system

Abstract

The system according to the embodiment aims to quickly generate optimal websites, advertisements, and apps based on photos and descriptions. [Solution] A system according to an embodiment includes an upload unit, an analysis unit, a generation unit, and a provision unit. The upload unit uploads photos. The analysis unit analyzes the photos and descriptions uploaded by the upload unit. The generation unit generates an appropriate website, advertisement, or app based on the results of the analysis by the analysis unit. The provision unit provides the content generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to quickly generate optimal websites, advertisements, and apps based on photos and descriptions.

[0005] The system according to the embodiment aims to quickly generate optimal websites, advertisements, and apps based on photos and descriptions. [Means for solving the problem]

[0006] The system according to the embodiment includes an upload unit, an analysis unit, a generation unit, and a provision unit. The upload unit uploads photos. The analysis unit analyzes the photos and descriptions uploaded by the upload unit. The generation unit generates an appropriate website, advertisement, or app based on the results of the analysis by the analysis unit. The provision unit provides the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly generate optimal websites, advertisements, and apps based on photos and descriptions. [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) In an embodiment of the present invention, an automatic content generation system automatically creates optimal websites, advertisements, apps, etc. for target customers, such as tourists and foreigners, simply by uploading multiple photos taken in the area and writing descriptions. This automatic content generation system operates by allowing users to upload multiple photos taken in the area and write descriptions for each photo. AI then analyzes the uploaded photos and descriptions to automatically create optimal websites, advertisements, apps, etc. for target customers, such as tourists and foreigners. This effectively promotes local culture and the appeal of local businesses. For example, a user uploads multiple photos taken in the area and writes descriptions for each photo. The user is required to provide detailed descriptions of the content and characteristics of the photos. For example, specific information such as scenery from tourist destinations and products from local businesses can be provided. Next, AI analyzes the uploaded photos and descriptions. The AI ​​uses image recognition technology to analyze the content of the photos and compare them with the descriptions to generate optimal content. For example, for a landscape photo of a tourist destination, a description emphasizing the destination's appeal can be used to generate a tourist-oriented website or advertisement. The generated content is then provided to target customers, such as tourists and foreigners. For example, the generated websites and advertisements can be provided in a format that is easy for tourists to access, effectively promoting the attractions of tourist destinations and the products of local businesses. This system can effectively promote the appeal of local culture and local businesses, and is expected to attract tourists and foreign visitors. In this way, the automatic content generation system can effectively promote the appeal of local culture and local businesses, and encourage the attraction of tourists and foreign visitors.

[0029] An automatic content generation system according to an embodiment includes an uploading unit, an analyzing unit, a generating unit, and a providing unit. The uploading unit has a function for uploading multiple photos taken by a user in a local area. The user can upload photos of, for example, scenery from tourist spots or products from local businesses. The uploading unit provides, for example, a drag-and-drop function and a file selection function, enabling the user to easily upload photos. The uploading unit also provides a function for entering captions for uploaded photos. The user is required to enter detailed information about the content and characteristics of the photos. For example, the user can enter information about the history of the tourist spot or product descriptions from local businesses. The analysis unit has a function for analyzing the photos and captions uploaded by the uploading unit. The analysis unit analyzes the content of the photos using image recognition technology and compares them with the captions to generate optimal content. For example, the analysis unit analyzes the content of the photos using deep learning technology to recognize scenery from tourist spots or products from local businesses. The analysis unit also analyzes the captions using natural language processing technology to extract information that matches the content of the photos. For example, the analysis unit uses keyword extraction technology to extract important information from the description and compare it with the content of the photo. The generation unit has a function to generate optimal websites, advertisements, and apps based on the results of the analysis by the analysis unit. The generation unit generates content tailored to user needs and target audiences, for example, to generate content optimal for tourists and foreigners. For example, the generation unit generates websites and advertisements that highlight the attractions of tourist destinations. The generation unit can also generate apps that effectively promote the products of local businesses. The generation unit generates content using a generation AI. The generation AI generates content tailored to user needs using text generation AI (e.g., LLM) or multimodal generation AI. The provision unit has a function to provide the content generated by the generation unit. For example, the provision unit provides the generated website or advertisement to the user. The provision unit has a function to publish the generated content on a website. The provision unit also has a function to send the generated content by email. The provision unit also has a function to distribute the generated app in an app store.This allows users to effectively provide the generated content to target audiences such as tourists, foreigners, etc. As a result, the automatic content generation system according to the embodiment can automatically generate and provide optimal websites, advertisements, and apps simply by users uploading photos and entering descriptions.

[0030] The analysis unit may include an image analysis unit that analyzes the content of a photo using image recognition technology. The image analysis unit analyzes the content of a photo using, for example, deep learning technology. For example, the image analysis unit analyzes landscape photos of tourist spots and extracts the features of the location. The image analysis unit can also analyze product photos of local businesses and recognize the features of the products. For example, the image analysis unit recognizes the shape, color, logo, etc. of the product and extracts the product features. The image analysis unit can also recognize a person in a photo and analyze the attributes of the person. For example, the image analysis unit analyzes the age, gender, facial expression, etc. of the person in the photo to identify the person's attributes. In this way, the content of a photo can be accurately analyzed using image recognition technology.

[0031] The analysis unit may include a text analysis unit that analyzes the description. The text analysis unit analyzes the description using, for example, natural language processing technology. For example, the text analysis unit extracts keywords from the description and compares them with the content of the photo. The text analysis unit can also analyze the grammar and structure of the description to extract important information. For example, the text analysis unit analyzes the subject, predicate, object, etc. in the description to understand the meaning of the sentence. The text analysis unit can also analyze the emotional nuances of the description. For example, the text analysis unit analyzes emotional expressions in the description to identify the emotional tone of the sentence. In this way, by analyzing the description, it is possible to generate optimal content by comparing the content of the photo with the description.

[0032] The generation unit can generate websites, advertisements, and apps that are appropriate for tourists or foreigners. The generation unit, for example, generates websites aimed at tourists. For example, the generation unit generates websites that are easy for tourists to access based on photos and descriptions that highlight the attractions of tourist destinations. The generation unit can also generate advertisements aimed at foreigners. For example, the generation unit generates advertisements that correspond to languages ​​and cultures that are easy for foreigners to understand. The generation unit can also generate apps aimed at tourists and foreigners. For example, the generation unit generates apps that provide information on tourist destinations and product information from local businesses. This makes it possible to provide optimal content to tourists and foreigners.

