system

Abstract

The system according to this embodiment aims to automatically generate hashtags suitable for images used by the user. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives images uploaded by the user. The analysis unit analyzes the images received by the reception unit and understands the elements and themes of the images. The generation unit automatically generates appropriate hashtags based on the elements and themes understood by the analysis unit. The provision unit provides the hashtags generated by the generation unit to the user.

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 performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes time and effort for a user to select a hash tag suitable for an image.

[0005] The system according to an embodiment aims to automatically generate a hash tag suitable for an image by a user. <00​​​​​The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives images uploaded by the user. The analysis unit analyzes the images received by the reception unit and understands the elements and themes of the images. The generation unit automatically generates appropriate hashtags based on the elements and themes understood by the analysis unit. The provision unit provides the hashtags generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows the user to automatically generate hashtags appropriate for images. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 social media support system according to an embodiment of the present invention is a system in which AI analyzes images and proposes the most suitable hashtags for the content. This social media support system analyzes images uploaded by users and automatically generates effective hashtags that match the content. The generating AI understands the elements and themes of the image and proposes the most suitable keywords. This allows creators to save time on selecting hashtags and communicate their messages more effectively. In addition, users can obtain natural and effective hashtags and maximize their impact on social media. First, the user uploads an image. At this time, the user does not need to perform any special operations and simply uploads the image using the normal posting procedure. For example, the user selects an image from a smartphone or personal computer and clicks the upload button. This information is input to the AI. Next, the AI ​​analyzes the uploaded image. The AI ​​analyzes the content of the image in detail and understands the elements and themes contained in the image. For example, in the case of a landscape photograph, it identifies elements such as mountains, rivers, and sky, and understands themes such as "nature" or "travel". As a result, the AI ​​can grasp the content of the image and generate appropriate hashtags. The generating AI automatically generates the most suitable hashtags based on the elements and themes of the analyzed image. For example, in the case of a landscape photograph, it suggests hashtags such as "#nature," "#travel," and "#landscape." In this way, the AI ​​generates and provides the most suitable hashtags for the image content. This system eliminates the effort required for creators to select hashtags. Creators can simply upload images, and the AI ​​automatically suggests the most suitable hashtags, allowing them to focus on their creative work without worrying about hashtags. Furthermore, users can obtain natural and effective hashtags, maximizing their impact on social media. For example, using appropriate hashtags can lead to posts being seen by more people and increased engagement. Thus, by analyzing images and suggesting the most suitable hashtags for the content, this innovative tool reduces the burden on creators and maximizes their impact on social media.This allows social media support systems to reduce the burden on creators and maximize their impact on social media.

[0029] The social media support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives images uploaded by users. The reception unit can receive images, for example, when a user selects an image from a smartphone or personal computer and clicks an upload button. The reception unit can also receive images uploaded by users using the normal posting procedure without requiring any special operations. For example, the reception unit can receive images simply by a user selecting an image and clicking an upload button. The analysis unit analyzes the images received by the reception unit and understands the elements and themes of the images. The analysis unit can, for example, analyze the content of the images in detail and understand the elements and themes contained in the images. For example, if it is a landscape photograph, the analysis unit can identify elements such as mountains, rivers, and sky, and understand themes such as "nature" or "travel". The generation unit automatically generates optimal hashtags based on the elements and themes understood by the analysis unit. The generation unit can, for example, automatically generate optimal hashtags based on the elements and themes of the analyzed images. For example, the generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape" in the case of a landscape photograph. The provision unit provides the hashtags generated by the generation unit to the user. The provision unit can provide the generated hashtags to the user. For example, the provision unit can notify the user of the generated hashtags. As a result, the social media support system according to the embodiment can reduce the burden on creators and maximize their impact on social media by analyzing images uploaded by users and automatically generating optimal hashtags. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without using AI. For example, the reception unit can input images uploaded by users into the AI ​​and have the AI ​​perform image reception. The analysis unit can input images received by the reception unit into the AI ​​and have the AI ​​perform image analysis.The generation unit inputs elements and themes understood by the analysis unit into the AI, allowing the AI ​​to generate hashtags. The provision unit inputs the hashtags generated by the generation unit into the AI, allowing the AI ​​to provide the hashtags.

[0030] The reception desk accepts images uploaded by users. For example, users can select images from their smartphones or personal computers and click the upload button to receive them. The reception desk can also accept images uploaded using the standard posting procedure without requiring any special operations from the user. For example, the reception desk can accept images simply by the user selecting an image and clicking the upload button. Furthermore, the reception desk allows users to upload multiple images at once, improving user convenience. For example, users can select and upload multiple photos taken during a trip at once, saving time and effort. The reception desk can also provide basic image editing functions during image upload. For example, users can perform simple edits such as cropping, rotating, and applying filters before uploading. This allows users to optimize their images before uploading. Additionally, the reception desk has a function to automatically retrieve metadata during image upload. For example, it can automatically retrieve the image's shooting date and time, location information, and camera settings, and provide this information to the analysis department. This allows the analysis department to analyze images based on more detailed information. The reception desk also includes security features to safely store images uploaded by users. For example, uploaded images are encrypted to protect them from unauthorized access by third parties. The system also provides a function to set the visibility of images to protect user privacy. For instance, users can choose who can view their images, allowing only specific friends or followers to see them. This allows the system to provide an environment where users can upload images with peace of mind.

