function to display real-time congestion status and waiting times in which the generation AI displays real-time congestion status and waiting times. For example, the congestion level is displayed as an icon and waiting times are displayed in text. This makes it easier for viewers to adjust the timing of their visit by displaying real-time congestion status and waiting times.

[0039] The video viewing unit can automatically tag and display photos and videos taken by past visitors. For example, the video viewing unit adds a function whereby the generation AI automatically tags and displays photos and videos taken by past visitors to store information. For example, photos of food and videos of the store interior are displayed by category. The video viewing unit also adds a function whereby the generation AI automatically tags and displays photos and videos taken by past visitors. For example, photos taken by visitors are tagged and displayed. The video viewing unit also adds a function whereby the generation AI automatically tags and displays photos and videos taken by past visitors. For example, videos taken by visitors are tagged and displayed. This makes it easier for viewers to refer to photos and videos taken by past visitors by automatically tagging and displaying them.

[0040] The video viewing unit can add a link that allows visitors to make a reservation on the spot. The video viewing unit provides a function that allows the generation AI to add a link to store information that allows visitors to make a reservation on the spot. For example, it displays a link that allows visitors to directly access the reservation page of a store that they are interested in. The video viewing unit also provides a function that allows the generation AI to add a link that allows visitors to make a reservation on the spot. For example, it displays a link that allows visitors to directly access the reservation form of a store that they are interested in. The video viewing unit also provides a function that allows the generation AI to add a link that allows visitors to make a reservation on the spot. For example, it displays a link that allows visitors to directly access the reservation system of a store that they are interested in. By adding a link that allows visitors to make a reservation on the spot, reservations can be made easily.

[0041] The video viewing unit can suggest a customized menu that matches the visitor's preferences. The video viewing unit, for example, adds a function to store information that allows the generation AI to suggest a customized menu that matches the visitor's preferences. For example, the optimal menu is suggested based on the visitor's past order history and preferences. The video viewing unit also adds a function to store information that allows the generation AI to suggest a customized menu that matches the visitor's preferences. For example, the optimal menu is suggested based on the visitor's viewing ... emotions. This makes it possible to improve visitor satisfaction by suggesting a customized menu that matches the visitor's preferences.

[0042] The review function can use generation AI to automatically suggest other related reviews and information. For example, when a review is posted, the generation AI automatically suggests other related reviews and information. For example, it may display other reviews about the same restaurant. The review function also uses generation AI to automatically suggest other related reviews and information. For example, it may display reviews about restaurants in the same genre. The review function also uses generation AI to automatically suggest other related reviews and information. For example, it may display reviews of related dishes. This automatically suggests other related reviews and information, which is helpful to users.

[0043] The review function can implement an algorithm that takes into account the poster's past review history and ratings to evaluate the reliability of a review. For example, the review function implements an algorithm in which the generation AI takes into account the poster's past review history and ratings when posting a review, and evaluates the reliability. For example, it may prioritize displaying reviews from posters who have received high ratings in the past. The review function can also implement an algorithm in which the generation AI takes into account the poster's past review history and ratings to evaluate the reliability. For example, it may prioritize displaying reviews from posters who have posted many reviews in the past. The review function can also implement an algorithm in which the generation AI takes into account the poster's past review history and ratings to evaluate the reliability. For example, it may prioritize displaying reviews from posters who have received many positive ratings in the past. This allows the reliability of reviews to be evaluated, making them easier for other users to use as reference.

[0044] The review function can add a real-time feedback function that allows visitors to change their ratings on the spot. For example, the review function adds a function whereby the generation AI collects real-time feedback from visitors when they view a review, allowing them to change their ratings on the spot. For example, a visitor changes their rating after reading a review. The review function also adds a function whereby the generation AI collects real-time feedback from visitors, allowing them to change their ratings on the spot. For example, a visitor changes their rating after reading a review. The review function also adds a function whereby the generation AI collects real-time feedback from visitors, allowing them to change their ratings on the spot. For example, a visitor changes their rating after reading a review and gains new information. This allows visitors to change their ratings on the spot, improving the reliability of reviews.

[0045] The review function can add a feature that allows visitors to express their emotions with emojis and stamps. For example, the review function adds a feature that allows the generation AI to express visitors' emotions with emojis and stamps when viewing reviews. For example, visitors add emojis and stamps to their reviews. The review function also allows the generation AI to analyze the visitor's emotions and suggest appropriate emojis and stamps. For example, it would suggest a tearful emoji for a review that moved the visitor. The review function also allows the generation AI to analyze the visitor's emotions in real time and automatically add emojis and stamps. For example, it would automatically add a smiling emoji for a review that made the visitor laugh. This allows visitors to express their emotions with emojis and stamps, making reviews more expressive.

[0046] The social share function can automatically generate optimal captions and hashtags for the content to be shared. For example, when sharing socially, the generation AI analyzes the content to be shared and automatically generates optimal captions and hashtags. For example, it generates captions based on the content of the video. The social share function also analyzes the content to be shared and automatically generates related hashtags. For example, it generates hashtags based on the genre of the video. The social share function also analyzes the content to be shared and automatically generates captions and hashtags that match trends. For example, it generates captions and hashtags based on current trends. This automatically generates optimal captions and hashtags, increasing the effectiveness of sharing.

