system

Abstract

The system according to this embodiment aims to predict trends according to the user's preferences and provide fashion image videos based on those trends. [Solution] The system according to the embodiment comprises a trend prediction unit, a video generation unit, and a video recommendation unit. The trend prediction unit predicts trends according to the user's preferences. The video generation unit automatically generates fashion image videos of the user wearing clothes based on the trends predicted by the trend prediction unit. The video recommendation unit recommends the videos generated by the video generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, predicting a trend according to a user's preference and providing a fashion image based on it have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to predict a trend according to a user's preference and provide a fashion image video based on it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a trend prediction unit, a video generation unit, and a video recommendation unit. The trend prediction unit predicts trends according to the user's preferences. The video generation unit automatically generates fashion image videos of the user wearing clothes based on the trends predicted by the trend prediction unit. The video recommendation unit recommends the videos generated by the video generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict trends according to the user's preferences and provide fashion image videos based on those trends. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fashion suggestion system according to an embodiment of the present invention is a system for users to find clothes that suit them. This fashion suggestion system utilizes generative AI to create a generative AI that incorporates fashion trends and items from different countries, eras, and age groups, predicts trends according to the user's preferences, and automatically generates a fashion image video of the user wearing the clothes. This video allows the user to see themselves moving around in the clothes, making it easier to visualize how they would look when wearing them. Furthermore, it allows users to easily check trends across different eras, countries, and age groups without effort, and to discover their own preferences by viewing a wide variety of trends. For example, the fashion suggestion system analyzes fashion trends from different countries, eras, and age groups, grasps the trends, and systematizes them. For example, it incorporates information from magazines, purchase data, and social media in various countries, and the generative AI analyzes it. Next, the fashion suggestion system automatically generates a video of the customer wearing the clothes that the user has searched for. The generative AI predicts trends according to the user's preferences and generates a fashion image video of the user wearing the clothes. For example, if a user searches for a specific piece of clothing, a video of them wearing that clothing will be generated. The generated video is delivered to the user in short video format. This allows the user to easily see themselves moving around in the clothes. Furthermore, the system predicts trends based on user preferences, enabling users to discover their own tastes. For example, if a user becomes interested in a particular trend, videos related to that trend will be recommended. This system allows users to find clothes that suit them, reducing the stress of information overload from keeping up with trends, the stress of starting to wear similar styles to others, and the stress of not being able to pursue the fashion they truly love. In this way, the fashion suggestion system allows users to find clothes that suit them, reducing the stress of information overload from keeping up with trends, the stress of starting to wear similar styles to others, and the stress of not being able to pursue the fashion they truly love.

[0029] The fashion suggestion system according to this embodiment comprises a trend prediction unit, a video generation unit, and a video recommendation unit. The trend prediction unit predicts trends according to the user's preferences. The trend prediction unit analyzes, for example, popular fashion trends by country, age group, and generation, grasps trends, and systematizes them. The trend prediction unit takes in information from magazines, purchase data, and social media in various countries, and the generation AI analyzes it. For example, the trend prediction unit analyzes popular items and styles in specific countries, age groups, and generation segments. The trend prediction unit uses the generation AI to predict trends according to the user's preferences. The video generation unit automatically generates fashion image videos of the user wearing clothes based on the trends predicted by the trend prediction unit. For example, the video generation unit automatically generates videos of the customer wearing clothes that the user has searched for. The video generation unit generates videos that match the user's body shape and posture. For example, the video generation unit generates videos that realistically reproduce the fit of clothes according to the user's body shape. The video generation unit generates videos that emphasize ease of movement based on the user's posture. The video generation unit generates videos that provide footage from different angles, depending on the user's body type and posture. The video recommendation unit recommends the videos generated by the video generation unit. The video recommendation unit delivers the generated videos to the user in short video format. The video recommendation unit predicts trends according to the user's preferences, enabling the user to discover their own preferences. For example, if the user is interested in a particular trend, the video recommendation unit recommends videos related to that trend. As a result, the fashion suggestion system according to this embodiment can predict trends, generate videos, and make recommendations according to the user's preferences.

[0030] The trend prediction unit predicts trends based on user preferences. For example, it analyzes fashion trends by country, age group, and generation to grasp and systematize trends. Specifically, the trend prediction unit gathers information from magazines, purchase data, and social media in various countries, and a generative AI analyzes it. The generative AI uses natural language processing technology to extract keywords and topics related to fashion from text data and image recognition technology to identify trendy items and styles from image data. For example, the generative AI analyzes fashion items and styles frequently posted on social media to understand their popularity and how trends change. It also analyzes purchase data to understand the trends in items and brands that consumers are actually buying and reflects this in trend predictions. Furthermore, the trend prediction unit learns the preferences and tastes of individual users based on their past purchase and search history, and performs personalized trend predictions. For example, it predicts new trends that a particular user is likely to like based on items they have purchased in the past and styles they have searched for. As a result, the trend prediction unit can predict trends that match user preferences with high accuracy and provide users with optimal fashion suggestions.

