System

Abstract

An object of the system according to the embodiment is to automatically generate and propose content or a story based on a photograph uploaded by a user.SOLUTION: A system includes a photograph analysis unit, a content generation unit, and a proposal unit. The photograph analysis unit analyzes a photograph uploaded by a user. The content generation unit generates a text or a story based on the content of the photo analyzed by the photo analysis unit. The proposal unit proposes the text or the story generated by the content generation unit 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 technologies do not adequately address the issue of automatically generating content and stories based on user-uploaded photos, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate and suggest content and stories based on photos uploaded by users. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, a content generation unit, and a suggestion unit. The photo analysis unit analyzes photos uploaded by users. The content generation unit generates text and stories based on the content of the photos analyzed by the photo analysis unit. The suggestion unit suggests the text and stories generated by the content generation unit to users. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and suggest content and stories based on photos uploaded by users. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) In the posting support system according to an embodiment of the present invention, when a user uploads a photo, a generation AI analyzes the photo and automatically generates content and stories to suggest to the user. This allows the posting support system to enable users to easily create attractive posts.

[0029] A posting support system according to an embodiment includes a photo analysis unit, a content generation unit, and a suggestion unit. The photo analysis unit analyzes photos uploaded by users. For example, the photo analysis unit identifies the content of the photos (such as landscapes, people, and objects) using image recognition technology. The photo analysis unit can also extract features of photos using machine learning algorithms. For example, the photo analysis unit analyzes landscape photos and identifies whether the landscape is mountain, ocean, city, or the like. The content generation unit generates text and stories based on the content of the photos analyzed by the photo analysis unit. For example, the content generation unit uses a generation AI to generate text such as "These beautiful mountains evoke the grandeur of nature" for landscape photos. For example, the content generation unit generates a story such as "This smile tells us that we are having a good time" for portrait photos. The suggestion unit suggests the text and stories generated by the content generation unit to the user. For example, the suggestion unit displays the generated text and stories to the user so that the user can review and edit them. The suggestion unit also allows the user to use the suggested text and stories as is or slightly modify them to create their own words. As a result, the posting support system according to the embodiment allows users to easily create attractive posts. For example, a user can post a landscape photo in combination with the generated text, thereby sharing the beautiful scenery with other users.

[0030] The photo analysis unit can automatically acquire background information of a photo and reflect it in the analysis. For example, when a user uploads a photo, the photo analysis unit analyzes the metadata of the photo and automatically acquires the location and time of the photo. For example, the photo analysis unit identifies the location of the photo based on GPS information and reflects information related to that location in the analysis. The photo analysis unit can also identify the time of the photo based on a timestamp and reflect information related to that time period in the analysis. In this way, by reflecting the background information of the photo in the analysis, more detailed analysis is possible.

[0031] The photo analysis unit provides a function that allows a user to add a voice memo when uploading a photo, and the voice can also be used for analysis. For example, the photo analysis unit provides an interface that allows a user to add a voice memo when uploading a photo. For example, the photo analysis unit records a voice description of the photo or thoughts about the photo. The photo analysis unit also analyzes the voice memo and reflects the content in the photo analysis. For example, the photo analysis unit can convert the voice memo into text data using voice recognition technology and use the text data for analysis. The photo analysis unit can also analyze the emotion in the voice memo and classify the content of the photo based on that emotion. This allows for more detailed analysis by using the voice memo in the analysis.

[0032] The content generation unit can automatically suggest related videos and music based on the results of photo analysis. For example, the content generation unit automatically suggests related videos based on the results of photo analysis. For example, if the photo is a landscape, a tourist video of the location can be suggested. The content generation unit can also automatically suggest related music based on the results of photo analysis. For example, if the photo is a landscape, music related to the location can be suggested. The content generation unit can also develop video and music suggestion algorithms to suggest optimal content based on the user's preferences. This improves the user experience by suggesting related videos and music.

[0033] The content generation unit can generate text and stories in multiple different styles based on the results of photo analysis, providing users with options. For example, the content generation unit uses a generative AI to generate text in multiple different styles based on the results of photo analysis. For example, it generates formal writing, casual writing, humorous writing, etc. The content generation unit also develops an algorithm that suggests the optimal style of text and story based on the user's preferences. For example, it analyzes past posting history to identify the user's preferred style. This allows users to have multiple options, enabling more diverse posts.