[0031] The analysis unit analyzes images received by the reception unit to understand the elements and themes of the images. For example, the analysis unit can analyze the content of an image in detail to understand the elements and themes contained within it. For example, in the case of a landscape photograph, the analysis unit can identify elements such as mountains, rivers, and sky, and understand themes such as "nature" or "travel." The analysis unit uses AI to analyze images. Specifically, it uses image recognition technology to detect objects in the image and extract the characteristics of each object. For example, in the case of a landscape photograph, it detects objects such as mountains, rivers, and sky, and analyzes the characteristics of each. The AI ​​also analyzes information such as the color, composition, and texture of the image to understand the overall theme of the image. Furthermore, the analysis unit improves the accuracy of the analysis by utilizing image metadata. For example, based on the date and time the image was taken and location information, it can identify themes related to a specific season or place. This allows the analysis unit to understand the elements and themes of the image more accurately. The analysis unit can continuously improve its analysis accuracy by learning from past analysis results and user feedback. For example, it can adjust the analysis algorithm based on user feedback and reflect it in the next analysis. Furthermore, the analysis unit can flexibly handle different types of images. For example, it can analyze various types of images, including not only landscape photographs but also portraits, food photos, and event photos, and identify their respective elements and themes. This allows the analysis unit to perform highly accurate analysis on a wide range of images and build a foundation for providing users with the most suitable hashtags.

[0032] The generation unit automatically generates optimal hashtags based on the elements and themes understood by the analysis unit. For example, the generation unit can automatically generate optimal hashtags based on the elements and themes of an analyzed image. For example, in the case of a landscape photograph, the generation unit can suggest hashtags such as "#nature," "#travel," and "#landscape." The generation unit uses AI to generate hashtags. Specifically, based on the elements and themes provided by the analysis unit, it searches a database for relevant hashtags and selects the most suitable ones. The AI ​​learns from past posting data and trend information and can suggest hashtags that match current social media trends. For example, it can prioritize suggesting hashtags related to specific seasons or events. Furthermore, the generation unit can also suggest individually optimized hashtags by considering the user's past posting history and the interests of their followers. For example, it can analyze the user's preferences and follower reactions based on images and hashtags the user has previously posted and generate optimal hashtags based on that. This allows the generation unit to help users' posts be seen by more people. The generation unit can evaluate the effectiveness of the generated hashtags and continuously improve them. For example, the system analyzes how much engagement generated hashtags receive and uses that information to improve future hashtag generation. This ensures that the generation unit consistently provides the most suitable hashtags. The generation unit also provides a function for users to customize suggested hashtags. For example, users can edit suggested hashtags or add new ones. This allows users to select the hashtags best suited to their posts. The generation unit can improve the accuracy of hashtag suggestions based on user feedback. For example, users can evaluate suggested hashtags, and the generation algorithm can be adjusted based on those evaluations. This allows the generation unit to provide the most suitable hashtags to meet user needs.

[0033] The provider unit provides users with hashtags generated by the generator unit. For example, the provider unit can provide users with generated hashtags. For example, the provider unit can notify users of generated hashtags. The provider unit visually displays generated hashtags through the user interface. For example, after a user uploads an image, generated hashtags are displayed in a pop-up window or notification bar. Users can review the suggested hashtags and edit or add them as needed. The provider unit makes it easy for users to select suggested hashtags and add them to posts. For example, users can add suggested hashtags to a post simply by clicking on them. The provider unit also provides a function to select suggested hashtags in bulk. This allows users to add multiple hashtags at once. Furthermore, the provider unit has a function to provide real-time feedback on the effectiveness of generated hashtags. For example, after a user publishes a post, it analyzes how much engagement the generated hashtags received and notifies the user of the results. This allows users to review the effectiveness of the generated hashtags and use that information for future posts. The provider unit collects user feedback and provides data to improve the accuracy of hashtag suggestions. For example, users evaluate suggested hashtags and feed the evaluation results back to the generation unit. This allows the generation unit to provide the optimal hashtags that meet the user's needs. The provision unit also has the functionality to support multiple platforms. For example, if a user posts to different social media platforms simultaneously, it can suggest the most suitable hashtags for each platform. This allows users to effectively post to multiple platforms with a single operation. The provision unit also provides a function that allows users to easily share the generated hashtags. For example, users can copy the generated hashtags and paste them into other posts or messages. This allows users to effectively utilize the generated hashtags and maximize their impact on social media.

[0034] The analysis unit can analyze the content of an image in detail and understand the elements and themes contained in the image. The analysis unit can analyze the content of an image in detail, for example, using image recognition technology. For example, the analysis unit can identify the elements contained in an image and understand the theme. For example, in the case of a landscape photograph, the analysis unit can identify elements such as mountains, rivers, and sky, and understand the theme as "nature" or "travel". As a result, the analysis unit can generate more appropriate hashtags by analyzing the content of the image in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input an image into AI and have the AI ​​perform the image analysis.

[0035] The generation unit can automatically generate optimal hashtags based on the elements and themes of the analyzed image. For example, in the case of a landscape photograph, the generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape". By generating hashtags based on the elements and themes of the analyzed image, the generation unit can provide more effective hashtags. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the elements and themes of the analyzed image into a generation AI and have the generation AI perform hashtag generation.

[0036] The service provider can provide the generated hashtags to the user. For example, the service provider can provide the generated hashtags to the user. For example, the service provider can notify the user of the generated hashtags. This allows the user to easily use effective hashtags by providing the generated hashtags to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated hashtags into AI and have AI perform the task of providing the hashtags.

[0037] The reception desk can accept image uploads using the normal posting procedure without requiring any special actions from the user. For example, the reception desk can accept an image simply by the user selecting the image and clicking the upload button. This allows the reception desk to accept images using the normal posting procedure without requiring any special actions from the user. 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 actions of the user selecting the image and clicking the upload button.

[0038] The generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape" in the case of landscape photographs. For example, the generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape" in the case of landscape photographs. By suggesting hashtags appropriate for landscape photographs, the generation unit can enhance the effectiveness of posts. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a landscape photograph into a generation AI and have the generation AI suggest hashtags.