[0031] The video generation unit automatically generates fashion image videos of users wearing clothes based on trends predicted by the trend prediction unit. Specifically, the video generation unit automatically generates videos of customers wearing clothes that the user has searched for. The video generation unit utilizes 3D modeling technology and motion capture technology to generate videos that match the user's body shape and posture. For example, the video generation unit generates a 3D model that realistically reproduces the fit of the clothes based on the user's body shape data, and then adds various poses and movements to that model to generate a video that emphasizes ease of movement. In addition, the video generation unit simulates multiple camera angles to provide footage from different angles, allowing users to check the design and fit of the clothes from various perspectives. Furthermore, the video generation unit provides more realistic and personalized fashion image videos by reflecting the user's individual characteristics such as skin color, hairstyle, and accessories. For example, if a user tries on clothes of a particular color, it realistically reproduces how that color looks on the user's skin tone. The video generation unit also provides visually appealing content by adding backgrounds, music, effects, etc. to the generated videos. This allows the video generation unit to provide realistic image videos that allow users to try out new fashions on their own faces, thereby increasing their desire to purchase.

[0032] The video recommendation unit recommends videos generated by the video generation unit. Specifically, the video recommendation unit delivers the generated videos to users in short video format. The video recommendation unit utilizes generation AI to predict trends based on user preferences and help users discover their own preferences. The generation AI analyzes the user's viewing history and rating data to identify videos that the user is likely to be interested in. For example, if a user shows interest in a particular trend, it recommends videos related to that trend. Furthermore, the video recommendation unit analyzes user behavior data in real time and updates its recommendations according to changes in the user's interests and preferences. For example, if a user shows interest in a new trend, it immediately recommends the latest videos related to that trend. The video recommendation unit also collects user feedback and continuously improves the accuracy of its recommendation algorithm. For example, if a user gives a high rating to a recommended video, it increases the frequency of recommending similar videos based on that rating. On the other hand, if a user gives a low rating, the frequency of recommendations for videos related to that trend will be reduced. This allows the video recommendation system to provide the most suitable videos according to the user's preferences, helping users discover new fashion items and styles. Furthermore, the video recommendation system provides a function for users to share recommended videos, promoting their spread on social media. This allows users to share their favorite fashion with others and helps to form a fashion community.

[0033] The trend prediction unit can analyze fashion trends by country, era, and age group, identify trends, and systematize them. For example, the trend prediction unit can analyze popular items and styles in specific countries, eras, and age groups. The trend prediction unit takes in information from magazines, purchase data, and social media in various countries, and its generating AI analyzes this information. For example, the trend prediction unit collects information from specific magazine names, purchase data ranges, and social media platforms. Using the generating AI, the trend prediction unit analyzes the collected information to identify and systematize fashion trends. This improves the accuracy of trend predictions by identifying and systematizing fashion trends.

[0034] The video generation unit can automatically generate videos of customers wearing clothes that the user has searched for. For example, the video generation unit automatically generates videos of customers wearing clothes that the user has searched for. The video generation unit generates videos that are tailored to the user's body type and posture. For example, the video generation unit generates videos that realistically reproduce the fit of the clothes according to the user's body type. The video generation unit generates videos that emphasize ease of movement based on the user's posture. The video generation unit generates videos that provide footage from different angles according to the user's body type and posture. As a result, by automatically generating videos of customers wearing clothes that the user has searched for, it becomes easier for users to see what they will look like wearing the clothes.

[0035] The video recommendation unit can deliver generated videos to users in short video format. For example, the video recommendation unit can deliver generated videos to users in short video format. The video recommendation unit predicts trends based on user preferences, allowing users to discover their own preferences. For example, if a user shows interest in a particular trend, the video recommendation unit will recommend videos related to that trend. By delivering generated videos in short video format, users can easily watch videos.

[0036] The trend prediction unit can gather information from magazines, purchase data, and social media in various countries, and have it analyzed by a generative AI. For example, the trend prediction unit can collect information from specific magazine names, purchase data ranges, and social media platforms. Using generative AI, the trend prediction unit analyzes the collected information to grasp and systematize trends in fashion. As a result, the accuracy of trend predictions is improved by gathering information from magazines, purchase data, and social media in various countries and having the generative AI analyze it.

[0037] The video generation unit can generate videos tailored to the user's body shape and posture. For example, it can generate videos that realistically reproduce the fit of clothing based on the user's body shape. It can also generate videos that emphasize ease of movement based on the user's posture. Furthermore, it can generate videos that offer footage from different angles depending on the user's body shape and posture. By generating videos tailored to the user's body shape and posture, it can provide a more realistic fashion image.

[0038] The video recommendation system can predict trends based on user preferences, enabling users to discover their own tastes. For example, if a user shows interest in a particular trend, the system will recommend videos related to that trend. By predicting trends based on user preferences and enabling users to discover their own tastes, the system improves user satisfaction.

[0039] The trend prediction unit can improve the accuracy of its predictions by analyzing the user's past fashion history. For example, the trend prediction unit analyzes the user's preferences based on data of items the user has purchased in the past and reflects this in its predictions. The trend prediction unit predicts styles the user is interested in based on the history of fashion items the user has viewed in the past. The trend prediction unit predicts appropriate trends based on the user's fashion history from past events and occasions they have attended. In this way, the accuracy of trend predictions is improved by analyzing the user's past fashion history.

[0040] The trend forecasting unit can customize its predictions based on the user's lifestyle and occupation. For example, if the user is a business person, the trend forecasting unit will prioritize predicting office casual or business styles. If the user has an active lifestyle, the trend forecasting unit will predict sportswear or casual styles. If the user is engaged in a creative occupation, the trend forecasting unit will predict unique and individual styles. By customizing predictions based on the user's lifestyle and occupation, more accurate trend forecasts become possible.