[0033] The providing unit can provide the generated content to the user. The providing unit, for example, provides the generated website to the user. For example, the providing unit publishes the generated website on the Internet so that the user can access it. The providing unit can also provide the generated advertisement to the user. For example, the providing unit sends the generated advertisement by email so that the user can receive it. The providing unit can also provide the generated app to the user. For example, the providing unit distributes the generated app in an app store so that the user can download it. In this way, providing the generated content to the user can be expected to attract tourists, foreigners, and other customers.

[0034] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, the upload unit stores the user's past upload history in a database and analyzes it. For example, the upload unit analyzes the types and contents of photos the user has uploaded in the past and suggests the optimal upload method. The upload unit can also preferentially suggest upload methods that the user has frequently used in the past. For example, if the user has frequently used the drag-and-drop function in the past, the upload unit can preferentially suggest the drag-and-drop function. The upload unit can also analyze the user's tendency to upload at specific time periods from the user's past upload history and suggest the optimal timing. For example, if the user has uploaded photos at specific time periods in the past, the upload unit can suggest uploading at those time periods. In this way, the optimal upload method can be suggested based on the user's past upload history.

[0035] The uploading unit can filter photos based on the user's current projects and areas of interest when uploading them. For example, the uploading unit stores the user's current projects and areas of interest in a database and filters the photos when uploading them. For example, the uploading unit filters the photos so that the user uploads only photos related to the project currently in progress. The uploading unit can also prioritize uploading highly relevant photos based on the user's areas of interest. For example, if the user is interested in a particular theme, the uploading unit filters the photos so that only photos related to that theme are uploaded. This allows photos related to the user's current projects and areas of interest to be uploaded with priority.

[0036] When uploading photos, the uploading unit can prioritize uploading highly relevant photos in consideration of the user's geographical location information. The uploading unit acquires the user's geographical location information using, for example, GPS data. For example, the uploading unit prioritizes uploading photos related to the user's current location. The uploading unit can also prioritize uploading photos related to places the user has visited in the past. For example, the uploading unit prioritizes uploading photos of tourist spots the user has visited in the past. The uploading unit can also prioritize uploading photos related to places the user plans to visit in the future. For example, the uploading unit prioritizes uploading photos of tourist spots the user plans to visit in the future. This makes it possible to prioritize uploading highly relevant photos based on the user's geographical location information.

[0037] The uploading unit may analyze the user's social media activity and upload related photos when uploading photos. For example, the uploading unit stores and analyzes the user's social media activity in a database. For example, the uploading unit may prioritize uploading photos recently shared by the user on social media. The uploading unit may also prioritize uploading photos that have received many likes and comments on social media. For example, the uploading unit may prioritize uploading photos that have received many responses on social media. The uploading unit may also prioritize uploading photos related to accounts that the user follows on social media. For example, the uploading unit may prioritize uploading photos of tourist spots or local businesses that the user follows. This allows photos related to the user's social media activity to be prioritized.

[0038] The image analysis unit can adjust the level of detail of the analysis based on the importance of the photo during image analysis. The image analysis unit, for example, stores the importance of the photo in a database and refers to it during analysis. For example, the image analysis unit performs detailed image analysis on photos that the user has rated as important. The image analysis unit can also perform standard image analysis on general photos. For example, the image analysis unit performs standard analysis on photos that the user has not rated as particularly important. The image analysis unit can also perform simplified image analysis on photos with low importance. For example, the image analysis unit performs simplified analysis on photos that the user has rated as low importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the photo.

[0039] The image analysis unit can apply different analysis algorithms depending on the category of the photo during image analysis. For example, the image analysis unit stores the photo category in a database and refers to it during analysis. For example, the image analysis unit applies a landscape analysis algorithm to a landscape photo. The image analysis unit can also apply a face recognition algorithm to a portrait photo. For example, the image analysis unit performs analysis using face recognition technology on a portrait photo. The image analysis unit can also apply a product recognition algorithm to a product photo. For example, the image analysis unit applies an algorithm to a product photo that recognizes the shape, color, logo, etc. of the product. This makes it possible to apply the optimal analysis algorithm depending on the photo category.

[0040] During image analysis, the image analysis unit can determine the priority of analysis based on the time the photo was taken. For example, the image analysis unit stores the time the photo was taken in a database and refers to it during analysis. For example, the image analysis unit prioritizes analyzing recently taken photos. The image analysis unit can also prioritize analyzing photos taken during a specific event period. For example, the image analysis unit prioritizes analyzing photos taken during a specific event period. The image analysis unit can also prioritize analyzing photos taken by season. For example, the image analysis unit prioritizes analyzing photos taken by season. This makes it possible to determine the priority of analysis based on the time the photo was taken.

[0041] The image analysis unit can adjust the order of analysis based on the relevance of the photos during image analysis. The image analysis unit, for example, stores the relevance of the photos in a database and refers to it during analysis. For example, the image analysis unit analyzes photos taken in the same location together. The image analysis unit can also analyze photos related to the same theme together. For example, the image analysis unit analyzes photos related to the same theme together. The image analysis unit can also analyze photos related to the same event together. For example, the image analysis unit analyzes photos related to the same event together. This makes it possible to adjust the order of analysis based on the relevance of the photos.

[0042] The text analysis unit can adjust the level of detail of the analysis based on the importance of the description during text analysis. The text analysis unit, for example, stores the importance of the description in a database and refers to it during analysis. For example, the text analysis unit performs detailed text analysis on a description that the user has rated as important. The text analysis unit can also perform standard text analysis on general descriptions. For example, the text analysis unit performs standard analysis on a description that the user has not rated as particularly important. The text analysis unit can also perform simplified text analysis on a description that has a low level of importance. For example, the text analysis unit performs simplified analysis on a description that the user has rated as low in importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the description.

[0043] The text analysis unit can apply different analysis algorithms depending on the category of the description when analyzing the text. For example, the text analysis unit stores the category of the description in a database and refers to it during analysis. For example, the text analysis unit applies a tourism analysis algorithm to a description of a tourist attraction. The text analysis unit can also apply a product analysis algorithm to a product description. For example, the text analysis unit applies an algorithm that analyzes product features to a product description. The text analysis unit can also apply an event analysis algorithm to an event description. For example, the text analysis unit applies an algorithm that analyzes event details to an event description. This makes it possible to apply the optimal analysis algorithm depending on the category of the description.