[0039] The reception desk can analyze a user's past posting history and select the optimal upload method. For example, the reception desk can recommend uploading during times when the user has received a lot of engagement in the past. It can also suggest filters and effects that the user has used in the past. Furthermore, the reception desk can pre-suggest relevant hashtags based on the user's past posts. In this way, the reception desk can select the optimal upload method by analyzing the user's past posting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past posting history into an AI and have the AI ​​select the upload method.

[0040] The reception system can filter images based on the user's current projects and areas of interest when they are uploaded. For example, the reception system can allow only images related to the user's current project to be uploaded. It can also prioritize the upload of relevant images based on the user's areas of interest. Furthermore, it can allow only images that match a theme set by the user to be uploaded. This allows the reception system to upload highly relevant images by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0041] The reception system can prioritize uploading images that are highly relevant to the user's location, taking into account the user's geographical location. For example, if the user is traveling, the reception system can prioritize uploading images of tourist attractions related to the user's current location. It can also prioritize uploading images related to a specific event if the user is attending that event. Furthermore, if the user is at home, it can prioritize uploading images related to local scenery or events. This allows the reception system to prioritize uploading highly relevant images by considering the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location into an AI and have the AI ​​select highly relevant images.

[0042] The reception desk can analyze a user's social media activity when they upload an image and upload relevant images. For example, the reception desk can prioritize uploading images related to posts that the user has recently received a lot of attention on. It can also prioritize uploading images related to posts from accounts that the user follows. Furthermore, it can prioritize uploading images related to the themes of groups or communities that the user participates in. In this way, the reception desk can upload relevant images by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI and have the AI ​​select relevant images.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the image during image analysis. For example, the analysis unit can perform a detailed analysis on important images. It can also perform a simplified analysis on general images. Furthermore, the analysis unit can perform a special analysis on images of particular interest to the user. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the image into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the image category during image analysis. For example, the analysis unit can apply a natural elements analysis algorithm to landscape photographs. It can also apply a face recognition algorithm to portrait photographs. Furthermore, it can apply an object recognition algorithm to product photographs. By applying different analysis algorithms depending on the image category, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the image category into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of image analysis based on when the images were taken. For example, the analysis unit can prioritize the analysis of recently taken images. It can also prioritize the analysis of images taken during a specific event period. Furthermore, it can prioritize the analysis of images taken within a period specified by the user. By doing so, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on when the images were taken. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the image taking dates into the AI ​​and have the AI ​​determine the priority of analysis.

[0046] The analysis unit can adjust the order of image analysis based on the relevance of the images. For example, the analysis unit can prioritize the analysis of images related to a theme specified by the user. It can also prioritize the analysis of images related to the user's past posts. Furthermore, it can prioritize the analysis of images related to the user's areas of interest. By adjusting the order of analysis based on the relevance of the images, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the images into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail generated based on the importance of the image when generating hashtags. For example, the generation unit can generate detailed hashtags for important images. It can also generate concise hashtags for general images. Furthermore, it can generate special hashtags for images of particular interest to the user. In this way, the generation unit can generate more appropriate hashtags by adjusting the level of detail based on the importance of the image. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the image into the generation AI and have the generation AI perform the hashtag generation.

[0048] The generation unit can apply different generation algorithms depending on the image category when generating hashtags. For example, the generation unit can apply a hashtag generation algorithm related to nature elements to landscape photographs. It can also apply a hashtag generation algorithm related to people to portrait photographs. Furthermore, it can apply a hashtag generation algorithm related to products to product photographs. In this way, the generation unit can generate more appropriate hashtags by applying different generation algorithms depending on the image category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the image category into the generation AI and have the generation AI perform hashtag generation.

[0049] The generation unit can determine the priority of hashtag generation based on when the image was taken. For example, for recently taken images, the generation unit can generate hashtags based on the latest trends. The generation unit can also generate hashtags related to a specific event for images taken during that event period. Furthermore, the generation unit can generate hashtags related to a period specified by the user for images taken within that period. By doing so, the generation unit can generate more appropriate hashtags by determining the priority of generation based on when the image was taken. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the image's taking date into the generation AI and have the generation AI perform the hashtag generation.

[0050] The generation unit can adjust the generation order based on the relevance of the images when generating hashtags. For example, the generation unit can prioritize generating hashtags related to a theme specified by the user. It can also prioritize generating hashtags related to the user's past posts. Furthermore, it can prioritize generating hashtags related to the user's areas of interest. By doing so, the generation unit can generate more appropriate hashtags by adjusting the generation order based on the relevance of the images. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the images into the generation AI and have the generation AI perform the hashtag generation.

[0051] The hashtag provider can select the optimal method of providing hashtags by referring to the user's past posting history. For example, the provider can prioritize providing hashtags that have received a lot of engagement from the user in the past. The provider can also analyze the trends of hashtags the user has used in the past and provide the most suitable hashtags. Furthermore, the provider can provide relevant hashtags based on the user's past posting content. In this way, the provider can provide the most suitable hashtags by referring to the user's past posting history. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's past posting history into AI and have the AI ​​select the method of providing hashtags.

[0052] The service provider can select the optimal method of providing hashtags by considering the user's device information. For example, if the user is using a smartphone, the service provider can provide hashtags that are optimized for the screen size. If the user is using a tablet, the service provider can provide hashtags optimized for larger screens. If the user is using a smartwatch, the service provider can provide concise and highly visible hashtags. In this way, the service provider can provide the optimal hashtags by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the method of providing hashtags.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze a user's past posting history and select the optimal upload method. For example, it can recommend uploading during times when the user has received a lot of engagement in the past. It can also suggest filters and effects the user has used in the past. Furthermore, it can pre-suggest relevant hashtags based on the user's past posts. In this way, the reception desk can select the optimal upload method by analyzing the user's past posting history.