[0047] The social sharing function can analyze viewer reactions to shared videos in real time and provide feedback. For example, the social sharing function uses a generation AI to analyze viewer reactions to shared videos in real time and provide feedback. For example, it analyzes viewer comments and reactions and provides feedback to the poster. The social sharing function also uses a generation AI to analyze viewer emotions in real time and provide feedback. For example, it provides feedback to the poster about comments that moved the viewer. The social sharing function also uses a generation AI to analyze viewer reactions in real time and provide feedback on areas for improvement. For example, it provides feedback to the poster about parts that viewers were dissatisfied with. In this way, analyzing viewer reactions in real time and providing feedback helps the poster improve.

[0048] The social sharing function can analyze which regions and age groups a shared video is most popular with and provide feedback on the results to the poster. For example, the social sharing function uses a generation AI to analyze the region and age group of viewers for a shared video and provide feedback on the results to the poster. For example, it can identify videos that are popular in a particular region or age group. The social sharing function also uses a generation AI to analyze viewer attributes and provide feedback on the results to the poster. For example, it can identify popular videos based on the viewer's gender or occupation. The social sharing function also uses a generation AI to analyze the viewer's viewing history and provide feedback on the results to the poster. For example, it can identify popular videos based on the genre of videos the viewer has previously watched. This allows the popularity of shared videos to be analyzed and feedback to be provided to the poster, helping the poster improve.

[0049] The social sharing function can add an influencer analysis function that evaluates the influence of shared videos. For example, the social sharing function adds an influencer analysis function in which the generation AI analyzes viewer reactions to shared videos and evaluates their influence. For example, it evaluates influence based on the viewer engagement rate. The social sharing function also adds an influencer analysis function in which the generation AI analyzes viewer sharing history and evaluates their influence. For example, it evaluates influence based on the number of times a viewer shares. The social sharing function also adds an influencer analysis function in which the generation AI analyzes viewer comments and reactions and evaluates their influence. For example, it evaluates influence based on the number of positive comments from viewers. This allows posters to understand their own influence by evaluating the influence of shared videos.

[0050] The social sharing function can add interactive features that allow viewers to add comments and reactions on the spot. For example, the social sharing function uses a generation AI to collect viewers' comments and reactions to shared videos in real time, adding interactive features. For example, viewers can add comments and reactions to videos. The social sharing function also uses a generation AI to analyze viewers' emotions in real time and add interactive features. For example, it may suggest that viewers add comments when they are moved. The social sharing function also uses a generation AI to analyze viewers' reactions in real time and add interactive features. For example, it may suggest that viewers add reactions when they laugh. This allows viewers to add comments and reactions on the spot, providing an interactive experience.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The video posting unit can automatically collect information about the area around a restaurant visited by the poster and add it to the video. For example, information about tourist spots and shopping areas near a restaurant visited by the poster can be displayed in the video. In addition, the generation AI in the video posting unit can automatically collect traffic information about the area around the restaurant and add it to the video. For example, information about the nearest station and bus stop can be displayed in the video. In addition, the generation AI in the video posting unit can automatically collect weather information about the area around the restaurant and add it to the video. For example, the weather and temperature at the time of the visit can be displayed in the video. This allows viewers to know the area around the restaurant in advance, making it easier to plan their visit.

[0053] The video posting unit can automatically display special offer information for restaurants visited by the poster. For example, coupons and discount information that can be used at restaurants visited by the poster can be displayed in the video. In addition, the generation AI in the video posting unit automatically collects special offer information for restaurants and adds it to the video. For example, information about limited-time campaigns and special menus can be displayed in the video. In addition, the generation AI in the video posting unit analyzes the special offer information for restaurants and suggests the best special offers for viewers. For example, special offer information tailored to the viewer's preferences can be displayed in the video. This allows viewers to know the special offer information for restaurants in advance, motivating them to visit.

[0054] The video posting unit can automatically display hygiene information for restaurants visited by the poster. For example, the hygiene rating and cleaning status of the restaurant visited by the poster can be displayed in the video. In addition, the generation AI in the video posting unit automatically collects hygiene information for restaurants and adds it to the video. For example, it displays information on public health center ratings and past hygiene issues in the video. In addition, the generation AI in the video posting unit analyzes the hygiene information of restaurants and provides the most appropriate hygiene information to the viewer. For example, it displays hygiene information tailored to the viewer's concerns in the video. This allows viewers to know the hygiene information of the restaurant in advance and visit with peace of mind.

[0055] The video posting unit can automatically display menu information for restaurants visited by the poster. For example, the video displays a menu list and price information for restaurants visited by the poster. In addition, the video posting unit's generation AI automatically collects restaurant menu information and adds it to the video. For example, it displays information about popular and recommended menu items in the video. In addition, the video posting unit's generation AI analyzes restaurant menu information and suggests the best menu for the viewer. For example, it displays menu information tailored to the viewer's preferences in the video. This allows viewers to know the restaurant's menu information in advance and use it as a reference when visiting.