[0044] During text analysis, the text analysis unit can determine the priority of analysis based on the time when the description was created. For example, the text analysis unit stores the time when the description was created in a database and refers to it during analysis. For example, the text analysis unit prioritizes analyzing recently created descriptions. The text analysis unit can also prioritize analyzing descriptions created during a specific event period. For example, the text analysis unit prioritizes analyzing descriptions created during a specific event period. The text analysis unit can also prioritize analyzing seasonal descriptions. For example, the text analysis unit prioritizes analyzing descriptions created for each season. This makes it possible to determine the priority of analysis based on the time when the description was created.

[0045] The text analysis unit can adjust the order of analysis based on the relevance of the explanatory text during text analysis. The text analysis unit, for example, stores the relevance of the explanatory text in a database and refers to it during analysis. For example, the text analysis unit collectively analyzes explanatory text related to the same location. The text analysis unit can also collectively analyze explanatory text related to the same theme. For example, the text analysis unit collectively analyzes explanatory text related to the same theme. The text analysis unit can also collectively analyze explanatory text related to the same event. For example, the text analysis unit collectively analyzes explanatory text related to the same event. This makes it possible to adjust the order of analysis based on the relevance of the explanatory text.

[0046] The generation unit can adjust the level of detail of the content generated based on the importance of the photo and the description when generating the content. For example, the generation unit stores the importance of the photo and the description in a database and refers to it when generating the content. For example, the generation unit generates detailed content for a photo and a description that the user has rated as important. The generation unit can also generate standard content for a general photo and description. For example, the generation unit generates standard content for a photo and a description that the user has not rated as particularly important. The generation unit can also generate simplified content for a photo and a description that has a low importance. For example, the generation unit generates simplified content for a photo and a description that the user has rated as low importance. This makes it possible to adjust the level of detail of the content generated according to the importance of the photo and the description.

[0047] The generation unit can apply different generation algorithms depending on the attributes of the target customers when generating content. For example, the generation unit stores the attributes of the target customers in a database and refers to them when generating content. For example, the generation unit applies a tourism analysis algorithm to content aimed at tourists. The generation unit can also apply a multilingual generation algorithm to content aimed at foreigners. For example, the generation unit applies a multilingual generation algorithm to content aimed at foreigners. The generation unit can also apply a region-specific generation algorithm to content aimed at local residents. For example, the generation unit applies a region-specific generation algorithm to content aimed at local residents. This makes it possible to apply the optimal generation algorithm depending on the attributes of the target customers.

[0048] The generation unit can determine a generation priority based on the submission dates of the photos and descriptions when generating content. For example, the generation unit stores the submission dates of the photos and descriptions in a database and refers to them when generating content. For example, the generation unit preferentially reflects recently submitted photos and descriptions in the content. The generation unit can also preferentially reflect photos and descriptions submitted during a specific event period in the content. For example, the generation unit preferentially reflects photos and descriptions submitted during a specific event period in the content. The generation unit can also preferentially reflect photos and descriptions submitted by season in the content. For example, the generation unit preferentially reflects photos and descriptions submitted by season in the content. This makes it possible to determine a generation priority based on the submission dates of the photos and descriptions.

[0049] The generation unit can adjust the order of generation based on the relevance between photos and descriptions when generating content. The generation unit, for example, stores the relevance between photos and descriptions in a database and refers to it when generating content. For example, the generation unit reflects photos and descriptions taken at the same location together in the content. The generation unit can also reflect photos and descriptions related to the same theme together in the content. For example, the generation unit reflects photos and descriptions related to the same theme together in the content. The generation unit can also reflect photos and descriptions related to the same event together in the content. For example, the generation unit reflects photos and descriptions related to the same event together in the content. This makes it possible to adjust the order of generation based on the relevance between photos and descriptions.

[0050] The providing unit can select the optimal display method by referring to the user's past browsing history when providing content. The providing unit, for example, stores the user's past browsing history in a database and refers to it when providing content. For example, the providing unit preferentially provides a display method for content that the user has frequently browsed in the past. The providing unit can also suggest the optimal display method for a specific time period based on the user's past browsing history. For example, the providing unit suggests a display method for content that the user has browsed in a specific time period in the past. The providing unit can also analyze the type and content of content that the user has browsed in the past and suggest the optimal display method. For example, the providing unit suggests the optimal display method based on the type and content of content that the user has browsed in the past. This makes it possible to provide the optimal display method based on the user's past browsing history.

[0051] The providing unit can select the optimal display method by taking into consideration the user's device information when providing content. The providing unit, for example, stores the user's device information in a database and refers to it when providing content. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method that includes detailed information. For example, if the user is using a desktop, the providing unit provides a display method that includes detailed information. This makes it possible to provide the optimal display method based on the user's device information.

[0052] The providing unit can select an optimal display method in consideration of the user's geographical location information when providing content. The providing unit, for example, stores the user's geographical location information in a database and refers to it when providing the content. For example, the providing unit can preferentially display content related to the user's current location. The providing unit can also preferentially display content related to places the user has visited in the past. For example, the providing unit can preferentially display content related to places the user has visited in the past. The providing unit can also preferentially display content related to places the user plans to visit in the future. For example, the providing unit can preferentially display content related to places the user plans to visit in the future. This makes it possible to provide an optimal display method based on the user's geographical location information.

[0053] The providing unit can analyze the user's social media activity and provide relevant content when providing content. For example, the providing unit stores the user's social media activity in a database and refers to it when providing content. For example, the providing unit can prioritize displaying content that the user recently shared on social media. The providing unit can also prioritize displaying content that the user has received many likes and comments on social media. For example, the providing unit can prioritize displaying content that the user has received many responses on social media. The providing unit can also prioritize displaying content related to accounts the user follows on social media. For example, the providing unit can prioritize displaying content related to tourist attractions and local businesses that the user follows. This makes it possible to provide relevant content based on the user's social media activity.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, it can preferentially suggest upload methods that the user has frequently used in the past. It can also analyze the user's tendency to upload at specific time periods in the past and suggest uploading at those time periods. It can also analyze the types and contents of photos that the user has uploaded in the past and suggest the optimal upload method. This makes it possible to suggest the optimal upload method based on the user's past upload history.

[0056] The providing unit can select the optimal display method taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. This makes it possible to provide the optimal display method based on the user's device information.

[0057] The providing unit can analyze the user's social media activity and provide relevant content. For example, the providing unit can prioritize displaying content that the user recently shared on social media. The providing unit can also prioritize displaying content that the user has received many likes or comments on social media. Furthermore, the providing unit can prioritize displaying content related to accounts that the user follows on social media. This makes it possible to provide relevant content based on the user's social media activity.

