System

Abstract

An object of the system according to the embodiment is to allow a user to automatically generate and propose a hashtag suitable for an image.SOLUTION: A system according to an embodiment includes an image analysis section, a hashtag generation section, and a proposal section. The image analysis unit analyzes an image uploaded by a user. The hashtag generation section automatically generates an optimum hashtag based on the content of the image analyzed by the image analysis section. The proposal section proposes the hashtag generated by the hashtag generation section to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires users to manually select hashtags appropriate for images, which takes time and effort.

[0005] The system according to the embodiment aims to allow users to automatically generate and suggest hashtags suitable for images. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a hashtag generation unit, and a suggestion unit. The image analysis unit analyzes images uploaded by users. The hashtag generation unit automatically generates optimal hashtags based on the content of the images analyzed by the image analysis unit. The suggestion unit suggests hashtags generated by the hashtag generation unit to users. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to automatically generate and suggest hashtags suitable for an image. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The social media support system according to an embodiment of the present invention is a system that automatically analyzes images uploaded by users, and uses a generation AI to generate and suggest optimal hashtags. This allows the social media support system to save users the trouble of selecting hashtags and to more effectively communicate their messages.

[0029] A social media support system according to an embodiment includes an image analysis unit, a hashtag generation unit, and a suggestion unit. The image analysis unit analyzes images uploaded by users. For example, the image analysis unit recognizes elements within the image using deep learning technology. The image analysis unit can also understand the theme of the image using computer vision technology. The image analysis unit can also analyze the color and composition of the image. For example, deep learning technology learns from a large amount of image data to perform highly accurate image recognition. Computer vision technology analyzes objects and scenes within the image to understand the content of the image. The analysis of the color and composition of the image is used to evaluate the aesthetic elements of the image. The hashtag generation unit automatically generates optimal hashtags based on the content of the image analyzed by the image analysis unit. For example, the hashtag generation unit generates hashtags related to elements of the image using keyword extraction technology. The hashtag generation unit can also generate currently popular hashtags using trend analysis technology. The hashtag generation unit can also generate hashtags related to the theme of the image using natural language processing technology. For example, keyword extraction technology extracts important keywords from the content of an image and generates hashtags based on them. Trend analysis technology analyzes trends on social media and generates popular hashtags. Natural language processing technology understands the theme of an image and generates hashtags related to it. The suggestion unit suggests hashtags generated by the hashtag generation unit to the user. For example, the suggestion unit displays hashtags through a user interface. The suggestion unit can also record hashtags selected by the user and reflect them in the next suggestion. The suggestion unit can also collect user feedback and improve the accuracy of hashtag suggestions. For example, the user interface has an intuitive and easy-to-use design, allowing users to easily select hashtags. By recording user selection history, it is possible to suggest the most suitable hashtags for each individual user. By collecting user feedback and improving the suggestion algorithm, the accuracy of suggestions can be improved.As a result, the social media support system according to the embodiment saves users the trouble of selecting hashtags and allows them to more effectively communicate their messages. For example, users can make effective posts simply by selecting hashtags suggested by the generation AI. Users can concentrate on creative activities without spending time selecting hashtags. Users can maximize their influence on social media by using hashtags suggested by the generation AI.

[0030] The image analysis unit can generate hashtags with greater accuracy based on image metadata. For example, the image analysis unit uses a generation AI to analyze image metadata and generate hashtags based on the location and time of the photo. For example, hashtags such as "#Paris" and "#EiffelTower" are suggested for images taken in Paris. The image analysis unit also uses a generation AI to consider image metadata and generate hashtags based on camera settings. For example, hashtags such as "#nightphotography" and "#citylights" are suggested for images taken in night mode. The image analysis unit also uses a generation AI to analyze image metadata and generate hashtags related to seasons and events based on the date and time of the photo. For example, hashtags such as "#Christmas" and "#holidayseason" are suggested for images taken at Christmas. This allows for more accurate hashtag generation by taking metadata into consideration.

