System

Abstract

An object of the system according to the embodiment is to display a video according to a user's preference and reflect actual weather and time.SOLUTION: A system according to an embodiment includes a reception unit, a selection unit, a reflection unit, and a display unit. The reception unit receives an input of a type of image preferred by the user. The selection unit selects a video on the basis of the information input by the reception unit. The reflection unit reflects the actual weather and time on the image selected by the selection unit. The display unit displays the image reflected by the reflection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to display images according to the user's preferences and that it is not possible to reflect the actual weather or time.

[0005] The system according to the embodiment aims to display an image according to the user's preferences and to reflect the actual weather and time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a selection unit, a reflection unit, and a display unit. The reception unit inputs the type of video the user prefers. The selection unit selects a video based on the information input by the reception unit. The reflection unit reflects the actual weather and time in the video selected by the selection unit. The display unit displays the video reflected by the reflection unit. [Effects of the Invention]

[0007] The system according to the embodiment can display images according to the user's preferences and reflect the actual weather and time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A video display system according to an embodiment of the present invention displays video tailored to a user's preferences and reflects actual weather and time. In the video display system, a user inputs their preferred video type, and AI selects and displays an appropriate video based on the user's preferences. Furthermore, the selected video can reflect actual weather and time. For example, in the video display system, a user selects a video type, such as a natural landscape, a cityscape, or a seascape. This information is input to AI. The AI ​​then analyzes the input information and selects an appropriate video based on the user's preferences. For example, if the user selects a natural landscape, the AI ​​searches for natural landscape videos and selects the most appropriate video. Furthermore, the selected video can reflect actual weather and time. For example, if the current weather is sunny, a video of a sunny natural landscape is displayed, and if it is evening, a video of an evening natural landscape is displayed. This allows the user to enjoy video that changes in real time. This allows the video display system to enjoy video tailored to the user's preferences. For example, a user can select a natural landscape video when they want to relax, and an urban landscape video when they are working. Furthermore, reflecting actual weather and time can provide a more realistic video experience.

[0029] A video display system according to an embodiment includes a reception unit, a selection unit, a reflection unit, and a display unit. The reception unit inputs a user's preferred video type. The user's preferred video type may include, but is not limited to, natural landscapes, urban landscapes, and ocean views. The reception unit may receive, for example, voice input or text input. The selection unit selects a video based on the information input by the reception unit. The selection unit may also select a video by taking into account, for example, the user's past selection history. The selection unit may use AI to select an optimal video based on the user's preferences. The reflection unit reflects actual weather and time in the video selected by the selection unit. The reflection unit may, for example, obtain weather and time information from the Internet and reflect the information in the video. The reflection unit may use AI to analyze weather and time information and reflect the information in the video. The display unit displays the video reflected by the reflection unit. The display unit may display the video on a device such as a smartphone, tablet, or television. The display unit may also adjust the brightness and volume of the video. As a result, the video display system according to the embodiment can display video that matches the user's preferences and reflects the actual weather and time. For example, if the user selects a video of a natural landscape and the current weather is sunny, the video of a sunny natural landscape can be displayed. This allows the user to enjoy video that changes in real time.

[0030] The selection unit can select a video based on the user's past selection history. For example, the selection unit selects a video taking into consideration the user's past selection history. For example, the selection unit selects an optimal video based on the types of videos the user has selected in the past. The selection unit can also predict and select a video that the user will prefer at a specific time period from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and select the most preferred video. This allows for a more appropriate video to be selected by taking the user's past selection history into consideration. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's past selection history into AI and have the AI ​​select the optimal video.

[0031] The reflection unit can acquire weather and time information from the Internet. The reflection unit acquires weather and time information from the Internet, for example. For example, the reflection unit acquires weather forecast data and the current time using an API. The reflection unit can also acquire weather and time information using web scraping technology. Furthermore, the reflection unit can acquire weather and time information from a public database on the Internet. In this way, by acquiring weather and time information from the Internet, real-time information can be reflected. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input weather and time information acquired from the Internet into AI and have the AI ​​reflect the information on the video.

[0032] The display unit can display images on devices such as smartphones, tablets, and televisions. The display unit can display images on devices such as smartphones, tablets, and televisions. For example, the display unit can display images on iOS devices, Android devices, smart TVs, and the like. The display unit can also display images on devices such as computers and projectors. Furthermore, the display unit can display images on multiple devices simultaneously. This allows images to be displayed on various devices. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input information about the device displaying the image into AI and have the AI ​​execute the optimal display method.

[0033] The display unit can adjust the brightness and volume of the image. The display unit adjusts, for example, the brightness and volume of the image. For example, the display unit adjusts the brightness and volume of the image based on user settings. The display unit can also automatically adjust the brightness of the image according to the ambient brightness using an ambient light sensor. Furthermore, the display unit can adjust the volume based on a user's voice command using voice recognition technology. This allows the display to be tailored to the user's preferences by adjusting the brightness and volume of the image. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can have AI adjust the brightness and volume of the image.

[0034] The reception unit can accept voice input or text input. The reception unit accepts, for example, voice input or text input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also accept keyboard input. Furthermore, the reception unit can also accept text input using a touch screen of a smartphone or tablet. This allows the user to easily input the type of video by accepting voice input or text input. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can have AI execute processing of the voice input or text input.

[0035] The selection unit can collect user feedback and improve the video selection. For example, the selection unit collects user feedback and improves the video selection. For example, the selection unit collects user feedback through a questionnaire. The selection unit can also collect user ratings using a rating system. Furthermore, the selection unit can analyze user feedback and improve the video selection algorithm. In this way, by collecting user feedback, the accuracy of video selection is improved. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input user feedback into AI and cause the AI ​​to improve the video selection algorithm.

[0036] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays the types of video that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the types of video that the user will prefer at a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0037] The reception unit can perform filtering based on the user's current activity and environment when inputting the type of video. For example, the reception unit can perform filtering based on the user's current activity and environment when inputting the type of video. For example, if the user is working, the reception unit can suggest videos that will help the user concentrate. Furthermore, if the user is relaxing, the reception unit can suggest videos that have a relaxing effect. Furthermore, if the user is exercising, the reception unit can suggest energetic videos. In this way, by filtering videos based on the user's current activity and environment, more appropriate videos can be provided. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's current activity and environment into AI and have the AI ​​perform video filtering.

