system

Abstract

The system according to the embodiment aims to automatically generate text based on a photograph and post it to a social networking site or blog. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a posting unit. The reception unit uploads photos. The analysis unit analyzes the photos uploaded by the reception unit. The generation unit generates text based on the content of the photos analyzed by the analysis unit. The posting unit posts the text generated by the generation unit to an SNS or blog.

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] With conventional technology, the task of creating text based on photos and posting it to social media or blogs was time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to automatically generate text based on a photograph and post it to a social networking site or blog. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a posting unit. The reception unit uploads photos. The analysis unit analyzes the photos uploaded by the reception unit. The generation unit generates text based on the content of the photos analyzed by the analysis unit. The posting unit posts the text generated by the generation unit to an SNS or blog. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate text based on a photograph and post it to social networking sites or blogs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic text generation system according to an embodiment of the present invention analyzes photos taken by a user and automatically generates and uploads text to social media sites, blogs, and other platforms based on the photos. This automatic text generation system analyzes photos taken by a user, generates text based on the photos, and automatically uploads the text to the social media site or blog. This allows users to effortlessly create text related to the photos and post it to the social media site or blog. For example, a user uploads landscape photos taken on a trip or photos of food at a restaurant to the service. These photos are input into a generation AI. The generation AI then analyzes the input photos. The generation AI understands the content of the photos and generates text based on that content. For example, for a landscape photo, a text such as "I enjoyed the beautiful mountain scenery" is generated. For a food photo, a text such as "Today's lunch was delicious pasta" is generated. The generated text is automatically uploaded to the user's social media site or blog. For example, the user logs in to their social media account and posts the generated text and photo. The user can then review the content and make any necessary edits. This system allows users to effortlessly create text related to photos and post it to social media sites or blogs. For example, by uploading photos taken during a trip and having the AI ​​automatically generate and post text, users can share their travel experiences in real time. Also, by taking photos of food and having the AI ​​automatically generate and post text, users can easily keep a record of their meals. This allows the automatic text generation system to analyze photos taken by users and automatically generate and upload text for social media, blogs, etc. based on the photos.

[0029] An automatic sentence generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a posting unit. The reception unit uploads photos taken by a user. Photos taken by a user include, but are not limited to, landscape photos, portrait photos, and food photos. For example, the reception unit uploads the photos taken by the user to a service. The analysis unit analyzes the uploaded photos. For example, the analysis unit analyzes the content of the photos using image recognition technology. The analysis unit understands the content of the photos and extracts features based on the content. For example, in the case of landscape photos, the analysis unit extracts features such as mountains and rivers. The generation unit generates sentences based on the content of the photos analyzed by the analysis unit. For example, the generation unit generates sentences based on the content of the photos using a generation AI. The generation AI can use natural language generation technologies such as GPT-4 (registered trademark) and Gemini. Based on the content of the photos, the generation unit generates sentences such as "I enjoyed the beautiful mountain scenery" in the case of landscape photos, and "Today's lunch was delicious pasta" in the case of food photos. The posting unit posts the text generated by the generation unit to an SNS or blog. The posting unit, for example, logs in to the user's SNS account and posts the generated text and photo. The posting unit can log in to the user's SNS account using OAuth authentication, an API key, or the like. The posting unit includes a confirmation unit that allows the user to check the posted content and correct it as necessary when posting the generated text and photo. As a result, the automatic text generation system according to the embodiment can analyze photos taken by the user and automatically generate and upload text for an SNS, blog, or the like based on the photo.

[0030] The generation unit can analyze the content of a photo using a generation AI and generate a sentence based on that content. For example, the generation unit can analyze the content of a photo using a generation AI and generate a sentence based on that content. The generation AI can use natural language generation technologies such as GPT-4 or Gemini. Based on the content of the photo, the generation unit can generate a sentence such as "I enjoyed the beautiful mountain scenery" for a landscape photo, or "Today's lunch was delicious pasta" for a food photo. This makes it possible to generate a sentence based on the content of a photo using a generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can analyze the content of a photo and generate a sentence using a generation AI model for generating a sentence based on that content.

[0031] The posting unit can log in to the user's SNS account and post the generated text and photos. For example, the posting unit can log in to the user's SNS account and post the generated text and photos. The posting unit can log in to the user's SNS account using OAuth authentication, an API key, or the like. This allows the posting unit to automatically log in to the user's SNS account and post the generated text and photos. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can log in to the user's SNS account and post the generated text and photos using an AI model for posting.

[0032] The posting unit may include a confirmation unit that allows the user to check the posted content and correct it as necessary. The posting unit may include, for example, a confirmation unit that allows the user to check the posted content and correct it as necessary. The confirmation unit may provide, for example, a preview display or an editing function, thereby allowing the user to check the posted content and correct it as necessary. Some or all of the above-described processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit may perform the confirmation and correction using an AI model that allows the user to check the posted content and correct it as necessary.

[0033] The analysis unit can analyze multiple types of photos, such as landscape photos taken at a travel destination or photos of food taken at a restaurant. The analysis unit analyzes various types of photos, such as landscape photos taken at a travel destination or photos of food taken at a restaurant. The analysis unit uses image recognition technology to analyze the content of the photo and extract features based on the content. For example, in the case of a landscape photo, features such as mountains and rivers are extracted. In the case of a food photo, features such as the type of food and presentation are extracted. This makes it possible to analyze various types of photos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may perform analysis using an AI model that analyzes the content of the photo and extracts features based on the content.

[0034] Based on the analyzed content of the photo, the generation unit can generate a sentence such as "I enjoyed the scenery with the mountains in the distance" for a landscape photo, or "Today's lunch was pasta" for a food photo. For example, based on the analyzed content of the photo, the generation unit can generate a sentence such as "I enjoyed the scenery with the beautiful mountains in the distance" for a landscape photo, or "Today's lunch was delicious pasta" for a food photo. The generation unit generates a sentence based on the content of the photo using a generative AI. The generative AI can use natural language generation technologies such as GPT-4 and Gemini. This allows for the generation of specific sentences based on the content of the photo. Some or all of the above-described processing in the generation unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the generation unit can analyze the content of the photo and generate sentences based on that content using a generative AI model.

