system

Abstract

The system according to this embodiment aims to improve user motivation by generating repetitive short videos (GIFs) based on still images. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives an image and instructions as input. The generation unit analyzes the image and instructions input by the reception unit and generates a repeating short video (GIF). The display unit displays the GIF generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is not easy to generate repetitive short videos (GIFs) based on still images, and there is a problem that there is a lack of effective means for maintaining the motivation of people with intellectual and developmental disabilities.

[0005] The system according to the embodiment aims to generate repetitive short videos (GIFs) based on still images and improve the motivation of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives an image and instructions as input. The generation unit analyzes the image and instructions input by the reception unit and generates a repeating short video (GIF). The display unit displays the GIF generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate repetitive short videos (GIFs) based on still images, thereby improving user motivation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The GIF generation system according to an embodiment of the present invention is a system that generates eye-catching short videos (GIFs) based on images (still images). The GIF generation system takes an image along with instructions such as "attract attention," "inspirational," or "praise for achieving a goal." The generation AI analyzes these instructions and generates a repeating short video (GIF). This GIF can be used to support the work of individuals with intellectual and developmental disabilities. By setting a target time for the task and displaying the GIF upon achieving the goal, it aims to maintain continuous results and increase motivation. Furthermore, changing the GIF according to the season or time of year provides a fresh and surprising display. In addition, the generated GIF can be used in presentations in presentation material creation apps or as a smartphone wallpaper, enhancing its visual impact. For example, the user inputs an image along with instructions such as "attract attention," "inspirational," or "praise for achieving a goal." The user only needs to input specific instructions. For example, they might instruct, "Upload a photo of fireworks and enter the comment 'Congratulations'." This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a repeating short video (GIF). The AI ​​generates the most suitable GIF based on an image and instructions. For example, based on a photo of fireworks and the comment "Congratulations," it generates a GIF of fireworks going off. The generated GIFs are used to support the work of individuals with intellectual and developmental disabilities. Specifically, a target time is set for the task, and the GIF is displayed when the target is achieved. For example, if the task is completed within the target time, the fireworks GIF is displayed. In this way, the results of the work can be visually confirmed, helping to maintain continuous achievement and increase motivation. Furthermore, by changing the GIF according to the season or time of year, a fresh and surprising display can be provided. For example, a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter can be displayed, providing a constantly fresh visual stimulus. In addition, the generated GIFs can be used in presentations in presentation material creation apps and as smartphone wallpapers. For example, inserting a fireworks GIF into presentation materials can enhance the visual effect.Furthermore, by setting a fireworks GIF as the smartphone's wallpaper, users can enjoy a more visually appealing wallpaper. Thus, a GIF generation system is a system that generates eye-catching short videos (GIFs) from images (still images), and can be used for various purposes such as work support for people with intellectual and developmental disabilities, presentation materials, and smartphone wallpapers. In this way, a GIF generation system can enhance visual impact.

[0029] The GIF generation system according to the embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives an image and instructions as input. Images include, but are not limited to, formats such as JPEG, PNG, and GIF. Instructions include, but are not limited to, text instructions such as "attract attention," "inspirational," and "praise for achieving a goal," as well as voice instructions. The reception unit can, for example, allow a user to give instructions such as "upload a photo of fireworks and enter the comment 'Congratulations' along with an image." The generation unit uses a generation AI to analyze the image and instructions input by the reception unit and generates a repeating short video (GIF). The generation unit generates the optimal GIF based on the image and instructions, for example, the generation AI generates a GIF of fireworks being launched based on a photo of fireworks and the comment "Congratulations." Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is implemented using, for example, a deep learning model or a generative opposite network (GAN). The display unit displays the GIF generated by the generation unit. The display unit can be used, for example, to support the work of individuals with intellectual or developmental disabilities. It allows users to set a target time for their tasks and displays a GIF upon achieving that time. For instance, if a task is completed within the target time, a fireworks GIF is displayed. The display unit can also change the GIF displayed depending on the season or time of year. For example, it can display a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. The display unit can also be used in presentations using presentation materials and as a smartphone wallpaper. For example, inserting a fireworks GIF into a presentation can enhance its visual impact. Furthermore, setting a fireworks GIF as a smartphone wallpaper allows users to enjoy the wallpaper. Thus, the GIF generation system according to this embodiment can enhance its visual impact.

[0030] The reception desk accepts images and instructions. Images can be in various formats, including but not limited to JPEG, PNG, and GIF. This allows users to utilize diverse image formats, increasing the system's flexibility. Instructions can be text-based or voice-based, such as "attract attention," "inspirational," or "praise for achieving a goal." This allows users to clearly communicate their intentions and customize the content of the generated GIF. The reception desk can, for example, receive instructions such as "upload a photo of fireworks and enter the comment 'Congratulations'" along with an image. Users upload images through a dedicated interface and enter instructions using text boxes or voice input. The interface is intuitively designed and easy to use, making it simple for users to operate. Furthermore, the reception desk includes an image preview function, allowing users to review uploaded images. This reduces the risk of users uploading the wrong image. The reception desk also has the ability to analyze the content of the entered instructions and convert them to the appropriate format. For example, by using speech recognition technology to convert voice instructions to text, voice instructions can be treated the same as text instructions. This allows the reception desk to accommodate diverse user input methods and improve the system's usability.