[0038] The reception unit can select the optimal input means depending on the user's input method when inputting the type of video. For example, when inputting the type of video, the reception unit selects the optimal input means depending on the user's input method (voice, text, gesture, etc.). For example, the reception unit can automatically set the type of video simply by the user inputting "natural scenery" by voice. The reception unit can also easily set the type of video by the user performing a specific gesture on the smartphone screen. Furthermore, the reception unit can also allow the user to set the type of video more intuitively by combining voice input and gesture input. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input data on the user's input method to AI and have the AI ​​select the optimal input means.

[0039] The reception unit can suggest highly relevant videos by taking into account the user's geographical location information when the type of video is input. For example, the reception unit suggests highly relevant videos by taking into account the user's geographical location information when the type of video is input. For example, if the user is at the seaside, the reception unit can suggest videos of seascapes. Furthermore, if the user is in a mountainous area, the reception unit can also suggest videos of mountain scenery. Furthermore, if the user is in an urban area, the reception unit can suggest videos of urban landscapes. In this way, more relevant videos can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and cause the AI ​​to suggest highly relevant videos.

[0040] The reception unit can suggest related videos based on the user's social media activity when the type of video is input. For example, when the type of video is input, the reception unit analyzes the user's social media activity and suggests related videos. For example, the reception unit can suggest videos related to places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related videos. Furthermore, the reception unit can suggest related videos by referring to the activities of the user's friends on social media. In this way, more relevant videos can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​suggest related videos.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting the type of video. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting the type of video. For example, the reception unit preferentially displays video types that the user has previously preferred. The reception unit can also suggest the optimal input method based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback into AI and have the AI ​​customize the input method.

[0042] The selection unit can select the optimal video by referring to the user's past selection history when selecting a video. For example, the selection unit selects the optimal video by referring to the user's past selection history when selecting a video. For example, the selection unit selects the optimal video based on the type of video the user has previously selected. The selection unit can also predict and select a video that the user will prefer at a specific time period from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and select the most preferred video. In this way, the optimal video can be selected by referring to the user's past selection history. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's past selection history into AI and have the AI ​​select the optimal video.

[0043] The selection unit can customize the video based on the user's current activity and environment when selecting the video. For example, the selection unit customizes the video based on the user's current activity and environment when selecting the video. For example, if the user is working, the selection unit selects a video that will increase concentration. If the user is relaxing, the selection unit can also select a video that has a relaxing effect. Furthermore, if the user is exercising, the selection unit can also select an energetic video. In this way, by customizing the video based on the user's current activity and environment, more appropriate video can be provided. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input data on the user's current activity and environment into AI and have the AI ​​customize the video.

[0044] The selection unit can improve the selection algorithm by reflecting user feedback when selecting a video. For example, the selection unit improves the selection algorithm by reflecting user feedback when selecting a video. For example, the selection unit improves the video selection algorithm based on user feedback. The selection unit can also analyze past user feedback and select optimal videos. Furthermore, the selection unit can customize video selection criteria by reflecting user feedback. In this way, the selection algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input user feedback data into AI and have the AI ​​improve the selection algorithm.

[0045] When selecting a video, the selection unit can select the optimal video by taking into consideration the user's geographical location information. For example, when selecting a video, the selection unit selects the optimal video by taking into consideration the user's geographical location information. For example, when the user is at the seaside, the selection unit selects a video of an ocean view. Furthermore, when the user is in a mountainous area, the selection unit can also select a video of a mountain view. Furthermore, when the user is in an urban area, the selection unit can also select a video of an urban landscape. In this way, by taking into consideration the user's geographical location information, more relevant video can be provided. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's geographical location information into AI and have the AI ​​select the optimal video.

[0046] The selection unit can analyze the user's social media activity and select related videos when selecting a video. For example, the selection unit can analyze the user's social media activity and select related videos when selecting a video. For example, the selection unit can select videos related to places the user has checked in to on social media. The selection unit can also analyze the content of the user's social media posts and select related videos. Furthermore, the selection unit can select related videos with reference to the activities of the user's friends on social media. In this way, more relevant videos can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the selection unit can be performed using AI, or can be performed without using AI. For example, the selection unit can input data on the user's social media activity into AI and have the AI ​​select related videos.

[0047] The selection unit can customize the selection criteria by reflecting the user's past feedback when selecting a video. For example, the selection unit customizes the selection criteria by reflecting the user's past feedback when selecting a video. For example, the selection unit selects the optimal video based on the user's past feedback. The selection unit can also analyze the user's past feedback and customize the video selection criteria. Furthermore, the selection unit can improve the video selection algorithm by reflecting the user's feedback. In this way, the selection criteria can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the selection unit may be performed using AI or without AI. For example, the selection unit can input data of the user's past feedback into AI and have the AI ​​customize the selection criteria.

[0048] The reflection unit can select the optimal reflection method by referring to the user's past viewing history when reflecting the weather or time. For example, the reflection unit can select the optimal reflection method by referring to the user's past viewing history when reflecting the weather or time. For example, the reflection unit prioritizes reflecting videos of weather and time periods that the user previously preferred. The reflection unit can also select videos of the optimal weather and time period based on the user's past viewing history. Furthermore, the reflection unit can analyze the user's past viewing history and reflect videos of the most preferred weather and time period. In this way, the optimal weather and time reflection method can be selected by referring to the user's past viewing history. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input the user's past viewing history into AI and have the AI ​​select the optimal reflection method.

[0049] The reflection unit can customize the reflection content based on the user's current activity and environment when reflecting the weather and time. For example, when reflecting the weather and time, the reflection unit customizes the reflection content based on the user's current activity and environment. For example, when the user is working, the reflection unit reflects an image of weather and a time period that enhances concentration. Furthermore, when the user is relaxing, the reflection unit can also reflect an image of weather and a time period that has a relaxing effect. Furthermore, when the user is exercising, the reflection unit can reflect an image of energetic weather and a time period. In this way, by customizing the reflection content based on the user's current activity and environment, more appropriate images can be provided. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input data on the user's current activity and environment into AI and have the AI ​​customize the reflection content.