[0056] The video viewing unit can provide a function that enables viewers to add comments in real time while watching a video. For example, viewers can enter comments while watching a video and communicate with other viewers in real time. In addition, the video viewing unit uses a generation AI to analyze viewers' comments and automatically display related comments. For example, comments from viewers who share the same opinion are highlighted. In addition, the video viewing unit uses a generation AI to analyze viewers' comments and display comments based on emotions. For example, moving comments are highlighted. This allows viewers to add comments in real time and communicate with other viewers.

[0057] The video viewing unit can provide a voting function in real time while the viewer is watching the video. For example, the viewer can vote on the content of the video and the results are displayed in real time. In addition, the video viewing unit uses a generation AI to analyze the viewer's voting results and display related information. For example, related videos are recommended based on the content of the viewer's vote. In addition, the video viewing unit uses a generation AI to analyze the viewer's voting results and display information based on emotions. For example, moving voting results are highlighted. This allows viewers to vote in real time and share their opinions with other viewers.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The video posting unit records a video of the dining experience at a restaurant the consumer visits, the atmosphere of the cafe, and details of the food, and posts it on the service. For example, a video can capture the moment when a consumer enjoys a meal with friends, the cafe's interior, the moment the food is served, and the ingredients and cooking method of the food. Step 2: The video viewing unit views the videos posted by the video posting unit. For example, by viewing videos posted by other consumers, consumers can have a simulated experience of actually visiting the restaurant. They can also check the atmosphere of the cafe, the appearance of the food, and the quality of the service provided through the videos.

[0060] (Example 2) The video simulation system according to an embodiment of the present invention is a system that allows consumers to film videos of their meals at restaurants they visit, the atmosphere of the cafe, and details of the food, and then post the videos on the service. This allows consumers to check the atmosphere of the restaurant and the quality of the food in advance, and also allows restaurants to effectively promote the appeal of their own establishments.

[0061] The video simulation system according to the embodiment includes a video posting unit and a video viewing unit. The video posting unit shoots videos of the meal at a restaurant visited by a consumer, the atmosphere of the cafe, and details of the food, and posts the videos on the service. For example, the video posts videos of the consumer enjoying a meal with friends. The video posting unit can also record videos of the cafe's interior and the moment the food is served. The video posting unit can also record details of the food in the video. For example, the video viewing unit can record videos of the ingredients and cooking methods of the food. The video viewing unit views videos posted by the video posting unit. For example, a consumer can view videos posted by other consumers and have a simulated experience of actually visiting the restaurant. The video viewing unit can check the atmosphere of the cafe and the appearance of the food through the videos. The video viewing unit can check the quality of the service provided through the videos. For example, the video viewing unit can check the atmosphere of the cafe and get a feel for the atmosphere before visiting. In this way, the video simulation system according to the embodiment allows consumers to check the atmosphere of a restaurant and the quality of the food in advance. For example, consumers can watch a video and think, "I want to go to this cafe." Restaurants can also effectively promote the appeal of their establishments. For example, restaurants can communicate the appeal of their establishments through videos.

[0062] The video posting unit can analyze the poster's emotions and prioritize displaying videos with strong positive emotions. For example, when posting a video, the generation AI of the video posting unit analyzes the poster's facial expressions and voice to automatically detect videos with strong positive emotions. For example, the generation AI calculates an emotion score based on smiles and a happy tone of voice, and prioritizes displaying videos with high scores. The video posting unit also analyzes the poster's text comments to prioritize displaying videos containing positive emotions. For example, the generation AI calculates an emotion score based on positive keywords and phrases, and prioritizes displaying videos with high scores. The video posting unit also analyzes the poster's past posting history to prioritize displaying videos with a high proportion of positive emotions. For example, the generation AI calculates an emotion score based on the proportion of positive emotions in past posts, and prioritizes displaying videos with high scores. This prioritizes displaying videos with strong positive emotions, making a good impression on viewers.

[0063] The video posting unit automatically adds subtitles to videos, making it accessible to those with hearing impairments. For example, when a video is posted, the video posting unit uses a generation AI to automatically analyze the audio and generate subtitles. For example, what the poster is saying is converted into text in real time and displayed as subtitles on the video. The video posting unit also uses a generation AI to analyze the content of the video and add subtitles to important parts. For example, subtitles are added to descriptions of dishes or parts related to the atmosphere of the cafe. The video posting unit also uses a generation AI to generate subtitles in multiple languages. For example, subtitles are added in the viewer's language, such as English or Spanish. This makes it accessible to those with hearing impairments and allows it to be used by a wider range of users.

[0064] The video posting unit allows the generation AI to automatically provide additional information such as recipes and the history of the cafe for posted videos. For example, when a video is posted, the generation AI in the video posting unit analyzes the content of the video and automatically generates related recipes. For example, the recipe for the dish introduced by the poster is displayed below the video. The video posting unit also allows the generation AI to automatically generate information about the history of the cafe. For example, the year the cafe was founded and information about past events are displayed below the video. The video posting unit also allows the generation AI to provide additional information about the ingredients and cooking methods of the dishes. For example, a list of ingredients and cooking steps for the dish are displayed below the video. This allows viewers to deepen their understanding by providing additional information.