[0035] The reception unit can analyze the user's past photo upload history and select an appropriate upload method. For example, the reception unit analyzes the user's past photo upload history and selects the optimal upload method. Based on the past upload history, the reception unit can prioritize and suggest upload methods that the user has frequently used in the past. For example, if the user previously only uploaded photos when connected to Wi-Fi, the reception unit can prompt the user to upload photos when connected to Wi-Fi. Furthermore, if the user tends to upload photos during a specific time of day, the reception unit can prompt the user to upload photos during that time of day. Furthermore, if the user tends to upload photos during a specific event (e.g., while traveling), the reception unit can prompt the user to upload photos during that event. This makes it possible to select the optimal upload method based on the user's past photo upload history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past photo upload history and select the upload method using an AI model for selecting the optimal upload method.

[0036] The reception unit can filter photos based on the user's current activity status and areas of interest when uploading photos. For example, the reception unit can filter photos based on the user's current activity status and areas of interest when uploading photos. The reception unit can select photos to upload based on the user's activity status and areas of interest. For example, if the user is traveling, the reception unit can suggest uploading only photos related to the travel. Also, if the user is interested in cooking, the reception unit can suggest uploading only photos of cooking. Furthermore, if the user is participating in a sporting event, the reception unit can suggest uploading only photos related to the event. This allows photos to be filtered based on the user's current activity status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can perform filtering using an AI model for filtering photos based on the user's current activity status and areas of interest.

[0037] The reception unit can prioritize uploading highly relevant photos based on the user's geographical location information when uploading photos. For example, when uploading photos, the reception unit prioritizes uploading highly relevant photos based on the user's geographical location information. The reception unit can acquire the user's geographical location information using GPS data, location information services, etc. For example, when the user is traveling, photos of the travel destination can be prioritized. Furthermore, when the user is participating in a specific event, photos related to the event can be prioritized. Furthermore, when the user is at home, photos related to daily life can be prioritized. This allows highly relevant photos to be prioritized uploaded based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform uploading using an AI model for prioritized uploading highly relevant photos based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and upload related photos when uploading a photo. For example, the reception unit can analyze the user's social media activity and upload related photos when uploading a photo. The reception unit can select related photos based on the user's posting history, number of likes, etc. For example, if a user frequently uses a specific hashtag, photos related to that hashtag can be preferentially uploaded. Also, if a user frequently interacts with a specific friend, photos related to that friend can be preferentially uploaded. Furthermore, if a user belongs to a specific group, photos related to that group can be preferentially uploaded. This makes it possible to upload related photos based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can analyze the user's social media activity and upload related photos using an AI model.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the photo during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo during analysis. The analysis unit can evaluate the importance of the photo and adjust the level of detail of the analysis based on the evaluation. For example, for photos of important events, a detailed analysis can be performed to generate detailed sentences. For everyday photos, a concise analysis can be performed to generate short sentences. Furthermore, for photos related to a specific theme, a detailed analysis appropriate for that theme can be performed. This makes it possible to adjust the level of detail of the analysis based on the importance of the photo. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model for evaluating the importance of the photo and adjusting the level of detail of the analysis based on the evaluation.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the photo during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the photo during analysis. The analysis unit can classify the category of the photo and apply an appropriate analysis algorithm based on the classification. For example, in the case of a landscape photo, an analysis algorithm specialized for landscapes can be applied. In addition, in the case of a food photo, an analysis algorithm specialized for food can be applied. Furthermore, in the case of a portrait photo, an analysis algorithm specialized for people can be applied. This makes it possible to apply an appropriate analysis algorithm depending on the category of the photo. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can classify the category of the photo and perform the analysis using an AI model for applying an appropriate analysis algorithm based on the classification.

[0041] The analysis unit can determine the analysis priority based on when the photos were taken during analysis. For example, the analysis unit can determine the analysis priority based on when the photos were taken during analysis. The analysis unit can evaluate when the photos were taken and determine the analysis priority based on the evaluation. For example, the analysis unit can prioritize analysis of recently taken photos. Furthermore, it can prioritize analysis of photos taken during a specific event period. Furthermore, it can prioritize analysis of photos taken within a period specified by the user. This makes it possible to determine the analysis priority based on when the photos were taken. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can evaluate when the photos were taken and determine the analysis priority based on the evaluation using an AI model.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the photos during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the photos during analysis. The analysis unit can evaluate the relevance of the photos and adjust the order of analysis based on the evaluation. For example, photos taken at the same event can be analyzed together. Also, photos taken at the same location can be analyzed together. Furthermore, photos related to the same theme can be analyzed together. This makes it possible to adjust the order of analysis based on the relevance of the photos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the relevance of the photos and adjust the order of analysis based on the evaluation using an AI model.

[0043] The generation unit can adjust the level of detail of the sentence based on the content of the photo when generating the sentence. For example, the generation unit can adjust the level of detail of the sentence based on the content of the photo when generating the sentence. The generation unit can evaluate the content of the photo and adjust the level of detail of the sentence based on the evaluation. For example, in the case of a landscape photo, a sentence including a detailed description of the landscape can be generated. Also, in the case of a food photo, a sentence including a detailed description of the food can be generated. Furthermore, in the case of a portrait photo, a sentence including a detailed description of the person can be generated. This makes it possible to adjust the level of detail of the sentence based on the content of the photo. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model for evaluating the content of the photo and adjusting the level of detail of the sentence based on the evaluation.

[0044] The generation unit can apply different generation algorithms depending on the category of the photo when generating a sentence. For example, the generation unit can apply different generation algorithms depending on the category of the photo when generating a sentence. The generation unit can classify the category of the photo and apply an appropriate generation algorithm based on the classification. For example, in the case of a landscape photo, a generation algorithm specialized for landscapes can be applied. In addition, in the case of a food photo, a generation algorithm specialized for food can be applied. Furthermore, in the case of a portrait photo, a generation algorithm specialized for people can be applied. This makes it possible to apply an appropriate generation algorithm depending on the category of the photo. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can perform generation using a generation AI model that classifies the category of the photo and applies an appropriate generation algorithm based on the classification.

[0045] The generation unit can determine the priority of sentences based on the time when the photos were taken when generating the sentences. For example, the generation unit can determine the priority of sentences based on the time when the photos were taken when generating the sentences. The generation unit can evaluate the time when the photos were taken and determine the priority of sentences based on the evaluation. For example, the generation unit can preferentially generate sentences based on recently taken photos. Also, the generation unit can preferentially generate sentences based on photos taken during a specific event period. Furthermore, the generation unit can preferentially generate sentences based on photos taken within a period specified by the user. This makes it possible to determine the priority of sentences based on the time when the photos were taken. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority using a generation AI model for evaluating the time when the photos were taken and determining the priority of sentences based on the evaluation.