[0041] The trend prediction unit can predict region-specific trends by considering the user's geographical location information during trend prediction. For example, if the user lives in an urban area, the trend prediction unit will predict a sophisticated, urban style. If the user lives in a rural area, the trend prediction unit will predict a casual, relaxed style. If the user lives in a specific country or region, the trend prediction unit will predict region-specific trends. In this way, by considering the user's geographical location information, it is possible to predict region-specific trends.

[0042] The trend prediction unit analyzes users' social media activity to predict relevant trends. For example, it predicts trends based on the fashion styles of influencers that users follow. It analyzes fashion-related photos and comments posted by users to predict their preferred styles. It makes predictions based on trends in fashion communities that users participate in. In this way, by analyzing users' social media activity, it can predict relevant trends.

[0043] The video generation unit can adjust the level of detail in the video based on the user's body shape and posture during video generation. For example, the video generation unit can generate a video that realistically reproduces the fit of clothing to match the user's body shape. The video generation unit can generate a video that emphasizes ease of movement based on the user's posture. The video generation unit can generate a video that provides footage from different angles depending on the user's body shape and posture. By adjusting the level of detail in the video based on the user's body shape and posture, it is possible to generate more realistic videos.

[0044] The video generation unit can apply different video generation algorithms depending on the user's fashion history when generating videos. For example, the video generation unit can generate videos based on the styles of brands the user has previously purchased. The video generation unit can generate videos that highlight styles of interest based on the user's past browsing history of fashion items. The video generation unit can generate videos that suggest appropriate styles based on the user's fashion history of events and occasions they have attended. In this way, by applying different video generation algorithms according to the user's fashion history, more appropriate videos can be generated.

[0045] The video generation unit can prioritize videos based on when the user submits them. For example, if a user submits a video immediately before attending a specific event, the unit will prioritize generating a video suitable for that event. If a user submits a video at the change of seasons, the unit will prioritize generating a video in a style appropriate for that season. If a user submits a video during a period when a particular trend is popular, the unit will prioritize generating a video related to that trend. By prioritizing videos based on when the user submits them, the unit can generate more appropriate videos.

[0046] The video generation unit can adjust the order of videos based on user relevance during video generation. For example, it can prioritize displaying highly relevant videos based on the order in which the user has previously viewed videos. If the user is interested in a particular brand or style, the video generation unit will prioritize displaying related videos. If the user has plans to attend a particular event or occasion, the video generation unit will prioritize displaying related videos. By adjusting the order of videos based on user relevance, it is possible to generate more appropriate videos.

[0047] The video recommendation system can analyze a user's past viewing history to select the most suitable recommendation method. For example, it can analyze a user's preferences based on data from videos they have previously watched and reflect this in its recommendations. It can also recommend styles that a user might be interested in based on their past viewing history of fashion items. Furthermore, it can make appropriate recommendations based on a user's viewing history of past events and activities. In this way, by analyzing a user's past viewing history, the system can select the most suitable recommendation method.

[0048] The video recommendation system can customize its recommendations based on the user's current lifestyle. For example, if the user is a business professional, it will recommend videos featuring office casual or business attire. If the user has an active lifestyle, it will recommend videos featuring sportswear or casual styles. If the user is in a creative profession, it will recommend videos featuring unique and individual styles. By customizing recommendations based on the user's current lifestyle, more appropriate recommendations become possible.

[0049] The video recommendation system can select the optimal recommendation method by considering the user's geographical location. For example, if a user lives in an urban area, the system will recommend videos with a sophisticated, urban style. If a user lives in a rural area, the system will recommend videos with a casual, relaxed style. If a user lives in a specific country or region, the system will recommend videos that reflect the trends specific to that region. In this way, the system can select the optimal recommendation method by considering the user's geographical location.

[0050] The video recommendation team can analyze users' social media activity and suggest recommendation methods when making recommendations. For example, the video recommendation team can make recommendations based on the fashion styles of influencers that users follow. The video recommendation team can analyze fashion-related photos and comments posted by users and recommend styles they like. The video recommendation team can make recommendations based on trends in fashion communities that users participate in. In this way, by analyzing users' social media activity, the team can suggest the most suitable recommendation method.

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

[0052] A fashion suggestion system can improve the accuracy of trend predictions by analyzing a user's past fashion history. For example, it can analyze a user's preferences based on data from items they have purchased in the past and incorporate that into predictions. It can predict styles a user is interested in based on the fashion items they have viewed in the past. It can predict appropriate trends based on the fashion history of events and occasions a user has attended in the past. In this way, analyzing a user's past fashion history improves the accuracy of trend predictions.

[0053] The fashion suggestion system can customize trend predictions based on the user's lifestyle and occupation. For example, if the user is a business person, the system can prioritize predicting office casual and business styles. If the user has an active lifestyle, it can predict sportswear and casual styles. If the user is engaged in a creative profession, it can predict unique and individual styles. By customizing predictions based on the user's lifestyle and occupation, more appropriate trend predictions can be made.

[0054] The fashion suggestion system can predict region-specific trends by considering the user's geographical location. For example, if the user lives in an urban area, it can predict sophisticated, urban styles. If the user lives in a rural area, it can predict casual, relaxed styles. If the user lives in a specific country or region, it can predict region-specific trends. In this way, by considering the user's geographical location, it can predict region-specific trends.