[0034] The content generation unit can generate a detailed story that includes relevant historical background and cultural information based on the results of photo analysis. For example, the content generation unit generates a story that includes historical background related to a place or object based on the results of photo analysis. For example, if the photo is of a historical building, a story that includes the history of the building and important events is generated. The content generation unit can also generate a story that includes cultural information. For example, if the photo is of a landscape from a specific region, a story that includes information about the culture and customs of that region is generated. This allows for the generation of detailed stories, thereby enriching user posts.

[0035] The content generation unit can generate related quizzes and trivia based on the analysis results of the photo and provide them to the user. For example, the content generation unit generates a quiz related to the content based on the analysis results of the photo. For example, if the photo is of a historical building, a quiz related to the building is generated. The content generation unit can also generate related trivia based on the analysis results of the photo. For example, if the photo is a landscape photo of a specific area, trivia about that area is provided. In this way, by providing quizzes and trivia, content is provided that attracts the user's interest and allows them to learn while having fun.

[0036] The content generation unit can automatically suggest related products and services based on the content of the photo. For example, the content generation unit automatically suggests products related to the content based on the results of analyzing the photo. For example, if the photo is a landscape, travel products related to that location will be suggested. The content generation unit can also automatically suggest related services based on the content of the photo. For example, if the photo is a landscape of a specific area, tourist services related to that area will be suggested. This improves user convenience by suggesting related products and services.

[0037] The suggestion unit can analyze a user's past posting history and suggest optimal texts and stories based on that history. For example, the suggestion unit analyzes a user's past posting history and suggests optimal texts and stories based on those trends. For example, it refers to the style of posts that have received many likes in the past. The suggestion unit also identifies the user's favorite themes and topics based on the user's posting history and develops an algorithm that suggests optimal content based on that. For example, it suggests texts and stories related to themes that the user has posted about frequently in the past. This allows for more personalized content to be provided by making optimal suggestions based on the user's past posting history.

[0038] The suggestion unit can predict other users' reactions to the proposed text or story and adjust the proposed content based on the prediction. The suggestion unit, for example, develops an algorithm that predicts other users' reactions to the proposed text or story. For example, it builds a prediction model based on reaction data for past posts. The suggestion unit can also adjust the proposed content based on the predicted reactions. For example, it preferentially suggests text or stories that the prediction model shows a high reaction to. This allows for more effective suggestions by predicting other users' reactions and adjusting the proposed content based on that.

[0039] The suggestion unit can collect user feedback on proposed texts and stories and improve the generative AI's suggestion algorithm based on that feedback. The suggestion unit, for example, builds a system that collects user feedback on proposed texts and stories. For example, it allows users to leave ratings and comments on the suggestions. The suggestion unit also improves the generative AI's suggestion algorithm based on the collected feedback. For example, it learns suggestions that are highly rated by users and reflects that in the next suggestion. This allows the suggestion algorithm to be improved based on user feedback, enabling more effective suggestions.

[0040] The suggestion unit can automatically translate the proposed text or story into different languages ​​and obtain feedback from an international perspective. The suggestion unit, for example, builds a system that automatically translates the proposed text or story into different languages ​​and collects feedback from an international perspective. For example, the suggestion unit translates into multiple languages, such as English, French, and Chinese. The suggestion unit can also analyze feedback on the translated text or story and reflect the results in the proposal. For example, the suggestion unit can adjust the proposal based on feedback from users from different cultural backgrounds. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0041] The suggestion unit allows the generation AI to automatically generate hashtags to promote the spread of posts. For example, when creating a post, the generation AI automatically generates relevant hashtags based on the results of analyzing the photo. For example, for a landscape photo, it generates hashtags such as "#nature" and "#scenery." The suggestion unit also develops an algorithm to promote the spread of posts based on the generated hashtags. For example, it performs trend analysis and suggests optimal hashtags. This makes it possible to promote the spread of posts by automatically generating hashtags.