[0046] The generation unit can adjust the order of sentences based on the relevance of photos when generating sentences. For example, the generation unit can adjust the order of sentences based on the relevance of photos when generating sentences. The generation unit can evaluate the relevance of photos and adjust the order of sentences based on the evaluation. For example, sentences can be generated collectively based on photos taken at the same event. Also, sentences can be generated collectively based on photos taken at the same location. Furthermore, sentences can be generated collectively based on photos related to the same theme. This makes it possible to adjust the order of sentences based on the relevance of photos. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order using a generative AI model for evaluating the relevance of photos and adjusting the order of sentences based on the evaluation.

[0047] The posting unit can select an appropriate posting method by referring to the user's past posting history when posting. For example, the posting unit can select the optimal posting method by referring to the user's past posting history when posting. The posting unit can preferentially suggest posting methods that the user has frequently used in the past based on the past posting history. For example, if the user has frequently used a specific social networking platform in the past, the posting unit can encourage the user to post on that platform. Also, if the user tends to post during a specific time of day, the posting unit can encourage the user to post during that time of day. Furthermore, if the user tends to post during a specific event (e.g., while traveling), the posting unit can encourage the user to post during that event. This makes it possible to select the optimal posting method based on the user's past posting history. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can select the posting method using an AI model that selects the optimal posting method by referring to the user's past posting history.

[0048] The posting unit can customize the posted content based on the user's current activity status when posting. For example, the posting unit customizes the posted content based on the user's current activity status when posting. The posting unit can select the posted content based on the user's activity status. For example, if the user is traveling, the posting unit can suggest that the user post content related to the travel. Also, if the user is cooking, the posting unit can suggest that the user post content related to cooking. Furthermore, if the user is participating in a sporting event, the posting unit can suggest that the user post content related to the event. This makes it possible to customize the posted content based on the user's current activity status. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can customize the posted content using an AI model for customizing the posted content based on the user's current activity status.

[0049] The posting unit can select the optimal posting method by taking into account the user's geographical location information when posting. For example, the posting unit can select the optimal posting method by taking into account the user's geographical location information when posting. The posting unit can acquire the user's geographical location information by using GPS data, location information services, etc. For example, if the user is traveling, posts including information about the travel destination can be suggested. Furthermore, if the user is participating in a specific event, posts related to the event can be suggested. Furthermore, if the user is at home, posts related to daily life can be suggested. This makes it possible to select the optimal posting method based on the user's geographical location information. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can select the posting method by using an AI model for selecting the optimal posting method by taking into account the user's geographical location information.

[0050] The posting unit can analyze the user's social media activity at the time of posting and suggest post content. For example, the posting unit can analyze the user's social media activity at the time of posting and suggest post content. The posting unit can suggest related post content based on the user's posting history, the number of likes, etc. For example, if the user frequently uses a specific hashtag, posts related to the hashtag can be suggested. Furthermore, if the user frequently interacts with a specific friend, posts related to the friend can be suggested. Furthermore, if the user belongs to a specific group, posts related to the group can be suggested. In this way, post content can be suggested based on the user's social media activity. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can suggest post content using an AI model for analyzing the user's social media activity and suggesting post content.

[0051] The confirmation unit can select the optimal confirmation method by referring to the user's past revision history when confirming. For example, the confirmation unit can select the optimal confirmation method by referring to the user's past revision history when confirming. The confirmation unit can prioritize displaying content that the user has frequently revised in the past based on the past revision history. For example, if the user has frequently revised specific content in the past, the confirmation unit can prioritize displaying that content. Furthermore, if the user tends to make revisions during a specific time period, the confirmation unit can prompt the user to confirm during that time period. Furthermore, if the user tends to make revisions during a specific event (e.g., while traveling), the confirmation unit can prompt the user to confirm during that event. This makes it possible to select the optimal confirmation method based on the user's past revision history. Some or all of the above-described processing in the confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the confirmation unit can select the confirmation method using an AI model for selecting the optimal confirmation method by referring to the user's past revision history.

[0052] The confirmation unit can select an appropriate confirmation method based on the user's device information during confirmation. For example, the confirmation unit can select an appropriate confirmation method based on the user's device information during confirmation. The confirmation unit can provide a confirmation method optimized for the device used by the user based on the device information. For example, if the user is using a smartphone, a confirmation method tailored to the screen size can be provided. Also, if the user is using a tablet, a confirmation method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible confirmation method can be provided. This makes it possible to select the optimal confirmation method based on the user's device information. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can select the confirmation method using an AI model for selecting an appropriate confirmation method based on the user's device information.

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

[0054] The generator can add information related to the text to be generated based on the content of the photo. For example, in the case of a landscape photo, the history and tourist information of the place can be added. In addition, in the case of a food photo, the recipe and nutritional information of the food can be added. Furthermore, in the case of a portrait photo, anecdotes and background information related to the person can be added. This makes it possible to generate text that includes a wealth of information related to the photo.

[0055] The posting unit can support two-step authentication when logging in to a user's SNS account. For example, when a user logs in to their SNS account, they are prompted to enter an authentication code sent via SMS or email. If the user uses an authentication app, they can be authenticated through that app. Furthermore, if the user uses biometric authentication, they can be logged in through fingerprint or face authentication. This can strengthen the security of the user's SNS account.

[0056] The posting unit can adjust the privacy settings of posts when a user checks the content of a post. For example, the user can choose whether to make the post public, public only to friends, or public only to a specific group. The posting unit can also set the visibility of the post to a time limit. Furthermore, the posting unit can provide a function to automatically mask personal information and location information included in the post. This allows the user to appropriately manage the privacy of the content of the post.

[0057] When analyzing photos, the analysis unit can improve the accuracy of the analysis by referring to the user's past posts and reactions. For example, the analysis unit can improve the accuracy of analysis of similar photos based on photos posted by the user in the past and the reactions to them. The analysis results can also be adjusted by referring to the user's past popular posts. Furthermore, if the user has used specific tags or keywords in the past, the analysis results can be supplemented based on that information. This allows for more accurate analysis based on the user's past posting history.