[0055] A fashion suggestion system can analyze a user's social media activity and predict relevant trends. For example, it can predict trends based on the fashion styles of influencers the user follows. It can also analyze fashion-related photos and comments posted by the user to predict their preferred style. Predictions can be made based on trends in the fashion communities the user participates in. In this way, by analyzing a user's social media activity, it can predict relevant trends.

[0056] A fashion recommendation system can analyze a user's past viewing history to select the most suitable recommendation method. For example, it can analyze a user's preferences based on data from videos they have watched in the past and reflect this in recommendations. It can recommend styles that the user is interested in based on their viewing history of fashion items they have watched in the past. It can also provide appropriate recommendations based on the user's viewing history of events and activities they have attended in the past. In this way, by analyzing a user's past viewing history, the system can select the most suitable recommendation method.

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

[0058] Step 1: The trend prediction unit predicts trends based on user preferences. The trend prediction unit analyzes fashion trends by country, age group, and demographic, identifying and systematizing the trends. Information is gathered from magazines, purchase data, and social media in various countries, and analyzed by the generating AI. For example, it analyzes popular items and styles in specific countries, age groups, and demographics to predict trends based on user preferences. Step 2: The video generation unit automatically generates fashion image videos of the user wearing clothes based on the trends predicted by the trend prediction unit. For example, it automatically generates videos of customers wearing clothes that the user has searched for, and generates videos that are tailored to the user's body type and posture. It generates videos that realistically reproduce the fit of the clothes according to the user's body type, videos that emphasize ease of movement based on the user's posture, and videos that provide footage from different angles. Step 3: The video recommendation unit recommends videos generated by the video generation unit. The generated videos are delivered to the user in short video format, and trends are predicted based on the user's preferences, allowing the user to discover their own preferences. For example, if a user is interested in a particular trend, videos related to that trend are recommended.

[0059] (Example of form 2) The fashion suggestion system according to an embodiment of the present invention is a system for users to find clothes that suit them. This fashion suggestion system utilizes generative AI to create a generative AI that incorporates fashion trends and items from different countries, eras, and age groups, predicts trends according to the user's preferences, and automatically generates a fashion image video of the user wearing the clothes. This video allows the user to see themselves moving around in the clothes, making it easier to visualize how they would look when wearing them. Furthermore, it allows users to easily check trends across different eras, countries, and age groups without effort, and to discover their own preferences by viewing a wide variety of trends. For example, the fashion suggestion system analyzes fashion trends from different countries, eras, and age groups, grasps the trends, and systematizes them. For example, it incorporates information from magazines, purchase data, and social media in various countries, and the generative AI analyzes it. Next, the fashion suggestion system automatically generates a video of the customer wearing the clothes that the user has searched for. The generative AI predicts trends according to the user's preferences and generates a fashion image video of the user wearing the clothes. For example, if a user searches for a specific piece of clothing, a video of them wearing that clothing will be generated. The generated video is delivered to the user in short video format. This allows the user to easily see themselves moving around in the clothes. Furthermore, the system predicts trends based on user preferences, enabling users to discover their own tastes. For example, if a user becomes interested in a particular trend, videos related to that trend will be recommended. This system allows users to find clothes that suit them, reducing the stress of information overload from keeping up with trends, the stress of starting to wear similar styles to others, and the stress of not being able to pursue the fashion they truly love. In this way, the fashion suggestion system allows users to find clothes that suit them, reducing the stress of information overload from keeping up with trends, the stress of starting to wear similar styles to others, and the stress of not being able to pursue the fashion they truly love.

[0060] The fashion suggestion system according to this embodiment comprises a trend prediction unit, a video generation unit, and a video recommendation unit. The trend prediction unit predicts trends according to the user's preferences. The trend prediction unit analyzes, for example, popular fashion trends by country, age group, and generation, grasps trends, and systematizes them. The trend prediction unit takes in information from magazines, purchase data, and social media in various countries, and the generation AI analyzes it. For example, the trend prediction unit analyzes popular items and styles in specific countries, age groups, and generation segments. The trend prediction unit uses the generation AI to predict trends according to the user's preferences. The video generation unit automatically generates fashion image videos of the user wearing clothes based on the trends predicted by the trend prediction unit. For example, the video generation unit automatically generates videos of the customer wearing clothes that the user has searched for. The video generation unit generates videos that match the user's body shape and posture. For example, the video generation unit generates videos that realistically reproduce the fit of clothes according to the user's body shape. The video generation unit generates videos that emphasize ease of movement based on the user's posture. The video generation unit generates videos that provide footage from different angles, depending on the user's body type and posture. The video recommendation unit recommends the videos generated by the video generation unit. The video recommendation unit delivers the generated videos to the user in short video format. The video recommendation unit predicts trends according to the user's preferences, enabling the user to discover their own preferences. For example, if the user is interested in a particular trend, the video recommendation unit recommends videos related to that trend. As a result, the fashion suggestion system according to this embodiment can predict trends, generate videos, and make recommendations according to the user's preferences.

[0061] The trend prediction unit predicts trends based on user preferences. For example, it analyzes fashion trends by country, age group, and generation to grasp and systematize trends. Specifically, the trend prediction unit gathers information from magazines, purchase data, and social media in various countries, and a generative AI analyzes it. The generative AI uses natural language processing technology to extract keywords and topics related to fashion from text data and image recognition technology to identify trendy items and styles from image data. For example, the generative AI analyzes fashion items and styles frequently posted on social media to understand their popularity and how trends change. It also analyzes purchase data to understand the trends in items and brands that consumers are actually buying and reflects this in trend predictions. Furthermore, the trend prediction unit learns the preferences and tastes of individual users based on their past purchase and search history, and performs personalized trend predictions. For example, it predicts new trends that a particular user is likely to like based on items they have purchased in the past and styles they have searched for. As a result, the trend prediction unit can predict trends that match user preferences with high accuracy and provide users with optimal fashion suggestions.