[0031] The generation unit uses a generation AI to analyze the image and instructions input by the reception unit and generate a repeating short video (GIF). For example, the generation unit generates the optimal GIF based on the image and the instructions. The generation AI is implemented using technologies such as deep learning models and generative opposite networks (GANs). Specifically, the generation AI first analyzes the input image and extracts the main elements and features within the image. Next, it analyzes the instructions and understands the user's intent. For example, if the comment "Congratulations" is input, the generation AI understands the intention of congratulations and generates a GIF that is appropriate for that. The generation AI generates animation patterns to move the elements in the image and creates a GIF by repeatedly playing these patterns. For example, if a picture of fireworks is input, the generation AI generates an animation of fireworks going off and creates a GIF that repeatedly plays this animation. The generation AI can also create more visually appealing GIFs by adjusting the colors and effects. Furthermore, the generation unit has a function to evaluate the quality of the generated GIF and make corrections as needed. For example, if the generated GIF does not meet the user's intent, the generation unit will run the generation process again to produce a more appropriate GIF. This allows the generation unit to provide high-quality GIFs that meet the user's expectations.

[0032] The display unit shows GIFs generated by the generation unit. The display unit can be used, for example, to support the work of individuals with intellectual and developmental disabilities. A target time is set for the task, and a GIF is displayed upon completion. For instance, if the task is completed within the target time, a fireworks GIF is displayed. This enhances the sense of accomplishment and improves motivation. The display unit can also change the GIFs displayed according to the season or time of year. For example, a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter can be displayed. This creates a sense of the season and provides visual enjoyment. The display unit can also be used in presentations in presentation materials creation apps and as smartphone wallpapers. For example, inserting a fireworks GIF into a presentation can enhance its visual impact. Additionally, setting a fireworks GIF as a smartphone wallpaper can enhance the wallpaper's appeal. Furthermore, the display unit can collect user feedback and use it to improve the displayed content. For example, if a user prefers a particular GIF, the display unit can adjust its display based on that information to show more similar GIFs. This allows the display unit to provide a customized display according to the user's preferences, maximizing the visual effect.

[0033] The generation unit can generate the optimal GIF based on an image and instructions using a generation AI. For example, the generation unit generates the optimal GIF based on an image and instructions. For example, the generation unit generates a GIF of fireworks going off based on a photo of fireworks and the comment "Congratulations." Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is implemented using technologies such as deep learning models or generative opposite networks (GANs). As a result, the optimal GIF can be generated by using a generation AI.

[0034] The display unit is used to support the work of individuals with intellectual and developmental disabilities. It allows users to set target times for their tasks and displays a GIF when the target is achieved. For example, if a task is completed within the target time, a GIF of fireworks can be displayed. This allows for the maintenance of continuous achievement and increased motivation by displaying a GIF upon goal completion. Some or all of the above-described processes in the display unit may be performed using AI or not. For example, the display unit can have AI execute the process of displaying a GIF of fireworks when a task is completed within the target time.

[0035] The display unit can change the GIFs displayed depending on the season or time of year. For example, the display unit can display a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. The display unit changes the GIFs displayed depending on the season or time of year. For example, it can display a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. By changing the GIFs according to the season or time of year, it is possible to provide a fresh and surprising display. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of changing the GIFs according to the season or time of year.

[0036] The display unit can be used for presentations in presentation material creation apps or as a smartphone wallpaper. For example, the display unit can enhance the visual effect by inserting a GIF of fireworks into a presentation. Furthermore, the display unit can enhance the wallpaper by setting a GIF of fireworks as the smartphone wallpaper. The display unit can be used for presentations in presentation material creation apps or as a smartphone wallpaper. For example, it can enhance the visual effect by inserting a GIF of fireworks into a presentation. Furthermore, it can enhance the wallpaper by setting a GIF of fireworks as the smartphone wallpaper. This allows for enhanced visual effects when used in presentations in presentation material creation apps or as a smartphone wallpaper. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of inserting a GIF of fireworks into a presentation.

[0037] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display images and instructions that the user has frequently used in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest images and instructions to be used during specific time periods based on the user's past input history. For example, it can predict and suggest images and instructions to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have AI perform the process of analyzing the user's past input history and suggesting the optimal input method.

[0038] The reception desk can filter images and instructions based on the user's current activity status and areas of interest when the user inputs them. For example, if the user is at work, the reception desk will prioritize displaying work-related images and instructions. The reception desk can also prioritize displaying relaxing images and instructions if the user is on vacation. The reception desk can also prioritize displaying images and instructions related to a particular hobby if the user is interested in that hobby. By filtering based on the user's current activity status and areas of interest, more appropriate images and instructions can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have AI perform the processing of filtering based on the user's current activity status and areas of interest.

[0039] The reception desk can prioritize accepting highly relevant inputs based on the user's geographical location when images and instructions are input. For example, if the user is in a specific region, the reception desk will prioritize displaying images and instructions related to that region. The reception desk can also prioritize displaying images and instructions related to the user's travel destination if the user is traveling. For example, if the user is traveling, the reception desk will prioritize displaying images and instructions related to the travel destination if the user is at home. For example, if the user is at home, the reception desk will prioritize displaying images and instructions related to home. By prioritizing the acceptance of highly relevant inputs based on the user's geographical location, more appropriate images and instructions can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have the AI ​​perform a process that prioritizes accepting inputs that are highly relevant based on the user's geographical location information.

[0040] The reception desk can analyze the user's social media activity when images and instructions are input and suggest relevant inputs. For example, the reception desk can suggest relevant inputs based on images and instructions shared by the user on social media. The reception desk can also suggest relevant inputs based on posts the user has "liked" on social media. For example, the reception desk can suggest relevant inputs based on posts from accounts the user follows on social media. In this way, relevant inputs can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have AI perform the process of analyzing the user's social media activity and suggesting relevant inputs.

[0041] The generation unit can adjust the level of detail of the GIF based on the importance of the image during generation. For example, if the image is important, the generation unit can generate a GIF with detailed effects. The generation unit can also generate a GIF with simple effects for general images. For example, if the image is general, it can generate a GIF with simple effects. The generation unit can also generate a GIF with special effects for images specifically designated by the user. For example, if the image is specifically designated by the user, it can generate a GIF with special effects. By adjusting the level of detail of the GIF based on the importance of the image, a more appropriate GIF can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can have a generation AI perform the process of adjusting the level of detail of the GIF based on the importance of the image.