[0058] The reception unit can analyze the user's past photo upload history and suggest the optimal upload timing. For example, if the user has tended to upload photos at a specific time in the past, the reception unit can prompt the user to upload at that time. If the user has tended to upload photos during a specific event, the reception unit can prompt the user to upload during that event. If the user has tended to upload photos at a specific location, the reception unit can prompt the user to upload at that location. This makes it possible to suggest the optimal upload timing based on the user's past upload history.

[0059] The reception unit can filter photos based on the user's current activity status and areas of interest when uploading photos. For example, if the user is traveling, the reception unit can suggest uploading only photos related to the travel. If the user is interested in cooking, the reception unit can suggest uploading only photos of cooking. Furthermore, if the user is participating in a sporting event, the reception unit can suggest uploading only photos related to the event. This makes it possible to filter photos based on the user's current activity status and areas of interest.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit uploads photos taken by the user. Photos taken by the user include landscape photos, portrait photos, food photos, etc. The reception unit uploads the photos taken by the user to the service. Step 2: The analysis unit analyzes the uploaded photo. The analysis unit uses image recognition technology to analyze the content of the photo and extract features based on that content. For example, in the case of a landscape photo, features such as mountains and rivers are extracted. Step 3: The generation unit generates text based on the content of the photo analyzed by the analysis unit. The generation unit uses generative AI to generate text based on the content of the photo. For example, in the case of a landscape photo, it generates a text such as "I enjoyed the beautiful mountain scenery," and in the case of a food photo, it generates a text such as "Today's lunch was excellent pasta." Step 4: The posting unit posts the text generated by the generation unit to an SNS or blog. The posting unit logs in to the user's SNS account and posts the generated text and photo. The posting unit can log in to the user's SNS account using OAuth authentication, an API key, etc. The posting unit includes a confirmation unit that allows the user to check the posted content and make corrections as necessary when posting the generated text and photo.

[0062] (Example 2) An automatic text generation system according to an embodiment of the present invention analyzes photos taken by a user and automatically generates and uploads text to social media sites, blogs, and other platforms based on the photos. This automatic text generation system analyzes photos taken by a user, generates text based on the photos, and automatically uploads the text to the social media site or blog. This allows users to effortlessly create text related to the photos and post it to the social media site or blog. For example, a user uploads landscape photos taken on a trip or photos of food at a restaurant to the service. These photos are input into a generation AI. The generation AI then analyzes the input photos. The generation AI understands the content of the photos and generates text based on that content. For example, for a landscape photo, a text such as "I enjoyed the beautiful mountain scenery" is generated. For a food photo, a text such as "Today's lunch was delicious pasta" is generated. The generated text is automatically uploaded to the user's social media site or blog. For example, the user logs in to their social media account and posts the generated text and photo. The user can then review the content and make any necessary edits. This system allows users to effortlessly create text related to photos and post it to social media sites or blogs. For example, by uploading photos taken during a trip and having the AI ​​automatically generate and post text, users can share their travel experiences in real time. Also, by taking photos of food and having the AI ​​automatically generate and post text, users can easily keep a record of their meals. This allows the automatic text generation system to analyze photos taken by users and automatically generate and upload text for social media, blogs, etc. based on the photos.

[0063] The automatic sentence generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a posting unit. The reception unit uploads photos taken by a user. The photos taken by a user include, but are not limited to, landscape photos, portrait photos, and food photos. For example, the reception unit uploads the photos taken by the user to a service. The analysis unit analyzes the uploaded photos. For example, the analysis unit analyzes the content of the photos using image recognition technology. The analysis unit understands the content of the photos and extracts features based on the content. For example, in the case of a landscape photo, the analysis unit extracts features such as mountains and rivers. The generation unit generates sentences based on the content of the photos analyzed by the analysis unit. For example, the generation unit generates sentences based on the content of the photos using a generation AI. The generation AI can use natural language generation technologies such as GPT-4 and Gemini. Based on the content of the photos, the generation unit generates sentences such as "I enjoyed the beautiful mountain scenery" in the case of a landscape photo, and "Today's lunch was delicious pasta" in the case of a food photo. The posting unit posts the text generated by the generation unit to an SNS or blog. The posting unit, for example, logs in to the user's SNS account and posts the generated text and photo. The posting unit can log in to the user's SNS account using OAuth authentication, an API key, or the like. The posting unit includes a confirmation unit that allows the user to check the posted content and correct it as necessary when posting the generated text and photo. As a result, the automatic text generation system according to the embodiment can analyze photos taken by the user and automatically generate and upload text for an SNS, blog, or the like based on the photo.

[0064] The generation unit can analyze the content of a photo using a generation AI and generate a sentence based on that content. For example, the generation unit can analyze the content of a photo using a generation AI and generate a sentence based on that content. The generation AI can use natural language generation technologies such as GPT-4 or Gemini. Based on the content of the photo, the generation unit can generate a sentence such as "I enjoyed the beautiful mountain scenery" for a landscape photo, or "Today's lunch was delicious pasta" for a food photo. This makes it possible to generate a sentence based on the content of a photo using a generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can analyze the content of a photo and generate a sentence using a generation AI model for generating a sentence based on that content.

[0065] The posting unit can log in to the user's SNS account and post the generated text and photos. For example, the posting unit can log in to the user's SNS account and post the generated text and photos. The posting unit can log in to the user's SNS account using OAuth authentication, an API key, or the like. This allows the posting unit to automatically log in to the user's SNS account and post the generated text and photos. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can log in to the user's SNS account and post the generated text and photos using an AI model for posting.

[0066] The posting unit may include a confirmation unit that allows the user to check the posted content and correct it as necessary. The posting unit may include, for example, a confirmation unit that allows the user to check the posted content and correct it as necessary. The confirmation unit may provide, for example, a preview display or an editing function, thereby allowing the user to check the posted content and correct it as necessary. Some or all of the above-described processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit may perform the confirmation and correction using an AI model that allows the user to check the posted content and correct it as necessary.