[0031] The image analysis unit can understand the relationships between objects in an image and generate highly relevant hashtags based on that. For example, the image analysis unit uses a generative AI to analyze the relationships between objects in an image and generate hashtags based on the interaction between a person and their pet. For example, for an image of someone playing with a dog, the generative AI would suggest hashtags such as "#doglover" and "#petplaytime." The image analysis unit also uses a generative AI to understand the relationships between objects in an image and suggest hashtags such as "#familytime" and "#parenting" for an image showing a parent-child relationship. The image analysis unit also uses a generative AI to analyze the relationships between objects in an image and suggest hashtags such as "#friendship" and "#besties" for an image showing a relationship between friends. This makes it possible to generate highly relevant hashtags by understanding the relationships between objects.

[0032] The image analysis unit can analyze video data and generate appropriate hashtags based on the content of the video. For example, the image analysis unit uses a generation AI to analyze video data and generate hashtags based on scenes and objects within the video. For example, hashtags such as "#travelvlog" and "#adventure" are suggested for travel videos. The image analysis unit also uses a generation AI to analyze video data and generate hashtags based on the video's storyline. For example, hashtags such as "#cooking" and "#recipe" are suggested for cooking videos. The image analysis unit also uses a generation AI to analyze video data and generate hashtags based on emotional moments within the video. For example, hashtags such as "#tearjerker" and "#heartwarming" are suggested for moving scenes. This makes it possible to generate hashtags appropriate for videos by analyzing video data.

[0033] The image analysis unit can suggest consistent hashtags based on a user's past posting history. For example, the image analysis unit uses a generation AI to analyze a user's past posting history and suggest consistent hashtags. For example, a user who has frequently used "#travel" in the past may be suggested hashtags such as "#wanderlust" and "#explore." The image analysis unit also uses a generation AI to refer to a user's past posting history and suggest hashtags related to specific themes. For example, a user who posts a lot about cooking may be suggested hashtags such as "#foodie" and "#homemade." The image analysis unit also uses a generation AI to analyze a user's past posting history and suggest hashtags based on past popular posts. For example, hashtags from posts that have received many likes in the past may be reused. This allows consistent hashtags to be suggested by referring to past posting history.

[0034] The hashtag generation unit can analyze trends in real time and generate hashtags based on the latest trends. In the hashtag generation unit, for example, a generation AI analyzes trends in real time and generates hashtags based on the latest trends. For example, hashtags related to currently popular events or topics are suggested. In addition, the hashtag generation unit can analyze trends in real time and generate hashtags based on the latest trends. For example, hashtags related to popular challenges or memes are suggested. In addition, the hashtag generation unit can analyze trends in real time and generate hashtags based on the latest trends. For example, hashtags related to seasons or holidays are suggested. This allows trends to be analyzed in real time and hashtags based on the latest trends to be generated.

[0035] The hashtag generator can generate hashtags that include literary or art quotes related to the theme of the image. For example, the hashtag generator uses a generation AI to generate hashtags that include literary quotes related to the theme of the image. For example, for images of nature, the generation AI suggests hashtags such as "#naturepoetry" and "#wildbeauty." The hashtag generator also generates hashtags that include art quotes related to the theme of the image. For example, for images of artworks, the generation AI suggests hashtags such as "#artinspiration" and "#masterpiece." The hashtag generator also generates hashtags that include literary or art quotes related to the theme of the image. For example, for historical images, the generation AI suggests hashtags such as "#historicalquotes" and "#timelessart." This allows hashtags with deeper meaning to be generated by including literary or art quotes.

[0036] The hashtag generation unit simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, the generation AI of the hashtag generation unit simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, hashtags in English and Spanish are proposed. The hashtag generation unit also simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, hashtags in French and German are proposed. The hashtag generation unit also simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, hashtags in Chinese and Japanese are proposed. In this way, by generating hashtags in different languages, influence from an international perspective can be increased.