[0050] The reflection unit can reflect user feedback when reflecting the weather and time to improve the reflection algorithm. For example, the reflection unit reflects user feedback when reflecting the weather and time to improve the reflection algorithm. For example, the reflection unit improves the weather and time reflection algorithm based on user feedback. The reflection unit can also analyze the user's past feedback and select the optimal weather and time reflection method. Furthermore, the reflection unit can reflect user feedback to customize the weather and time reflection criteria. In this way, the reflection algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input user feedback data into AI and have the AI ​​improve the reflection algorithm.

[0051] The reflection unit can select the optimal reflection method by taking into account the user's geographical location information when reflecting the weather and time. For example, when reflecting the weather and time, the reflection unit selects the optimal reflection method by taking into account the user's geographical location information. For example, when the user is at the seaside, the reflection unit reflects the current weather and time in an image of a seascape. Furthermore, when the user is in a mountainous area, the reflection unit can also reflect the current weather and time in an image of a mountain landscape. Furthermore, when the user is in an urban area, the reflection unit can reflect the current weather and time in an image of an urban landscape. In this way, by taking into account the user's geographical location information, more relevant images can be provided. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information into AI and have the AI ​​select the optimal reflection method.

[0052] The reflection unit can analyze the user's social media activity and reflect related information when reflecting the weather and time. For example, the reflection unit analyzes the user's social media activity and reflects related information when reflecting the weather and time. For example, the reflection unit reflects weather and time information related to a location where the user checked in on social media. The reflection unit can also analyze the content of the user's social media posts and reflect related weather and time information. Furthermore, the reflection unit can reflect related weather and time information based on the activities of the user's friends on social media. This makes it possible to provide more relevant images by analyzing the user's social media activity. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input data on the user's social media activity into AI and have the AI ​​reflect the related information.

[0053] The reflection unit can customize the reflection criteria by reflecting the user's past feedback when reflecting the weather or time. For example, the reflection unit customizes the reflection criteria by reflecting the user's past feedback when reflecting the weather or time. For example, the reflection unit selects an optimal weather or time reflection method based on the user's past feedback. The reflection unit can also analyze the user's past feedback and customize the weather or time reflection criteria. Furthermore, the reflection unit can improve the weather or time reflection algorithm by reflecting the user's feedback. In this way, the reflection criteria can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reflection unit may be performed using AI or without AI. For example, the reflection unit can input data of the user's past feedback into AI and have the AI ​​customize the reflection criteria.

[0054] The display unit can select the optimal display method by referring to the user's past viewing history when displaying video. For example, the display unit selects the optimal display method by referring to the user's past viewing history when displaying video. For example, the display unit preferentially applies a display method that the user has previously preferred. The display unit can also select the optimal display method based on the user's past viewing history. Furthermore, the display unit can analyze the user's past viewing history and apply the most preferred display method. In this way, the optimal display method can be selected by referring to the user's past viewing history. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's past viewing history into AI and have the AI ​​select the optimal display method.

[0055] The display unit can customize the display content based on the user's current activity and environment when displaying the video. For example, the display unit customizes the display content based on the user's current activity and environment when displaying the video. For example, when the user is working, the display unit applies a display method that enhances concentration. Furthermore, when the user is relaxing, the display unit can apply a display method that has a relaxing effect. Furthermore, when the user is exercising, the display unit can apply an energetic display method. In this way, by customizing the display content based on the user's current activity and environment, more appropriate video can be provided. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input data on the user's current activity and environment into AI and have the AI ​​customize the display content.

[0056] The display unit can improve the display algorithm by reflecting user feedback when displaying the image. For example, the display unit improves the display algorithm by reflecting user feedback when displaying the image. For example, the display unit improves the image display algorithm based on user feedback. The display unit can also analyze the user's past feedback and select the optimal display method. Furthermore, the display unit can customize the image display criteria by reflecting user feedback. In this way, the display algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input user feedback data into AI and have the AI ​​improve the display algorithm.

[0057] The display unit can select the optimal display method by taking into consideration the user's geographical location information when displaying an image. For example, the display unit selects the optimal display method by taking into consideration the user's geographical location information when displaying an image. For example, if the user is at the seaside, the display unit can preferentially display an image of an ocean view. Furthermore, if the user is in a mountainous area, the display unit can preferentially display an image of a mountain view. Furthermore, if the user is in an urban area, the display unit can preferentially display an image of an urban landscape. In this way, by taking into consideration the user's geographical location information, more relevant images can be provided. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's geographical location information into AI and have the AI ​​select the optimal display method.

[0058] The display unit can analyze the user's social media activity and display related information when displaying the video. For example, the display unit can analyze the user's social media activity and display related information when displaying the video. For example, the display unit can display video related to places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and display related video. Furthermore, the display unit can display related video by referring to the activity of the user's friends on social media. In this way, more relevant video can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input data on the user's social media activity into AI and have the AI ​​display related information.

[0059] The display unit can customize display criteria by reflecting the user's past feedback when displaying video. For example, the display unit customizes display criteria by reflecting the user's past feedback when displaying video. For example, the display unit selects an optimal display method based on the user's past feedback. The display unit can also analyze the user's past feedback and customize the video display criteria. Furthermore, the display unit can improve the video display algorithm by reflecting the user's feedback. In this way, the display criteria can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the display unit may be performed using AI or may be performed without using AI. For example, the display unit can input data of the user's past feedback into AI and have the AI ​​customize the display criteria.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The selection unit can analyze the user's music playback history to gain a deeper understanding of the user's preferences and reflect this in the video selection. For example, if the user frequently listens to relaxing music, the selection unit can prioritize selecting videos with a relaxing effect. If the user prefers energetic music, the selection unit can select videos of action scenes or sports. Furthermore, if the user prefers a particular artist or genre, the selection unit can select videos related to that artist or genre. This allows for a more personalized video experience by selecting videos based on the user's musical preferences.