[0067] The analysis unit can analyze multiple types of photos, such as landscape photos taken at a travel destination or photos of food taken at a restaurant. The analysis unit analyzes various types of photos, such as landscape photos taken at a travel destination or photos of food taken at a restaurant. The analysis unit uses image recognition technology to analyze the content of the photo and extract features based on the content. For example, in the case of a landscape photo, features such as mountains and rivers are extracted. In the case of a food photo, features such as the type of food and presentation are extracted. This makes it possible to analyze various types of photos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may perform analysis using an AI model that analyzes the content of the photo and extracts features based on the content.

[0068] Based on the analyzed content of the photo, the generation unit can generate a sentence such as "I enjoyed the scenery with the mountains in the distance" for a landscape photo, or "Today's lunch was pasta" for a food photo. For example, based on the analyzed content of the photo, the generation unit can generate a sentence such as "I enjoyed the scenery with the beautiful mountains in the distance" for a landscape photo, or "Today's lunch was delicious pasta" for a food photo. The generation unit generates a sentence based on the content of the photo using a generative AI. The generative AI can use natural language generation technologies such as GPT-4 and Gemini. This allows for the generation of specific sentences based on the content of the photo. Some or all of the above-described processing in the generation unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the generation unit can analyze the content of the photo and generate sentences based on that content using a generative AI model.

[0069] The reception unit can estimate a user's emotions and adjust the timing of photo uploads based on the estimated user emotions. For example, the reception unit can estimate a user's emotions and adjust the timing of photo uploads based on the estimated user emotions. The reception unit can estimate a user's emotions using technologies such as facial expression recognition and voice analysis. For example, if a user is excited, the reception unit can prompt the user to upload photos immediately. Also, if a user is relaxed, the reception unit can suggest that the user can postpone uploading photos. Furthermore, if a user is tired, a reminder can be set to upload photos later. This makes it possible to adjust the timing of photo uploads according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can estimate a user's emotions and adjust the upload timing using an AI model for adjusting the timing of photo uploads based on the estimated user emotions.

[0070] The reception unit can analyze the user's past photo upload history and select an appropriate upload method. For example, the reception unit analyzes the user's past photo upload history and selects the optimal upload method. Based on the past upload history, the reception unit can prioritize and suggest upload methods that the user has frequently used in the past. For example, if the user previously only uploaded photos when connected to Wi-Fi, the reception unit can prompt the user to upload photos when connected to Wi-Fi. Furthermore, if the user tends to upload photos during a specific time of day, the reception unit can prompt the user to upload photos during that time of day. Furthermore, if the user tends to upload photos during a specific event (e.g., while traveling), the reception unit can prompt the user to upload photos during that event. This makes it possible to select the optimal upload method based on the user's past photo upload history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past photo upload history and select the upload method using an AI model for selecting the optimal upload method.

[0071] The reception unit can filter photos based on the user's current activity status and areas of interest when uploading photos. For example, the reception unit can filter photos based on the user's current activity status and areas of interest when uploading photos. The reception unit can select photos to upload based on the user's activity status and areas of interest. For example, if the user is traveling, the reception unit can suggest uploading only photos related to the travel. Also, if the user is interested in cooking, the reception unit can suggest uploading only photos of cooking. Furthermore, if the user is participating in a sporting event, the reception unit can suggest uploading only photos related to the event. This allows photos to be filtered based on the user's current activity status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can perform filtering using an AI model for filtering photos based on the user's current activity status and areas of interest.

[0072] The reception unit can estimate the user's emotions and determine the priority of photos to be uploaded based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the priority of photos to be uploaded based on the estimated user emotions. The reception unit can estimate the user's emotions using technologies such as facial expression recognition and voice analysis. For example, if the user is excited, the most recent photos can be uploaded first. Also, if the user is relaxed, older photos can be uploaded first. Furthermore, if the user is tired, less important photos can be postponed. This makes it possible to determine the priority of photos to be uploaded based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can estimate the user's emotions and determine the priority of photos to be uploaded based on the estimated user emotions using an AI model.

[0073] The reception unit can prioritize uploading highly relevant photos based on the user's geographical location information when uploading photos. For example, when uploading photos, the reception unit prioritizes uploading highly relevant photos based on the user's geographical location information. The reception unit can acquire the user's geographical location information using GPS data, location information services, etc. For example, when the user is traveling, photos of the travel destination can be prioritized. Furthermore, when the user is participating in a specific event, photos related to the event can be prioritized. Furthermore, when the user is at home, photos related to daily life can be prioritized. This allows highly relevant photos to be prioritized uploaded based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform uploading using an AI model for prioritized uploading highly relevant photos based on the user's geographical location information.

[0074] The reception unit can analyze the user's social media activity and upload related photos when uploading a photo. For example, the reception unit can analyze the user's social media activity and upload related photos when uploading a photo. The reception unit can select related photos based on the user's posting history, number of likes, etc. For example, if a user frequently uses a specific hashtag, photos related to that hashtag can be preferentially uploaded. Also, if a user frequently interacts with a specific friend, photos related to that friend can be preferentially uploaded. Furthermore, if a user belongs to a specific group, photos related to that group can be preferentially uploaded. This makes it possible to upload related photos based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can analyze the user's social media activity and upload related photos using an AI model.

[0075] The analysis unit can estimate the user's emotions and adjust the photo analysis method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the photo analysis method based on the estimated user emotions. The analysis unit can estimate the user's emotions using technologies such as facial expression recognition and voice analysis. For example, if the user is excited, a detailed analysis can be performed to generate sentences that match the emotions. If the user is relaxed, a concise analysis can be performed to generate sentences with a relaxed atmosphere. Furthermore, if the user is tired, a simple analysis can be performed to generate short sentences. This allows the photo analysis method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotions and adjust the analysis method using an AI model for adjusting the photo analysis method based on the estimated user emotions.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the photo during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo during analysis. The analysis unit can evaluate the importance of the photo and adjust the level of detail of the analysis based on the evaluation. For example, for photos of important events, a detailed analysis can be performed to generate detailed sentences. For everyday photos, a concise analysis can be performed to generate short sentences. Furthermore, for photos related to a specific theme, a detailed analysis appropriate for that theme can be performed. This makes it possible to adjust the level of detail of the analysis based on the importance of the photo. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model for evaluating the importance of the photo and adjusting the level of detail of the analysis based on the evaluation.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the photo during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the photo during analysis. The analysis unit can classify the category of the photo and apply an appropriate analysis algorithm based on the classification. For example, in the case of a landscape photo, an analysis algorithm specialized for landscapes can be applied. In addition, in the case of a food photo, an analysis algorithm specialized for food can be applied. Furthermore, in the case of a portrait photo, an analysis algorithm specialized for people can be applied. This makes it possible to apply an appropriate analysis algorithm depending on the category of the photo. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can classify the category of the photo and perform the analysis using an AI model for applying an appropriate analysis algorithm based on the classification.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using technologies such as facial expression recognition and voice analysis. For example, if the user is excited, detailed analysis results can be displayed. If the user is relaxed, concise analysis results can be displayed. Furthermore, if the user is tired, only the main points can be displayed. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotions and adjust the display method using an AI model for adjusting the display method of the analysis results based on the estimated user emotions.