[0037] The hashtag generation unit can analyze the interests of a user's followers and generate optimal hashtags based on that. For example, the hashtag generation unit uses a generation AI to analyze the interests of a user's followers and generate optimal hashtags based on that. For example, if a follower is interested in travel, it suggests hashtags such as "#travel" and "#explore." The hashtag generation unit also analyzes the interests of a user's followers and generates optimal hashtags based on that. For example, if a follower is interested in cooking, it suggests hashtags such as "#foodie" and "#cooking." The hashtag generation unit also analyzes the interests of a user's followers and generates optimal hashtags based on that. For example, if a follower is interested in fashion, it suggests hashtags such as "#fashionista" and "#style." This allows optimal hashtags to be generated by analyzing the interests of followers.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The social media support system can further include a content suggestion unit that automatically suggests related articles and content based on the user's posts. For example, if a user uploads travel-related images, the content suggestion unit can suggest travel guides and tourist spot information. If a user uploads cooking images, the content suggestion unit can suggest articles about recipes and cooking tips. Furthermore, if a user uploads sports-related images, the content suggestion unit can suggest content about the latest sports news and training methods. This allows users to easily obtain information related to their interests and increases the value of their posts.

[0040] The social media support system may further include a product suggestion unit that suggests related products based on the content posted by the user. For example, if a user uploads fashion-related images, the product suggestion unit may suggest related fashion items and accessories. If a user uploads images related to outdoor activities, the product suggestion unit may suggest outdoor products and gear. If a user uploads images related to interior design, the product suggestion unit may suggest products related to interior design and furniture. This allows users to easily find products related to their interests, increasing their desire to purchase.

[0041] The social media support system may further include an event suggestion unit that suggests related events based on the content posted by the user. For example, if a user uploads a music-related image, the event suggestion unit may suggest nearby concerts and live events. If a user uploads a sports-related image, the event suggestion unit may suggest information about sports events and matches. Furthermore, if a user uploads an art-related image, the event suggestion unit may suggest information about art exhibitions and workshops. This allows users to easily find events related to their interests and increases their opportunities to participate.

[0042] The social media support system may further include a community suggestion unit that suggests related communities and groups based on the user's posted content. For example, if a user uploads an image related to pets, the community suggestion unit may suggest groups and forums for pet lovers. If a user uploads an image related to fitness, the community suggestion unit may suggest fitness communities and training groups. If a user uploads an image related to reading, the system may suggest book clubs and book review groups. This allows users to easily find communities and groups related to their interests and increases opportunities for interaction.

[0043] The social media support system may further include a learning suggestion unit that suggests related learning resources based on the user's posted content. For example, if a user uploads images related to science, the learning suggestion unit may suggest related science articles or online courses. If a user uploads images related to history, the learning suggestion unit may suggest historical documentaries or books. Furthermore, if a user uploads images related to art, the learning suggestion unit may suggest learning resources related to art techniques or history. This allows users to easily find learning resources related to their interests and deepen their knowledge.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The image analysis unit analyzes the image uploaded by the user. For example, the image analysis unit uses deep learning technology to recognize elements within the image and computer vision technology to understand the theme of the image. It can also analyze the color and composition of the image. This allows for a detailed understanding of the content of the image. Step 2: The hashtag generator automatically generates optimal hashtags based on the content of the image analyzed by the image analyzer. For example, it uses keyword extraction, trend analysis, and natural language processing techniques to generate hashtags related to the elements and themes of the image. Step 3: The suggestion unit suggests the hashtags generated by the hashtag generation unit to the user. For example, it displays the hashtags through a user interface, records the hashtags selected by the user, and reflects them in the next suggestion. It also collects user feedback to improve the accuracy of the suggestions.

[0046] (Example 2) The social media support system according to an embodiment of the present invention is a system that automatically analyzes images uploaded by users, and uses a generation AI to generate and suggest optimal hashtags. This allows the social media support system to save users the trouble of selecting hashtags and to more effectively communicate their messages.