[0058] When generating content, the generation unit can apply different generation algorithms depending on the attributes of the target customer. For example, a tourism analysis algorithm can be applied to content aimed at tourists. A multilingual generation algorithm can be applied to content aimed at foreigners. Furthermore, a region-specific generation algorithm can be applied to content aimed at local residents. This makes it possible to apply the optimal generation algorithm depending on the attributes of the target customer.

[0059] The analysis unit can determine the priority of analysis based on when the photos were taken. For example, it can prioritize analysis of recently taken photos. It can also prioritize analysis of photos taken during a specific event period. It can also prioritize analysis of photos taken by season. This makes it possible to determine the priority of analysis based on when the photos were taken.

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

[0061] Step 1: The upload unit has a function that allows users to upload multiple photos taken in the area. For example, users can upload photos of scenery from tourist spots or products from local businesses. The upload unit provides, for example, a drag-and-drop function or a file selection function, allowing users to easily upload photos. The upload unit also provides a function for entering descriptions for uploaded photos. Users are required to enter detailed information about the content and characteristics of the photos. For example, users can enter the history of tourist spots or product descriptions of local businesses. Step 2: The analysis unit has the function of analyzing the photos and descriptions uploaded by the upload unit. The analysis unit uses image recognition technology to analyze the content of the photos and compares it with the descriptions to generate optimal content. For example, the analysis unit uses deep learning technology to analyze the content of the photos and recognize scenery from tourist spots or products from local businesses. The analysis unit also uses natural language processing technology to analyze the descriptions and extract information that matches the content of the photos. For example, the analysis unit uses keyword extraction technology to extract important information from the descriptions and compare it with the content of the photos. Step 3: The generation unit has the function of generating optimal websites, advertisements, and apps based on the results of analysis by the analysis unit. The generation unit generates content according to the user's needs and target audience, for example, to generate content optimal for tourists and foreigners. For example, the generation unit generates websites and advertisements that emphasize the attractions of tourist destinations. The generation unit can also generate apps that effectively promote the products of local companies. The generation unit generates content using generation AI. The generation AI generates content according to the user's needs using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit has a function of providing the content generated by the generating unit. The providing unit provides, for example, the generated website or advertisement to the user. The providing unit has a function of publishing the generated content on a website. The providing unit also provides a function of sending the generated content by email. Furthermore, the providing unit has a function of distributing the generated app in an app store. This allows the user to effectively provide the generated content to a target audience, such as tourists and foreigners.

[0062] (Example 2) In an embodiment of the present invention, an automatic content generation system automatically creates optimal websites, advertisements, apps, etc. for target customers, such as tourists and foreigners, simply by uploading multiple photos taken in the area and writing descriptions. This automatic content generation system operates by allowing users to upload multiple photos taken in the area and write descriptions for each photo. AI then analyzes the uploaded photos and descriptions to automatically create optimal websites, advertisements, apps, etc. for target customers, such as tourists and foreigners. This effectively promotes local culture and the appeal of local businesses. For example, a user uploads multiple photos taken in the area and writes descriptions for each photo. The user is required to provide detailed descriptions of the content and characteristics of the photos. For example, specific information such as scenery from tourist destinations and products from local businesses can be provided. Next, AI analyzes the uploaded photos and descriptions. The AI ​​uses image recognition technology to analyze the content of the photos and compare them with the descriptions to generate optimal content. For example, for a landscape photo of a tourist destination, a description emphasizing the destination's appeal can be used to generate a tourist-oriented website or advertisement. The generated content is then provided to target customers, such as tourists and foreigners. For example, the generated websites and advertisements can be provided in a format that is easy for tourists to access, effectively promoting the attractions of tourist destinations and the products of local businesses. This system can effectively promote the appeal of local culture and local businesses, and is expected to attract tourists and foreign visitors. In this way, the automatic content generation system can effectively promote the appeal of local culture and local businesses, and encourage the attraction of tourists and foreign visitors.

[0063] An automatic content generation system according to an embodiment includes an uploading unit, an analyzing unit, a generating unit, and a providing unit. The uploading unit has a function for uploading multiple photos taken by a user in a local area. The user can upload photos of, for example, scenery from tourist spots or products from local businesses. The uploading unit provides, for example, a drag-and-drop function and a file selection function, enabling the user to easily upload photos. The uploading unit also provides a function for entering captions for uploaded photos. The user is required to enter detailed information about the content and characteristics of the photos. For example, the user can enter information about the history of the tourist spot or product descriptions from local businesses. The analysis unit has a function for analyzing the photos and captions uploaded by the uploading unit. The analysis unit analyzes the content of the photos using image recognition technology and compares them with the captions to generate optimal content. For example, the analysis unit analyzes the content of the photos using deep learning technology to recognize scenery from tourist spots or products from local businesses. The analysis unit also analyzes the captions using natural language processing technology to extract information that matches the content of the photos. For example, the analysis unit uses keyword extraction technology to extract important information from the description and compare it with the content of the photo. The generation unit has a function to generate optimal websites, advertisements, and apps based on the results of the analysis by the analysis unit. The generation unit generates content tailored to user needs and target audiences, for example, to generate content optimal for tourists and foreigners. For example, the generation unit generates websites and advertisements that highlight the attractions of tourist destinations. The generation unit can also generate apps that effectively promote the products of local businesses. The generation unit generates content using a generation AI. The generation AI generates content tailored to user needs using text generation AI (e.g., LLM) or multimodal generation AI. The provision unit has a function to provide the content generated by the generation unit. For example, the provision unit provides the generated website or advertisement to the user. The provision unit has a function to publish the generated content on a website. The provision unit also has a function to send the generated content by email. The provision unit also has a function to distribute the generated app in an app store.This allows users to effectively provide the generated content to target audiences such as tourists, foreigners, etc. As a result, the automatic content generation system according to the embodiment can automatically generate and provide optimal websites, advertisements, and apps simply by users uploading photos and entering descriptions.

[0064] The analysis unit may include an image analysis unit that analyzes the content of a photo using image recognition technology. The image analysis unit analyzes the content of a photo using, for example, deep learning technology. For example, the image analysis unit analyzes landscape photos of tourist spots and extracts the features of the location. The image analysis unit can also analyze product photos of local businesses and recognize the features of the products. For example, the image analysis unit recognizes the shape, color, logo, etc. of the product and extracts the product features. The image analysis unit can also recognize a person in a photo and analyze the attributes of the person. For example, the image analysis unit analyzes the age, gender, facial expression, etc. of the person in the photo to identify the person's attributes. In this way, the content of a photo can be accurately analyzed using image recognition technology.