[0042] The generation unit can apply different generation algorithms depending on the image category during generation. For example, in the case of a landscape image, the generation unit applies a generation algorithm that emphasizes natural movement. The generation unit can also apply a generation algorithm that emphasizes facial expressions and movements in the case of a portrait image. For example, in the case of a portrait image, a generation algorithm that emphasizes facial expressions and movements is applied. The generation unit can also apply an animation-style generation algorithm in the case of an illustration image. For example, in the case of an illustration image, an animation-style generation algorithm is applied. By applying different generation algorithms depending on the image category, a more appropriate GIF can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can cause a generation AI to perform the process of applying different generation algorithms depending on the image category.

[0043] The generation unit can determine the priority of GIFs based on when the images were taken during generation. For example, the generation unit can prioritize the use of recently taken images when generating GIFs. The generation unit can also prioritize the use of images taken during a specific event when generating GIFs. For example, it can prioritize the use of images taken during a specific event when generating GIFs. The generation unit can also prioritize the use of images taken during a specific season when generating GIFs. For example, it can prioritize the use of images taken during a specific season when generating GIFs. By determining the priority of GIFs based on when the images were taken, it is possible to generate more appropriate GIFs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can have a generation AI perform the process of determining the priority of GIFs based on when the images were taken.

[0044] The generation unit can adjust the order of GIFs based on the relevance of the images during generation. For example, the generation unit can generate a GIF that displays highly relevant images consecutively. The generation unit can also generate a GIF that displays less relevant images with intervals in between. For example, the generation unit can generate a GIF that displays less relevant images with intervals in between. The generation unit can also generate a GIF based on an order specified by the user. For example, the generation unit can generate a GIF based on an order specified by the user. By adjusting the order of GIFs based on the relevance of the images, a more appropriate GIF can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can have a generation AI perform the process of adjusting the order of GIFs based on the relevance of the images.

[0045] The display unit can select the optimal display method by referring to the user's past viewing history when displaying content. For example, the display unit may prioritize displaying GIFs that the user has previously enjoyed viewing. The display unit can also predict and suggest GIFs to display at specific times based on the user's past viewing history. For example, it may predict and suggest GIFs to display at specific times based on the user's past viewing history. The display unit can also analyze the user's past viewing history and suggest the most effective display method. For example, it may analyze the user's past viewing history and suggest the most effective display method. This allows the display unit to select the optimal display method by referring to the user's past viewing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of selecting the optimal display method by referring to the user's past viewing history.

[0046] The display unit can customize the displayed content based on the user's current activity status when displaying content. For example, if the user is working, the display unit will display a GIF related to work. The display unit can also customize the displayed content based on the user's current activity status when displaying content. For example, if the user is working, it will display a GIF related to work. The display unit can also display a relaxing GIF if the user is on vacation. For example, if the user is on vacation, it will display a relaxing GIF. The display unit can also display a GIF related to a particular hobby if the user is interested in that hobby. For example, if the user is interested in a particular hobby, it will display a GIF related to that hobby. By customizing the displayed content based on the user's current activity status, more appropriate GIFs can be displayed. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of customizing the displayed content based on the user's current activity status.

[0047] The display unit can select the optimal display method when displaying content, taking into account the user's geographical location information. For example, if the user is in a specific region, the display unit will display a GIF related to that region. The display unit can also select the optimal display method when displaying content, taking into account the user's geographical location information. For example, if the user is in a specific region, it will display a GIF related to that region. The display unit can also display a GIF related to the user's travel destination if the user is traveling. For example, if the user is traveling, it will display a GIF related to the travel destination. The display unit can also display a GIF related to the user's home if the user is at home. For example, if the user is at home, it will display a GIF related to the user's home. This allows for the display of more appropriate GIFs by taking into account the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can have AI perform the process of selecting the optimal display method, taking into account the user's geographical location information.

[0048] The display unit can analyze the user's social media activity and suggest content to display at the time of display. For example, the display unit can display relevant GIFs based on images and instructions shared by the user on social media. The display unit can also display relevant GIFs based on posts the user has "liked" on social media. For example, the display unit can display relevant GIFs based on posts the user has "liked" on social media. The display unit can also display relevant GIFs based on posts from accounts the user follows on social media. For example, the display unit can display relevant GIFs based on posts from accounts the user follows on social media. This allows for the display of more appropriate GIFs by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of analyzing the user's social media activity and suggesting content to display.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The generation unit can adjust the level of detail in the GIF based on the importance of the image during generation. For example, for important images, it can generate a GIF with detailed effects. For general images, it can generate a GIF with simple effects. Furthermore, for images specifically designated by the user, it can generate a GIF with special effects. By adjusting the level of detail in the GIF based on the importance of the image, it is possible to generate a more appropriate GIF.

[0051] The reception desk can analyze a user's past input history and suggest the optimal input method. For example, it can automatically display images and instructions that the user has frequently used in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest images and instructions that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method.

[0052] The reception system can filter images and instructions based on the user's current activities and areas of interest. For example, if the user is at work, work-related images and instructions will be prioritized. If the user is on vacation, relaxing images and instructions will be prioritized. Furthermore, if the user has a particular hobby, images and instructions related to that hobby will be prioritized. This allows for more appropriate images and instructions to be provided by filtering based on the user's current activities and areas of interest.

[0053] The generation unit can apply different generation algorithms depending on the image category during generation. For example, for landscape images, a generation algorithm that emphasizes natural movement can be applied. For portrait images, a generation algorithm that emphasizes facial expressions and movements can be applied. Furthermore, for illustration images, an animation-style generation algorithm can be applied. By applying different generation algorithms depending on the image category, it is possible to generate more appropriate GIFs.