[0047] A social media support system according to an embodiment includes an image analysis unit, a hashtag generation unit, and a suggestion unit. The image analysis unit analyzes images uploaded by users. For example, the image analysis unit recognizes elements within the image using deep learning technology. The image analysis unit can also understand the theme of the image using computer vision technology. The image analysis unit can also analyze the color and composition of the image. For example, deep learning technology learns from a large amount of image data to perform highly accurate image recognition. Computer vision technology analyzes objects and scenes within the image to understand the content of the image. The analysis of the color and composition of the image is used to evaluate the aesthetic elements of the image. The hashtag generation unit automatically generates optimal hashtags based on the content of the image analyzed by the image analysis unit. For example, the hashtag generation unit generates hashtags related to elements of the image using keyword extraction technology. The hashtag generation unit can also generate currently popular hashtags using trend analysis technology. The hashtag generation unit can also generate hashtags related to the theme of the image using natural language processing technology. For example, keyword extraction technology extracts important keywords from the content of an image and generates hashtags based on them. Trend analysis technology analyzes trends on social media and generates popular hashtags. Natural language processing technology understands the theme of an image and generates hashtags related to it. The suggestion unit suggests hashtags generated by the hashtag generation unit to the user. For example, the suggestion unit displays hashtags through a user interface. The suggestion unit can also record hashtags selected by the user and reflect them in the next suggestion. The suggestion unit can also collect user feedback and improve the accuracy of hashtag suggestions. For example, the user interface has an intuitive and easy-to-use design, allowing users to easily select hashtags. By recording user selection history, it is possible to suggest the most suitable hashtags for each individual user. By collecting user feedback and improving the suggestion algorithm, the accuracy of suggestions can be improved.As a result, the social media support system according to the embodiment saves users the trouble of selecting hashtags and allows them to more effectively communicate their messages. For example, users can make effective posts simply by selecting hashtags suggested by the generation AI. Users can concentrate on creative activities without spending time selecting hashtags. Users can maximize their influence on social media by using hashtags suggested by the generation AI.

[0048] The image analysis unit can detect emotional elements in an image and generate emotion-related hashtags based on that. For example, the image analysis unit uses a generation AI to detect emotional elements such as smiles and tears in an image and generate emotion-related hashtags based on that. For example, it suggests hashtags such as "#happy" and "#joyful" for an image of a smile. The image analysis unit also uses a generation AI to analyze emotional elements in an image and generate hashtags according to the intensity of the emotion. For example, it suggests hashtags such as "#intenseemotion" and "#deepfeeling" for an image that shows strong emotion. The image analysis unit also uses a generation AI to detect emotional elements in an image and generate hashtags according to the type of emotion. For example, it suggests hashtags such as "#sad" and "#emotional" for an image of crying. In this way, by generating hashtags based on emotional elements, it is possible to provide hashtags that are easy to empathize with.

[0049] The image analysis unit can generate hashtags with greater accuracy based on image metadata. For example, the image analysis unit uses a generation AI to analyze image metadata and generate hashtags based on the location and time of the photo. For example, hashtags such as "#Paris" and "#EiffelTower" are suggested for images taken in Paris. The image analysis unit also uses a generation AI to consider image metadata and generate hashtags based on camera settings. For example, hashtags such as "#nightphotography" and "#citylights" are suggested for images taken in night mode. The image analysis unit also uses a generation AI to analyze image metadata and generate hashtags related to seasons and events based on the date and time of the photo. For example, hashtags such as "#Christmas" and "#holidayseason" are suggested for images taken at Christmas. This allows for more accurate hashtag generation by taking metadata into consideration.

[0050] The image analysis unit can understand the relationships between objects in an image and generate highly relevant hashtags based on that. For example, the image analysis unit uses a generative AI to analyze the relationships between objects in an image and generate hashtags based on the interaction between a person and their pet. For example, for an image of someone playing with a dog, the generative AI would suggest hashtags such as "#doglover" and "#petplaytime." The image analysis unit also uses a generative AI to understand the relationships between objects in an image and suggest hashtags such as "#familytime" and "#parenting" for an image showing a parent-child relationship. The image analysis unit also uses a generative AI to analyze the relationships between objects in an image and suggest hashtags such as "#friendship" and "#besties" for an image showing a relationship between friends. This makes it possible to generate highly relevant hashtags by understanding the relationships between objects.

[0051] The image analysis unit can analyze video data and generate appropriate hashtags based on the content of the video. For example, the image analysis unit uses a generation AI to analyze video data and generate hashtags based on scenes and objects within the video. For example, hashtags such as "#travelvlog" and "#adventure" are suggested for travel videos. The image analysis unit also uses a generation AI to analyze video data and generate hashtags based on the video's storyline. For example, hashtags such as "#cooking" and "#recipe" are suggested for cooking videos. The image analysis unit also uses a generation AI to analyze video data and generate hashtags based on emotional moments within the video. For example, hashtags such as "#tearjerker" and "#heartwarming" are suggested for moving scenes. This makes it possible to generate hashtags appropriate for videos by analyzing video data.