[0065] The video posting unit can add effects that visually express the aroma and taste of food. For example, when posting a video, the video posting unit uses a generation AI to automatically generate effects that visually express the aroma and taste of food. For example, an aroma effect is added to the video to express the aroma of the food. The video posting unit also uses a generation AI to automatically generate effects that visually express the taste of the food. For example, a taste effect is added to the video to express the sweetness or sourness of the food. The video posting unit also uses a generation AI to automatically generate effects that visually express the texture of the food. For example, a texture effect is added to the video to express the creaminess or crunchiness of the food. This visually expresses the aroma and taste of the food, thereby attracting the interest of viewers.

[0066] The video posting unit can add a tutorial mode in which the poster explains how to make a dish. For example, when posting a video, the video posting unit has the generation AI automatically analyze the poster's cooking method and add the tutorial mode. For example, when the poster explains the steps of a dish, the steps are displayed in the video. The video posting unit also has the generation AI automatically generate a list of ingredients for the dish and add it to the tutorial mode. For example, the ingredients used by the poster are displayed in the video. The video posting unit also has the generation AI automatically calculate the cooking time of the dish and add it to the tutorial mode. For example, the cooking time for each step is displayed in the video. In this way, adding the tutorial mode makes it easier for viewers to learn how to make the dish.

[0067] The video posting unit can analyze the poster's emotions in real time and suggest the best shooting angle and timing. For example, when shooting a video, the generation AI of the video posting unit analyzes the poster's emotions in real time and suggests the best shooting angle and timing. For example, it suggests starting shooting when the poster is smiling. The video posting unit also analyzes the poster's emotions and suggests the best shooting angle. For example, it suggests an angle that brings out the beauty of the food. The video posting unit also analyzes the poster's emotions and suggests the best shooting timing. For example, it suggests shooting the moment the food is being served. This allows for more appealing videos to be shot by suggesting the best shooting angle and timing.

[0068] The video browsing unit can analyze the viewer's emotions in real time and recommend videos that match the viewer's preferences. For example, the generation AI in the video browsing unit analyzes the viewer's facial expressions and voice while they are watching a video, and analyzes the viewer's emotions in real time. For example, if the viewer seems to be enjoying a video, it will recommend similar videos. The generation AI in the video browsing unit also analyzes the viewer's viewing history and recommends videos that match the viewer's preferences. For example, it recommends related videos based on the genre of videos the viewer has previously watched. The generation AI in the video browsing unit also analyzes the viewer's emotions and recommends videos that match the viewer's preferences. For example, it recommends videos in the same genre as a video that moved the viewer. This improves the viewing experience by recommending videos that match the viewer's preferences.

[0069] The video viewing unit can automatically adjust the playback speed of the video and display important scenes in slow motion. For example, when a video is viewed, the generation AI in the video viewing unit analyzes the content of the video and automatically detects important scenes. For example, the moment when a dish is served is displayed in slow motion. The generation AI in the video viewing unit also analyzes the viewer's reaction and displays important scenes in slow motion. For example, scenes that the viewer is interested in are displayed in slow motion. The generation AI in the video viewing unit also automatically adjusts the playback speed of the video and displays important scenes in slow motion. For example, the cooking process of a dish is displayed in slow motion. This allows the viewer to deepen their understanding by displaying important scenes in slow motion.

[0070] The video viewing unit can add an interactive function that displays detailed information when the viewer clicks on a dish or drink introduced in the video. For example, when the video is viewed, the generation AI analyzes the content of the video and automatically tags the introduced dishes and drinks. For example, when the viewer clicks on a dish, detailed information is displayed. The video viewing unit also analyzes the viewer's click history and displays related detailed information. For example, it displays detailed information about dishes that the viewer is interested in. The video viewing unit also displays detailed information that matches the viewer's preferences. For example, it displays detailed information about dishes that the viewer has clicked on in the past. This allows the viewer to easily check detailed information about dishes and drinks that interest them.

[0071] The video viewing unit can provide a link that allows a viewer to order food on the spot. For example, when a video is viewed, the generation AI of the video viewing unit analyzes the content of the video and automatically generates a link that allows the viewer to order the food featured in the video. For example, when a viewer clicks on a food, they are taken to an order page. The video viewing unit also analyzes the viewer's order history and provides a link that allows the viewer to order related food. For example, it provides a link that allows the viewer to order the same food that they have ordered in the past. The video viewing unit also provides a link that allows the generation AI to order food that suits the viewer's preferences. For example, it provides a link that allows the viewer to order related food based on the genre of food that the viewer has ordered in the past. This allows the viewer to order food that they are interested in on the spot.

[0072] The video viewing unit can suggest customized restaurant tours to the viewer. For example, the generation AI in the video viewing unit analyzes the viewer's preferences when the viewer is viewing a video and suggests a customized restaurant tour. For example, the video viewing unit suggests a tour based on the viewer's favorite food or cafe. The generation AI in the video viewing unit also analyzes the viewer's viewing history and suggests a customized restaurant tour. For example, the video viewing unit suggests a tour based on the genre of videos the viewer has previously watched. The generation AI in the video viewing unit also analyzes the viewer's emotions and suggests a customized restaurant tour. For example, the video viewing unit suggests a tour that includes restaurants in the same genre as a video that impressed the viewer. This makes it possible to attract the viewer's interest by suggesting restaurant tours that match the viewer's preferences.