[0042] The suggestion unit allows the generation AI to evaluate the quality of the post content and suggest areas for improvement before the post is published. The suggestion unit, for example, builds a system in which the generation AI analyzes the post content and evaluates the quality before the post is published. For example, it evaluates the appropriateness of the grammar and expression of the text. The suggestion unit also develops an algorithm that suggests areas for improvement of the post content based on the quality evaluation. For example, it suggests correcting grammar and improving expression. In this way, by evaluating the quality of the post and suggesting areas for improvement, it becomes possible to post higher quality posts.

[0043] The suggestion unit can enable the generation AI to automatically monitor the performance of a post after it is published and suggest areas for improvement. For example, the suggestion unit will build a system where the generation AI automatically monitors the performance of a post after it is published. For example, it will track the number of views and likes in real time. The suggestion unit will also develop an algorithm that suggests areas for improvement in the next post based on the performance data. For example, if the number of views is low, it will suggest improvements to the title or content. This allows the quality of the next post to be improved by monitoring the performance of the post and suggesting areas for improvement.

[0044] The suggestion unit, when a post is published, uses a generation AI to automatically recommend related posts from other users, thereby revitalizing the community. For example, the suggestion unit builds a system in which, when a post is published, a generation AI automatically recommends related posts from other users. For example, it recommends posts on the same theme or topic. The suggestion unit also develops an algorithm to recommend optimal posts based on a user's interests and concerns. For example, it recommends related posts based on past browsing history and "like" data. This makes it possible to revitalize the community by recommending related posts from other users.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The photo analysis unit can analyze the movement of objects in a photo and classify the content of the photo based on that movement. For example, the photo analysis unit can analyze a photo of an animal and determine whether the animal is running, flying, or resting. The photo analysis unit can also analyze a sports photo and determine the movement of the players. For example, in a soccer photo, it can determine whether the player is kicking the ball or passing the ball. This allows for more detailed analysis by classifying the content based on the movement of the photo.

[0047] The content generation unit can automatically suggest related literary works or poems based on the results of photo analysis. For example, if a photo is of a landscape, it can suggest poems or passages from literary works related to that landscape. The content generation unit can also develop an algorithm that suggests literary works tailored to the user's preferences based on the results of photo analysis. For example, it can suggest the most suitable works based on data on works the user has read in the past. This improves the user's experience by suggesting related literary works and poems.

[0048] The suggestion unit can analyze a user's past posting history and suggest optimal texts and stories based on that history. For example, it can analyze a user's past posting history and suggest optimal texts and stories based on those trends. For example, it can refer to the style of posts that have received many likes in the past. The suggestion unit can also identify themes and topics that the user likes based on the user's posting history and develop an algorithm that suggests optimal content based on that. For example, it can suggest texts and stories related to themes that the user has posted about frequently in the past. This allows for more personalized content to be provided by making optimal suggestions based on the user's past posting history.

[0049] The content generation unit can generate related quizzes and trivia based on the results of photo analysis and provide them to the user. For example, based on the results of photo analysis, a quiz related to the content is generated. For example, if the photo is of a historical building, a quiz related to the building is generated. The content generation unit can also generate related trivia based on the results of photo analysis. For example, if the photo is a landscape photo of a specific area, trivia about that area is provided. In this way, by providing quizzes and trivia, content is provided that attracts the user's interest and allows them to learn while having fun.

[0050] The suggestion unit can predict other users' reactions to the proposed text or story and adjust the proposed content based on the prediction. For example, an algorithm can be developed to predict other users' reactions to the proposed text or story. For example, a prediction model can be constructed based on reaction data for past posts. The suggestion unit can also adjust the proposed content based on the predicted reactions. For example, the prediction model can preferentially suggest text or stories that show high reactions. This allows for more effective suggestions by predicting other users' reactions and adjusting the proposed content based on that.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The photo analysis unit analyzes photos uploaded by users. For example, the photo analysis unit uses image recognition technology to identify the content of the photo (such as scenery, people, or objects). The photo analysis unit can also extract features of the photo using machine learning algorithms. For example, the photo analysis unit analyzes landscape photos and identifies whether the scenery is a mountain, ocean, city, etc. Step 2: The content generation unit generates text and stories based on the content of the photo analyzed by the photo analysis unit. For example, the content generation unit uses a generative AI to generate text such as "These beautiful mountains give a sense of the grandeur of nature" for a landscape photo. Similarly, the content generation unit generates a story such as "This smile tells us that we are having a good time" for a portrait photo. Step 3: The suggestion unit suggests the text or story generated by the content generation unit to the user. For example, the suggestion unit displays the generated text or story to the user so that the user can review and edit it. The suggestion unit also allows the user to use the suggested text or story as is or slightly modify it to express it in their own words.