[0062] The video generation unit automatically generates fashion image videos of users wearing clothes based on trends predicted by the trend prediction unit. Specifically, the video generation unit automatically generates videos of customers wearing clothes that the user has searched for. The video generation unit utilizes 3D modeling technology and motion capture technology to generate videos that match the user's body shape and posture. For example, the video generation unit generates a 3D model that realistically reproduces the fit of the clothes based on the user's body shape data, and then adds various poses and movements to that model to generate a video that emphasizes ease of movement. In addition, the video generation unit simulates multiple camera angles to provide footage from different angles, allowing users to check the design and fit of the clothes from various perspectives. Furthermore, the video generation unit provides more realistic and personalized fashion image videos by reflecting the user's individual characteristics such as skin color, hairstyle, and accessories. For example, if a user tries on clothes of a particular color, it realistically reproduces how that color looks on the user's skin tone. The video generation unit also provides visually appealing content by adding backgrounds, music, effects, etc. to the generated videos. This allows the video generation unit to provide realistic image videos that allow users to try out new fashions on their own faces, thereby increasing their desire to purchase.

[0063] The video recommendation unit recommends videos generated by the video generation unit. Specifically, the video recommendation unit delivers the generated videos to users in short video format. The video recommendation unit utilizes generation AI to predict trends based on user preferences and help users discover their own preferences. The generation AI analyzes the user's viewing history and rating data to identify videos that the user is likely to be interested in. For example, if a user shows interest in a particular trend, it recommends videos related to that trend. Furthermore, the video recommendation unit analyzes user behavior data in real time and updates its recommendations according to changes in the user's interests and preferences. For example, if a user shows interest in a new trend, it immediately recommends the latest videos related to that trend. The video recommendation unit also collects user feedback and continuously improves the accuracy of its recommendation algorithm. For example, if a user gives a high rating to a recommended video, it increases the frequency of recommending similar videos based on that rating. On the other hand, if a user gives a low rating, the frequency of recommendations for videos related to that trend will be reduced. This allows the video recommendation system to provide the most suitable videos according to the user's preferences, helping users discover new fashion items and styles. Furthermore, the video recommendation system provides a function for users to share recommended videos, promoting their spread on social media. This allows users to share their favorite fashion with others and helps to form a fashion community.

[0064] The trend prediction unit can analyze fashion trends by country, era, and age group, identify trends, and systematize them. For example, the trend prediction unit can analyze popular items and styles in specific countries, eras, and age groups. The trend prediction unit takes in information from magazines, purchase data, and social media in various countries, and its generating AI analyzes this information. For example, the trend prediction unit collects information from specific magazine names, purchase data ranges, and social media platforms. Using the generating AI, the trend prediction unit analyzes the collected information to identify and systematize fashion trends. This improves the accuracy of trend predictions by identifying and systematizing fashion trends.

[0065] The video generation unit can automatically generate videos of customers wearing clothes that the user has searched for. For example, the video generation unit automatically generates videos of customers wearing clothes that the user has searched for. The video generation unit generates videos that are tailored to the user's body type and posture. For example, the video generation unit generates videos that realistically reproduce the fit of the clothes according to the user's body type. The video generation unit generates videos that emphasize ease of movement based on the user's posture. The video generation unit generates videos that provide footage from different angles according to the user's body type and posture. As a result, by automatically generating videos of customers wearing clothes that the user has searched for, it becomes easier for users to see what they will look like wearing the clothes.

[0066] The video recommendation unit can deliver generated videos to users in short video format. For example, the video recommendation unit can deliver generated videos to users in short video format. The video recommendation unit predicts trends based on user preferences, allowing users to discover their own preferences. For example, if a user shows interest in a particular trend, the video recommendation unit will recommend videos related to that trend. By delivering generated videos in short video format, users can easily watch videos.

[0067] The trend prediction unit can gather information from magazines, purchase data, and social media in various countries, and have it analyzed by a generative AI. For example, the trend prediction unit can collect information from specific magazine names, purchase data ranges, and social media platforms. Using generative AI, the trend prediction unit analyzes the collected information to grasp and systematize trends in fashion. As a result, the accuracy of trend predictions is improved by gathering information from magazines, purchase data, and social media in various countries and having the generative AI analyze it.

[0068] The video generation unit can generate videos tailored to the user's body shape and posture. For example, it can generate videos that realistically reproduce the fit of clothing based on the user's body shape. It can also generate videos that emphasize ease of movement based on the user's posture. Furthermore, it can generate videos that offer footage from different angles depending on the user's body shape and posture. By generating videos tailored to the user's body shape and posture, it can provide a more realistic fashion image.

[0069] The video recommendation system can predict trends based on user preferences, enabling users to discover their own tastes. For example, if a user shows interest in a particular trend, the system will recommend videos related to that trend. By predicting trends based on user preferences and enabling users to discover their own tastes, the system improves user satisfaction.