[0079] The analysis unit can determine the analysis priority based on when the photos were taken during analysis. For example, the analysis unit can determine the analysis priority based on when the photos were taken during analysis. The analysis unit can evaluate when the photos were taken and determine the analysis priority based on the evaluation. For example, the analysis unit can prioritize analysis of recently taken photos. Furthermore, it can prioritize analysis of photos taken during a specific event period. Furthermore, it can prioritize analysis of photos taken within a period specified by the user. This makes it possible to determine the analysis priority based on when the photos were taken. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can evaluate when the photos were taken and determine the analysis priority based on the evaluation using an AI model.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the photos during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the photos during analysis. The analysis unit can evaluate the relevance of the photos and adjust the order of analysis based on the evaluation. For example, photos taken at the same event can be analyzed together. Also, photos taken at the same location can be analyzed together. Furthermore, photos related to the same theme can be analyzed together. This makes it possible to adjust the order of analysis based on the relevance of the photos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the relevance of the photos and adjust the order of analysis based on the evaluation using an AI model.

[0081] The generation unit can estimate the user's emotion and adjust the expression style of the generated sentence based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the expression style of the generated sentence based on the estimated user's emotion. The generation unit can estimate the user's emotion using technologies such as facial expression recognition and voice analysis. For example, if the user is excited, an expressive expression can be used. If the user is relaxed, a calm expression can be used. Furthermore, if the user is tired, a concise and simple expression can be used. This allows the expression style of the generated sentence to be adjusted based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can estimate the user's emotion and adjust the expression style using an AI model for adjusting the expression style of the generated sentence based on the estimated user's emotion.

[0082] The generation unit can adjust the level of detail of the sentence based on the content of the photo when generating the sentence. For example, the generation unit can adjust the level of detail of the sentence based on the content of the photo when generating the sentence. The generation unit can evaluate the content of the photo and adjust the level of detail of the sentence based on the evaluation. For example, in the case of a landscape photo, a sentence including a detailed description of the landscape can be generated. Also, in the case of a food photo, a sentence including a detailed description of the food can be generated. Furthermore, in the case of a portrait photo, a sentence including a detailed description of the person can be generated. This makes it possible to adjust the level of detail of the sentence based on the content of the photo. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model for evaluating the content of the photo and adjusting the level of detail of the sentence based on the evaluation.

[0083] The generation unit can apply different generation algorithms depending on the category of the photo when generating a sentence. For example, the generation unit can apply different generation algorithms depending on the category of the photo when generating a sentence. The generation unit can classify the category of the photo and apply an appropriate generation algorithm based on the classification. For example, in the case of a landscape photo, a generation algorithm specialized for landscapes can be applied. In addition, in the case of a food photo, a generation algorithm specialized for food can be applied. Furthermore, in the case of a portrait photo, a generation algorithm specialized for people can be applied. This makes it possible to apply an appropriate generation algorithm depending on the category of the photo. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can perform generation using a generation AI model that classifies the category of the photo and applies an appropriate generation algorithm based on the classification.

[0084] The generation unit can estimate the user's emotion and adjust the length of the generated sentence based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the generated sentence based on the estimated user's emotion. The generation unit can estimate the user's emotion using technologies such as facial expression recognition and voice analysis. For example, if the user is excited, the generation unit can generate longer sentences. Also, if the user is relaxed, the generation unit can generate sentences of appropriate length. Furthermore, if the user is tired, the generation unit can generate shorter sentences. This allows the length of the generated sentences to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can estimate the user's emotion and adjust the length of the generated sentence based on the estimated user's emotion using a generation AI model.

[0085] The generation unit can determine the priority of sentences based on the time when the photos were taken when generating the sentences. For example, the generation unit can determine the priority of sentences based on the time when the photos were taken when generating the sentences. The generation unit can evaluate the time when the photos were taken and determine the priority of sentences based on the evaluation. For example, the generation unit can preferentially generate sentences based on recently taken photos. Also, the generation unit can preferentially generate sentences based on photos taken during a specific event period. Furthermore, the generation unit can preferentially generate sentences based on photos taken within a period specified by the user. This makes it possible to determine the priority of sentences based on the time when the photos were taken. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority using a generation AI model for evaluating the time when the photos were taken and determining the priority of sentences based on the evaluation.

[0086] The generation unit can adjust the order of sentences based on the relevance of photos when generating sentences. For example, the generation unit can adjust the order of sentences based on the relevance of photos when generating sentences. The generation unit can evaluate the relevance of photos and adjust the order of sentences based on the evaluation. For example, sentences can be generated collectively based on photos taken at the same event. Also, sentences can be generated collectively based on photos taken at the same location. Furthermore, sentences can be generated collectively based on photos related to the same theme. This makes it possible to adjust the order of sentences based on the relevance of photos. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order using a generative AI model for evaluating the relevance of photos and adjusting the order of sentences based on the evaluation.

[0087] The posting unit can estimate a user's emotions and adjust the timing of posting based on the estimated user emotions. For example, the posting unit can estimate a user's emotions and adjust the timing of posting based on the estimated user emotions. The posting unit can estimate a user's emotions using technologies such as facial expression recognition and voice analysis. For example, if a user is excited, the posting unit can prompt the user to post immediately. Furthermore, if the user is relaxed, the posting unit can suggest that the user can postpone posting. Furthermore, if the user is tired, a reminder can be set to post later. This allows the timing of posting to be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the posting unit can be performed using, for example, AI, or without AI. For example, the posting unit can estimate a user's emotions and adjust the timing of posting based on the estimated user emotions using an AI model.