[0055] The analysis unit can analyze the content of an image in detail and understand the elements and themes contained within it. For example, it can use image recognition technology to analyze the content of an image in detail. In the case of a landscape photograph, it can identify elements such as mountains, rivers, and the sky, and understand themes such as "nature" or "travel." As a result, the analysis unit can generate more appropriate hashtags by analyzing the content of the image in detail.

[0056] The generation unit can automatically generate the most suitable hashtags based on the elements and themes of the analyzed image. For example, in the case of a landscape photograph, it can suggest hashtags such as "#nature," "#travel," and "#landscape." This allows the generation unit to provide more effective hashtags by generating them based on the elements and themes of the analyzed image.

[0057] The service provider can provide the generated hashtags to users. For example, it can notify users of the generated hashtags. By providing the generated hashtags to users, the service provider can enable users to easily use effective hashtags.

[0058] The reception desk can accept image uploads using the standard posting procedure without requiring any special actions from the user. For example, the user can simply select an image and click the upload button to have it accepted. This allows the reception desk to accept images using the standard posting procedure without requiring any special actions from the user.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk accepts images uploaded by users. For example, users can select an image from their smartphone or personal computer and click the upload button to submit it. It also accepts images uploaded using the standard posting procedure without requiring any special operations from the user. Step 2: The analysis unit analyzes the image received by the reception unit to understand the image's elements and theme. For example, in the case of a landscape photograph, it identifies elements such as mountains, rivers, and sky, and understands the theme as "nature" or "travel." Step 3: The generation unit automatically generates the most suitable hashtags based on the elements and themes understood by the analysis unit. For example, in the case of a landscape photograph, it suggests hashtags such as "#nature", "#travel", and "#landscape". Step 4: The providing unit provides the user with the hashtag generated by the generating unit. For example, it notifies the user of the generated hashtag.

[0061] (Example of form 2) The social media support system according to an embodiment of the present invention is a system in which AI analyzes images and proposes the most suitable hashtags for the content. This social media support system analyzes images uploaded by users and automatically generates effective hashtags that match the content. The generating AI understands the elements and themes of the image and proposes the most suitable keywords. This allows creators to save time on selecting hashtags and communicate their messages more effectively. In addition, users can obtain natural and effective hashtags and maximize their impact on social media. First, the user uploads an image. At this time, the user does not need to perform any special operations and simply uploads the image using the normal posting procedure. For example, the user selects an image from a smartphone or personal computer and clicks the upload button. This information is input to the AI. Next, the AI ​​analyzes the uploaded image. The AI ​​analyzes the content of the image in detail and understands the elements and themes contained in the image. For example, in the case of a landscape photograph, it identifies elements such as mountains, rivers, and sky, and understands themes such as "nature" or "travel". As a result, the AI ​​can grasp the content of the image and generate appropriate hashtags. The generating AI automatically generates the most suitable hashtags based on the elements and themes of the analyzed image. For example, in the case of a landscape photograph, it suggests hashtags such as "#nature," "#travel," and "#landscape." In this way, the AI ​​generates and provides the most suitable hashtags for the image content. This system eliminates the effort required for creators to select hashtags. Creators can simply upload images, and the AI ​​automatically suggests the most suitable hashtags, allowing them to focus on their creative work without worrying about hashtags. Furthermore, users can obtain natural and effective hashtags, maximizing their impact on social media. For example, using appropriate hashtags can lead to posts being seen by more people and increased engagement. Thus, by analyzing images and suggesting the most suitable hashtags for the content, this innovative tool reduces the burden on creators and maximizes their impact on social media.This allows social media support systems to reduce the burden on creators and maximize their impact on social media.

[0062] The social media support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives images uploaded by users. The reception unit can receive images, for example, when a user selects an image from a smartphone or personal computer and clicks an upload button. The reception unit can also receive images uploaded by users using the normal posting procedure without requiring any special operations. For example, the reception unit can receive images simply by a user selecting an image and clicking an upload button. The analysis unit analyzes the images received by the reception unit and understands the elements and themes of the images. The analysis unit can, for example, analyze the content of the images in detail and understand the elements and themes contained in the images. For example, if it is a landscape photograph, the analysis unit can identify elements such as mountains, rivers, and sky, and understand themes such as "nature" or "travel". The generation unit automatically generates optimal hashtags based on the elements and themes understood by the analysis unit. The generation unit can, for example, automatically generate optimal hashtags based on the elements and themes of the analyzed images. For example, the generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape" in the case of a landscape photograph. The provision unit provides the hashtags generated by the generation unit to the user. The provision unit can provide the generated hashtags to the user. For example, the provision unit can notify the user of the generated hashtags. As a result, the social media support system according to the embodiment can reduce the burden on creators and maximize their impact on social media by analyzing images uploaded by users and automatically generating optimal hashtags. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without using AI. For example, the reception unit can input images uploaded by users into the AI ​​and have the AI ​​perform image reception. The analysis unit can input images received by the reception unit into the AI ​​and have the AI ​​perform image analysis.The generation unit inputs elements and themes understood by the analysis unit into the AI, allowing the AI ​​to generate hashtags. The provision unit inputs the hashtags generated by the generation unit into the AI, allowing the AI ​​to provide the hashtags.