[0053] (Example 2) In the posting support system according to an embodiment of the present invention, when a user uploads a photo, a generation AI analyzes the photo and automatically generates content and stories to suggest to the user. This allows the posting support system to enable users to easily create attractive posts.

[0054] A posting support system according to an embodiment includes a photo analysis unit, a content generation unit, and a suggestion unit. The photo analysis unit analyzes photos uploaded by users. For example, the photo analysis unit identifies the content of the photos (such as landscapes, people, and objects) using image recognition technology. The photo analysis unit can also extract features of photos using machine learning algorithms. For example, the photo analysis unit analyzes landscape photos and identifies whether the landscape is mountain, ocean, city, or the like. The content generation unit generates text and stories based on the content of the photos analyzed by the photo analysis unit. For example, the content generation unit uses a generation AI to generate text such as "These beautiful mountains evoke the grandeur of nature" for landscape photos. For example, the content generation unit generates a story such as "This smile tells us that we are having a good time" for portrait photos. The suggestion unit suggests the text and stories generated by the content generation unit to the user. For example, the suggestion unit displays the generated text and stories to the user so that the user can review and edit them. The suggestion unit also allows the user to use the suggested text and stories as is or slightly modify them to create their own words. As a result, the posting support system according to the embodiment allows users to easily create attractive posts. For example, a user can post a landscape photo in combination with the generated text, thereby sharing the beautiful scenery with other users.

[0055] The photo analysis unit can automatically acquire background information of a photo and reflect it in the analysis. For example, when a user uploads a photo, the photo analysis unit analyzes the metadata of the photo and automatically acquires the location and time of the photo. For example, the photo analysis unit identifies the location of the photo based on GPS information and reflects information related to that location in the analysis. The photo analysis unit can also identify the time of the photo based on a timestamp and reflect information related to that time period in the analysis. In this way, by reflecting the background information of the photo in the analysis, more detailed analysis is possible.

[0056] The photo analysis unit can analyze the facial expressions and poses of people in photos to infer their emotions and actions. The photo analysis unit, for example, analyzes the facial expressions of people in photos to infer their emotions. For example, a smile identifies an emotion such as "joy," and a frown identifies an emotion such as "anxiety." The photo analysis unit also analyzes the person's pose to infer their behavior. For example, an open-armed person identifies an emotion such as "welcoming," and a folded-armed person identifies an emotion such as "alert." The photo analysis unit can also combine facial expression and pose data to infer more detailed emotions and actions. This allows for more detailed analysis by inferring the emotions and actions of people in photos.

[0057] The photo analysis unit can use the emotion estimation function to analyze the emotions of people in a photo and classify the content of the photo based on those emotions. For example, the photo analysis unit analyzes the emotions of people in a photo and classifies the photo into categories such as "joy," "sadness," and "surprise" based on those emotions. For example, a photo of a smiling person would be classified as "joy." The photo analysis unit also uses the emotion estimation function to develop an algorithm that automatically classifies the content of a photo. For example, the emotion estimation function identifies emotions using facial expression recognition technology and classifies the photo based on those emotions. This allows for more detailed analysis by classifying the content of a photo based on emotions.

[0058] The photo analysis unit provides a function that allows a user to add a voice memo when uploading a photo, and the voice can also be used for analysis. For example, the photo analysis unit provides an interface that allows a user to add a voice memo when uploading a photo. For example, the photo analysis unit records a voice description of the photo or thoughts about the photo. The photo analysis unit also analyzes the voice memo and reflects the content in the photo analysis. For example, the photo analysis unit can convert the voice memo into text data using voice recognition technology and use the text data for analysis. The photo analysis unit can also analyze the emotion in the voice memo and classify the content of the photo based on that emotion. This allows for more detailed analysis by using the voice memo in the analysis.