[0065] The analysis unit may include a text analysis unit that analyzes the description. The text analysis unit analyzes the description using, for example, natural language processing technology. For example, the text analysis unit extracts keywords from the description and compares them with the content of the photo. The text analysis unit can also analyze the grammar and structure of the description to extract important information. For example, the text analysis unit analyzes the subject, predicate, object, etc. in the description to understand the meaning of the sentence. The text analysis unit can also analyze the emotional nuances of the description. For example, the text analysis unit analyzes emotional expressions in the description to identify the emotional tone of the sentence. In this way, by analyzing the description, it is possible to generate optimal content by comparing the content of the photo with the description.

[0066] The generation unit can generate websites, advertisements, and apps that are appropriate for tourists or foreigners. The generation unit, for example, generates websites aimed at tourists. For example, the generation unit generates websites that are easy for tourists to access based on photos and descriptions that highlight the attractions of tourist destinations. The generation unit can also generate advertisements aimed at foreigners. For example, the generation unit generates advertisements that correspond to languages ​​and cultures that are easy for foreigners to understand. The generation unit can also generate apps aimed at tourists and foreigners. For example, the generation unit generates apps that provide information on tourist destinations and product information from local businesses. This makes it possible to provide optimal content to tourists and foreigners.

[0067] The providing unit can provide the generated content to the user. The providing unit, for example, provides the generated website to the user. For example, the providing unit publishes the generated website on the Internet so that the user can access it. The providing unit can also provide the generated advertisement to the user. For example, the providing unit sends the generated advertisement by email so that the user can receive it. The providing unit can also provide the generated app to the user. For example, the providing unit distributes the generated app in an app store so that the user can download it. In this way, providing the generated content to the user can be expected to attract tourists, foreigners, and other customers.

[0068] The upload unit can estimate a user's emotions and adjust the timing of photo uploads based on the estimated user emotions. The upload unit can estimate a user's emotions using, for example, facial expression recognition technology. For example, the upload unit can capture a user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The upload unit can also estimate a user's emotions using voice analysis technology. For example, the upload unit can analyze the tone and speed of the user's voice to estimate the emotion. The upload unit can also estimate a user's emotions using text analysis technology. For example, the upload unit can analyze the content of text entered by the user to estimate the emotion. This makes it possible to upload photos at the optimal timing based on the user's emotions. For example, if the user is relaxed, the photo can be uploaded immediately. If the user is busy, the user can set a reminder to upload later. If the user is stressed, the upload can be temporarily postponed and a notification can be sent at an appropriate time. This makes it possible to adjust the timing of photo uploads based on the user's emotions.

[0069] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, the upload unit stores the user's past upload history in a database and analyzes it. For example, the upload unit analyzes the types and contents of photos the user has uploaded in the past and suggests the optimal upload method. The upload unit can also preferentially suggest upload methods that the user has frequently used in the past. For example, if the user has frequently used the drag-and-drop function in the past, the upload unit can preferentially suggest the drag-and-drop function. The upload unit can also analyze the user's tendency to upload at specific time periods from the user's past upload history and suggest the optimal timing. For example, if the user has uploaded photos at specific time periods in the past, the upload unit can suggest uploading at those time periods. In this way, the optimal upload method can be suggested based on the user's past upload history.

[0070] The uploading unit can filter photos based on the user's current projects and areas of interest when uploading them. For example, the uploading unit stores the user's current projects and areas of interest in a database and filters the photos when uploading them. For example, the uploading unit filters the photos so that the user uploads only photos related to the project currently in progress. The uploading unit can also prioritize uploading highly relevant photos based on the user's areas of interest. For example, if the user is interested in a particular theme, the uploading unit filters the photos so that only photos related to that theme are uploaded. This allows photos related to the user's current projects and areas of interest to be uploaded with priority.

[0071] The uploading unit can estimate a user's emotions and determine the priority of photos to be uploaded based on the estimated user's emotions. The uploading unit can estimate a user's emotions using, for example, facial expression recognition technology. For example, the uploading unit can capture a user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The uploading unit can also estimate a user's emotions using voice analysis technology. For example, the uploading unit can analyze the tone and speed of the user's voice to estimate the emotion. The uploading unit can also estimate a user's emotions using text analysis technology. For example, the uploading unit can analyze the content of text entered by the user to estimate the emotion. This makes it possible to prioritize uploading optimal photos according to the user's emotions. For example, if the user is excited, the most recent photos can be prioritized. Alternatively, if the user is relaxed, older photos can be prioritized. Alternatively, if the user is in a hurry, important photos can be prioritized. This makes it possible to prioritize uploading important photos based on the user's emotions.

[0072] When uploading photos, the uploading unit can prioritize uploading highly relevant photos in consideration of the user's geographical location information. The uploading unit acquires the user's geographical location information using, for example, GPS data. For example, the uploading unit prioritizes uploading photos related to the user's current location. The uploading unit can also prioritize uploading photos related to places the user has visited in the past. For example, the uploading unit prioritizes uploading photos of tourist spots the user has visited in the past. The uploading unit can also prioritize uploading photos related to places the user plans to visit in the future. For example, the uploading unit prioritizes uploading photos of tourist spots the user plans to visit in the future. This makes it possible to prioritize uploading highly relevant photos based on the user's geographical location information.

[0073] The uploading unit may analyze the user's social media activity and upload related photos when uploading photos. For example, the uploading unit stores and analyzes the user's social media activity in a database. For example, the uploading unit may prioritize uploading photos recently shared by the user on social media. The uploading unit may also prioritize uploading photos that have received many likes and comments on social media. For example, the uploading unit may prioritize uploading photos that have received many responses on social media. The uploading unit may also prioritize uploading photos related to accounts that the user follows on social media. For example, the uploading unit may prioritize uploading photos of tourist spots or local businesses that the user follows. This allows photos related to the user's social media activity to be prioritized.

[0074] The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated user's emotions. The image analysis unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the image analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The image analysis unit can also estimate the user's emotions using voice analysis technology. For example, the image analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The image analysis unit can also estimate the user's emotions using text analysis technology. For example, the image analysis unit analyzes the content of text entered by the user to estimate the emotions. This makes it possible to adjust the accuracy of the image analysis according to the user's emotions. For example, if the user is relaxed, detailed image analysis can be performed. If the user is in a hurry, simplified image analysis can be performed. If the user is excited, image analysis can be performed that emphasizes visually stimulating elements. This makes it possible to adjust the accuracy of the image analysis based on the user's emotions.