[0054] The display unit can select the optimal display method by referring to the user's past viewing history. For example, it can prioritize displaying GIFs that the user has previously viewed frequently. It can also predict and suggest GIFs to display at specific times based on the user's past viewing history. Furthermore, it can analyze the user's past viewing history and suggest the most effective display method. In this way, the optimal display method can be selected by referring to the user's past viewing history.

[0055] The display unit can select the optimal display method by considering the user's geographical location information. For example, if the user is in a specific region, it can display GIFs related to that region. If the user is traveling, it can display GIFs related to their travel destination. Furthermore, if the user is at home, it can display GIFs related to their home. This allows for the display of more appropriate GIFs by considering the user's geographical location.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk inputs images and instructions. Images may include, but are not limited to, formats such as JPEG, PNG, and GIF. Instructions may include text or audio instructions such as "attract attention," "inspirational," or "praise for achieving a goal." Users can provide instructions such as "upload a photo of fireworks and enter the comment 'Congratulations'" along with an image. Step 2: The generation unit uses a generation AI to analyze the image and instructions input by the reception unit and generate a repeating short video (GIF). For example, the generation unit generates the optimal GIF based on the image and instructions. For example, based on a photo of fireworks and the comment "Congratulations," it generates a GIF of fireworks going off. The generation AI is implemented using technologies such as deep learning models and generative opposite networks (GANs). Step 3: The display unit displays the GIF generated by the generation unit. The display unit can be used, for example, to support the work of individuals with intellectual or developmental disabilities. A target time is set for the task, and a GIF is displayed when the target is achieved. For example, if the task is completed within the target time, a fireworks GIF is displayed. The display unit can also change the GIF depending on the season or time of year. For example, a cherry blossom GIF can be displayed in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. The display unit can also be used in presentations using presentation material creation apps and as a smartphone wallpaper. For example, inserting a fireworks GIF into a presentation can enhance its visual effect. Also, setting a fireworks GIF as a smartphone wallpaper can provide a more enjoyable wallpaper.

[0058] (Example of form 2) The GIF generation system according to an embodiment of the present invention is a system that generates eye-catching short videos (GIFs) based on images (still images). The GIF generation system takes an image along with instructions such as "attract attention," "inspirational," or "praise for achieving a goal." The generation AI analyzes these instructions and generates a repeating short video (GIF). This GIF can be used to support the work of individuals with intellectual and developmental disabilities. By setting a target time for the task and displaying the GIF upon achieving the goal, it aims to maintain continuous results and increase motivation. Furthermore, changing the GIF according to the season or time of year provides a fresh and surprising display. In addition, the generated GIF can be used in presentations in presentation material creation apps or as a smartphone wallpaper, enhancing its visual impact. For example, the user inputs an image along with instructions such as "attract attention," "inspirational," or "praise for achieving a goal." The user only needs to input specific instructions. For example, they might instruct, "Upload a photo of fireworks and enter the comment 'Congratulations'." This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a repeating short video (GIF). The AI ​​generates the most suitable GIF based on an image and instructions. For example, based on a photo of fireworks and the comment "Congratulations," it generates a GIF of fireworks going off. The generated GIFs are used to support the work of individuals with intellectual and developmental disabilities. Specifically, a target time is set for the task, and the GIF is displayed when the target is achieved. For example, if the task is completed within the target time, the fireworks GIF is displayed. In this way, the results of the work can be visually confirmed, helping to maintain continuous achievement and increase motivation. Furthermore, by changing the GIF according to the season or time of year, a fresh and surprising display can be provided. For example, a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter can be displayed, providing a constantly fresh visual stimulus. In addition, the generated GIFs can be used in presentations in presentation material creation apps and as smartphone wallpapers. For example, inserting a fireworks GIF into presentation materials can enhance the visual effect.Furthermore, by setting a fireworks GIF as the smartphone's wallpaper, users can enjoy a more visually appealing wallpaper. Thus, a GIF generation system is a system that generates eye-catching short videos (GIFs) from images (still images), and can be used for various purposes such as work support for people with intellectual and developmental disabilities, presentation materials, and smartphone wallpapers. In this way, a GIF generation system can enhance visual impact.

[0059] The GIF generation system according to the embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives an image and instructions as input. Images include, but are not limited to, formats such as JPEG, PNG, and GIF. Instructions include, but are not limited to, text instructions such as "attract attention," "inspirational," and "praise for achieving a goal," as well as voice instructions. The reception unit can, for example, allow a user to give instructions such as "upload a photo of fireworks and enter the comment 'Congratulations' along with an image." The generation unit uses a generation AI to analyze the image and instructions input by the reception unit and generates a repeating short video (GIF). The generation unit generates the optimal GIF based on the image and instructions, for example, the generation AI generates a GIF of fireworks being launched based on a photo of fireworks and the comment "Congratulations." Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is implemented using, for example, a deep learning model or a generative opposite network (GAN). The display unit displays the GIF generated by the generation unit. The display unit can be used, for example, to support the work of individuals with intellectual or developmental disabilities. It allows users to set a target time for their tasks and displays a GIF upon achieving that time. For instance, if a task is completed within the target time, a fireworks GIF is displayed. The display unit can also change the GIF displayed depending on the season or time of year. For example, it can display a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. The display unit can also be used in presentations using presentation materials and as a smartphone wallpaper. For example, inserting a fireworks GIF into a presentation can enhance its visual impact. Furthermore, setting a fireworks GIF as a smartphone wallpaper allows users to enjoy the wallpaper. Thus, the GIF generation system according to this embodiment can enhance its visual impact.