[0070] The trend prediction unit can estimate the user's emotions and adjust the accuracy of trend predictions based on those emotions. For example, if the user is excited, the trend prediction unit will prioritize predicting and suggesting the latest trends. If the user is relaxed, the trend prediction unit will predict and suggest past trends or classic styles. If the user is stressed, the trend prediction unit will predict and suggest simple and calming styles. By adjusting the accuracy of trend predictions based on the user's emotions, more appropriate trend predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The trend prediction unit can improve the accuracy of its predictions by analyzing the user's past fashion history. For example, the trend prediction unit analyzes the user's preferences based on data of items the user has purchased in the past and reflects this in its predictions. The trend prediction unit predicts styles the user is interested in based on the history of fashion items the user has viewed in the past. The trend prediction unit predicts appropriate trends based on the user's fashion history from past events and occasions they have attended. In this way, the accuracy of trend predictions is improved by analyzing the user's past fashion history.

[0072] The trend forecasting unit can customize its predictions based on the user's lifestyle and occupation. For example, if the user is a business person, the trend forecasting unit will prioritize predicting office casual or business styles. If the user has an active lifestyle, the trend forecasting unit will predict sportswear or casual styles. If the user is engaged in a creative occupation, the trend forecasting unit will predict unique and individual styles. By customizing predictions based on the user's lifestyle and occupation, more accurate trend forecasts become possible.

[0073] The trend prediction unit can estimate the user's emotions and determine the priority of trend predictions based on the estimated emotions. For example, if the user is excited, the trend prediction unit will prioritize suggesting the latest trends. If the user is relaxed, the trend prediction unit will prioritize suggesting past trends or classic styles. If the user is stressed, the trend prediction unit will prioritize suggesting simple and calming styles. By prioritizing trend predictions based on the user's emotions, more appropriate trend predictions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The trend prediction unit can predict region-specific trends by considering the user's geographical location information during trend prediction. For example, if the user lives in an urban area, the trend prediction unit will predict a sophisticated, urban style. If the user lives in a rural area, the trend prediction unit will predict a casual, relaxed style. If the user lives in a specific country or region, the trend prediction unit will predict region-specific trends. In this way, by considering the user's geographical location information, it is possible to predict region-specific trends.

[0075] The trend prediction unit analyzes users' social media activity to predict relevant trends. For example, it predicts trends based on the fashion styles of influencers that users follow. It analyzes fashion-related photos and comments posted by users to predict their preferred styles. It makes predictions based on trends in fashion communities that users participate in. In this way, by analyzing users' social media activity, it can predict relevant trends.

[0076] The video generation unit can estimate the user's emotions and adjust the video's presentation based on those emotions. For example, if the user is relaxed, the video generation unit will generate a video that progresses at a leisurely pace. If the user is in a hurry, the video generation unit will generate a video that emphasizes the shortest route. If the user is excited, the video generation unit will generate a video with visually stimulating effects. In this way, by adjusting the video's presentation based on the user's emotions, a more appropriate video can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The video generation unit can adjust the level of detail in the video based on the user's body shape and posture during video generation. For example, the video generation unit can generate a video that realistically reproduces the fit of clothing to match the user's body shape. The video generation unit can generate a video that emphasizes ease of movement based on the user's posture. The video generation unit can generate a video that provides footage from different angles depending on the user's body shape and posture. By adjusting the level of detail in the video based on the user's body shape and posture, it is possible to generate more realistic videos.

[0078] The video generation unit can apply different video generation algorithms depending on the user's fashion history when generating videos. For example, the video generation unit can generate videos based on the styles of brands the user has previously purchased. The video generation unit can generate videos that highlight styles of interest based on the user's past browsing history of fashion items. The video generation unit can generate videos that suggest appropriate styles based on the user's fashion history of events and occasions they have attended. In this way, by applying different video generation algorithms according to the user's fashion history, more appropriate videos can be generated.

[0079] The video generation unit can estimate the user's emotions and adjust the video length based on the estimated emotions. For example, if the user is in a hurry, the video generation unit will generate a short, concise video. If the user is relaxed, the video generation unit will generate a longer video with detailed explanations. If the user is excited, the video generation unit will generate a video with visually stimulating effects. By adjusting the video length based on the user's emotions, a more appropriate video can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The video generation unit can prioritize videos based on when the user submits them. For example, if a user submits a video immediately before attending a specific event, the unit will prioritize generating a video suitable for that event. If a user submits a video at the change of seasons, the unit will prioritize generating a video in a style appropriate for that season. If a user submits a video during a period when a particular trend is popular, the unit will prioritize generating a video related to that trend. By prioritizing videos based on when the user submits them, the unit can generate more appropriate videos.

[0081] The video generation unit can adjust the order of videos based on user relevance during video generation. For example, it can prioritize displaying highly relevant videos based on the order in which the user has previously viewed videos. If the user is interested in a particular brand or style, the video generation unit will prioritize displaying related videos. If the user has plans to attend a particular event or occasion, the video generation unit will prioritize displaying related videos. By adjusting the order of videos based on user relevance, it is possible to generate more appropriate videos.