[0088] The posting unit can select an appropriate posting method by referring to the user's past posting history when posting. For example, the posting unit can select the optimal posting method by referring to the user's past posting history when posting. The posting unit can preferentially suggest posting methods that the user has frequently used in the past based on the past posting history. For example, if the user has frequently used a specific social networking platform in the past, the posting unit can encourage the user to post on that platform. Also, if the user tends to post during a specific time of day, the posting unit can encourage the user to post during that time of day. Furthermore, if the user tends to post during a specific event (e.g., while traveling), the posting unit can encourage the user to post during that event. This makes it possible to select the optimal posting method based on the user's past posting history. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can select the posting method using an AI model that selects the optimal posting method by referring to the user's past posting history.

[0089] The posting unit can customize the posted content based on the user's current activity status when posting. For example, the posting unit customizes the posted content based on the user's current activity status when posting. The posting unit can select the posted content based on the user's activity status. For example, if the user is traveling, the posting unit can suggest that the user post content related to the travel. Also, if the user is cooking, the posting unit can suggest that the user post content related to cooking. Furthermore, if the user is participating in a sporting event, the posting unit can suggest that the user post content related to the event. This makes it possible to customize the posted content based on the user's current activity status. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can customize the posted content using an AI model for customizing the posted content based on the user's current activity status.

[0090] The posting unit can estimate a user's emotions and determine the priority of posts based on the estimated user emotions. The posting unit, for example, estimates a user's emotions and determines the priority of posts based on the estimated user emotions. The posting unit can estimate a user's emotions using technologies such as facial expression recognition and voice analysis. For example, if a user is excited, the latest post can be prioritized. Also, if a user is relaxed, older posts can be prioritized. Furthermore, if a user is tired, less important posts can be postponed. This allows the priority of posts to be determined based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can estimate a user's emotions and determine the priority of posts based on the estimated user emotions using an AI model.

[0091] The posting unit can select the optimal posting method by taking into account the user's geographical location information when posting. For example, the posting unit can select the optimal posting method by taking into account the user's geographical location information when posting. The posting unit can acquire the user's geographical location information by using GPS data, location information services, etc. For example, if the user is traveling, posts including information about the travel destination can be suggested. Furthermore, if the user is participating in a specific event, posts related to the event can be suggested. Furthermore, if the user is at home, posts related to daily life can be suggested. This makes it possible to select the optimal posting method based on the user's geographical location information. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can select the posting method by using an AI model for selecting the optimal posting method by taking into account the user's geographical location information.

[0092] The posting unit can analyze the user's social media activity at the time of posting and suggest post content. For example, the posting unit can analyze the user's social media activity at the time of posting and suggest post content. The posting unit can suggest related post content based on the user's posting history, the number of likes, etc. For example, if the user frequently uses a specific hashtag, posts related to the hashtag can be suggested. Furthermore, if the user frequently interacts with a specific friend, posts related to the friend can be suggested. Furthermore, if the user belongs to a specific group, posts related to the group can be suggested. In this way, post content can be suggested based on the user's social media activity. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can suggest post content using an AI model for analyzing the user's social media activity and suggesting post content.

[0093] The confirmation unit can estimate the user's emotion and adjust the confirmation display method based on the estimated user's emotion. For example, the confirmation unit can estimate the user's emotion and adjust the confirmation display method based on the estimated user's emotion. The confirmation unit can estimate the user's emotion using technologies such as facial expression recognition and voice analysis. For example, if the user is excited, a detailed confirmation screen can be displayed. If the user is relaxed, a concise confirmation screen can be displayed. Furthermore, if the user is tired, only the main points can be displayed. This allows the confirmation display method to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the confirmation unit can be performed using, for example, AI, or without AI. For example, the confirmation unit can estimate the user's emotion and adjust the display method using an AI model for adjusting the confirmation display method based on the estimated user's emotion.

[0094] The confirmation unit can select the optimal confirmation method by referring to the user's past revision history when confirming. For example, the confirmation unit can select the optimal confirmation method by referring to the user's past revision history when confirming. The confirmation unit can prioritize displaying content that the user has frequently revised in the past based on the past revision history. For example, if the user has frequently revised specific content in the past, the confirmation unit can prioritize displaying that content. Furthermore, if the user tends to make revisions during a specific time period, the confirmation unit can prompt the user to confirm during that time period. Furthermore, if the user tends to make revisions during a specific event (e.g., while traveling), the confirmation unit can prompt the user to confirm during that event. This makes it possible to select the optimal confirmation method based on the user's past revision history. Some or all of the above-described processing in the confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the confirmation unit can select the confirmation method using an AI model for selecting the optimal confirmation method by referring to the user's past revision history.

[0095] The confirmation unit can estimate the user's emotions and determine the priority of confirmations based on the estimated user emotions. For example, the confirmation unit can estimate the user's emotions and determine the priority of confirmations based on the estimated user emotions. The confirmation unit can estimate the user's emotions using technologies such as facial expression recognition and voice analysis. For example, if the user is excited, the most recent confirmation can be prioritized. Also, if the user is relaxed, older confirmations can be prioritized. Furthermore, if the user is tired, less important confirmations can be postponed. This allows the priority of confirmations to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can estimate the user's emotions and determine the priority of confirmations based on the estimated user emotions using an AI model.

[0096] The confirmation unit can select an appropriate confirmation method based on the user's device information during confirmation. For example, the confirmation unit can select an appropriate confirmation method based on the user's device information during confirmation. The confirmation unit can provide a confirmation method optimized for the device used by the user based on the device information. For example, if the user is using a smartphone, a confirmation method tailored to the screen size can be provided. Also, if the user is using a tablet, a confirmation method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible confirmation method can be provided. This makes it possible to select the optimal confirmation method based on the user's device information. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can select the confirmation method using an AI model for selecting an appropriate confirmation method based on the user's device information. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and posting unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and uploads photos taken by the user to the service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates text based on the analyzed content of the photo. The posting unit is realized by the control unit 46A of the smart device 14 and posts the generated text and photos to an SNS or blog. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and posting unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and uploads photos taken by the user to a service. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text based on the analyzed content of the photo. The posting unit is realized, for example, by the control unit 46A of the smart glasses 214 and posts the generated text and photos to an SNS or blog. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and posting unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and uploads photos taken by the user to the service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates text based on the analyzed content of the photo. The posting unit is realized by the control unit 46A of the headset type terminal 314 and posts the generated text and photos to an SNS or blog. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and posting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and uploads photos taken by the user to the service. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text based on the analyzed content of the photo. The posting unit is realized, for example, by the control unit 46A of the robot 414 and posts the generated text and photos to an SNS or blog.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the user's interests and concerns based on the content of the photos and adjust the tone and style of the generated text based on the estimation results. For example, if the user takes many landscape photos, it can estimate that the user has an interest in nature and travel, and reflect that interest in the generated text. Also, if the user takes many photos of food, it can estimate that the user has an interest in cooking and gourmet food, and reflect that interest in the generated text. Furthermore, if the user takes many photos of sporting events, it can estimate that the user has an interest in sports, and reflect that interest in the generated text. This makes it possible to generate more personalized text based on the user's interests and concerns.