[0052] The image analysis unit can suggest consistent hashtags based on a user's past posting history. For example, the image analysis unit uses a generation AI to analyze a user's past posting history and suggest consistent hashtags. For example, a user who has frequently used "#travel" in the past may be suggested hashtags such as "#wanderlust" and "#explore." The image analysis unit also uses a generation AI to refer to a user's past posting history and suggest hashtags related to specific themes. For example, a user who posts a lot about cooking may be suggested hashtags such as "#foodie" and "#homemade." The image analysis unit also uses a generation AI to analyze a user's past posting history and suggest hashtags based on past popular posts. For example, hashtags from posts that have received many likes in the past may be reused. This allows consistent hashtags to be suggested by referring to past posting history.

[0053] The image analysis unit can estimate the user's emotions and suggest hashtags that will elicit positive emotions. For example, the image analysis unit uses a generative AI to analyze an image, estimate the user's emotions, and suggest hashtags that will elicit positive emotions. For example, hashtags such as "#happy" and "#smile" are suggested for an image of a smiling face. The image analysis unit also uses a generative AI to analyze an image, estimate the user's emotions, and suggest hashtags that will elicit positive emotions. For example, hashtags such as "#achievement" and "#success" are suggested for an image that shows success. The image analysis unit also uses a generative AI to analyze an image, estimate the user's emotions, and suggest hashtags that will elicit positive emotions. For example, hashtags such as "#fun" and "#goodtimes" are suggested for an image of someone having fun. This makes it possible to estimate the user's emotions and suggest hashtags that will elicit positive emotions.

[0054] The hashtag generation unit uses an emotion estimation function to consider the user's emotions and can generate hashtags that are likely to resonate emotionally. In the hashtag generation unit, for example, the generation AI uses the emotion estimation function to generate hashtags that consider the user's emotions. For example, hashtags such as "#inspiring" and "#touching" are suggested for moving images. In addition, the hashtag generation unit uses the emotion estimation function to generate hashtags that consider the user's emotions. For example, hashtags such as "#joyful" and "#happiness" are suggested for images that show joy. In addition, the hashtag generation unit uses the emotion estimation function to generate hashtags that consider the user's emotions. For example, hashtags such as "#amazing" and "#wow" are suggested for images that show surprise. In this way, hashtags that are likely to resonate emotionally can be generated by considering the user's emotions.

[0055] The hashtag generation unit can analyze trends in real time and generate hashtags based on the latest trends. In the hashtag generation unit, for example, a generation AI analyzes trends in real time and generates hashtags based on the latest trends. For example, hashtags related to currently popular events or topics are suggested. In addition, the hashtag generation unit can analyze trends in real time and generate hashtags based on the latest trends. For example, hashtags related to popular challenges or memes are suggested. In addition, the hashtag generation unit can analyze trends in real time and generate hashtags based on the latest trends. For example, hashtags related to seasons or holidays are suggested. This allows trends to be analyzed in real time and hashtags based on the latest trends to be generated.

[0056] The hashtag generator can generate hashtags that include literary or art quotes related to the theme of the image. For example, the hashtag generator uses a generation AI to generate hashtags that include literary quotes related to the theme of the image. For example, for images of nature, the generation AI suggests hashtags such as "#naturepoetry" and "#wildbeauty." The hashtag generator also generates hashtags that include art quotes related to the theme of the image. For example, for images of artworks, the generation AI suggests hashtags such as "#artinspiration" and "#masterpiece." The hashtag generator also generates hashtags that include literary or art quotes related to the theme of the image. For example, for historical images, the generation AI suggests hashtags such as "#historicalquotes" and "#timelessart." This allows hashtags with deeper meaning to be generated by including literary or art quotes.