[0060] The reception desk accepts images and instructions. Images can be in various formats, including but not limited to JPEG, PNG, and GIF. This allows users to utilize diverse image formats, increasing the system's flexibility. Instructions can be text-based or voice-based, such as "attract attention," "inspirational," or "praise for achieving a goal." This allows users to clearly communicate their intentions and customize the content of the generated GIF. The reception desk can, for example, receive instructions such as "upload a photo of fireworks and enter the comment 'Congratulations'" along with an image. Users upload images through a dedicated interface and enter instructions using text boxes or voice input. The interface is intuitively designed and easy to use, making it simple for users to operate. Furthermore, the reception desk includes an image preview function, allowing users to review uploaded images. This reduces the risk of users uploading the wrong image. The reception desk also has the ability to analyze the content of the entered instructions and convert them to the appropriate format. For example, by using speech recognition technology to convert voice instructions to text, voice instructions can be treated the same as text instructions. This allows the reception desk to accommodate diverse user input methods and improve the system's usability.

[0061] The generation unit uses a generation AI to analyze the image and instructions input by the reception unit and generate a repeating short video (GIF). For example, the generation unit generates the optimal GIF based on the image and the instructions. The generation AI is implemented using technologies such as deep learning models and generative opposite networks (GANs). Specifically, the generation AI first analyzes the input image and extracts the main elements and features within the image. Next, it analyzes the instructions and understands the user's intent. For example, if the comment "Congratulations" is input, the generation AI understands the intention of congratulations and generates a GIF that is appropriate for that. The generation AI generates animation patterns to move the elements in the image and creates a GIF by repeatedly playing these patterns. For example, if a picture of fireworks is input, the generation AI generates an animation of fireworks going off and creates a GIF that repeatedly plays this animation. The generation AI can also create more visually appealing GIFs by adjusting the colors and effects. Furthermore, the generation unit has a function to evaluate the quality of the generated GIF and make corrections as needed. For example, if the generated GIF does not meet the user's intent, the generation unit will run the generation process again to produce a more appropriate GIF. This allows the generation unit to provide high-quality GIFs that meet the user's expectations.

[0062] The display unit shows GIFs generated by the generation unit. The display unit can be used, for example, to support the work of individuals with intellectual and developmental disabilities. A target time is set for the task, and a GIF is displayed upon completion. For instance, if the task is completed within the target time, a fireworks GIF is displayed. This enhances the sense of accomplishment and improves motivation. The display unit can also change the GIFs displayed according to the season or time of year. For example, a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter can be displayed. This creates a sense of the season and provides visual enjoyment. The display unit can also be used in presentations in presentation materials creation apps and as smartphone wallpapers. For example, inserting a fireworks GIF into a presentation can enhance its visual impact. Additionally, setting a fireworks GIF as a smartphone wallpaper can enhance the wallpaper's appeal. Furthermore, the display unit can collect user feedback and use it to improve the displayed content. For example, if a user prefers a particular GIF, the display unit can adjust its display based on that information to show more similar GIFs. This allows the display unit to provide a customized display according to the user's preferences, maximizing the visual effect.

[0063] The generation unit can generate the optimal GIF based on an image and instructions using a generation AI. For example, the generation unit generates the optimal GIF based on an image and instructions. For example, the generation unit generates a GIF of fireworks going off based on a photo of fireworks and the comment "Congratulations." Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is implemented using technologies such as deep learning models or generative opposite networks (GANs). As a result, the optimal GIF can be generated by using a generation AI.

[0064] The display unit is used to support the work of individuals with intellectual and developmental disabilities. It allows users to set target times for their tasks and displays a GIF when the target is achieved. For example, if a task is completed within the target time, a GIF of fireworks can be displayed. This allows for the maintenance of continuous achievement and increased motivation by displaying a GIF upon goal completion. Some or all of the above-described processes in the display unit may be performed using AI or not. For example, the display unit can have AI execute the process of displaying a GIF of fireworks when a task is completed within the target time.

[0065] The display unit can change the GIFs displayed depending on the season or time of year. For example, the display unit can display a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. The display unit changes the GIFs displayed depending on the season or time of year. For example, it can display a cherry blossom GIF in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. By changing the GIFs according to the season or time of year, it is possible to provide a fresh and surprising display. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of changing the GIFs according to the season or time of year.

[0066] The display unit can be used for presentations in presentation material creation apps or as a smartphone wallpaper. For example, the display unit can enhance the visual effect by inserting a GIF of fireworks into a presentation. Furthermore, the display unit can enhance the wallpaper by setting a GIF of fireworks as the smartphone wallpaper. The display unit can be used for presentations in presentation material creation apps or as a smartphone wallpaper. For example, it can enhance the visual effect by inserting a GIF of fireworks into a presentation. Furthermore, it can enhance the wallpaper by setting a GIF of fireworks as the smartphone wallpaper. This allows for enhanced visual effects when used in presentations in presentation material creation apps or as a smartphone wallpaper. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of inserting a GIF of fireworks into a presentation.

[0067] The reception unit can estimate the user's emotions and determine the priority of input images and instructions based on the estimated emotions. For example, if the user is happy, the reception unit will prioritize emotionally moving images and instructions. The reception unit can also prioritize images and instructions containing encouraging messages if the user is sad. For example, if the user is sad, the reception unit will prioritize images and instructions containing encouraging messages. The reception unit can also prioritize energetic images and instructions if the user is excited. For example, if the user is excited, the reception unit will prioritize energetic images and instructions. By determining the priority of input images and instructions based on the user's emotions, it is possible to generate more appropriate GIFs. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI or not. For example, the reception area may have the AI ​​perform the process of estimating the user's emotions and determining the priority of input images and instructions based on the estimated user emotions.

[0068] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display images and instructions that the user has frequently used in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest images and instructions to be used during specific time periods based on the user's past input history. For example, it can predict and suggest images and instructions to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have AI perform the process of analyzing the user's past input history and suggesting the optimal input method.

[0069] The reception desk can filter images and instructions based on the user's current activity status and areas of interest when the user inputs them. For example, if the user is at work, the reception desk will prioritize displaying work-related images and instructions. The reception desk can also prioritize displaying relaxing images and instructions if the user is on vacation. The reception desk can also prioritize displaying images and instructions related to a particular hobby if the user is interested in that hobby. By filtering based on the user's current activity status and areas of interest, more appropriate images and instructions can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have AI perform the processing of filtering based on the user's current activity status and areas of interest.