[0082] The video recommendation unit can estimate the user's emotions and adjust its recommendation method based on those emotions. For example, if the user is relaxed, the video recommendation unit will recommend videos that proceed at a leisurely pace. If the user is in a hurry, the video recommendation unit will recommend videos that emphasize the shortest route. If the user is excited, the video recommendation unit will recommend videos with visually stimulating effects. By adjusting the recommendation method based on the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The video recommendation system can analyze a user's past viewing history to select the most suitable recommendation method. For example, it can analyze a user's preferences based on data from videos they have previously watched and reflect this in its recommendations. It can also recommend styles that a user might be interested in based on their past viewing history of fashion items. Furthermore, it can make appropriate recommendations based on a user's viewing history of past events and activities. In this way, by analyzing a user's past viewing history, the system can select the most suitable recommendation method.

[0084] The video recommendation system can customize its recommendations based on the user's current lifestyle. For example, if the user is a business professional, it will recommend videos featuring office casual or business attire. If the user has an active lifestyle, it will recommend videos featuring sportswear or casual styles. If the user is in a creative profession, it will recommend videos featuring unique and individual styles. By customizing recommendations based on the user's current lifestyle, more appropriate recommendations become possible.

[0085] The video recommendation system can estimate the user's emotions and determine recommendation priorities based on those emotions. For example, if the user is excited, the system will prioritize recommending the latest trends. If the user is relaxed, the system will prioritize recommending past trends or classic styles. If the user is stressed, the system will prioritize recommending simple and calming styles. By prioritizing recommendations based on the user's emotions, more appropriate recommendations can be made. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The video recommendation system can select the optimal recommendation method by considering the user's geographical location. For example, if a user lives in an urban area, the system will recommend videos with a sophisticated, urban style. If a user lives in a rural area, the system will recommend videos with a casual, relaxed style. If a user lives in a specific country or region, the system will recommend videos that reflect the trends specific to that region. In this way, the system can select the optimal recommendation method by considering the user's geographical location.

[0087] The video recommendation team can analyze users' social media activity and suggest recommendation methods when making recommendations. For example, the video recommendation team can make recommendations based on the fashion styles of influencers that users follow. The video recommendation team can analyze fashion-related photos and comments posted by users and recommend styles they like. The video recommendation team can make recommendations based on trends in fashion communities that users participate in. In this way, by analyzing users' social media activity, the team can suggest the most suitable recommendation method.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The fashion suggestion system can estimate the user's emotions and adjust the accuracy of trend predictions based on those emotions. For example, if the user is excited, it can prioritize predicting and suggesting the latest trends. If the user is relaxed, it can predict and suggest past trends or classic styles. If the user is stressed, it can predict and suggest simple and calming styles. By adjusting the accuracy of trend predictions based on the user's emotions, more appropriate trend predictions become possible.

[0090] A fashion suggestion system can improve the accuracy of trend predictions by analyzing a user's past fashion history. For example, it can analyze a user's preferences based on data from items they have purchased in the past and incorporate that into predictions. It can predict styles a user is interested in based on the fashion items they have viewed in the past. It can predict appropriate trends based on the fashion history of events and occasions a user has attended in the past. In this way, analyzing a user's past fashion history improves the accuracy of trend predictions.

[0091] The fashion suggestion system can customize trend predictions based on the user's lifestyle and occupation. For example, if the user is a business person, the system can prioritize predicting office casual and business styles. If the user has an active lifestyle, it can predict sportswear and casual styles. If the user is engaged in a creative profession, it can predict unique and individual styles. By customizing predictions based on the user's lifestyle and occupation, more appropriate trend predictions can be made.

[0092] The fashion suggestion system can predict region-specific trends by considering the user's geographical location. For example, if the user lives in an urban area, it can predict sophisticated, urban styles. If the user lives in a rural area, it can predict casual, relaxed styles. If the user lives in a specific country or region, it can predict region-specific trends. In this way, by considering the user's geographical location, it can predict region-specific trends.

[0093] A fashion suggestion system can analyze a user's social media activity and predict relevant trends. For example, it can predict trends based on the fashion styles of influencers the user follows. It can also analyze fashion-related photos and comments posted by the user to predict their preferred style. Predictions can be made based on trends in the fashion communities the user participates in. In this way, by analyzing a user's social media activity, it can predict relevant trends.

[0094] The fashion suggestion system can estimate the user's emotions and adjust the video's presentation based on those emotions. For example, if the user is relaxed, it can generate a video that progresses at a leisurely pace. If the user is in a hurry, it can generate a video that emphasizes the shortest route. If the user is excited, it can generate a video with visually stimulating effects. In this way, by adjusting the video's presentation based on the user's emotions, it can generate more appropriate videos.

[0095] The fashion suggestion system can estimate the user's emotions and adjust the video length based on those emotions. For example, if the user is in a hurry, it can generate a short, to-the-point video. If the user is relaxed, it can generate a longer video with detailed explanations. If the user is excited, it can generate a video with visually stimulating effects. By adjusting the video length based on the user's emotions, it can generate more appropriate videos.

[0096] The fashion recommendation system can estimate the user's emotions and adjust its recommendation methods based on those emotions. For example, if the user is relaxed, it can recommend videos that proceed at a leisurely pace. If the user is in a hurry, it can recommend videos that emphasize the shortest route. If the user is excited, it can recommend videos with visually stimulating effects. By adjusting the recommendation method based on the user's emotions, it becomes possible to provide more appropriate recommendations.

[0097] The fashion recommendation system can estimate the user's emotions and prioritize recommendations based on those emotions. For example, if the user is excited, it can prioritize recommending the latest trends. If the user is relaxed, it can prioritize recommending past trends or classic styles. If the user is stressed, it can prioritize recommending simple and calming styles. By prioritizing recommendations based on the user's emotions, it becomes possible to provide more appropriate recommendations.