[0057] The hashtag generation unit simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, the generation AI of the hashtag generation unit simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, hashtags in English and Spanish are proposed. The hashtag generation unit also simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, hashtags in French and German are proposed. The hashtag generation unit also simultaneously generates hashtags in different languages, thereby increasing influence from an international perspective. For example, hashtags in Chinese and Japanese are proposed. In this way, by generating hashtags in different languages, influence from an international perspective can be increased.

[0058] The hashtag generation unit can analyze the interests of a user's followers and generate optimal hashtags based on that. For example, the hashtag generation unit uses a generation AI to analyze the interests of a user's followers and generate optimal hashtags based on that. For example, if a follower is interested in travel, it suggests hashtags such as "#travel" and "#explore." The hashtag generation unit also analyzes the interests of a user's followers and generates optimal hashtags based on that. For example, if a follower is interested in cooking, it suggests hashtags such as "#foodie" and "#cooking." The hashtag generation unit also analyzes the interests of a user's followers and generates optimal hashtags based on that. For example, if a follower is interested in fashion, it suggests hashtags such as "#fashionista" and "#style." This allows optimal hashtags to be generated by analyzing the interests of followers.

[0059] The hashtag generation unit can estimate the user's emotions and suggest hashtags that elicit a positive emotional response. For example, the hashtag generation unit uses a generation AI to estimate the user's emotions and suggest hashtags that elicit a positive emotional response. For example, hashtags such as "#happy" and "#smile" are suggested for an image of a smiling face. The hashtag generation unit also uses a generation AI to estimate the user's emotions and suggest hashtags that elicit a positive emotional response. For example, hashtags such as "#achievement" and "#success" are suggested for an image of success. The hashtag generation unit also uses a generation AI to estimate the user's emotions and suggest hashtags that elicit a positive emotional response. For example, hashtags such as "#fun" and "#goodtimes" are suggested for an image of someone having fun. This makes it possible to estimate the user's emotions and suggest hashtags that elicit a positive response.

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

[0061] The social media support system can further include a content suggestion unit that automatically suggests related articles and content based on the user's posts. For example, if a user uploads travel-related images, the content suggestion unit can suggest travel guides and tourist spot information. If a user uploads cooking images, the content suggestion unit can suggest articles about recipes and cooking tips. Furthermore, if a user uploads sports-related images, the content suggestion unit can suggest content about the latest sports news and training methods. This allows users to easily obtain information related to their interests and increases the value of their posts.

[0062] The social media support system may further include a music suggestion unit that estimates a user's emotions and suggests appropriate music based on the estimated emotions. For example, if a user uploads an image showing sad emotions, the music suggestion unit may suggest relaxing music or music containing encouraging messages. Alternatively, if a user uploads an image showing joy, the music suggestion unit may suggest upbeat, fun music. Furthermore, the music suggestion unit may suggest music that is useful when the user wants to concentrate. This allows users to easily find music that matches their emotions, providing a richer experience.

[0063] The social media support system may further include a product suggestion unit that suggests related products based on the content posted by the user. For example, if a user uploads fashion-related images, the product suggestion unit may suggest related fashion items and accessories. If a user uploads images related to outdoor activities, the product suggestion unit may suggest outdoor products and gear. If a user uploads images related to interior design, the product suggestion unit may suggest products related to interior design and furniture. This allows users to easily find products related to their interests, increasing their desire to purchase.

[0064] The social media support system may further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit may provide advice on how to relax or relieve stress. If the user uploads an image showing success, the feedback unit may provide a congratulatory message or advice on how to achieve further success. Furthermore, if the user is facing a difficult situation, the feedback unit may suggest an encouraging message or a solution. This allows the user to receive appropriate feedback according to their emotions, providing a better experience.

[0065] The social media support system may further include an event suggestion unit that suggests related events based on the content posted by the user. For example, if a user uploads a music-related image, the event suggestion unit may suggest nearby concerts and live events. If a user uploads a sports-related image, the event suggestion unit may suggest information about sports events and matches. Furthermore, if a user uploads an art-related image, the event suggestion unit may suggest information about art exhibitions and workshops. This allows users to easily find events related to their interests and increases their opportunities to participate.