[0070] The reception system can estimate the user's emotions and adjust the display method of the input interface based on the estimated emotions. For example, if the user is nervous, the reception system can provide an interface with calming colors to reduce visual stress. The reception system can also provide an interface with bright colors if the user is enjoying themselves to make the input process more enjoyable. For example, if the user is enjoying themselves, the reception system can provide an interface with bright colors to make the input process more enjoyable. The reception system can also provide a simple and highly visible interface if the user is tired to make the input process easier. For example, if the user is tired, the system can provide a simple and highly visible interface to make the input process easier. By adjusting the display method of the input interface based on the user's emotions, a more appropriate interface can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, or they may not. For example, the reception area can have the AI ​​perform the process of estimating the user's emotions and adjusting the display method of the input interface based on the estimated user emotions.

[0071] The reception desk can prioritize accepting highly relevant inputs based on the user's geographical location when images and instructions are input. For example, if the user is in a specific region, the reception desk will prioritize displaying images and instructions related to that region. The reception desk can also prioritize displaying images and instructions related to the user's travel destination if the user is traveling. For example, if the user is traveling, the reception desk will prioritize displaying images and instructions related to the travel destination if the user is at home. For example, if the user is at home, the reception desk will prioritize displaying images and instructions related to home. By prioritizing the acceptance of highly relevant inputs based on the user's geographical location, more appropriate images and instructions can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have the AI ​​perform a process that prioritizes accepting inputs that are highly relevant based on the user's geographical location information.

[0072] The reception desk can analyze the user's social media activity when images and instructions are input and suggest relevant inputs. For example, the reception desk can suggest relevant inputs based on images and instructions shared by the user on social media. The reception desk can also suggest relevant inputs based on posts the user has "liked" on social media. For example, the reception desk can suggest relevant inputs based on posts from accounts the user follows on social media. In this way, relevant inputs can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have AI perform the process of analyzing the user's social media activity and suggesting relevant inputs.

[0073] The generation unit can estimate the user's emotions and adjust the way the generated GIF is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a GIF that progresses at a leisurely pace. The generation unit can also generate a GIF that emphasizes the shortest route if the user is in a hurry. For example, if the user is in a hurry, the generation unit will generate a GIF that emphasizes the shortest route. The generation unit can also generate a GIF with visually stimulating effects if the user is excited. For example, if the user is excited, the generation unit will generate a GIF with visually stimulating effects. In this way, by adjusting the way the generated GIF is expressed based on the user's emotions, it is possible to generate more appropriate GIFs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can have the generation AI perform a process to estimate the user's emotions and adjust the expression method of the generated GIF based on the estimated user emotions.

[0074] The generation unit can adjust the level of detail of the GIF based on the importance of the image during generation. For example, if the image is important, the generation unit can generate a GIF with detailed effects. The generation unit can also generate a GIF with simple effects for general images. For example, if the image is general, it can generate a GIF with simple effects. The generation unit can also generate a GIF with special effects for images specifically designated by the user. For example, if the image is specifically designated by the user, it can generate a GIF with special effects. By adjusting the level of detail of the GIF based on the importance of the image, a more appropriate GIF can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can have a generation AI perform the process of adjusting the level of detail of the GIF based on the importance of the image.

[0075] The generation unit can apply different generation algorithms depending on the image category during generation. For example, in the case of a landscape image, the generation unit applies a generation algorithm that emphasizes natural movement. The generation unit can also apply a generation algorithm that emphasizes facial expressions and movements in the case of a portrait image. For example, in the case of a portrait image, a generation algorithm that emphasizes facial expressions and movements is applied. The generation unit can also apply an animation-style generation algorithm in the case of an illustration image. For example, in the case of an illustration image, an animation-style generation algorithm is applied. By applying different generation algorithms depending on the image category, a more appropriate GIF can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can cause a generation AI to perform the process of applying different generation algorithms depending on the image category.

[0076] The generation unit can estimate the user's emotions and adjust the length of the generated GIF based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise GIF. The generation unit can also generate a longer GIF with detailed explanations if the user is relaxed. For example, if the user is relaxed, the generation unit will generate a longer GIF with detailed explanations. The generation unit can also generate a GIF with visually stimulating effects if the user is excited. For example, if the user is excited, the generation unit will generate a GIF with visually stimulating effects. By adjusting the length of the generated GIF based on the user's emotions, it is possible to generate more appropriate GIFs. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can have the generation AI perform the process of estimating the user's emotions and adjusting the length of the generated GIF based on the estimated user emotions.

[0077] The generation unit can determine the priority of GIFs based on when the images were taken during generation. For example, the generation unit can prioritize the use of recently taken images when generating GIFs. The generation unit can also prioritize the use of images taken during a specific event when generating GIFs. For example, it can prioritize the use of images taken during a specific event when generating GIFs. The generation unit can also prioritize the use of images taken during a specific season when generating GIFs. For example, it can prioritize the use of images taken during a specific season when generating GIFs. By determining the priority of GIFs based on when the images were taken, it is possible to generate more appropriate GIFs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can have a generation AI perform the process of determining the priority of GIFs based on when the images were taken.

[0078] The generation unit can adjust the order of GIFs based on the relevance of the images during generation. For example, the generation unit can generate a GIF that displays highly relevant images consecutively. The generation unit can also generate a GIF that displays less relevant images with intervals in between. For example, the generation unit can generate a GIF that displays less relevant images with intervals in between. The generation unit can also generate a GIF based on an order specified by the user. For example, the generation unit can generate a GIF based on an order specified by the user. By adjusting the order of GIFs based on the relevance of the images, a more appropriate GIF can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can have a generation AI perform the process of adjusting the order of GIFs based on the relevance of the images.