[0062] The reflection unit can acquire the user's calendar information and adjust the content of the video based on the schedule. For example, if the user has a meeting scheduled, it can reflect video that will help the user concentrate. If the user is on vacation, it can reflect video that has a relaxing effect. Furthermore, if the user has plans to exercise, it can reflect energetic video. In this way, by adjusting the content of the video based on the user's schedule, it is possible to provide more appropriate video.

[0063] The display unit monitors the remaining battery level of the user's device and can display images in power-saving mode when the battery is low. For example, if the battery level is 20% or less, the display unit automatically adjusts the image brightness and lowers the volume. If the battery level is 10% or less, the display unit can also lower the image resolution. Furthermore, if the battery level is 5% or less, the display unit can pause video playback and display a message urging the user to charge the device. This allows users to enjoy videos while reducing battery consumption.

[0064] The reception unit can acquire the user's health data and suggest a type of video based on the user's health condition. For example, if the user's heart rate is high, it can suggest a video with a relaxing effect. It can also analyze the user's sleep data and suggest a video with a relaxing effect if the user is sleep deprived. It can also acquire the user's exercise data and suggest a video with a recovery effect after exercise. This allows the system to provide more appropriate videos by suggesting a type of video based on the user's health condition.

[0065] The display unit can acquire location information of the user's device and adjust the way the image is displayed based on the location information. For example, when the user is outdoors, the display unit can automatically adjust the brightness to make the image easier to see. Also, when the user is moving, the display unit can lower the image resolution to reduce data usage. Furthermore, when the user is in a specific location, the display unit can prioritize displaying images related to that location. This allows the display unit to provide more appropriate images by adjusting the way the image is displayed based on the user's location information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs the type of video that the user prefers. The type of video that the user prefers includes natural landscapes, cityscapes, seascapes, etc. The reception unit can accept voice input and text input. Step 2: The selection unit selects a video based on the information input by the reception unit. The selection unit can also select a video taking into account the user's past selection history, and uses AI to select the optimal video based on the user's preferences. Step 3: The reflection unit reflects the actual weather and time in the video selected by the selection unit. The reflection unit obtains weather and time information from the internet, analyzes this information using AI, and reflects it in the video. Step 4: The display unit displays the image reflected by the reflecting unit. The display unit displays the image on a device such as a smartphone, tablet, or TV, and can also adjust the brightness and volume of the image.

[0068] (Example 2) A video display system according to an embodiment of the present invention displays video tailored to a user's preferences and reflects actual weather and time. In the video display system, a user inputs their preferred video type, and AI selects and displays an appropriate video based on the user's preferences. Furthermore, the selected video can reflect actual weather and time. For example, in the video display system, a user selects a video type, such as a natural landscape, a cityscape, or a seascape. This information is input to AI. The AI ​​then analyzes the input information and selects an appropriate video based on the user's preferences. For example, if the user selects a natural landscape, the AI ​​searches for natural landscape videos and selects the most appropriate video. Furthermore, the selected video can reflect actual weather and time. For example, if the current weather is sunny, a video of a sunny natural landscape is displayed, and if it is evening, a video of an evening natural landscape is displayed. This allows the user to enjoy video that changes in real time. This allows the video display system to enjoy video tailored to the user's preferences. For example, a user can select a natural landscape video when they want to relax, and an urban landscape video when they are working. Furthermore, reflecting actual weather and time can provide a more realistic video experience.

[0069] A video display system according to an embodiment includes a reception unit, a selection unit, a reflection unit, and a display unit. The reception unit inputs a user's preferred video type. The user's preferred video type may include, but is not limited to, natural landscapes, urban landscapes, and ocean views. The reception unit may receive, for example, voice input or text input. The selection unit selects a video based on the information input by the reception unit. The selection unit may also select a video by taking into account, for example, the user's past selection history. The selection unit may use AI to select an optimal video based on the user's preferences. The reflection unit reflects actual weather and time in the video selected by the selection unit. The reflection unit may, for example, obtain weather and time information from the Internet and reflect the information in the video. The reflection unit may use AI to analyze weather and time information and reflect the information in the video. The display unit displays the video reflected by the reflection unit. The display unit may display the video on a device such as a smartphone, tablet, or television. The display unit may also adjust the brightness and volume of the video. As a result, the video display system according to the embodiment can display video that matches the user's preferences and reflects the actual weather and time. For example, if the user selects a video of a natural landscape and the current weather is sunny, the video of a sunny natural landscape can be displayed. This allows the user to enjoy video that changes in real time.

[0070] The selection unit can select a video based on the user's past selection history. For example, the selection unit selects a video taking into consideration the user's past selection history. For example, the selection unit selects an optimal video based on the types of videos the user has selected in the past. The selection unit can also predict and select a video that the user will prefer at a specific time period from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and select the most preferred video. This allows for a more appropriate video to be selected by taking the user's past selection history into consideration. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's past selection history into AI and have the AI ​​select the optimal video.

[0071] The reflection unit can acquire weather and time information from the Internet. The reflection unit acquires weather and time information from the Internet, for example. For example, the reflection unit acquires weather forecast data and the current time using an API. The reflection unit can also acquire weather and time information using web scraping technology. Furthermore, the reflection unit can acquire weather and time information from a public database on the Internet. In this way, by acquiring weather and time information from the Internet, real-time information can be reflected. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input weather and time information acquired from the Internet into AI and have the AI ​​reflect the information on the video.

[0072] The display unit can display images on devices such as smartphones, tablets, and televisions. The display unit can display images on devices such as smartphones, tablets, and televisions. For example, the display unit can display images on iOS devices, Android devices, smart TVs, and the like. The display unit can also display images on devices such as computers and projectors. Furthermore, the display unit can display images on multiple devices simultaneously. This allows images to be displayed on various devices. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input information about the device displaying the image into AI and have the AI ​​execute the optimal display method.

[0073] The display unit can adjust the brightness and volume of the image. The display unit adjusts, for example, the brightness and volume of the image. For example, the display unit adjusts the brightness and volume of the image based on user settings. The display unit can also automatically adjust the brightness of the image according to the ambient brightness using an ambient light sensor. Furthermore, the display unit can adjust the volume based on a user's voice command using voice recognition technology. This allows the display to be tailored to the user's preferences by adjusting the brightness and volume of the image. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can have AI adjust the brightness and volume of the image.