[0099] The generator can add information related to the text to be generated based on the content of the photo. For example, in the case of a landscape photo, the history and tourist information of the place can be added. In addition, in the case of a food photo, the recipe and nutritional information of the food can be added. Furthermore, in the case of a portrait photo, anecdotes and background information related to the person can be added. This makes it possible to generate text that includes a wealth of information related to the photo.

[0100] The posting unit can support two-step authentication when logging in to a user's SNS account. For example, when a user logs in to their SNS account, they are prompted to enter an authentication code sent via SMS or email. If the user uses an authentication app, they can be authenticated through that app. Furthermore, if the user uses biometric authentication, they can be logged in through fingerprint or face authentication. This can strengthen the security of the user's SNS account.

[0101] The posting unit can adjust the privacy settings of posts when a user checks the content of a post. For example, the user can choose whether to make the post public, public only to friends, or public only to a specific group. The posting unit can also set the visibility of the post to a time limit. Furthermore, the posting unit can provide a function to automatically mask personal information and location information included in the post. This allows the user to appropriately manage the privacy of the content of the post.

[0102] When analyzing photos, the analysis unit can improve the accuracy of the analysis by referring to the user's past posts and reactions. For example, the analysis unit can improve the accuracy of analysis of similar photos based on photos posted by the user in the past and the reactions to them. The analysis results can also be adjusted by referring to the user's past popular posts. Furthermore, if the user has used specific tags or keywords in the past, the analysis results can be supplemented based on that information. This allows for more accurate analysis based on the user's past posting history.

[0103] The generation unit can estimate the user's emotions and adjust the tempo and rhythm of the generated sentences based on the estimated user emotions. For example, if the user is excited, sentences with a fast tempo can be generated. If the user is relaxed, sentences with a slow rhythm can be generated. Furthermore, if the user is tired, sentences that are concise and easy to read can be generated. This makes it possible to adjust the tempo and rhythm of the sentences according to the user's emotions.

[0104] The reception unit can estimate the user's emotions and suggest a photo uploading method based on the estimated user emotions. For example, if the user is excited, the reception unit can prompt the user to upload photos immediately. If the user is relaxed, the reception unit can suggest uploading later. Furthermore, if the user is tired, the reception unit can suggest a method that allows the user to upload photos with simple operations. In this way, the reception unit can suggest the optimal uploading method according to the user's emotions.

[0105] The reception unit can analyze the user's past photo upload history and suggest the optimal upload timing. For example, if the user has tended to upload photos at a specific time in the past, the reception unit can prompt the user to upload at that time. If the user has tended to upload photos during a specific event, the reception unit can prompt the user to upload during that event. If the user has tended to upload photos at a specific location, the reception unit can prompt the user to upload at that location. This makes it possible to suggest the optimal upload timing based on the user's past upload history.

[0106] The reception unit can filter photos based on the user's current activity status and areas of interest when uploading photos. For example, if the user is traveling, the reception unit can suggest uploading only photos related to the travel. If the user is interested in cooking, the reception unit can suggest uploading only photos of cooking. Furthermore, if the user is participating in a sporting event, the reception unit can suggest uploading only photos related to the event. This makes it possible to filter photos based on the user's current activity status and areas of interest.

[0107] The reception unit can estimate the user's emotions and determine the priority of photos to be uploaded based on the estimated user's emotions. For example, if the user is excited, the most recent photos can be uploaded with priority. Also, if the user is relaxed, older photos can be uploaded with priority. Furthermore, if the user is tired, photos of lower importance can be postponed. In this way, the priority of photos to be uploaded can be determined based on the user's emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit uploads photos taken by the user. Photos taken by the user include landscape photos, portrait photos, food photos, etc. The reception unit uploads the photos taken by the user to the service. Step 2: The analysis unit analyzes the uploaded photo. The analysis unit uses image recognition technology to analyze the content of the photo and extract features based on that content. For example, in the case of a landscape photo, features such as mountains and rivers are extracted. Step 3: The generation unit generates text based on the content of the photo analyzed by the analysis unit. The generation unit uses generative AI to generate text based on the content of the photo. For example, in the case of a landscape photo, it generates a text such as "I enjoyed the beautiful mountain scenery," and in the case of a food photo, it generates a text such as "Today's lunch was excellent pasta." Step 4: The posting unit posts the text generated by the generation unit to an SNS or blog. The posting unit logs in to the user's SNS account and posts the generated text and photo. The posting unit can log in to the user's SNS account using OAuth authentication, an API key, etc. The posting unit includes a confirmation unit that allows the user to check the posted content and make corrections as necessary when posting the generated text and photo.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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, in order to avoid confusion and to 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where you can upload photos, an analysis unit that analyzes the photo uploaded by the reception unit; a generation unit that generates a sentence based on the content of the photograph analyzed by the analysis unit; a posting unit that posts the text generated by the generation unit to an SNS or a blog; Equipped with A system characterized by:

2. The generation unit Generative AI analyzes the content of a photo and generates text based on that content.

2. The system of claim 1.

3. The posting unit: Log in to the user's SNS account and post the generated text and photo.

2. The system of claim 1.

4. The posting unit: A confirmation section is provided that allows users to check the content of their posts and make corrections as necessary.

2. The system of claim 1.

5. The analysis unit Analyze multiple types of photos, such as landscape photos taken on a trip or food photos taken at a restaurant 2. The system of claim 1.

6. The reception unit Estimate user emotions and adjust the timing of photo uploads based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past photo upload history and select the appropriate upload method 2. The system of claim 1.

Images