[0073] The video viewing unit can analyze emotional changes while the viewer is watching a video and highlight the scenes that resonated most emotionally. For example, in the video viewing unit, the generation AI analyzes the viewer's emotional changes in real time while the video is being viewed and highlights the scenes that resonated most emotionally. For example, it highlights scenes that moved the viewer. Furthermore, in the video viewing unit, the generation AI analyzes the viewer's emotional changes and highlights scenes that resonated emotionally. For example, it highlights scenes that made the viewer laugh. Furthermore, in the video viewing unit, the generation AI analyzes the viewer's emotional changes and highlights scenes that resonated emotionally. For example, it highlights scenes that surprised the viewer. This makes it possible to improve the viewing experience by highlighting important scenes based on the viewer's emotions.

[0074] The video viewing unit can use the generation AI on the store information to display an emotion map based on the emotion analysis results of past visitors. For example, the video viewing unit displays an emotion map on the store information based on the emotion analysis results of past visitors by the generation AI. For example, areas that visitors enjoyed are displayed in different colors. The video viewing unit also displays an emotion map on the store information based on the emotion analysis results of past visitors by the generation AI. For example, areas where visitors felt relaxed are displayed in different colors. The video viewing unit also displays an emotion map on the store information based on the emotion analysis results of past visitors by the generation AI. For example, areas where visitors felt excited are displayed in different colors. In this way, by displaying an emotion map based on the emotion analysis results of past visitors, viewers can better understand the atmosphere of the store.

[0075] The video viewing unit can display real-time congestion status and waiting times. For example, the video viewing unit adds a function to store information in which the generation AI displays real-time congestion status and waiting times. For example, the current congestion level is displayed in color and waiting times are displayed in minutes. The video viewing unit also adds a function to display real-time congestion status and waiting times in which the generation AI displays real-time congestion status and waiting times. For example, the congestion level is displayed in a graph and waiting times are displayed numerically. The video viewing unit also adds a function to display real-time congestion status and waiting times in which the generation AI displays real-time congestion status and waiting times. For example, the congestion level is displayed as an icon and waiting times are displayed in text. This makes it easier for viewers to adjust the timing of their visit by displaying real-time congestion status and waiting times.

[0076] The video viewing unit can automatically tag and display photos and videos taken by past visitors. For example, the video viewing unit adds a function whereby the generation AI automatically tags and displays photos and videos taken by past visitors to store information. For example, photos of food and videos of the store interior are displayed by category. The video viewing unit also adds a function whereby the generation AI automatically tags and displays photos and videos taken by past visitors. For example, photos taken by visitors are tagged and displayed. The video viewing unit also adds a function whereby the generation AI automatically tags and displays photos and videos taken by past visitors. For example, videos taken by visitors are tagged and displayed. This makes it easier for viewers to refer to photos and videos taken by past visitors by automatically tagging and displaying them.

[0077] The video viewing unit can add a link that allows visitors to make a reservation on the spot. The video viewing unit provides a function that allows the generation AI to add a link to store information that allows visitors to make a reservation on the spot. For example, it displays a link that allows visitors to directly access the reservation page of a store that they are interested in. The video viewing unit also provides a function that allows the generation AI to add a link that allows visitors to make a reservation on the spot. For example, it displays a link that allows visitors to directly access the reservation form of a store that they are interested in. The video viewing unit also provides a function that allows the generation AI to add a link that allows visitors to make a reservation on the spot. For example, it displays a link that allows visitors to directly access the reservation system of a store that they are interested in. By adding a link that allows visitors to make a reservation on the spot, reservations can be made easily.

[0078] The video viewing unit can suggest a customized menu that matches the visitor's preferences. The video viewing unit, for example, adds a function to store information that allows the generation AI to suggest a customized menu that matches the visitor's preferences. For example, the optimal menu is suggested based on the visitor's past order history and preferences. The video viewing unit also adds a function to store information that allows the generation AI to suggest a customized menu that matches the visitor's preferences. For example, the optimal menu is suggested based on the visitor's viewing ... emotions. This makes it possible to improve visitor satisfaction by suggesting a customized menu that matches the visitor's preferences.

[0079] The video viewing unit can analyze the emotions of visitors when they are viewing store information and prioritize displaying the most appropriate information. In the video viewing unit, for example, the generation AI analyzes the emotions of visitors in real time when they are viewing store information and prioritizes displaying the most appropriate information. For example, it highlights information that the visitor is interested in. In addition, the generation AI analyzes the emotions of visitors and prioritizes displaying the most appropriate information. For example, it highlights information that impresses the visitor. In addition, the video viewing unit analyzes the emotions of visitors and prioritizes displaying the most appropriate information. For example, it highlights information that surprises the visitor. In this way, it is possible to attract the visitor's interest by prioritize displaying the most appropriate information based on the visitor's emotions.