[0079] The display unit can estimate the user's emotions and select a GIF to display based on the estimated emotions. For example, if the user is happy, the display unit can display a GIF containing a congratulatory message. The display unit can also estimate the user's emotions and select a GIF to display based on the estimated emotions. For example, if the user is happy, the display unit can display a GIF containing a congratulatory message. The display unit can also display a GIF containing an encouraging message if the user is sad. For example, if the user is sad, the display unit can display a GIF containing an encouraging message. The display unit can also display an energetic GIF if the user is excited. For example, if the user is excited, the display unit can display an energetic GIF. By selecting a GIF to display based on the user's emotions, a more appropriate GIF can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have the AI ​​perform a process to estimate the user's emotions and select a GIF to display based on those estimated emotions.

[0080] The display unit can select the optimal display method by referring to the user's past viewing history when displaying content. For example, the display unit may prioritize displaying GIFs that the user has previously enjoyed viewing. The display unit can also predict and suggest GIFs to display at specific times based on the user's past viewing history. For example, it may predict and suggest GIFs to display at specific times based on the user's past viewing history. The display unit can also analyze the user's past viewing history and suggest the most effective display method. For example, it may analyze the user's past viewing history and suggest the most effective display method. This allows the display unit to select the optimal display method by referring to the user's past viewing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of selecting the optimal display method by referring to the user's past viewing history.

[0081] The display unit can customize the displayed content based on the user's current activity status when displaying content. For example, if the user is working, the display unit will display a GIF related to work. The display unit can also customize the displayed content based on the user's current activity status when displaying content. For example, if the user is working, it will display a GIF related to work. The display unit can also display a relaxing GIF if the user is on vacation. For example, if the user is on vacation, it will display a relaxing GIF. The display unit can also display a GIF related to a particular hobby if the user is interested in that hobby. For example, if the user is interested in a particular hobby, it will display a GIF related to that hobby. By customizing the displayed content based on the user's current activity status, more appropriate GIFs can be displayed. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of customizing the displayed content based on the user's current activity status.

[0082] The display unit can estimate the user's emotions and determine the priority of GIFs to display based on the estimated emotions. For example, if the user is happy, the display unit will prioritize displaying GIFs containing congratulatory messages. The display unit can also prioritize displaying GIFs containing encouraging messages if the user is sad. For example, if the user is sad, the display unit will prioritize displaying GIFs containing encouraging messages. The display unit can also prioritize displaying energetic GIFs if the user is excited. For example, if the user is excited, the display unit will prioritize displaying energetic GIFs. In this way, by determining the priority of GIFs to display based on the user's emotions, more appropriate GIFs can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the display unit may be performed using AI or not. For example, the display unit can have the AI ​​perform the process of estimating the user's emotions and determining the priority of GIFs to display based on the estimated user emotions.

[0083] The display unit can select the optimal display method when displaying content, taking into account the user's geographical location information. For example, if the user is in a specific region, the display unit will display a GIF related to that region. The display unit can also select the optimal display method when displaying content, taking into account the user's geographical location information. For example, if the user is in a specific region, it will display a GIF related to that region. The display unit can also display a GIF related to the user's travel destination if the user is traveling. For example, if the user is traveling, it will display a GIF related to the travel destination. The display unit can also display a GIF related to the user's home if the user is at home. For example, if the user is at home, it will display a GIF related to the user's home. This allows for the display of more appropriate GIFs by taking into account the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI. For example, the display unit can have AI perform the process of selecting the optimal display method, taking into account the user's geographical location information.

[0084] The display unit can analyze the user's social media activity and suggest content to display at the time of display. For example, the display unit can display relevant GIFs based on images and instructions shared by the user on social media. The display unit can also display relevant GIFs based on posts the user has "liked" on social media. For example, the display unit can display relevant GIFs based on posts the user has "liked" on social media. The display unit can also display relevant GIFs based on posts from accounts the user follows on social media. For example, the display unit can display relevant GIFs based on posts from accounts the user follows on social media. This allows for the display of more appropriate GIFs by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can have AI perform the process of analyzing the user's social media activity and suggesting content to display.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The reception system can estimate the user's emotions and prioritize the input images and instructions based on those emotions. For example, if the user is happy, it will prioritize emotionally moving images and instructions. If the user is sad, it can also prioritize images and instructions containing encouraging messages. Furthermore, if the user is excited, it can prioritize energetic images and instructions. By prioritizing the input images and instructions based on the user's emotions, it can generate more appropriate GIFs.

[0087] The generation unit can adjust the level of detail in the GIF based on the importance of the image during generation. For example, for important images, it can generate a GIF with detailed effects. For general images, it can generate a GIF with simple effects. Furthermore, for images specifically designated by the user, it can generate a GIF with special effects. By adjusting the level of detail in the GIF based on the importance of the image, it is possible to generate a more appropriate GIF.

[0088] The display unit can estimate the user's emotions and select a GIF to display based on those emotions. For example, if the user is happy, it can display a GIF containing a congratulatory message. If the user is sad, it can display a GIF containing an encouraging message. Furthermore, if the user is excited, it can display an energetic GIF. By selecting a GIF to display based on the user's emotions, it is possible to display a more appropriate GIF.

[0089] The reception desk can analyze a user's past input history and suggest the optimal input method. For example, it can automatically display images and instructions that the user has frequently used in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest images and instructions that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method.

[0090] The generation unit can estimate the user's emotions and adjust the way the generated GIF is expressed based on those emotions. For example, if the user is relaxed, it can generate a GIF that progresses at a leisurely pace. If the user is in a hurry, it can generate a GIF that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a GIF with visually stimulating effects. In this way, by adjusting the way the generated GIF is expressed based on the user's emotions, it is possible to generate more appropriate GIFs.