[0063] The reception desk accepts images uploaded by users. For example, users can select images from their smartphones or personal computers and click the upload button to receive them. The reception desk can also accept images uploaded using the standard posting procedure without requiring any special operations from the user. For example, the reception desk can accept images simply by the user selecting an image and clicking the upload button. Furthermore, the reception desk allows users to upload multiple images at once, improving user convenience. For example, users can select and upload multiple photos taken during a trip at once, saving time and effort. The reception desk can also provide basic image editing functions during image upload. For example, users can perform simple edits such as cropping, rotating, and applying filters before uploading. This allows users to optimize their images before uploading. Additionally, the reception desk has a function to automatically retrieve metadata during image upload. For example, it can automatically retrieve the image's shooting date and time, location information, and camera settings, and provide this information to the analysis department. This allows the analysis department to analyze images based on more detailed information. The reception desk also includes security features to safely store images uploaded by users. For example, uploaded images are encrypted to protect them from unauthorized access by third parties. The system also provides a function to set the visibility of images to protect user privacy. For instance, users can choose who can view their images, allowing only specific friends or followers to see them. This allows the system to provide an environment where users can upload images with peace of mind.

[0064] The analysis unit analyzes images received by the reception unit to understand the elements and themes of the images. For example, the analysis unit can analyze the content of an image in detail to understand the elements and themes contained within it. For example, in the case of a landscape photograph, the analysis unit can identify elements such as mountains, rivers, and sky, and understand themes such as "nature" or "travel." The analysis unit uses AI to analyze images. Specifically, it uses image recognition technology to detect objects in the image and extract the characteristics of each object. For example, in the case of a landscape photograph, it detects objects such as mountains, rivers, and sky, and analyzes the characteristics of each. The AI ​​also analyzes information such as the color, composition, and texture of the image to understand the overall theme of the image. Furthermore, the analysis unit improves the accuracy of the analysis by utilizing image metadata. For example, based on the date and time the image was taken and location information, it can identify themes related to a specific season or place. This allows the analysis unit to understand the elements and themes of the image more accurately. The analysis unit can continuously improve its analysis accuracy by learning from past analysis results and user feedback. For example, it can adjust the analysis algorithm based on user feedback and reflect it in the next analysis. Furthermore, the analysis unit can flexibly handle different types of images. For example, it can analyze various types of images, including not only landscape photographs but also portraits, food photos, and event photos, and identify their respective elements and themes. This allows the analysis unit to perform highly accurate analysis on a wide range of images and build a foundation for providing users with the most suitable hashtags.

[0065] The generation unit automatically generates optimal hashtags based on the elements and themes understood by the analysis unit. For example, the generation unit can automatically generate optimal hashtags based on the elements and themes of an analyzed image. For example, in the case of a landscape photograph, the generation unit can suggest hashtags such as "#nature," "#travel," and "#landscape." The generation unit uses AI to generate hashtags. Specifically, based on the elements and themes provided by the analysis unit, it searches a database for relevant hashtags and selects the most suitable ones. The AI ​​learns from past posting data and trend information and can suggest hashtags that match current social media trends. For example, it can prioritize suggesting hashtags related to specific seasons or events. Furthermore, the generation unit can also suggest individually optimized hashtags by considering the user's past posting history and the interests of their followers. For example, it can analyze the user's preferences and follower reactions based on images and hashtags the user has previously posted and generate optimal hashtags based on that. This allows the generation unit to help users' posts be seen by more people. The generation unit can evaluate the effectiveness of the generated hashtags and continuously improve them. For example, the system analyzes how much engagement generated hashtags receive and uses that information to improve future hashtag generation. This ensures that the generation unit consistently provides the most suitable hashtags. The generation unit also provides a function for users to customize suggested hashtags. For example, users can edit suggested hashtags or add new ones. This allows users to select the hashtags best suited to their posts. The generation unit can improve the accuracy of hashtag suggestions based on user feedback. For example, users can evaluate suggested hashtags, and the generation algorithm can be adjusted based on those evaluations. This allows the generation unit to provide the most suitable hashtags to meet user needs.

[0066] The provider unit provides users with hashtags generated by the generator unit. For example, the provider unit can provide users with generated hashtags. For example, the provider unit can notify users of generated hashtags. The provider unit visually displays generated hashtags through the user interface. For example, after a user uploads an image, generated hashtags are displayed in a pop-up window or notification bar. Users can review the suggested hashtags and edit or add them as needed. The provider unit makes it easy for users to select suggested hashtags and add them to posts. For example, users can add suggested hashtags to a post simply by clicking on them. The provider unit also provides a function to select suggested hashtags in bulk. This allows users to add multiple hashtags at once. Furthermore, the provider unit has a function to provide real-time feedback on the effectiveness of generated hashtags. For example, after a user publishes a post, it analyzes how much engagement the generated hashtags received and notifies the user of the results. This allows users to review the effectiveness of the generated hashtags and use that information for future posts. The provider unit collects user feedback and provides data to improve the accuracy of hashtag suggestions. For example, users evaluate suggested hashtags and feed the evaluation results back to the generation unit. This allows the generation unit to provide the optimal hashtags that meet the user's needs. The provision unit also has the functionality to support multiple platforms. For example, if a user posts to different social media platforms simultaneously, it can suggest the most suitable hashtags for each platform. This allows users to effectively post to multiple platforms with a single operation. The provision unit also provides a function that allows users to easily share the generated hashtags. For example, users can copy the generated hashtags and paste them into other posts or messages. This allows users to effectively utilize the generated hashtags and maximize their impact on social media.

[0067] The analysis unit can analyze the content of an image in detail and understand the elements and themes contained in the image. The analysis unit can analyze the content of an image in detail, for example, using image recognition technology. For example, the analysis unit can identify the elements contained in an image and understand the theme. For example, in the case of a landscape photograph, the analysis unit can identify elements such as mountains, rivers, and sky, and understand the theme as "nature" or "travel". As a result, the analysis unit can generate more appropriate hashtags by analyzing the content of the image in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input an image into AI and have the AI ​​perform the image analysis.