[0080] The review function can analyze the emotions of the poster and prioritize displaying reviews that are likely to resonate emotionally. For example, when a review is posted, the generation AI analyzes the poster's emotions in real time and prioritizes displaying reviews that are likely to resonate emotionally. For example, reviews with strong positive emotions are displayed at the top. The review function also analyzes the poster's emotions and prioritizes displaying reviews that are likely to resonate emotionally. For example, moving reviews are displayed at the top. The review function also analyzes the poster's emotions and prioritizes displaying reviews that are likely to resonate emotionally. For example, humorous reviews are displayed at the top. This prioritizes reviews that are likely to resonate emotionally, which can be helpful to other users.

[0081] The review function can use generation AI to automatically suggest other related reviews and information. For example, when a review is posted, the generation AI automatically suggests other related reviews and information. For example, it may display other reviews about the same restaurant. The review function also uses generation AI to automatically suggest other related reviews and information. For example, it may display reviews about restaurants in the same genre. The review function also uses generation AI to automatically suggest other related reviews and information. For example, it may display reviews of related dishes. This automatically suggests other related reviews and information, which is helpful to users.

[0082] The review function can implement an algorithm that takes into account the poster's past review history and ratings to evaluate the reliability of a review. For example, the review function implements an algorithm in which the generation AI takes into account the poster's past review history and ratings when posting a review, and evaluates the reliability. For example, it may prioritize displaying reviews from posters who have received high ratings in the past. The review function can also implement an algorithm in which the generation AI takes into account the poster's past review history and ratings to evaluate the reliability. For example, it may prioritize displaying reviews from posters who have posted many reviews in the past. The review function can also implement an algorithm in which the generation AI takes into account the poster's past review history and ratings to evaluate the reliability. For example, it may prioritize displaying reviews from posters who have received many positive ratings in the past. This allows the reliability of reviews to be evaluated, making them easier for other users to use as reference.

[0083] The review function can add a real-time feedback function that allows visitors to change their ratings on the spot. For example, the review function adds a function whereby the generation AI collects real-time feedback from visitors when they view a review, allowing them to change their ratings on the spot. For example, a visitor changes their rating after reading a review. The review function also adds a function whereby the generation AI collects real-time feedback from visitors, allowing them to change their ratings on the spot. For example, a visitor changes their rating after reading a review. The review function also adds a function whereby the generation AI collects real-time feedback from visitors, allowing them to change their ratings on the spot. For example, a visitor changes their rating after reading a review and gains new information. This allows visitors to change their ratings on the spot, improving the reliability of reviews.

[0084] The review function can add a feature that allows visitors to express their emotions with emojis and stamps. For example, the review function adds a feature that allows the generation AI to express visitors' emotions with emojis and stamps when viewing reviews. For example, visitors add emojis and stamps to their reviews. The review function also allows the generation AI to analyze the visitor's emotions and suggest appropriate emojis and stamps. For example, it would suggest a tearful emoji for a review that moved the visitor. The review function also allows the generation AI to analyze the visitor's emotions in real time and automatically add emojis and stamps. For example, it would automatically add a smiling emoji for a review that made the visitor laugh. This allows visitors to express their emotions with emojis and stamps, making reviews more expressive.

[0085] The review function can analyze the emotions of visitors when they are viewing reviews and highlight the reviews that resonated with them most emotionally. For example, the review function uses a generation AI to analyze the emotions of visitors in real time when they are viewing reviews and highlight the reviews that resonated with them most emotionally. For example, it highlights reviews that moved the visitor. The review function also uses a generation AI to analyze the emotions of visitors and highlight reviews that resonated with them emotionally. For example, it highlights reviews that made the visitor laugh. The review function also uses a generation AI to analyze the emotions of visitors and highlight reviews that resonated with them emotionally. For example, it highlights reviews that surprised the visitor. In this way, highlighting reviews that resonated with them emotionally can be helpful to other users.

[0086] The social share function can automatically generate optimal captions and hashtags for the content to be shared. For example, when sharing socially, the generation AI analyzes the content to be shared and automatically generates optimal captions and hashtags. For example, it generates captions based on the content of the video. The social share function also analyzes the content to be shared and automatically generates related hashtags. For example, it generates hashtags based on the genre of the video. The social share function also analyzes the content to be shared and automatically generates captions and hashtags that match trends. For example, it generates captions and hashtags based on current trends. This automatically generates optimal captions and hashtags, increasing the effectiveness of sharing.

[0087] The social sharing function can analyze viewer reactions to shared videos in real time and provide feedback. For example, the social sharing function uses a generation AI to analyze viewer reactions to shared videos in real time and provide feedback. For example, it analyzes viewer comments and reactions and provides feedback to the poster. The social sharing function also uses a generation AI to analyze viewer emotions in real time and provide feedback. For example, it provides feedback to the poster about comments that moved the viewer. The social sharing function also uses a generation AI to analyze viewer reactions in real time and provide feedback on areas for improvement. For example, it provides feedback to the poster about parts that viewers were dissatisfied with. In this way, analyzing viewer reactions in real time and providing feedback helps the poster improve.