[0059] The content generation unit can automatically suggest related videos and music based on the results of photo analysis. For example, the content generation unit automatically suggests related videos based on the results of photo analysis. For example, if the photo is a landscape, a tourist video of the location can be suggested. The content generation unit can also automatically suggest related music based on the results of photo analysis. For example, if the photo is a landscape, music related to the location can be suggested. The content generation unit can also develop video and music suggestion algorithms to suggest optimal content based on the user's preferences. This improves the user experience by suggesting related videos and music.

[0060] The photo analysis unit can use the emotion estimation function to analyze the emotions of users when they upload photos in real time and adjust the analysis results based on those emotions. For example, the photo analysis unit analyzes emotions in real time using a camera or microphone when a user uploads a photo. For example, it identifies emotions by analyzing facial expressions and tone of voice. The photo analysis unit also develops an algorithm to adjust the photo analysis results based on the emotions analyzed in real time. For example, if the user is happy, it prioritizes positive analysis results. This allows for more personalized analysis by adjusting the analysis results based on the user's emotions.

[0061] The content generation unit can generate text and stories in multiple different styles based on the results of photo analysis, providing users with options. For example, the content generation unit uses a generative AI to generate text in multiple different styles based on the results of photo analysis. For example, it generates formal writing, casual writing, humorous writing, etc. The content generation unit also develops an algorithm that suggests the optimal style of text and story based on the user's preferences. For example, it analyzes past posting history to identify the user's preferred style. This allows users to have multiple options, enabling more diverse posts.

[0062] The content generation unit can generate a detailed story that includes relevant historical background and cultural information based on the results of photo analysis. For example, the content generation unit generates a story that includes historical background related to a place or object based on the results of photo analysis. For example, if the photo is of a historical building, a story that includes the history of the building and important events is generated. The content generation unit can also generate a story that includes cultural information. For example, if the photo is of a landscape from a specific region, a story that includes information about the culture and customs of that region is generated. This allows for the generation of detailed stories, thereby enriching user posts.

[0063] The content generation unit can use the emotion estimation function to estimate the user's emotion regarding the content of a photo and generate text or a story based on that emotion. The content generation unit, for example, estimates the user's emotion regarding the content of a photo and generates text based on that emotion. For example, if the user is moved, it generates moving text. The content generation unit also uses the emotion estimation function to develop an algorithm for generating a story based on the user's emotion. For example, if the user is having fun, it generates an enjoyable story. In this way, by generating text or a story based on the user's emotion, more personalized content can be provided.

[0064] The content generation unit can generate related quizzes and trivia based on the analysis results of the photo and provide them to the user. For example, the content generation unit generates a quiz related to the content based on the analysis results of the photo. For example, if the photo is of a historical building, a quiz related to the building is generated. The content generation unit can also generate related trivia based on the analysis results of the photo. For example, if the photo is a landscape photo of a specific area, trivia about that area is provided. In this way, by providing quizzes and trivia, content is provided that attracts the user's interest and allows them to learn while having fun.

[0065] The content generation unit can automatically suggest related products and services based on the content of the photo. For example, the content generation unit automatically suggests products related to the content based on the results of analyzing the photo. For example, if the photo is a landscape, travel products related to that location will be suggested. The content generation unit can also automatically suggest related services based on the content of the photo. For example, if the photo is a landscape of a specific area, tourist services related to that area will be suggested. This improves user convenience by suggesting related products and services.

[0066] The content generation unit can use the emotion estimation function to analyze the user's emotions toward the generated text or story in real time and adjust the content based on those emotions. For example, the content generation unit analyzes the user's emotions while reading the generated text or story in real time and adjusts the content based on those emotions. For example, if the user shows interest, more detailed information is provided. The content generation unit also uses the emotion estimation function to develop an algorithm for adjusting the content based on the user's emotions. For example, if the user is enjoying it, fun content is added. In this way, by adjusting the content based on the user's emotions, more personalized content is provided.

[0067] The suggestion unit can analyze a user's past posting history and suggest optimal texts and stories based on that history. For example, the suggestion unit analyzes a user's past posting history and suggests optimal texts and stories based on those trends. For example, it refers to the style of posts that have received many likes in the past. The suggestion unit also identifies the user's favorite themes and topics based on the user's posting history and develops an algorithm that suggests optimal content based on that. For example, it suggests texts and stories related to themes that the user has posted about frequently in the past. This allows for more personalized content to be provided by making optimal suggestions based on the user's past posting history.