[0091] The reception system can filter images and instructions based on the user's current activities and areas of interest. For example, if the user is at work, work-related images and instructions will be prioritized. If the user is on vacation, relaxing images and instructions will be prioritized. Furthermore, if the user has a particular hobby, images and instructions related to that hobby will be prioritized. This allows for more appropriate images and instructions to be provided by filtering based on the user's current activities and areas of interest.

[0092] The generation unit can apply different generation algorithms depending on the image category during generation. For example, for landscape images, a generation algorithm that emphasizes natural movement can be applied. For portrait images, a generation algorithm that emphasizes facial expressions and movements can be applied. Furthermore, for illustration images, an animation-style generation algorithm can be applied. By applying different generation algorithms depending on the image category, it is possible to generate more appropriate GIFs.

[0093] The display unit can select the optimal display method by referring to the user's past viewing history. For example, it can prioritize displaying GIFs that the user has previously viewed frequently. It can also predict and suggest GIFs to display at specific times based on the user's past viewing history. Furthermore, it can analyze the user's past viewing history and suggest the most effective display method. In this way, the optimal display method can be selected by referring to the user's past viewing history.

[0094] The generation unit can estimate the user's emotions and adjust the length of the generated GIF based on those emotions. For example, if the user is in a hurry, it can generate a short, concise GIF. If the user is relaxed, it can generate a longer GIF with detailed explanations. Furthermore, if the user is excited, it can generate a GIF with visually stimulating effects. By adjusting the length of the generated GIF based on the user's emotions, it is possible to produce more appropriate GIFs.

[0095] The display unit can select the optimal display method by considering the user's geographical location information. For example, if the user is in a specific region, it can display GIFs related to that region. If the user is traveling, it can display GIFs related to their travel destination. Furthermore, if the user is at home, it can display GIFs related to their home. This allows for the display of more appropriate GIFs by considering the user's geographical location.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The reception desk inputs images and instructions. Images may include, but are not limited to, formats such as JPEG, PNG, and GIF. Instructions may include text or audio instructions such as "attract attention," "inspirational," or "praise for achieving a goal." Users can provide instructions such as "upload a photo of fireworks and enter the comment 'Congratulations'" along with an image. Step 2: The generation unit uses a generation AI to analyze the image and instructions input by the reception unit and generate a repeating short video (GIF). For example, the generation unit generates the optimal GIF based on the image and instructions. For example, based on a photo of fireworks and the comment "Congratulations," it generates a GIF of fireworks going off. The generation AI is implemented using technologies such as deep learning models and generative opposite networks (GANs). Step 3: The display unit displays the GIF generated by the generation unit. The display unit can be used, for example, to support the work of individuals with intellectual or developmental disabilities. A target time is set for the task, and a GIF is displayed when the target is achieved. For example, if the task is completed within the target time, a fireworks GIF is displayed. The display unit can also change the GIF depending on the season or time of year. For example, a cherry blossom GIF can be displayed in spring, a fireworks GIF in summer, an autumn foliage GIF in fall, and a snow GIF in winter. The display unit can also be used in presentations using presentation material creation apps and as a smartphone wallpaper. For example, inserting a fireworks GIF into a presentation can enhance its visual effect. Also, setting a fireworks GIF as a smartphone wallpaper can provide a more enjoyable wallpaper.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements, including the reception unit, generation unit, and display unit described above, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs images and instructions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI is used to analyze the images and instructions and generate a repeating short video (GIF). The display unit is implemented by the output device 40 of the smart device 14, where the generated GIF is displayed. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs images and instructions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the images and instructions and generates a repeating short video (GIF). The display unit is implemented by the speaker 240 of the smart glasses 214, where the generated GIF is displayed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs images and instructions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the images and instructions and generates a repeating short video (GIF). The display unit is implemented by the display 343 of the headset terminal 314, where the generated GIF is displayed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs images and instructions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI is used to analyze the images and instructions and generate a repeating short video (GIF). The display unit is implemented by the speaker 240 of the robot 414, where the generated GIF is displayed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A reception area where images and instructions are entered, A generation unit analyzes the image and instructions input by the reception unit and generates a repeating short video (GIF), The system includes a display unit that displays the GIF generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates the optimal GIF based on the image and instructions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Used to support the work of individuals with intellectual and developmental disabilities, it allows them to set target times for tasks and displays a GIF when the target is achieved. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Change the GIFs according to the season or time of year. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is It is used for presentations in presentation material creation apps and as a wallpaper on smartphones. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input images and instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting images and instructions, filtering is performed based on the user's current activity status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input images and instructions, the system prioritizes inputs that are highly relevant based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input images and instructions, the system analyzes their social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the way GIFs are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the GIF's detail level is adjusted based on the importance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the generated GIF based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the priority of GIFs is determined based on when the images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the order of GIFs is adjusted based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is The system estimates the user's emotions and selects a GIF to display based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying content, the system selects the optimal display method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displayed, the content displayed is customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is It estimates the user's emotions and determines the priority of GIFs to display based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying content, the system analyzes the user's social media activity and suggests what to show. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where images and instructions are entered, A generation unit analyzes the images and instructions input by the reception unit and generates a repeating short video, The system includes a display unit that displays the GIF generated by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates the optimal GIF based on the image and instructions. The system according to feature 1.

3. The aforementioned display unit is Used to support the work of individuals with intellectual and developmental disabilities, it allows them to set target times for tasks and displays a GIF when the target is achieved. The system according to feature 1.

4. The aforementioned display unit is Change the GIFs according to the season or time of year. The system according to feature 1.

5. The aforementioned display unit is It is used for presentations in presentation material creation apps and as a wallpaper on smartphones. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input images and instructions based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When inputting images and instructions, filtering is performed based on the user's current activity status and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When users input images and instructions, the system prioritizes inputs that are highly relevant based on their geographical location. The system according to feature 1.

Images