[0068] The generation unit can automatically generate optimal hashtags based on the elements and themes of the analyzed image. For example, in the case of a landscape photograph, the generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape". By generating hashtags based on the elements and themes of the analyzed image, the generation unit can provide more effective hashtags. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the elements and themes of the analyzed image into a generation AI and have the generation AI perform hashtag generation.

[0069] The service provider can provide the generated hashtags to the user. For example, the service provider can provide the generated hashtags to the user. For example, the service provider can notify the user of the generated hashtags. This allows the user to easily use effective hashtags by providing the generated hashtags to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated hashtags into AI and have AI perform the task of providing the hashtags.

[0070] The reception desk can accept image uploads using the normal posting procedure without requiring any special actions from the user. For example, the reception desk can accept an image simply by the user selecting the image and clicking the upload button. This allows the reception desk to accept images using the normal posting procedure without requiring any special actions from the user. 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 actions of the user selecting the image and clicking the upload button.

[0071] The generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape" in the case of landscape photographs. For example, the generation unit can suggest hashtags such as "#nature", "#travel", and "#landscape" in the case of landscape photographs. By suggesting hashtags appropriate for landscape photographs, the generation unit can enhance the effectiveness of posts. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a landscape photograph into a generation AI and have the generation AI suggest hashtags.

[0072] The reception desk can estimate the user's emotions and adjust the timing of image uploads based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can prompt them to upload images during a time when they can relax. It can also allow users to upload images immediately if they are excited. Furthermore, if the user is tired, the reception desk can set a reminder to upload later. This allows the reception desk to upload images at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotions into an AI and have the AI ​​perform emotion estimation.

[0073] The reception desk can analyze a user's past posting history and select the optimal upload method. For example, the reception desk can recommend uploading during times when the user has received a lot of engagement in the past. It can also suggest filters and effects that the user has used in the past. Furthermore, the reception desk can pre-suggest relevant hashtags based on the user's past posts. In this way, the reception desk can select the optimal upload method by analyzing the user's past posting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past posting history into an AI and have the AI ​​select the upload method.

[0074] The reception system can filter images based on the user's current projects and areas of interest when they are uploaded. For example, the reception system can allow only images related to the user's current project to be uploaded. It can also prioritize the upload of relevant images based on the user's areas of interest. Furthermore, it can allow only images that match a theme set by the user to be uploaded. This allows the reception system to upload highly relevant images by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0075] The reception desk can estimate the user's emotions and determine the priority of images to upload based on the estimated emotions. For example, if the user is happy, the reception desk can prioritize uploading images with positive content. If the user is sad, the reception desk can prioritize uploading images containing encouraging messages. If the user is excited, the reception desk can prioritize uploading images with energetic content. In this way, the reception desk can upload more appropriate images by prioritizing images according to the user's emotions. 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 processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotions into an AI and have the AI ​​perform emotion estimation.

[0076] The reception system can prioritize uploading images that are highly relevant to the user's location, taking into account the user's geographical location. For example, if the user is traveling, the reception system can prioritize uploading images of tourist attractions related to the user's current location. It can also prioritize uploading images related to a specific event if the user is attending that event. Furthermore, if the user is at home, it can prioritize uploading images related to local scenery or events. This allows the reception system to prioritize uploading highly relevant images by considering the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location into an AI and have the AI ​​select highly relevant images.

[0077] The reception desk can analyze a user's social media activity when they upload an image and upload relevant images. For example, the reception desk can prioritize uploading images related to posts that the user has recently received a lot of attention on. It can also prioritize uploading images related to posts from accounts that the user follows. Furthermore, it can prioritize uploading images related to the themes of groups or communities that the user participates in. In this way, the reception desk can upload relevant images by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI and have the AI ​​select relevant images.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the image analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the image analysis according to the user's emotions. 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotions into the AI ​​and have the AI ​​perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the image during image analysis. For example, the analysis unit can perform a detailed analysis on important images. It can also perform a simplified analysis on general images. Furthermore, the analysis unit can perform a special analysis on images of particular interest to the user. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the image into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the image category during image analysis. For example, the analysis unit can apply a natural elements analysis algorithm to landscape photographs. It can also apply a face recognition algorithm to portrait photographs. Furthermore, it can apply an object recognition algorithm to product photographs. By applying different analysis algorithms depending on the image category, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the image category into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can provide a visually appealing analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide more appropriate analysis results. 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 processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotions into the AI ​​and have the AI ​​perform emotion estimation.

[0082] The analysis unit can determine the priority of image analysis based on when the images were taken. For example, the analysis unit can prioritize the analysis of recently taken images. It can also prioritize the analysis of images taken during a specific event period. Furthermore, it can prioritize the analysis of images taken within a period specified by the user. By doing so, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on when the images were taken. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the image taking dates into the AI ​​and have the AI ​​determine the priority of analysis.

[0083] The analysis unit can adjust the order of image analysis based on the relevance of the images. For example, the analysis unit can prioritize the analysis of images related to a theme specified by the user. It can also prioritize the analysis of images related to the user's past posts. Furthermore, it can prioritize the analysis of images related to the user's areas of interest. By adjusting the order of analysis based on the relevance of the images, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the images into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0084] The generation unit can estimate the user's emotions and adjust the expression of the generated hashtags based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate soft-sounding hashtags. If the user is in a hurry, the generation unit can generate concise-sounding hashtags. If the user is excited, the generation unit can generate energetic-sounding hashtags. In this way, the generation unit can generate more appropriate hashtags by adjusting the expression of hashtags according to the user's emotions. 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 such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's emotions into the generation AI and have the generation AI adjust the expression of the hashtags.