[0088] The social sharing function can analyze which regions and age groups a shared video is most popular with and provide feedback on the results to the poster. For example, the social sharing function uses a generation AI to analyze the region and age group of viewers for a shared video and provide feedback on the results to the poster. For example, it can identify videos that are popular in a particular region or age group. The social sharing function also uses a generation AI to analyze viewer attributes and provide feedback on the results to the poster. For example, it can identify popular videos based on the viewer's gender or occupation. The social sharing function also uses a generation AI to analyze the viewer's viewing history and provide feedback on the results to the poster. For example, it can identify popular videos based on the genre of videos the viewer has previously watched. This allows the popularity of shared videos to be analyzed and feedback to be provided to the poster, helping the poster improve.

[0089] The social sharing function can add an influencer analysis function that evaluates the influence of shared videos. For example, the social sharing function adds an influencer analysis function in which the generation AI analyzes viewer reactions to shared videos and evaluates their influence. For example, it evaluates influence based on the viewer engagement rate. The social sharing function also adds an influencer analysis function in which the generation AI analyzes viewer sharing history and evaluates their influence. For example, it evaluates influence based on the number of times a viewer shares. The social sharing function also adds an influencer analysis function in which the generation AI analyzes viewer comments and reactions and evaluates their influence. For example, it evaluates influence based on the number of positive comments from viewers. This allows posters to understand their own influence by evaluating the influence of shared videos.

[0090] The social sharing function can add interactive features that allow viewers to add comments and reactions on the spot. For example, the social sharing function uses a generation AI to collect viewers' comments and reactions to shared videos in real time, adding interactive features. For example, viewers can add comments and reactions to videos. The social sharing function also uses a generation AI to analyze viewers' emotions in real time and add interactive features. For example, it may suggest that viewers add comments when they are moved. The social sharing function also uses a generation AI to analyze viewers' reactions in real time and add interactive features. For example, it may suggest that viewers add reactions when they laugh. This allows viewers to add comments and reactions on the spot, providing an interactive experience.

[0091] The social share function uses an emotion estimation function to analyze viewers' emotional reactions to shared videos in real time and suggest the optimal timing to share. For example, the social share function uses a generation AI to analyze viewers' emotional reactions to shared videos in real time and suggest the optimal timing to share. For example, it encourages viewers to share at the moment when their emotions are heightened. The social share function also uses a generation AI to analyze viewers' emotions in real time and suggest the optimal timing to share. For example, it encourages viewers to share at the moment when they are moved. The social share function also uses a generation AI to analyze viewers' emotional reactions in real time and suggest the optimal timing to share. For example, it encourages viewers to share at the moment when they laugh. In this way, by analyzing viewers' emotional reactions in real time and suggesting the optimal timing to share, the effectiveness of sharing is increased.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The video posting unit can automatically collect information about the area around a restaurant visited by the poster and add it to the video. For example, information about tourist spots and shopping areas near a restaurant visited by the poster can be displayed in the video. In addition, the generation AI in the video posting unit can automatically collect traffic information about the area around the restaurant and add it to the video. For example, information about the nearest station and bus stop can be displayed in the video. In addition, the generation AI in the video posting unit can automatically collect weather information about the area around the restaurant and add it to the video. For example, the weather and temperature at the time of the visit can be displayed in the video. This allows viewers to know the area around the restaurant in advance, making it easier to plan their visit.

[0094] The video posting unit can analyze the poster's emotions and automatically filter out videos with strong negative emotions. For example, it can analyze the poster's facial expressions and tone of voice to show signs of dissatisfaction and hide videos with strong negative emotions. In addition, the video posting unit's generation AI analyzes the poster's text comments and hides videos that contain negative emotions. For example, it calculates an emotion score based on negative keywords and phrases and hides videos with a low score. In addition, the video posting unit's generation AI analyzes the poster's past posting history and hides videos that contain a lot of negative emotions. For example, it calculates an emotion score based on the proportion of negative emotions in past posts and hides videos with a low score. This makes it possible to eliminate videos that give viewers an unpleasant impression.

[0095] The video posting unit can automatically add music to videos to enhance the viewer's emotions. For example, the generation AI selects appropriate music to match the content of the video and adds it to the video. The video posting unit also uses the generation AI to analyze the viewer's emotions and add music that matches those emotions. For example, it adds upbeat music when the viewer is enjoying themselves. The video posting unit also uses the generation AI to analyze the content of the video and add different music to each scene. For example, it adds upbeat music to scenes introducing dishes and relaxing music to scenes introducing the atmosphere of a cafe. This enhances the viewer's emotions and makes the video more appealing.

[0096] The video posting unit can automatically display special offer information for restaurants visited by the poster. For example, coupons and discount information that can be used at restaurants visited by the poster can be displayed in the video. In addition, the generation AI in the video posting unit automatically collects special offer information for restaurants and adds it to the video. For example, information about limited-time campaigns and special menus can be displayed in the video. In addition, the generation AI in the video posting unit analyzes the special offer information for restaurants and suggests the best special offers for viewers. For example, special offer information tailored to the viewer's preferences can be displayed in the video. This allows viewers to know the special offer information for restaurants in advance, motivating them to visit.