[0066] The social media support system can further include a relaxation suggestion unit that estimates the user's emotions and suggests appropriate meditation or relaxation methods based on the estimated emotions. For example, if the user feels anxious, the relaxation suggestion unit can suggest deep breathing or meditation methods. If the user feels tired, the relaxation suggestion unit can suggest relaxing yoga poses or stretching methods. Furthermore, if the user wants to improve their concentration, the relaxation suggestion unit can suggest mindfulness techniques to improve concentration. This allows the user to find an appropriate relaxation method according to their emotions and maintain their physical and mental health.

[0067] The social media support system may further include a community suggestion unit that suggests related communities and groups based on the user's posted content. For example, if a user uploads an image related to pets, the community suggestion unit may suggest groups and forums for pet lovers. If a user uploads an image related to fitness, the community suggestion unit may suggest fitness communities and training groups. If a user uploads an image related to reading, the system may suggest book clubs and book review groups. This allows users to easily find communities and groups related to their interests and increases opportunities for interaction.

[0068] The social media support system may further include a fitness suggestion unit that estimates the user's emotions and suggests appropriate exercises or fitness programs based on the estimated emotions. For example, if the user feels low in energy, the fitness suggestion unit may suggest exercises to increase energy. If the user feels stressed, the fitness suggestion unit may suggest yoga or Pilates programs to relieve stress. Furthermore, if the user wants to relax, the fitness suggestion unit may suggest light stretching or walking to promote relaxation. This allows the user to find an appropriate fitness program that matches their emotions and maintain their health.

[0069] The social media support system may further include a learning suggestion unit that suggests related learning resources based on the user's posted content. For example, if a user uploads images related to science, the learning suggestion unit may suggest related science articles or online courses. If a user uploads images related to history, the learning suggestion unit may suggest historical documentaries or books. Furthermore, if a user uploads images related to art, the learning suggestion unit may suggest learning resources related to art techniques or history. This allows users to easily find learning resources related to their interests and deepen their knowledge.

[0070] The social media support system may further include a mental health suggestion unit that estimates the user's emotions and suggests appropriate mental health resources based on the estimated emotions. For example, if the user is feeling anxious, the mental health suggestion unit may suggest counseling services or support groups to reduce anxiety. If the user is feeling depressed, the mental health suggestion unit may suggest encouraging messages or resources that promote positive thinking. Furthermore, if the user is feeling stressed, the mental health suggestion unit may suggest stress management techniques or relaxation methods. This allows the user to find appropriate mental health resources according to their emotions and maintain their mental health.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The image analysis unit analyzes the image uploaded by the user. For example, the image analysis unit uses deep learning technology to recognize elements within the image and computer vision technology to understand the theme of the image. It can also analyze the color and composition of the image. This allows for a detailed understanding of the content of the image. Step 2: The hashtag generator automatically generates optimal hashtags based on the content of the image analyzed by the image analyzer. For example, it uses keyword extraction, trend analysis, and natural language processing techniques to generate hashtags related to the elements and themes of the image. Step 3: The suggestion unit suggests the hashtags generated by the hashtag generation unit to the user. For example, it displays the hashtags through a user interface, records the hashtags selected by the user, and reflects them in the next suggestion. It also collects user feedback to improve the accuracy of the suggestions.

[0073] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0079] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0080] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0081] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0082] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0083] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0084] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0114] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0123] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0124] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0125] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0126] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0127] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0128] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0129] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0130] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0131] 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.

[0132] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0133] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0134] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0135] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0136] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0137] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that analyzes images uploaded by users; a hashtag generation unit that automatically generates an optimal hashtag based on the content of the image analyzed by the image analysis unit; a suggestion unit that suggests the hashtag generated by the hashtag generation unit to a user. A system characterized by:

2. The image analysis unit Detecting emotional elements in the image and generating emotion-related hashtags based thereon.

2. The system of claim 1.

3. The image analysis unit Generate more accurate hashtags based on the image metadata 2. The system of claim 1.

4. The image analysis unit Understand the relationships between objects in the image and generate relevant hashtags based on that 2. The system of claim 1.

5. The image analysis unit Analyze video data and generate appropriate hashtags based on the content of the video 2. The system of claim 1.

Images