[0074] The reception unit can accept voice input or text input. The reception unit accepts, for example, voice input or text input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also accept keyboard input. Furthermore, the reception unit can also accept text input using a touch screen of a smartphone or tablet. This allows the user to easily input the type of video by accepting voice input or text input. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can have AI execute processing of the voice input or text input.

[0075] The selection unit can collect user feedback and improve the video selection. For example, the selection unit collects user feedback and improves the video selection. For example, the selection unit collects user feedback through a questionnaire. The selection unit can also collect user ratings using a rating system. Furthermore, the selection unit can analyze user feedback and improve the video selection algorithm. In this way, by collecting user feedback, the accuracy of video selection is improved. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input user feedback into AI and cause the AI ​​to improve the video selection algorithm.

[0076] The reception unit can estimate the user's emotion and suggest a video type based on the estimated user's emotion. For example, the reception unit can estimate the user's emotion and suggest a video type based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can suggest a video of a natural landscape that has a relaxing effect. If the user is excited, the reception unit can also suggest an action scene or a sports video. If the user is sad, the reception unit can also suggest a video with a soothing effect. This allows for more appropriate video to be provided by suggesting a video type based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into AI and have the AI ​​execute the video type suggestion.

[0077] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays the types of video that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the types of video that the user will prefer at a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0078] The reception unit can perform filtering based on the user's current activity and environment when inputting the type of video. For example, the reception unit can perform filtering based on the user's current activity and environment when inputting the type of video. For example, if the user is working, the reception unit can suggest videos that will help the user concentrate. Furthermore, if the user is relaxing, the reception unit can suggest videos that have a relaxing effect. Furthermore, if the user is exercising, the reception unit can suggest energetic videos. In this way, by filtering videos based on the user's current activity and environment, more appropriate videos can be provided. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's current activity and environment into AI and have the AI ​​perform video filtering.

[0079] The reception unit can select the optimal input means depending on the user's input method when inputting the type of video. For example, when inputting the type of video, the reception unit selects the optimal input means depending on the user's input method (voice, text, gesture, etc.). For example, the reception unit can automatically set the type of video simply by the user inputting "natural scenery" by voice. The reception unit can also easily set the type of video by the user performing a specific gesture on the smartphone screen. Furthermore, the reception unit can also allow the user to set the type of video more intuitively by combining voice input and gesture input. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input data on the user's input method to AI and have the AI ​​select the optimal input means.

[0080] The reception unit can estimate the user's emotion and determine the priority of the input video based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the input video based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can prioritize displaying videos with a relaxing effect. Furthermore, if the user is excited, the reception unit can prioritize displaying action scenes or sports videos. Furthermore, if the user is sad, the reception unit can prioritize displaying videos with a soothing effect. This allows for more appropriate video to be provided by determining the priority of videos based on the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into AI and have the AI ​​determine the priority of the videos.

[0081] The reception unit can suggest highly relevant videos by taking into account the user's geographical location information when the type of video is input. For example, the reception unit suggests highly relevant videos by taking into account the user's geographical location information when the type of video is input. For example, if the user is at the seaside, the reception unit can suggest videos of seascapes. Furthermore, if the user is in a mountainous area, the reception unit can also suggest videos of mountain scenery. Furthermore, if the user is in an urban area, the reception unit can suggest videos of urban landscapes. In this way, more relevant videos can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and cause the AI ​​to suggest highly relevant videos.

[0082] The reception unit can suggest related videos based on the user's social media activity when the type of video is input. For example, when the type of video is input, the reception unit analyzes the user's social media activity and suggests related videos. For example, the reception unit can suggest videos related to places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related videos. Furthermore, the reception unit can suggest related videos by referring to the activities of the user's friends on social media. In this way, more relevant videos can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​suggest related videos.

[0083] The reception unit can customize the input method by reflecting the user's past feedback when inputting the type of video. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting the type of video. For example, the reception unit preferentially displays video types that the user has previously preferred. The reception unit can also suggest the optimal input method based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback into AI and have the AI ​​customize the input method.

[0084] The selection unit can estimate the user's emotion and adjust the video selection criteria based on the estimated user's emotion. For example, the selection unit can estimate the user's emotion and adjust the video selection criteria based on the estimated user's emotion. For example, if the user is relaxed, the selection unit can preferentially select videos with a relaxing effect. Furthermore, if the user is excited, the selection unit can preferentially select action scenes or sports videos. Furthermore, if the user is sad, the selection unit can preferentially select videos with a soothing effect. By adjusting the video selection criteria based on the user's emotion, more appropriate videos can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using AI, or without AI. For example, the selection unit can input the user's emotion data into AI and have the AI ​​adjust the video selection criteria.

[0085] The selection unit can select the optimal video by referring to the user's past selection history when selecting a video. For example, the selection unit selects the optimal video by referring to the user's past selection history when selecting a video. For example, the selection unit selects the optimal video based on the type of video the user has previously selected. The selection unit can also predict and select a video that the user will prefer at a specific time period from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and select the most preferred video. In this way, the optimal video can be selected by referring to the user's past selection history. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's past selection history into AI and have the AI ​​select the optimal video.

[0086] The selection unit can customize the video based on the user's current activity and environment when selecting the video. For example, the selection unit customizes the video based on the user's current activity and environment when selecting the video. For example, if the user is working, the selection unit selects a video that will increase concentration. If the user is relaxing, the selection unit can also select a video that has a relaxing effect. Furthermore, if the user is exercising, the selection unit can also select an energetic video. In this way, by customizing the video based on the user's current activity and environment, more appropriate video can be provided. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input data on the user's current activity and environment into AI and have the AI ​​customize the video.

[0087] The selection unit can improve the selection algorithm by reflecting user feedback when selecting a video. For example, the selection unit improves the selection algorithm by reflecting user feedback when selecting a video. For example, the selection unit improves the video selection algorithm based on user feedback. The selection unit can also analyze past user feedback and select optimal videos. Furthermore, the selection unit can customize video selection criteria by reflecting user feedback. In this way, the selection algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input user feedback data into AI and have the AI ​​improve the selection algorithm.