[0068] The suggestion unit can predict other users' reactions to the proposed text or story and adjust the proposed content based on the prediction. The suggestion unit, for example, develops an algorithm that predicts other users' reactions to the proposed text or story. For example, it builds a prediction model based on reaction data for past posts. The suggestion unit can also adjust the proposed content based on the predicted reactions. For example, it preferentially suggests text or stories that the prediction model shows a high reaction to. This allows for more effective suggestions by predicting other users' reactions and adjusting the proposed content based on that.

[0069] The suggestion unit can use the emotion estimation function to analyze the user's emotion toward the proposed text or story and optimize the proposed content based on that emotion. For example, the suggestion unit analyzes the user's emotion toward the proposed text or story in real time and optimizes the proposed content based on that emotion. For example, if the user shows interest, the suggestion unit provides more detailed information. The suggestion unit also uses the emotion estimation function to develop an algorithm that optimizes the proposed content based on the user's emotion. For example, if the user is enjoying something, the suggestion unit adds enjoyable content. This enables more personalized suggestions by optimizing the proposed content based on the user's emotion.

[0070] The suggestion unit can collect user feedback on proposed texts and stories and improve the generative AI's suggestion algorithm based on that feedback. The suggestion unit, for example, builds a system that collects user feedback on proposed texts and stories. For example, it allows users to leave ratings and comments on the suggestions. The suggestion unit also improves the generative AI's suggestion algorithm based on the collected feedback. For example, it learns suggestions that are highly rated by users and reflects that in the next suggestion. This allows the suggestion algorithm to be improved based on user feedback, enabling more effective suggestions.

[0071] The suggestion unit can automatically translate the proposed text or story into different languages ​​and obtain feedback from an international perspective. The suggestion unit, for example, builds a system that automatically translates the proposed text or story into different languages ​​and collects feedback from an international perspective. For example, the suggestion unit translates into multiple languages, such as English, French, and Chinese. The suggestion unit can also analyze feedback on the translated text or story and reflect the results in the proposal. For example, the suggestion unit can adjust the proposal based on feedback from users from different cultural backgrounds. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0072] The suggestion unit can use the emotion estimation function to monitor the user's emotion toward the proposed text or story in real time and continuously adjust the content of the suggestion based on that emotion. For example, the suggestion unit develops a system that monitors the user's emotion toward the proposed text or story in real time and continuously adjusts the content of the suggestion based on that emotion. For example, the suggestion unit identifies the emotion by analyzing the user's facial expression or voice. The suggestion unit also develops an algorithm that continuously adjusts the content of the suggestion based on the emotion analyzed in real time. For example, if the user shows interest, more detailed information is provided. This enables more effective suggestions by monitoring the user's emotion in real time and adjusting the content of the suggestion based on that emotion.

[0073] The suggestion unit allows the generation AI to automatically generate hashtags to promote the spread of posts. For example, when creating a post, the generation AI automatically generates relevant hashtags based on the results of analyzing the photo. For example, for a landscape photo, it generates hashtags such as "#nature" and "#scenery." The suggestion unit also develops an algorithm to promote the spread of posts based on the generated hashtags. For example, it performs trend analysis and suggests optimal hashtags. This makes it possible to promote the spread of posts by automatically generating hashtags.

[0074] The suggestion unit allows the generation AI to evaluate the quality of the post content and suggest areas for improvement before the post is published. The suggestion unit, for example, builds a system in which the generation AI analyzes the post content and evaluates the quality before the post is published. For example, it evaluates the appropriateness of the grammar and expression of the text. The suggestion unit also develops an algorithm that suggests areas for improvement of the post content based on the quality evaluation. For example, it suggests correcting grammar and improving expression. In this way, by evaluating the quality of the post and suggesting areas for improvement, it becomes possible to post higher quality posts.

[0075] The suggestion unit can use the emotion estimation function to analyze the emotion a user has when creating a post and optimize the content of the post based on that emotion. For example, the suggestion unit develops a system that analyzes the user's emotion in real time when creating a post and optimizes the content of the post based on that emotion. For example, the suggestion unit identifies the emotion by analyzing the user's facial expression or voice. The suggestion unit also develops an algorithm that optimizes the content of the post based on the emotion analyzed in real time. For example, if the user is having fun, the suggestion unit adds fun content. This enables more personalized posts by optimizing the content of the post based on the user's emotion.