[0075] The image analysis unit can adjust the level of detail of the analysis based on the importance of the photo during image analysis. The image analysis unit, for example, stores the importance of the photo in a database and refers to it during analysis. For example, the image analysis unit performs detailed image analysis on photos that the user has rated as important. The image analysis unit can also perform standard image analysis on general photos. For example, the image analysis unit performs standard analysis on photos that the user has not rated as particularly important. The image analysis unit can also perform simplified image analysis on photos with low importance. For example, the image analysis unit performs simplified analysis on photos that the user has rated as low importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the photo.

[0076] The image analysis unit can apply different analysis algorithms depending on the category of the photo during image analysis. For example, the image analysis unit stores the photo category in a database and refers to it during analysis. For example, the image analysis unit applies a landscape analysis algorithm to a landscape photo. The image analysis unit can also apply a face recognition algorithm to a portrait photo. For example, the image analysis unit performs analysis using face recognition technology on a portrait photo. The image analysis unit can also apply a product recognition algorithm to a product photo. For example, the image analysis unit applies an algorithm to a product photo that recognizes the shape, color, logo, etc. of the product. This makes it possible to apply the optimal analysis algorithm depending on the photo category.

[0077] The image analysis unit can estimate the user's emotions and adjust the order of image analysis based on the estimated user's emotions. The image analysis unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the image analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The image analysis unit can also estimate the user's emotions using voice analysis technology. For example, the image analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The image analysis unit can also estimate the user's emotions using text analysis technology. For example, the image analysis unit analyzes the content of text entered by the user to estimate the emotions. This makes it possible to adjust the order of image analysis based on the user's emotions. For example, if the user is relaxed, the analysis can be performed without worrying about the order. Also, if the user is in a hurry, the analysis can be prioritized starting with important photos. Also, if the user is excited, the analysis can be prioritized starting with visually stimulating photos. This makes it possible to adjust the order of image analysis based on the user's emotions.

[0078] During image analysis, the image analysis unit can determine the priority of analysis based on the time the photo was taken. For example, the image analysis unit stores the time the photo was taken in a database and refers to it during analysis. For example, the image analysis unit prioritizes analyzing recently taken photos. The image analysis unit can also prioritize analyzing photos taken during a specific event period. For example, the image analysis unit prioritizes analyzing photos taken during a specific event period. The image analysis unit can also prioritize analyzing photos taken by season. For example, the image analysis unit prioritizes analyzing photos taken by season. This makes it possible to determine the priority of analysis based on the time the photo was taken.

[0079] The image analysis unit can adjust the order of analysis based on the relevance of the photos during image analysis. The image analysis unit, for example, stores the relevance of the photos in a database and refers to it during analysis. For example, the image analysis unit analyzes photos taken in the same location together. The image analysis unit can also analyze photos related to the same theme together. For example, the image analysis unit analyzes photos related to the same theme together. The image analysis unit can also analyze photos related to the same event together. For example, the image analysis unit analyzes photos related to the same event together. This makes it possible to adjust the order of analysis based on the relevance of the photos.

[0080] The text analysis unit can estimate the user's emotions and adjust the accuracy of the text analysis based on the estimated user's emotions. The text analysis unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the text analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The text analysis unit can also estimate the user's emotions using voice analysis technology. For example, the text analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The text analysis unit can also estimate the user's emotions using text analysis technology. For example, the text analysis unit analyzes the content of text entered by the user to estimate the emotions. This makes it possible to adjust the accuracy of the text analysis according to the user's emotions. For example, if the user is relaxed, detailed text analysis can be performed. If the user is in a hurry, simplified text analysis can be performed. If the user is excited, text analysis that emphasizes emotional elements can be performed. This makes it possible to adjust the accuracy of the text analysis based on the user's emotions.

[0081] The text analysis unit can adjust the level of detail of the analysis based on the importance of the description during text analysis. The text analysis unit, for example, stores the importance of the description in a database and refers to it during analysis. For example, the text analysis unit performs detailed text analysis on a description that the user has rated as important. The text analysis unit can also perform standard text analysis on general descriptions. For example, the text analysis unit performs standard analysis on a description that the user has not rated as particularly important. The text analysis unit can also perform simplified text analysis on a description that has a low level of importance. For example, the text analysis unit performs simplified analysis on a description that the user has rated as low in importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the description.

[0082] The text analysis unit can apply different analysis algorithms depending on the category of the description when analyzing the text. For example, the text analysis unit stores the category of the description in a database and refers to it during analysis. For example, the text analysis unit applies a tourism analysis algorithm to a description of a tourist attraction. The text analysis unit can also apply a product analysis algorithm to a product description. For example, the text analysis unit applies an algorithm that analyzes product features to a product description. The text analysis unit can also apply an event analysis algorithm to an event description. For example, the text analysis unit applies an algorithm that analyzes event details to an event description. This makes it possible to apply the optimal analysis algorithm depending on the category of the description.

[0083] The text analysis unit can estimate the user's emotions and adjust the order of text analysis based on the estimated user's emotions. The text analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the text analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The text analysis unit can also estimate the user's emotions using voice analysis technology. For example, the text analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The text analysis unit can also estimate the user's emotions using text analysis technology. For example, the text analysis unit can analyze the content of text entered by the user to estimate the emotions. This makes it possible to adjust the order of text analysis based on the user's emotions. For example, if the user is relaxed, the analysis can be performed without worrying about the order. Also, if the user is in a hurry, the analysis can be prioritized starting with important explanatory text. Also, if the user is excited, the analysis can be prioritized starting with explanatory text containing emotional elements. This makes it possible to adjust the order of text analysis based on the user's emotions.

[0084] During text analysis, the text analysis unit can determine the priority of analysis based on the time when the description was created. For example, the text analysis unit stores the time when the description was created in a database and refers to it during analysis. For example, the text analysis unit prioritizes analyzing recently created descriptions. The text analysis unit can also prioritize analyzing descriptions created during a specific event period. For example, the text analysis unit prioritizes analyzing descriptions created during a specific event period. The text analysis unit can also prioritize analyzing seasonal descriptions. For example, the text analysis unit prioritizes analyzing descriptions created for each season. This makes it possible to determine the priority of analysis based on the time when the description was created.

[0085] The text analysis unit can adjust the order of analysis based on the relevance of the explanatory text during text analysis. The text analysis unit, for example, stores the relevance of the explanatory text in a database and refers to it during analysis. For example, the text analysis unit collectively analyzes explanatory text related to the same location. The text analysis unit can also collectively analyze explanatory text related to the same theme. For example, the text analysis unit collectively analyzes explanatory text related to the same theme. The text analysis unit can also collectively analyze explanatory text related to the same event. For example, the text analysis unit collectively analyzes explanatory text related to the same event. This makes it possible to adjust the order of analysis based on the relevance of the explanatory text.