[0088] The selection unit can estimate the user's emotion and adjust the selection order of the videos based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the selection order of the videos based on the estimated user's emotion. For example, if the user is relaxed, the selection unit can prioritize displaying videos with a relaxing effect. Furthermore, if the user is excited, the selection unit can prioritize displaying action scenes or sports videos. Furthermore, if the user is sad, the selection unit can prioritize displaying videos with a soothing effect. By adjusting the selection order of the videos based on the user's emotion, more appropriate videos can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without AI. For example, the selection unit can input the user's emotion data into AI and have the AI ​​adjust the selection order of the videos.

[0089] When selecting a video, the selection unit can select the optimal video by taking into consideration the user's geographical location information. For example, when selecting a video, the selection unit selects the optimal video by taking into consideration the user's geographical location information. For example, when the user is at the seaside, the selection unit selects a video of an ocean view. Furthermore, when the user is in a mountainous area, the selection unit can also select a video of a mountain view. Furthermore, when the user is in an urban area, the selection unit can also select a video of an urban landscape. In this way, by taking into consideration the user's geographical location information, more relevant video can be provided. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the user's geographical location information into AI and have the AI ​​select the optimal video.

[0090] The selection unit can analyze the user's social media activity and select related videos when selecting a video. For example, the selection unit can analyze the user's social media activity and select related videos when selecting a video. For example, the selection unit can select videos related to places the user has checked in to on social media. The selection unit can also analyze the content of the user's social media posts and select related videos. Furthermore, the selection unit can select related videos with reference to the activities of the user's friends on social media. In this way, more relevant videos can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the selection unit can be performed using AI, or can be performed without using AI. For example, the selection unit can input data on the user's social media activity into AI and have the AI ​​select related videos.

[0091] The selection unit can customize the selection criteria by reflecting the user's past feedback when selecting a video. For example, the selection unit customizes the selection criteria by reflecting the user's past feedback when selecting a video. For example, the selection unit selects the optimal video based on the user's past feedback. The selection unit can also analyze the user's past feedback and customize the video selection criteria. Furthermore, the selection unit can improve the video selection algorithm by reflecting the user's feedback. In this way, the selection criteria can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the selection unit may be performed using AI or without AI. For example, the selection unit can input data of the user's past feedback into AI and have the AI ​​customize the selection criteria.

[0092] The reflection unit can estimate the user's emotions and adjust the weather and time display method based on the estimated user emotions. The reflection unit, for example, estimates the user's emotions and adjusts the weather and time display method based on the estimated user emotions. For example, if the user is relaxed, the reflection unit displays images of calm weather and time periods. Furthermore, if the user is excited, the reflection unit can display images of dynamic weather and time periods. Furthermore, if the user is sad, the reflection unit can display images of soothing weather and time periods. This allows for more appropriate images to be provided by adjusting the weather and time display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without AI. For example, the reflection unit can input the user's emotion data into AI and have the AI ​​adjust the weather and time display method.

[0093] The reflection unit can select the optimal reflection method by referring to the user's past viewing history when reflecting the weather or time. For example, the reflection unit can select the optimal reflection method by referring to the user's past viewing history when reflecting the weather or time. For example, the reflection unit prioritizes reflecting videos of weather and time periods that the user previously preferred. The reflection unit can also select videos of the optimal weather and time period based on the user's past viewing history. Furthermore, the reflection unit can analyze the user's past viewing history and reflect videos of the most preferred weather and time period. In this way, the optimal weather and time reflection method can be selected by referring to the user's past viewing history. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input the user's past viewing history into AI and have the AI ​​select the optimal reflection method.

[0094] The reflection unit can customize the reflection content based on the user's current activity and environment when reflecting the weather and time. For example, when reflecting the weather and time, the reflection unit customizes the reflection content based on the user's current activity and environment. For example, when the user is working, the reflection unit reflects an image of weather and a time period that enhances concentration. Furthermore, when the user is relaxing, the reflection unit can also reflect an image of weather and a time period that has a relaxing effect. Furthermore, when the user is exercising, the reflection unit can reflect an image of energetic weather and a time period. In this way, by customizing the reflection content based on the user's current activity and environment, more appropriate images can be provided. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input data on the user's current activity and environment into AI and have the AI ​​customize the reflection content.

[0095] The reflection unit can reflect user feedback when reflecting the weather and time to improve the reflection algorithm. For example, the reflection unit reflects user feedback when reflecting the weather and time to improve the reflection algorithm. For example, the reflection unit improves the weather and time reflection algorithm based on user feedback. The reflection unit can also analyze the user's past feedback and select the optimal weather and time reflection method. Furthermore, the reflection unit can reflect user feedback to customize the weather and time reflection criteria. In this way, the reflection algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input user feedback data into AI and have the AI ​​improve the reflection algorithm.

[0096] The reflection unit can estimate the user's emotions and adjust the order in which weather and time are reflected based on the estimated user's emotions. The reflection unit, for example, estimates the user's emotions and adjusts the order in which weather and time are reflected based on the estimated user's emotions. For example, if the user is relaxed, the reflection unit can prioritize reflecting images of calm weather and time periods. Furthermore, if the user is excited, the reflection unit can prioritize reflecting images of moving weather and time periods. Furthermore, if the user is sad, the reflection unit can prioritize reflecting images of soothing weather and time periods. This allows for more appropriate images to be provided by adjusting the order in which weather and time are reflected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without AI. For example, the reflection unit can input the user's emotion data into AI and have the AI ​​adjust the order in which weather and time are reflected.

[0097] The reflection unit can select the optimal reflection method by taking into account the user's geographical location information when reflecting the weather and time. For example, when reflecting the weather and time, the reflection unit selects the optimal reflection method by taking into account the user's geographical location information. For example, when the user is at the seaside, the reflection unit reflects the current weather and time in an image of a seascape. Furthermore, when the user is in a mountainous area, the reflection unit can also reflect the current weather and time in an image of a mountain landscape. Furthermore, when the user is in an urban area, the reflection unit can reflect the current weather and time in an image of an urban landscape. In this way, by taking into account the user's geographical location information, more relevant images can be provided. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information into AI and have the AI ​​select the optimal reflection method.