[0085] The generation unit can adjust the level of detail generated based on the importance of the image when generating hashtags. For example, the generation unit can generate detailed hashtags for important images. It can also generate concise hashtags for general images. Furthermore, it can generate special hashtags for images of particular interest to the user. In this way, the generation unit can generate more appropriate hashtags by adjusting the level of detail based on the importance of the image. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the image into the generation AI and have the generation AI perform the hashtag generation.

[0086] The generation unit can apply different generation algorithms depending on the image category when generating hashtags. For example, the generation unit can apply a hashtag generation algorithm related to nature elements to landscape photographs. It can also apply a hashtag generation algorithm related to people to portrait photographs. Furthermore, it can apply a hashtag generation algorithm related to products to product photographs. In this way, the generation unit can generate more appropriate hashtags by applying different generation algorithms depending on the image category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the image category into the generation AI and have the generation AI perform hashtag generation.

[0087] The generation unit can estimate the user's emotions and adjust the length of the generated hashtags based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise hashtags. If the user is relaxed, the generation unit can generate longer hashtags with detailed descriptions. If the user is excited, the generation unit can generate visually stimulating hashtags. In this way, the generation unit can generate more appropriate hashtags by adjusting the length of the hashtags according to the user's emotions. 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 processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's emotions into a generation AI and have the generation AI adjust the length of the hashtags.

[0088] The generation unit can determine the priority of hashtag generation based on when the image was taken. For example, for recently taken images, the generation unit can generate hashtags based on the latest trends. The generation unit can also generate hashtags related to a specific event for images taken during that event period. Furthermore, the generation unit can generate hashtags related to a period specified by the user for images taken within that period. By doing so, the generation unit can generate more appropriate hashtags by determining the priority of generation based on when the image was taken. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the image's taking date into the generation AI and have the generation AI perform the hashtag generation.

[0089] The generation unit can adjust the generation order based on the relevance of the images when generating hashtags. For example, the generation unit can prioritize generating hashtags related to a theme specified by the user. It can also prioritize generating hashtags related to the user's past posts. Furthermore, it can prioritize generating hashtags related to the user's areas of interest. By doing so, the generation unit can generate more appropriate hashtags by adjusting the generation order based on the relevance of the images. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the images into the generation AI and have the generation AI perform the hashtag generation.

[0090] The service provider can estimate the user's emotions and adjust how hashtags are provided based on the estimated emotions. For example, if the user is relaxed, the service provider can provide hashtags with detailed descriptions. If the user is in a hurry, the service provider can provide concise hashtags. If the user is excited, the service provider can provide visually appealing hashtags. In this way, the service provider can provide more appropriate hashtags by adjusting how hashtags are provided according to the user's emotions. 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 processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the user's emotions into an AI and have the AI ​​adjust how hashtags are provided.

[0091] The hashtag provider can select the optimal method of providing hashtags by referring to the user's past posting history. For example, the provider can prioritize providing hashtags that have received a lot of engagement from the user in the past. The provider can also analyze the trends of hashtags the user has used in the past and provide the most suitable hashtags. Furthermore, the provider can provide relevant hashtags based on the user's past posting content. In this way, the provider can provide the most suitable hashtags by referring to the user's past posting history. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's past posting history into AI and have the AI ​​select the method of providing hashtags.

[0092] The service provider can estimate the user's emotions and adjust the order in which hashtags are provided based on the estimated emotions. For example, if the user is relaxed, the service provider can prioritize providing hashtags that include detailed descriptions. If the user is in a hurry, the service provider can prioritize providing concise hashtags. If the user is excited, the service provider can prioritize providing visually appealing hashtags. In this way, the service provider can provide more appropriate hashtags by adjusting the order in which hashtags are provided according to the user's emotions. 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 processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's emotions into an AI and have the AI ​​adjust the order in which hashtags are provided.

[0093] The service provider can select the optimal method of providing hashtags by considering the user's device information. For example, if the user is using a smartphone, the service provider can provide hashtags that are optimized for the screen size. If the user is using a tablet, the service provider can provide hashtags optimized for larger screens. If the user is using a smartwatch, the service provider can provide concise and highly visible hashtags. In this way, the service provider can provide the optimal hashtags by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and have the AI ​​select the method of providing hashtags.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze a user's past posting history and select the optimal upload method. For example, it can recommend uploading during times when the user has received a lot of engagement in the past. It can also suggest filters and effects the user has used in the past. Furthermore, it can pre-suggest relevant hashtags based on the user's past posts. In this way, the reception desk can select the optimal upload method by analyzing the user's past posting history.

[0096] The analysis unit can analyze the content of an image in detail and understand the elements and themes contained within it. For example, it can use image recognition technology to analyze the content of an image in detail. In the case of a landscape photograph, it can identify elements such as mountains, rivers, and the sky, and understand themes such as "nature" or "travel." As a result, the analysis unit can generate more appropriate hashtags by analyzing the content of the image in detail.

[0097] The generation unit can automatically generate the most suitable hashtags based on the elements and themes of the analyzed image. For example, in the case of a landscape photograph, it can suggest hashtags such as "#nature," "#travel," and "#landscape." This allows the generation unit to provide more effective hashtags by generating them based on the elements and themes of the analyzed image.

[0098] The service provider can provide the generated hashtags to users. For example, it can notify users of the generated hashtags. By providing the generated hashtags to users, the service provider can enable users to easily use effective hashtags.

[0099] The reception desk can accept image uploads using the standard posting procedure without requiring any special actions from the user. For example, the user can simply select an image and click the upload button to have it accepted. This allows the reception desk to accept images using the standard posting procedure without requiring any special actions from the user.

[0100] The reception desk can estimate the user's emotions and adjust the timing of image uploads based on those estimates. For example, if a user is stressed, it can prompt them to upload images during a time when they can relax. If a user is excited, it can allow them to upload images immediately. Furthermore, if a user is tired, it can set a reminder to upload later. In this way, the reception desk can adjust the timing of image uploads according to the user's emotions, allowing for more appropriate image uploads.