[0098] A fashion recommendation system can analyze a user's past viewing history to select the most suitable recommendation method. For example, it can analyze a user's preferences based on data from videos they have watched in the past and reflect this in recommendations. It can recommend styles that the user is interested in based on their viewing history of fashion items they have watched in the past. It can also provide appropriate recommendations based on the user's viewing history of events and activities they have attended in the past. In this way, by analyzing a user's past viewing history, the system can select the most suitable recommendation method.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The trend prediction unit predicts trends based on user preferences. The trend prediction unit analyzes fashion trends by country, age group, and demographic, identifying and systematizing the trends. Information is gathered from magazines, purchase data, and social media in various countries, and analyzed by the generating AI. For example, it analyzes popular items and styles in specific countries, age groups, and demographics to predict trends based on user preferences. Step 2: The video generation unit automatically generates fashion image videos of the user wearing clothes based on the trends predicted by the trend prediction unit. For example, it automatically generates videos of customers wearing clothes that the user has searched for, and generates videos that are tailored to the user's body type and posture. It generates videos that realistically reproduce the fit of the clothes according to the user's body type, videos that emphasize ease of movement based on the user's posture, and videos that provide footage from different angles. Step 3: The video recommendation unit recommends videos generated by the video generation unit. The generated videos are delivered to the user in short video format, and trends are predicted based on the user's preferences, allowing the user to discover their own preferences. For example, if a user is interested in a particular trend, videos related to that trend are recommended.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the trend prediction unit, video generation unit, and video recommendation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the trend prediction unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The video generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The video recommendation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the trend prediction unit, video generation unit, and video recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the trend prediction unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The video generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The video recommendation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the trend prediction unit, video generation unit, and video recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the trend prediction unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The video generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The video recommendation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the trend prediction unit, video generation unit, and video recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the trend prediction unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The video generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The video recommendation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[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] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A trend prediction unit that predicts trends according to user preferences, Based on the trends predicted by the aforementioned trend prediction unit, a video generation unit automatically generates fashion image videos of the user wearing the clothes, The system includes a video recommendation unit that recommends videos generated by the video generation unit. A system characterized by the following features. (Note 2) The aforementioned trend forecasting unit is Analyze fashion trends by country, era, and age group to identify and systematize them. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned video generation unit, Automatically generates videos of customers wearing clothes that users have searched for. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned video recommendation section is, The generated video is delivered to the user in a short video format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned trend forecasting unit is The AI ​​generates and analyzes information collected from magazines, purchase data, and social media in various countries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned video generation unit, Generates videos tailored to the user's body shape and posture. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned video recommendation section is, Predicting trends based on user preferences and enabling users to discover their own preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned trend forecasting unit is It estimates user sentiment and adjusts the accuracy of trend predictions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned trend forecasting unit is When predicting trends, we analyze the user's past fashion history to improve the accuracy of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned trend forecasting unit is When predicting trends, customize the predictions based on the user's lifestyle and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned trend forecasting unit is It estimates user sentiment and determines the priority of trend predictions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned trend forecasting unit is When predicting trends, the system takes into account the user's geographical location to predict region-specific trends. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned trend forecasting unit is When predicting trends, we analyze users' social media activity and predict relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned video generation unit, The system estimates the user's emotions and adjusts the video's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned video generation unit, When generating a video, the level of detail in the video is adjusted based on the user's body shape and posture. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned video generation unit, When generating videos, different video generation algorithms are applied depending on the user's fashion history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned video generation unit, It estimates the user's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned video generation unit, When generating videos, the system prioritizes videos based on when they were submitted by the users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned video generation unit, When generating videos, the order of the videos is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned video recommendation section is, It estimates the user's emotions and adjusts the recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned video recommendation section is, When making recommendations, the system analyzes the user's past viewing history to select the most suitable recommendation method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned video recommendation section is, When making recommendations, customize the recommendation method based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned video recommendation section is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned video recommendation section is, When making recommendations, the system selects the optimal recommendation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned video recommendation section is, When making recommendations, the system analyzes the user's social media activity to suggest appropriate recommendation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A trend prediction unit that predicts trends according to user preferences, Based on the trends predicted by the aforementioned trend prediction unit, a video generation unit automatically generates fashion image videos of the user wearing the clothes, The system includes a video recommendation unit that recommends videos generated by the video generation unit. A system characterized by the following features.

2. The aforementioned trend forecasting unit is Analyze fashion trends by country, era, and age group to identify and systematize them. The system according to feature 1.

3. The aforementioned video generation unit, Automatically generates videos of customers wearing clothes that users have searched for. The system according to feature 1.

4. The aforementioned video recommendation section is, The generated video is delivered to the user in a short video format. The system according to feature 1.

5. The aforementioned trend forecasting unit is The AI ​​generates and analyzes information collected from magazines, purchase data, and social media in various countries. The system according to feature 1.

6. The aforementioned video generation unit, Generates videos tailored to the user's body shape and posture. The system according to feature 1.

7. The aforementioned video recommendation section is, Predicting trends based on user preferences and enabling users to discover their own preferences. The system according to feature 1.

8. The aforementioned trend forecasting unit is It estimates user sentiment and adjusts the accuracy of trend predictions based on the estimated user sentiment. The system according to feature 1.

Images