[0098] The reflection unit can analyze the user's social media activity and reflect related information when reflecting the weather and time. For example, the reflection unit analyzes the user's social media activity and reflects related information when reflecting the weather and time. For example, the reflection unit reflects weather and time information related to a location where the user checked in on social media. The reflection unit can also analyze the content of the user's social media posts and reflect related weather and time information. Furthermore, the reflection unit can reflect related weather and time information based on the activities of the user's friends on social media. This makes it possible to provide more relevant images by analyzing the user's social media activity. Some or all of the above-described processing in the reflection unit may be performed using AI, or may be performed without using AI. For example, the reflection unit can input data on the user's social media activity into AI and have the AI ​​reflect the related information.

[0099] The reflection unit can customize the reflection criteria by reflecting the user's past feedback when reflecting the weather or time. For example, the reflection unit customizes the reflection criteria by reflecting the user's past feedback when reflecting the weather or time. For example, the reflection unit selects an optimal weather or time reflection method based on the user's past feedback. The reflection unit can also analyze the user's past feedback and customize the weather or time reflection criteria. Furthermore, the reflection unit can improve the weather or time reflection algorithm by reflecting the user's feedback. In this way, the reflection criteria can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reflection unit may be performed using AI or without AI. For example, the reflection unit can input data of the user's past feedback into AI and have the AI ​​customize the reflection criteria.

[0100] The display unit can estimate the user's emotion and adjust the image display method based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the image display method based on the estimated user's emotion. For example, if the user is relaxed, the display unit can display the image with calm colors and a soft volume. If the user is excited, the display unit can also display the image with vibrant colors and a louder volume. If the user is sad, the display unit can also display the image with a soothing color and a softer volume. This allows for more appropriate images to be provided by adjusting the image display method based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using AI, or without AI. For example, the display unit can input the user's emotion data into AI and have the AI ​​adjust the image display method.

[0101] The display unit can select the optimal display method by referring to the user's past viewing history when displaying video. For example, the display unit selects the optimal display method by referring to the user's past viewing history when displaying video. For example, the display unit preferentially applies a display method that the user has previously preferred. The display unit can also select the optimal display method based on the user's past viewing history. Furthermore, the display unit can analyze the user's past viewing history and apply the most preferred display method. In this way, the optimal display method can be selected by referring to the user's past viewing history. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's past viewing history into AI and have the AI ​​select the optimal display method.

[0102] The display unit can customize the display content based on the user's current activity and environment when displaying the video. For example, the display unit customizes the display content based on the user's current activity and environment when displaying the video. For example, when the user is working, the display unit applies a display method that enhances concentration. Furthermore, when the user is relaxing, the display unit can apply a display method that has a relaxing effect. Furthermore, when the user is exercising, the display unit can apply an energetic display method. In this way, by customizing the display content based on the user's current activity and environment, more appropriate video can be provided. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input data on the user's current activity and environment into AI and have the AI ​​customize the display content.

[0103] The display unit can improve the display algorithm by reflecting user feedback when displaying the image. For example, the display unit improves the display algorithm by reflecting user feedback when displaying the image. For example, the display unit improves the image display algorithm based on user feedback. The display unit can also analyze the user's past feedback and select the optimal display method. Furthermore, the display unit can customize the image display criteria by reflecting user feedback. In this way, the display algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input user feedback data into AI and have the AI ​​improve the display algorithm.

[0104] The display unit can estimate the user's emotion and adjust the display order of the images based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the display order of the images based on the estimated user's emotion. For example, if the user is relaxed, the display unit can prioritize displaying images with a relaxing effect. Furthermore, if the user is excited, the display unit can prioritize displaying action scenes or sports images. Furthermore, if the user is sad, the display unit can prioritize displaying images with a soothing effect. This allows for more appropriate images to be provided by adjusting the display order of the images based on the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without AI. For example, the display unit can input the user's emotion data into AI and have the AI ​​adjust the display order of the images.

[0105] The display unit can select the optimal display method by taking into consideration the user's geographical location information when displaying an image. For example, the display unit selects the optimal display method by taking into consideration the user's geographical location information when displaying an image. For example, if the user is at the seaside, the display unit can preferentially display an image of an ocean view. Furthermore, if the user is in a mountainous area, the display unit can preferentially display an image of a mountain view. Furthermore, if the user is in an urban area, the display unit can preferentially display an image of an urban landscape. In this way, by taking into consideration the user's geographical location information, more relevant images can be provided. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the user's geographical location information into AI and have the AI ​​select the optimal display method.

[0106] The display unit can analyze the user's social media activity and display related information when displaying the video. For example, the display unit can analyze the user's social media activity and display related information when displaying the video. For example, the display unit can display video related to places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and display related video. Furthermore, the display unit can display related video by referring to the activity of the user's friends on social media. In this way, more relevant video can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input data on the user's social media activity into AI and have the AI ​​display related information.

[0107] The display unit can customize display criteria by reflecting the user's past feedback when displaying video. For example, the display unit customizes display criteria by reflecting the user's past feedback when displaying video. For example, the display unit selects an optimal display method based on the user's past feedback. The display unit can also analyze the user's past feedback and customize the video display criteria. Furthermore, the display unit can improve the video display algorithm by reflecting the user's feedback. In this way, the display criteria can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the display unit may be performed using AI or may be performed without using AI. For example, the display unit can input data of the user's past feedback into AI and have the AI ​​customize the display criteria. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, selection unit, reflection unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's preferred video type via voice input or text input. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects the optimal video based on the user's preferences using AI. The reflection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes weather and time information obtained from the Internet and reflects the information in the video. The display unit is realized, for example, by the output device 40 of the smart device 14 and displays the reflected video on a device such as a smartphone or tablet. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, selection unit, reflection unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's preferred type of video by voice input. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects the optimal video based on the user's preferences using AI. The reflection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes weather and time information obtained from the Internet and reflects it in the video. The display unit is realized, for example, by the display of the smart glasses 214 and displays the reflected video on the display of the smart glasses. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, selection unit, reflection unit, and display unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives the user's preferred type of video by voice input. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to select the optimal video based on the user's preferences. The reflection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes weather and time information obtained from the Internet and reflects it in the video. The display unit is realized, for example, by the display 343 of the headset terminal 314 and displays the reflected video on the display of the headset terminal. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, selection unit, reflection unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's preferred type of video by voice input. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects the optimal video based on the user's preferences using AI. The reflection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes weather and time information obtained from the Internet and reflects it in the video. The display unit is realized, for example, by the display of the robot 414 and displays the reflected video on the robot's display.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The selection unit can analyze the user's music playback history to gain a deeper understanding of the user's preferences and reflect this in the video selection. For example, if the user frequently listens to relaxing music, the selection unit can prioritize selecting videos with a relaxing effect. If the user prefers energetic music, the selection unit can select videos of action scenes or sports. Furthermore, if the user prefers a particular artist or genre, the selection unit can select videos related to that artist or genre. This allows for a more personalized video experience by selecting videos based on the user's musical preferences.