[0097] The video posting unit can automatically display hygiene information for restaurants visited by the poster. For example, the hygiene rating and cleaning status of the restaurant visited by the poster can be displayed in the video. In addition, the generation AI in the video posting unit automatically collects hygiene information for restaurants and adds it to the video. For example, it displays information on public health center ratings and past hygiene issues in the video. In addition, the generation AI in the video posting unit analyzes the hygiene information of restaurants and provides the most appropriate hygiene information to the viewer. For example, it displays hygiene information tailored to the viewer's concerns in the video. This allows viewers to know the hygiene information of the restaurant in advance and visit with peace of mind.

[0098] The video posting unit can automatically display menu information for restaurants visited by the poster. For example, the video displays a menu list and price information for restaurants visited by the poster. In addition, the video posting unit's generation AI automatically collects restaurant menu information and adds it to the video. For example, it displays information about popular and recommended menu items in the video. In addition, the video posting unit's generation AI analyzes restaurant menu information and suggests the best menu for the viewer. For example, it displays menu information tailored to the viewer's preferences in the video. This allows viewers to know the restaurant's menu information in advance and use it as a reference when visiting.

[0099] The video posting unit can analyze the poster's emotions in real time and suggest optimal video editing. For example, it can suggest editing that emphasizes scenes in which the poster is enjoying themselves. The video posting unit's generation AI also analyzes the poster's emotions and suggests optimal cuts and transitions. For example, it can suggest cutting at moments when emotions are heightened. The video posting unit's generation AI also analyzes the poster's emotions and suggests optimal effects. For example, it can suggest adding effects to moving scenes. This allows the poster to optimally edit their video based on their emotions, providing viewers with more moving videos.

[0100] The video viewing unit can analyze the viewer's emotions in real time and display advertisements that match the viewer's emotions. For example, when the viewer is having fun, it displays advertisements for related products. In addition, the video viewing unit uses a generation AI to analyze the viewer's emotions and display advertisements that match the emotions. For example, when the viewer is relaxing, it displays advertisements for relaxation products. In addition, the video viewing unit uses a generation AI to analyze the viewer's emotions and display advertisements that match the emotions. For example, when the viewer is moved, it displays advertisements for products that share the emotion. In this way, by displaying advertisements that match the viewer's emotions, it is possible to increase the effectiveness of advertising.

[0101] The video viewing unit can provide a function that enables viewers to add comments in real time while watching a video. For example, viewers can enter comments while watching a video and communicate with other viewers in real time. In addition, the video viewing unit uses a generation AI to analyze viewers' comments and automatically display related comments. For example, comments from viewers who share the same opinion are highlighted. In addition, the video viewing unit uses a generation AI to analyze viewers' comments and display comments based on emotions. For example, moving comments are highlighted. This allows viewers to add comments in real time and communicate with other viewers.

[0102] The video viewing unit can provide a voting function in real time while the viewer is watching the video. For example, the viewer can vote on the content of the video and the results are displayed in real time. In addition, the video viewing unit uses a generation AI to analyze the viewer's voting results and display related information. For example, related videos are recommended based on the content of the viewer's vote. In addition, the video viewing unit uses a generation AI to analyze the viewer's voting results and display information based on emotions. For example, moving voting results are highlighted. This allows viewers to vote in real time and share their opinions with other viewers.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The video posting unit records a video of the dining experience at a restaurant the consumer visits, the atmosphere of the cafe, and details of the food, and posts it on the service. For example, a video can capture the moment when a consumer enjoys a meal with friends, the cafe's interior, the moment the food is served, and the ingredients and cooking method of the food. Step 2: The video viewing unit views the videos posted by the video posting unit. For example, by viewing videos posted by other consumers, consumers can have a simulated experience of actually visiting the restaurant. They can also check the atmosphere of the cafe, the appearance of the food, and the quality of the service provided through the videos.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0126] The 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.

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

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

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

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

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

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] 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.

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

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] 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.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 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 video posting unit that takes videos of the food served at restaurants visited by consumers, or of the atmosphere of the cafe and details of the food, and posts the videos on the service; a video viewing unit that views the video posted by the video posting unit. A system characterized by:

2. The video posting unit Analyze the poster's emotions and prioritize displaying videos with strong positive emotions 2. The system of claim 1.

3. The video posting unit Add effects that visually represent the aroma or taste of the dish 2. The system of claim 1.

4. The video viewing unit Analyzing the viewer's emotions in real time and recommending the video that matches the viewer's preferences 2. The system of claim 1.

5. The video viewing unit Using generative AI for store information, an emotion map is displayed based on the results of sentiment analysis of past visitors.

2. The system of claim 1.

6. The review function is Analyzes the poster's emotions and prioritizes reviews that resonate with them emotionally.

2. The system of claim 1.

7. Social sharing feature Automatically generate the best captions and hashtags for what you share 2. The system of claim 1.

8. Social sharing feature Using emotion estimation, the app analyzes viewers' emotional reactions to the shared video in real time and suggests the best time to share it.

2. The system of claim 1.

Images