[0101] The analysis unit can estimate the user's emotions and adjust the presentation of the image analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the image analysis according to the user's emotions.

[0102] The generation unit can estimate the user's emotions and adjust the expression of the generated hashtags based on those emotions. For example, if the user is relaxed, it can generate soft-sounding hashtags. If the user is in a hurry, it can generate concise hashtags. Furthermore, if the user is excited, it can generate energetic hashtags. In this way, the generation unit can generate more appropriate hashtags by adjusting the expression of hashtags according to the user's emotions.

[0103] The service provider can estimate the user's emotions and adjust how hashtags are provided based on those estimates. For example, if the user is relaxed, it can provide hashtags with detailed descriptions. If the user is in a hurry, it can provide concise hashtags. Furthermore, if the user is excited, it can provide visually appealing hashtags. In this way, the service provider can provide more appropriate hashtags by adjusting how hashtags are provided according to the user's emotions.

[0104] The service provider can estimate the user's emotions and adjust the order in which hashtags are provided based on those estimates. For example, if the user is relaxed, hashtags with detailed descriptions can be prioritized. If the user is in a hurry, concise hashtags can be prioritized. Furthermore, if the user is excited, visually appealing hashtags can be prioritized. In this way, the service provider can provide more appropriate hashtags by adjusting the order in which hashtags are provided according to the user's emotions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk accepts images uploaded by users. For example, users can select an image from their smartphone or personal computer and click the upload button to submit it. It also accepts images uploaded using the standard posting procedure without requiring any special operations from the user. Step 2: The analysis unit analyzes the image received by the reception unit to understand the image's elements and theme. For example, in the case of a landscape photograph, it identifies elements such as mountains, rivers, and sky, and understands the theme as "nature" or "travel." Step 3: The generation unit automatically generates the most suitable hashtags based on the elements and themes understood by the analysis unit. For example, in the case of a landscape photograph, it suggests hashtags such as "#nature", "#travel", and "#landscape". Step 4: The providing unit provides the user with the hashtag generated by the generating unit. For example, it notifies the user of the generated hashtag.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, and accepts images when the user selects an image from a smartphone or personal computer and clicks the upload button. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the image received by the reception unit to understand the elements and theme of the image. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates the optimal hashtag based on the elements and theme understood by the analysis unit. The provision unit is implemented by the control unit 46A of the smart device 14, and provides the hashtag generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 control unit 46A of the smart glasses 214, and accepts images when the user selects an image from a smartphone or personal computer and clicks the upload button. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the image received by the reception unit to understand the elements and theme of the image. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates the optimal hashtag based on the elements and theme understood by the analysis unit. The provision unit is implemented by the control unit 46A of the smart glasses 214, and provides the hashtag generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 control unit 46A of the headset terminal 314, and accepts images when the user selects an image from a smartphone or personal computer and clicks the upload button. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the image received by the reception unit to understand the elements and theme of the image. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates the optimal hashtag based on the elements and theme understood by the analysis unit. The provision unit is implemented by the control unit 46A of the headset terminal 314, and provides the hashtag generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 control unit 46A of the robot 414, and accepts images when the user selects an image from a smartphone or personal computer and clicks the upload button. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the image received by the reception unit to understand the elements and theme of the image. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates the optimal hashtag based on the elements and theme understood by the analysis unit. The provision unit is implemented by the control unit 46A of the robot 414, and provides the hashtag generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception area that accepts images uploaded by users, An analysis unit analyzes the image received by the reception unit and understands the elements and theme of the image, Based on the elements and themes understood by the analysis unit, a generation unit automatically generates appropriate hashtags, The system includes a providing unit that provides the hashtag generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the image content in detail and understand the elements and themes contained within the image. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the elements and themes of the analyzed image, the system automatically generates the most suitable hashtags. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated hashtag to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system allows users to upload images using the standard posting procedure without requiring any special actions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is For landscape photos, we suggest the hashtags "#nature", "#travel", and "#landscape". The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of image uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past posting history and select the appropriate upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When uploading images, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the images to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When uploading images, the system prioritizes uploading images that are more relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When uploading an image, the system analyzes the user's social media activity and uploads relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the image analysis representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During image analysis, the level of detail of the analysis is adjusted based on the importance of the image. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing images, different analysis algorithms are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During image analysis, the priority of the analysis is determined based on when the images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During image analysis, the order of analysis is adjusted based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates user sentiment and adjusts the way hashtags are expressed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating hashtags, adjust the level of detail based on the importance of the image. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating hashtags, different generation algorithms are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's sentiment and adjusts the length of the generated hashtags based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating hashtags, the priority of generation is determined based on when the image was taken. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating hashtags, the generation order is adjusted based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate user sentiment and adjust how hashtags are provided based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing hashtags, the system will refer to the user's past posting history to select the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates user sentiment and adjusts the order in which hashtags are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing hashtags, the appropriate method of provision is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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 that accepts images uploaded by users, An analysis unit analyzes the image received by the reception unit and understands the elements and theme of the image, Based on the elements and themes understood by the analysis unit, a generation unit automatically generates appropriate hashtags, The system includes a providing unit that provides the hashtag generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the image content in detail and understand the elements and themes contained within the image. The system according to feature 1.

3. The generating unit is Based on the elements and themes of the analyzed image, the system automatically generates the most suitable hashtags. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated hashtag to the user. The system according to feature 1.

5. The aforementioned reception unit is The system allows users to upload images using the standard posting procedure without requiring any special actions. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of image uploads based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past posting history and select the appropriate upload method. The system according to feature 1.

Images