[0076] The suggestion unit can enable the generation AI to automatically monitor the performance of a post after it is published and suggest areas for improvement. For example, the suggestion unit will build a system where the generation AI automatically monitors the performance of a post after it is published. For example, it will track the number of views and likes in real time. The suggestion unit will also develop an algorithm that suggests areas for improvement in the next post based on the performance data. For example, if the number of views is low, it will suggest improvements to the title or content. This allows the quality of the next post to be improved by monitoring the performance of the post and suggesting areas for improvement.

[0077] The suggestion unit, when a post is published, uses a generation AI to automatically recommend related posts from other users, thereby revitalizing the community. For example, the suggestion unit builds a system in which, when a post is published, a generation AI automatically recommends related posts from other users. For example, it recommends posts on the same theme or topic. The suggestion unit also develops an algorithm to recommend optimal posts based on a user's interests and concerns. For example, it recommends related posts based on past browsing history and "like" data. This makes it possible to revitalize the community by recommending related posts from other users.

[0078] The suggestion unit can use the emotion estimation function to analyze other users' emotional reactions to a post in real time and adjust the content of the next post based on that reaction. The suggestion unit, for example, develops a system that analyzes other users' emotional reactions to a post in real time and adjusts the content of the next post based on that reaction. For example, it analyzes the user's facial expressions and comments to identify emotions. The suggestion unit also develops an algorithm that adjusts the content of the next post based on the emotional reactions analyzed in real time. For example, if there are many positive reactions, that style is maintained. This enables more effective posting by analyzing other users' emotional reactions in real time and adjusting the content of the next post based on that.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The photo analysis unit can analyze the movement of objects in a photo and classify the content of the photo based on that movement. For example, the photo analysis unit can analyze a photo of an animal and determine whether the animal is running, flying, or resting. The photo analysis unit can also analyze a sports photo and determine the movement of the players. For example, in a soccer photo, it can determine whether the player is kicking the ball or passing the ball. This allows for more detailed analysis by classifying the content based on the movement of the photo.

[0081] The content generation unit can automatically suggest related literary works or poems based on the results of photo analysis. For example, if a photo is of a landscape, it can suggest poems or passages from literary works related to that landscape. The content generation unit can also develop an algorithm that suggests literary works tailored to the user's preferences based on the results of photo analysis. For example, it can suggest the most suitable works based on data on works the user has read in the past. This improves the user's experience by suggesting related literary works and poems.

[0082] The suggestion unit can analyze a user's past posting history and suggest optimal texts and stories based on that history. For example, it can analyze a user's past posting history and suggest optimal texts and stories based on those trends. For example, it can refer to the style of posts that have received many likes in the past. The suggestion unit can also identify themes and topics that the user likes based on the user's posting history and develop an algorithm that suggests optimal content based on that. For example, it can suggest texts and stories related to themes that the user has posted about frequently in the past. This allows for more personalized content to be provided by making optimal suggestions based on the user's past posting history.

[0083] The content generation unit can generate related quizzes and trivia based on the results of photo analysis and provide them to the user. For example, based on the results of photo analysis, a quiz related to the content is generated. For example, if the photo is of a historical building, a quiz related to the building is generated. The content generation unit can also generate related trivia based on the results of photo analysis. For example, if the photo is a landscape photo of a specific area, trivia about that area is provided. In this way, by providing quizzes and trivia, content is provided that attracts the user's interest and allows them to learn while having fun.

[0084] The suggestion unit can predict other users' reactions to the proposed text or story and adjust the proposed content based on the prediction. For example, an algorithm can be developed to predict other users' reactions to the proposed text or story. For example, a prediction model can be constructed based on reaction data for past posts. The suggestion unit can also adjust the proposed content based on the predicted reactions. For example, the prediction model can preferentially suggest text or stories that show high reactions. This allows for more effective suggestions by predicting other users' reactions and adjusting the proposed content based on that.