[0110] The reflection unit can acquire the user's calendar information and adjust the content of the video based on the schedule. For example, if the user has a meeting scheduled, it can reflect video that will help the user concentrate. If the user is on vacation, it can reflect video that has a relaxing effect. Furthermore, if the user has plans to exercise, it can reflect energetic video. In this way, by adjusting the content of the video based on the user's schedule, it is possible to provide more appropriate video.

[0111] The display unit monitors the remaining battery level of the user's device and can display images in power-saving mode when the battery is low. For example, if the battery level is 20% or less, the display unit automatically adjusts the image brightness and lowers the volume. If the battery level is 10% or less, the display unit can also lower the image resolution. Furthermore, if the battery level is 5% or less, the display unit can pause video playback and display a message urging the user to charge the device. This allows users to enjoy videos while reducing battery consumption.

[0112] The reception unit can acquire the user's health data and suggest a type of video based on the user's health condition. For example, if the user's heart rate is high, it can suggest a video with a relaxing effect. It can also analyze the user's sleep data and suggest a video with a relaxing effect if the user is sleep deprived. It can also acquire the user's exercise data and suggest a video with a recovery effect after exercise. This allows the system to provide more appropriate videos by suggesting a type of video based on the user's health condition.

[0113] The selection unit can estimate the user's emotions and adjust the video selection criteria based on the estimated user's emotions. For example, if the user is relaxed, the selection unit can preferentially select videos that have a relaxing effect. If the user is excited, the selection unit can also preferentially select videos of action scenes or sports. Furthermore, if the user is sad, the selection unit can also preferentially select videos that have a soothing effect. In this way, by adjusting the video selection criteria based on the user's emotions, more appropriate videos can be provided.

[0114] The reflection unit can estimate the user's emotions and adjust how the weather and time are reflected based on the estimated user's emotions. For example, if the user is relaxed, the reflection unit can reflect images of calm weather and time periods. If the user is excited, the reflection unit can also reflect images of dynamic weather and time periods. Furthermore, if the user is sad, the reflection unit can also reflect images of soothing weather and time periods. In this way, by adjusting how the weather and time are reflected based on the user's emotions, more appropriate images can be provided.

[0115] The display unit can estimate the user's emotions and adjust the way the video is displayed based on the estimated user's emotions. For example, if the user is relaxed, the display unit can display the video with calm colors and a soft volume. If the user is excited, the display unit can also display the video with vivid colors and a louder volume. Furthermore, if the user is sad, the display unit can also display the video with soothing colors and a softer volume. This allows the display unit to provide more appropriate video by adjusting the way the video is displayed based on the user's emotions.

[0116] The reception unit can estimate the user's emotions and suggest a type of video based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can suggest a video of natural scenery that has a relaxing effect. If the user is excited, the reception unit can also suggest an action scene or a sports video. Furthermore, if the user is sad, the reception unit can also suggest a video that has a soothing effect. In this way, by suggesting a type of video based on the user's emotions, more appropriate videos can be provided.

[0117] The selection unit can estimate the user's emotion and adjust the selection order of the videos based on the estimated user's emotion. For example, if the user is relaxed, the selection unit can preferentially display videos that have a relaxing effect. Also, if the user is excited, the selection unit can preferentially display videos of action scenes or sports. Furthermore, if the user is sad, the selection unit can preferentially display videos that have a soothing effect. In this way, by adjusting the selection order of the videos based on the user's emotion, more appropriate videos can be provided.

[0118] The display unit can acquire location information of the user's device and adjust the way the image is displayed based on the location information. For example, when the user is outdoors, the display unit can automatically adjust the brightness to make the image easier to see. Also, when the user is moving, the display unit can lower the image resolution to reduce data usage. Furthermore, when the user is in a specific location, the display unit can prioritize displaying images related to that location. This allows the display unit to provide more appropriate images by adjusting the way the image is displayed based on the user's location information.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit inputs the type of video that the user prefers. The type of video that the user prefers includes natural landscapes, cityscapes, seascapes, etc. The reception unit can accept voice input and text input. Step 2: The selection unit selects a video based on the information input by the reception unit. The selection unit can also select a video taking into account the user's past selection history, and uses AI to select the optimal video based on the user's preferences. Step 3: The reflection unit reflects the actual weather and time in the video selected by the selection unit. The reflection unit obtains weather and time information from the internet, analyzes this information using AI, and reflects it in the video. Step 4: The display unit displays the image reflected by the reflecting unit. The display unit displays the image on a device such as a smartphone, tablet, or TV, and can also adjust the brightness and volume of the image.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] 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.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the type of video the user prefers; a selection unit that selects a video based on the information input by the reception unit; a reflection unit that reflects actual weather and time in the image selected by the selection unit; a display unit that displays the image reflected by the reflection unit; A system characterized by:

2. The selection unit Selecting videos based on the user's past selection history 2. The system of claim 1.

3. The reflection unit Get weather and time information from the Internet 2. The system of claim 1.

4. The display unit Displaying images on smartphones, tablets, and TV devices 2. The system of claim 1.

5. The display unit Adjusting the image brightness and volume 2. The system of claim 1.

6. The reception unit Accepts voice or text input 2. The system of claim 1.

7. The selection unit Gather user feedback and improve video selection 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and suggest video types based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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