[0086] The generation unit can estimate the user's emotion and adjust the expression method of the generated content based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit can analyze the content of text entered by the user to estimate the emotion. This makes it possible to generate content using an optimal expression method depending on the user's emotion. For example, if the user is relaxed, a calm expression method can be used. If the user is excited, a visually stimulating expression method can be used. If the user is in a hurry, a concise and to-the-point expression method can be used. This makes it possible to adjust the expression method of the generated content based on the user's emotion.

[0087] The generation unit can adjust the level of detail of the content generated based on the importance of the photo and the description when generating the content. For example, the generation unit stores the importance of the photo and the description in a database and refers to it when generating the content. For example, the generation unit generates detailed content for a photo and a description that the user has rated as important. The generation unit can also generate standard content for a general photo and description. For example, the generation unit generates standard content for a photo and a description that the user has not rated as particularly important. The generation unit can also generate simplified content for a photo and a description that has a low importance. For example, the generation unit generates simplified content for a photo and a description that the user has rated as low importance. This makes it possible to adjust the level of detail of the content generated according to the importance of the photo and the description.

[0088] The generation unit can apply different generation algorithms depending on the attributes of the target customers when generating content. For example, the generation unit stores the attributes of the target customers in a database and refers to them when generating content. For example, the generation unit applies a tourism analysis algorithm to content aimed at tourists. The generation unit can also apply a multilingual generation algorithm to content aimed at foreigners. For example, the generation unit applies a multilingual generation algorithm to content aimed at foreigners. The generation unit can also apply a region-specific generation algorithm to content aimed at local residents. For example, the generation unit applies a region-specific generation algorithm to content aimed at local residents. This makes it possible to apply the optimal generation algorithm depending on the attributes of the target customers.

[0089] The generation unit can estimate the user's emotion and adjust the length of the generated content based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit can analyze the content of text entered by the user to estimate the emotion. This makes it possible to generate content with an optimal length depending on the user's emotion. For example, if the user is relaxed, detailed and longer content can be generated. If the user is in a hurry, short and to the point content can be generated. If the user is excited, visually stimulating content can be generated. This makes it possible to adjust the length of the generated content based on the user's emotion.

[0090] The generation unit can determine a generation priority based on the submission dates of the photos and descriptions when generating content. For example, the generation unit stores the submission dates of the photos and descriptions in a database and refers to them when generating content. For example, the generation unit preferentially reflects recently submitted photos and descriptions in the content. The generation unit can also preferentially reflect photos and descriptions submitted during a specific event period in the content. For example, the generation unit preferentially reflects photos and descriptions submitted during a specific event period in the content. The generation unit can also preferentially reflect photos and descriptions submitted by season in the content. For example, the generation unit preferentially reflects photos and descriptions submitted by season in the content. This makes it possible to determine a generation priority based on the submission dates of the photos and descriptions.

[0091] The generation unit can adjust the order of generation based on the relevance between photos and descriptions when generating content. The generation unit, for example, stores the relevance between photos and descriptions in a database and refers to it when generating content. For example, the generation unit reflects photos and descriptions taken at the same location together in the content. The generation unit can also reflect photos and descriptions related to the same theme together in the content. For example, the generation unit reflects photos and descriptions related to the same theme together in the content. The generation unit can also reflect photos and descriptions related to the same event together in the content. For example, the generation unit reflects photos and descriptions related to the same event together in the content. This makes it possible to adjust the order of generation based on the relevance between photos and descriptions.

[0092] The providing unit can estimate the user's emotions and adjust the display method of the content to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. The providing unit can also estimate the user's emotions using text analysis technology. For example, the providing unit analyzes the content of text entered by the user to estimate the emotions. This makes it possible to provide content in an optimal display method depending on the user's emotions. For example, if the user is relaxed, a calm display method can be used. If the user is excited, a visually stimulating display method can be used. If the user is in a hurry, a concise and to-the-point display method can be used. This makes it possible to adjust the display method of the content to be provided based on the user's emotions.

[0093] The providing unit can select the optimal display method by referring to the user's past browsing history when providing content. The providing unit, for example, stores the user's past browsing history in a database and refers to it when providing content. For example, the providing unit preferentially provides a display method for content that the user has frequently browsed in the past. The providing unit can also suggest the optimal display method for a specific time period based on the user's past browsing history. For example, the providing unit suggests a display method for content that the user has browsed in a specific time period in the past. The providing unit can also analyze the type and content of content that the user has browsed in the past and suggest the optimal display method. For example, the providing unit suggests the optimal display method based on the type and content of content that the user has browsed in the past. This makes it possible to provide the optimal display method based on the user's past browsing history.

[0094] The providing unit can select the optimal display method by taking into consideration the user's device information when providing content. The providing unit, for example, stores the user's device information in a database and refers to it when providing content. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method that includes detailed information. For example, if the user is using a desktop, the providing unit provides a display method that includes detailed information. This makes it possible to provide the optimal display method based on the user's device information.

[0095] The providing unit can estimate the user's emotion and adjust the operation procedures of the content to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotion. The providing unit can also estimate the user's emotion using text analysis technology. For example, the providing unit analyzes the content of text entered by the user to estimate the emotion. This makes it possible to provide content with optimal operation procedures according to the user's emotion. For example, if the user is relaxed, detailed operation procedures can be provided. If the user is in a hurry, concise and quick operation procedures can be provided. If the user is excited, visually stimulating operation procedures can be provided. This makes it possible to adjust the operation procedures of the content to be provided based on the user's emotion.

[0096] The providing unit can select an optimal display method in consideration of the user's geographical location information when providing content. The providing unit, for example, stores the user's geographical location information in a database and refers to it when providing the content. For example, the providing unit can preferentially display content related to the user's current location. The providing unit can also preferentially display content related to places the user has visited in the past. For example, the providing unit can preferentially display content related to places the user has visited in the past. The providing unit can also preferentially display content related to places the user plans to visit in the future. For example, the providing unit can preferentially display content related to places the user plans to visit in the future. This makes it possible to provide an optimal display method based on the user's geographical location information.