[0085] The photo analysis unit uses the emotion estimation function to analyze the emotions of users when they upload photos in real time and adjust the analysis results based on those emotions. For example, when a user uploads a photo, the camera and microphone are used to analyze emotions in real time. For example, facial expressions and tone of voice are analyzed to identify emotions. The photo analysis unit also develops an algorithm to adjust the photo analysis results based on the emotions analyzed in real time. For example, if the user is happy, positive analysis results are prioritized. This enables more personalized analysis by adjusting the analysis results based on the user's emotions.

[0086] The content generation unit can use the emotion estimation function to estimate the user's emotion regarding the content of a photo and generate text or a story based on that emotion. For example, the content generation unit estimates the user's emotion regarding the content of a photo and generates text based on that emotion. For example, if the user is moved, it generates moving text. The content generation unit also uses the emotion estimation function to develop an algorithm that generates a story based on the user's emotion. For example, if the user is having fun, it generates an enjoyable story. In this way, by generating text or a story based on the user's emotion, more personalized content can be provided.

[0087] The suggestion unit can use the emotion estimation function to analyze the user's emotions toward the proposed text or story in real time and optimize the suggested content based on the emotions. For example, the suggestion unit can analyze the user's emotions toward the proposed text or story in real time and optimize the suggested content based on the emotions. For example, if the user shows interest, more detailed information can be provided. The suggestion unit also uses the emotion estimation function to develop an algorithm that optimizes the suggested content based on the user's emotions. For example, if the user is enjoying something, fun content can be added. This allows the suggested content to be optimized based on the user's emotions, making it possible to make more personalized suggestions.

[0088] The suggestion unit can use the emotion estimation function to analyze other users' emotional reactions to a post in real time and adjust the content of the next post based on that reaction. For example, a system is developed that analyzes other users' emotional reactions to a post in real time and adjusts the content of the next post based on that reaction. For example, emotions are identified by analyzing the user's facial expressions and comments. The suggestion unit also develops an algorithm that adjusts the content of the next post based on the emotional reactions analyzed in real time. For example, if there are many positive reactions, that style is maintained. This allows for more effective posting by analyzing other users' emotional reactions in real time and adjusting the content of the next post based on that.

[0089] The suggestion unit can use the emotion estimation function to analyze the emotions of a user when creating a post and optimize the content of the post based on those emotions. For example, a system is developed that analyzes a user's emotions in real time when creating a post and optimizes the content of the post based on those emotions. For example, the system identifies emotions by analyzing the user's facial expressions and voice. The suggestion unit also develops an algorithm that optimizes the content of the post based on the emotions analyzed in real time. For example, if the user is having fun, the system adds fun content. This enables more personalized posts by optimizing the content of the post based on the user's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The photo analysis unit analyzes photos uploaded by users. For example, the photo analysis unit uses image recognition technology to identify the content of the photo (such as scenery, people, or objects). The photo analysis unit can also extract features of the photo using machine learning algorithms. For example, the photo analysis unit analyzes landscape photos and identifies whether the scenery is a mountain, ocean, city, etc. Step 2: The content generation unit generates text and stories based on the content of the photo analyzed by the photo analysis unit. For example, the content generation unit uses a generative AI to generate text such as "These beautiful mountains give a sense of the grandeur of nature" for a landscape photo. Similarly, the content generation unit generates a story such as "This smile tells us that we are having a good time" for a portrait photo. Step 3: The suggestion unit suggests the text or story generated by the content generation unit to the user. For example, the suggestion unit displays the generated text or story to the user so that the user can review and edit it. The suggestion unit also allows the user to use the suggested text or story as is or slightly modify it to express it in their own words.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

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

[0128] The 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.

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

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo analysis unit that analyzes photos uploaded by users; a content generation unit that generates text and a story based on the content of the photo analyzed by the photo analysis unit; a suggestion unit that suggests the text or story generated by the content generation unit to a user. A system characterized by:

2. The photo analysis unit When uploading a photo, the user is provided with a function that allows the user to add a voice memo, and the voice memo is also used in the analysis.

2. The system of claim 1.

3. The content generation unit Based on the analysis of the photo, a plurality of different styles of the text and the story are generated and the user is provided with a choice.

2. The system of claim 1.

4. The proposal unit Analyzing the user's past posting history and suggesting the most suitable text or story based on that history 2. The system of claim 1.

5. The photo analysis unit Analyze the facial expressions and poses of people in photos to estimate their emotions and behaviors 2. The system of claim 1.

Images