[0097] The providing unit can analyze the user's social media activity and provide relevant content when providing content. For example, the providing unit stores the user's social media activity in a database and refers to it when providing content. For example, the providing unit can prioritize displaying content that the user recently shared on social media. The providing unit can also prioritize displaying content that the user has received many likes and comments on social media. For example, the providing unit can prioritize displaying content that the user has received many responses on social media. The providing unit can also prioritize displaying content related to accounts the user follows on social media. For example, the providing unit can prioritize displaying content related to tourist attractions and local businesses that the user follows. This makes it possible to provide relevant content based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the smart device 14 and provides a function for users to upload photos taken in their local area. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos and descriptions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates optimal websites, advertisements, and apps based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated content to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the smart glasses 214 and provides a function for a user to upload photos taken in the area. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos and descriptions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates optimal websites, advertisements, and apps based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated content to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the headset type terminal 314 and provides a function for users to upload photos they have taken in their local area. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos and descriptions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates optimal websites, advertisements, and apps based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated content to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the robot 414 and provides a function for users to upload photos taken in their local area. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded photos and descriptions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates optimal websites, advertisements, and apps based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated content to users.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit will prioritize analyzing visually stimulating photos. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing important photos. This makes it possible to adjust the priority of analysis based on the user's emotions.

[0100] The generation unit can estimate the user's emotion and adjust the color of the generated content based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can use calm colors. If the user is excited, the generation unit can use bright colors. If the user is sad, the generation unit can use subdued colors. In this way, the color of the generated content can be adjusted based on the user's emotion.

[0101] The providing unit can estimate the user's emotion and adjust the font size of the content to be provided based on the estimated user's emotion. For example, if the user is relaxed, the providing unit uses a larger font size. If the user is excited, the providing unit can use a standard font size. Furthermore, if the user is in a hurry, the providing unit can use a smaller font size. In this way, the font size of the content to be provided can be adjusted based on the user's emotion.

[0102] The upload unit can estimate the user's emotion and select a filter for the photo to be uploaded based on the estimated user's emotion. For example, if the user is relaxed, the upload unit can apply a warm-colored filter. If the user is excited, the upload unit can apply a vivid filter. If the user is sad, the upload unit can apply a monochrome filter. In this way, a filter for the photo to be uploaded can be selected based on the user's emotion.

[0103] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a simplified analysis. Furthermore, if the user is excited, the analysis unit can perform an analysis that emphasizes visually stimulating elements. This makes it possible to adjust the level of detail of the analysis based on the user's emotions.

[0104] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, it can preferentially suggest upload methods that the user has frequently used in the past. It can also analyze the user's tendency to upload at specific time periods in the past and suggest uploading at those time periods. It can also analyze the types and contents of photos that the user has uploaded in the past and suggest the optimal upload method. This makes it possible to suggest the optimal upload method based on the user's past upload history.

[0105] The providing unit can select the optimal display method taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. This makes it possible to provide the optimal display method based on the user's device information.

[0106] The providing unit can analyze the user's social media activity and provide relevant content. For example, the providing unit can prioritize displaying content that the user recently shared on social media. The providing unit can also prioritize displaying content that the user has received many likes or comments on social media. Furthermore, the providing unit can prioritize displaying content related to accounts that the user follows on social media. This makes it possible to provide relevant content based on the user's social media activity.

[0107] When generating content, the generation unit can apply different generation algorithms depending on the attributes of the target customer. For example, a tourism analysis algorithm can be applied to content aimed at tourists. A multilingual generation algorithm can be applied to content aimed at foreigners. Furthermore, a region-specific generation algorithm can be applied to content aimed at local residents. This makes it possible to apply the optimal generation algorithm depending on the attributes of the target customer.

[0108] The analysis unit can determine the priority of analysis based on when the photos were taken. For example, it can prioritize analysis of recently taken photos. It can also prioritize analysis of photos taken during a specific event period. It can also prioritize analysis of photos taken by season. This makes it possible to determine the priority of analysis based on when the photos were taken.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The upload unit has a function that allows users to upload multiple photos taken in the area. For example, users can upload photos of scenery from tourist spots or products from local businesses. The upload unit provides, for example, a drag-and-drop function or a file selection function, allowing users to easily upload photos. The upload unit also provides a function for entering descriptions for uploaded photos. Users are required to enter detailed information about the content and characteristics of the photos. For example, users can enter the history of tourist spots or product descriptions of local businesses. Step 2: The analysis unit has the function of analyzing the photos and descriptions uploaded by the upload unit. The analysis unit uses image recognition technology to analyze the content of the photos and compares it with the descriptions to generate optimal content. For example, the analysis unit uses deep learning technology to analyze the content of the photos and recognize scenery from tourist spots or products from local businesses. The analysis unit also uses natural language processing technology to analyze the descriptions and extract information that matches the content of the photos. For example, the analysis unit uses keyword extraction technology to extract important information from the descriptions and compare it with the content of the photos. Step 3: The generation unit has the function of generating optimal websites, advertisements, and apps based on the results of analysis by the analysis unit. The generation unit generates content according to the user's needs and target audience, for example, to generate content optimal for tourists and foreigners. For example, the generation unit generates websites and advertisements that emphasize the attractions of tourist destinations. The generation unit can also generate apps that effectively promote the products of local companies. The generation unit generates content using generation AI. The generation AI generates content according to the user's needs using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit has a function of providing the content generated by the generating unit. The providing unit provides, for example, the generated website or advertisement to the user. The providing unit has a function of publishing the generated content on a website. The providing unit also provides a function of sending the generated content by email. Furthermore, the providing unit has a function of distributing the generated app in an app store. This allows the user to effectively provide the generated content to a target audience, such as tourists and foreigners.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An upload section for uploading photos; an analysis unit that analyzes the photos and descriptions uploaded by the upload unit; a generation unit that generates an appropriate website, advertisement, or app based on the results of the analysis by the analysis unit; a providing unit that provides the content generated by the generating unit; Equipped with A system characterized by:

2. The analysis unit Equipped with an image analysis unit that uses image recognition technology to analyze the content of photos 2. The system of claim 1.

3. The analysis unit Equipped with a text analysis section that analyzes explanatory text 2. The system of claim 1.

4. The generation unit Generate websites, ads, and apps that are suitable for tourists or foreigners 2. The system of claim 1.

5. The providing unit Providing generated content to users 2. The system of claim 1.

6. The upload 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 upload unit Analyze the user's upload history and select the optimal upload method 2. The system of claim 1.

8. The upload unit Filter photos based on your current projects and interests when uploading them 2. The system of claim 1.

9. The upload unit Estimate the user's emotions and prioritize the photos to upload based on the estimated user emotions.

2. The system of claim 1.

Images