System

Abstract

An object of the system according to the embodiment is to generate and present new content on the basis of things that a user encounters in daily life.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image from a user. The analysis unit analyzes the image received by the reception unit and extracts a feature of each image. The generation unit generates new content by combining the features extracted by the analysis unit. The presentation unit presents the content generated by the generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to generate novel content based on things that users encounter in their daily lives.

[0005] The system according to the embodiment aims to generate and present novel content based on things that a user encounters in their daily lives. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives images from a user. The analysis unit analyzes the images received by the reception unit and extracts features of each image. The generation unit generates novel content by combining the features extracted by the analysis unit. The provision unit presents the content generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate and present novel content based on things that a user encounters in their daily lives. [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) A system according to an embodiment of the present invention uses a generative AI to create novel experiences. This system accepts images from users, analyzes them, extracts features from each image, and combines the extracted features to generate novel content and present it to the user. For example, a user takes images of landscapes, food, animals, and other things they encounter in their daily lives using a device and uploads them to the system. These images are then input into the generative AI, which analyzes them and extracts features from each image. For example, features such as buildings and natural elements are extracted from landscape images, colors and shapes are extracted from food images, and species and movements are extracted from animal images. The generative AI combines the extracted features to generate novel content. For example, it can combine landscape elements with animal elements to generate new, nonexistent landscapes and animals. It can also combine food elements to suggest new cooking ideas. The generated novel content is presented to users, allowing them to discover new things and gain inspiration. This allows the system to increase novel encounters and boost people's vitality. This allows users to enjoy new discoveries in their daily lives and add color to their lives. Sharing the generated content also promotes interaction with other users.

[0029] A novel encounter creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives images from a user. Examples of images from a user include, but are not limited to, photographs, illustrations, scanned images, etc. The reception unit can, for example, allow the user to upload images taken with a device to the system. The reception unit can also allow the user to select and upload existing images. The analysis unit analyzes the images received by the reception unit and extracts features of each image. Examples of features include, but are not limited to, color, shape, texture, etc. The analysis unit can, for example, extract buildings and natural elements from landscape images. The analysis unit can also extract color and shape from food images. The analysis unit can also extract species and movements from animal images. The generation unit combines the features extracted by the analysis unit to generate novel content. Examples of novel content include, but are not limited to, new landscapes and animals that do not actually exist, new cooking ideas, etc. The generation unit, for example, combines landscape elements and animal elements to generate new landscapes and animals that do not actually exist. The generation unit can also combine food elements to generate new cooking ideas. The provision unit presents the content generated by the generation unit to the user. The provision unit, for example, displays the generated content on the user's device. The provision unit can also share the generated content on the user's social media account. This allows the novel encounter creation system according to the embodiment to enable the user to gain new discoveries and inspiration. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate content using a generation AI model that receives the features extracted by the analysis unit as input and outputs novel content.

[0030] The analysis unit can extract buildings or natural elements from landscape images. Landscape images include, but are not limited to, natural landscapes and urban landscapes. For example, the analysis unit extracts the shape of buildings from landscape images. The analysis unit can also extract natural plants and terrain from landscape images. For example, the analysis unit can use image analysis technology to identify the outline and structure of buildings. The analysis unit can also use image analysis technology to identify plant types and terrain features. In this way, by extracting buildings and natural elements from landscape images, more detailed features can be grasped. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input landscape images into AI and cause the AI ​​to extract buildings and natural elements.

[0031] The analysis unit can extract colors and shapes from food images. Food images include, but are not limited to, for example, dishes and ingredients. The analysis unit, for example, extracts colors from food images. The analysis unit can also extract shapes from food images. For example, the analysis unit can use image analysis technology to identify the color range of food. The analysis unit can also use image analysis technology to identify the shape characteristics of food. In this way, by extracting colors and shapes from food images, the characteristics of food can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food images into AI and have the AI ​​extract colors and shapes.

[0032] The analysis unit can extract the type or behavior of an animal from an image of the animal. Examples of animal images include, but are not limited to, pets and wild animals. For example, the analysis unit extracts the type from the image of the animal. The analysis unit can also extract behavior from the image of the animal. For example, the analysis unit can identify the type of animal using image analysis technology. The analysis unit can also identify the animal's behavior pattern using image analysis technology. In this way, by extracting the type and behavior from the image of the animal, the characteristics of the animal can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the image of the animal into AI and have the AI ​​extract the type and behavior.

[0033] The generation unit can combine the extracted features to generate new non-existent landscapes and animals. Examples of non-existent landscapes and animals include, but are not limited to, combinations of landscape elements and animal elements. For example, the generation unit can combine landscape elements and animal elements to generate new non-existent landscapes. The generation unit can also combine animal elements to generate new non-existent animals. For example, the generation unit can use a generation AI to generate new landscapes that combine landscape elements and animal elements. The generation unit can also use a generation AI to generate new animals that combine animal elements. In this way, novel landscapes and animals can be generated by combining the extracted features. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the extracted features to the generation AI and cause the generation AI to generate novel landscapes and animals.

[0034] The generation unit can generate new dish ideas by combining the extracted features. New dish ideas include, but are not limited to, combinations of food elements, for example. The generation unit generates new dish ideas by combining food elements, for example. For example, the generation unit uses a generation AI to generate new dish ideas by combining food elements. In this way, new dish ideas can be generated by combining the extracted features. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the extracted features into the generation AI and cause the generation AI to generate new dish ideas.

[0035] The providing unit can present the generated content to the user. Examples of the generated content include, but are not limited to, images, videos, and text. The providing unit can, for example, display the generated content on the user's device. The providing unit can also share the generated content to the user's social media account. This allows the user to gain new discoveries and inspiration by presenting the generated content to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the generated content into AI and cause the AI ​​to execute a method of presenting the content to the user.

[0036] The reception unit can analyze the user's past image submission history and select the optimal reception method. For example, the reception unit can accept images according to the time period in which the user frequently submitted images in the past. The reception unit can also preferentially accept images of a specific category based on the types of images the user has submitted in the past. The reception unit can also predict images that will be submitted on a specific day of the week or time period from the user's past submission history and adjust the reception method. In this way, the optimal reception method can be selected by analyzing the user's past image submission history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past image submission history into AI and have the AI ​​select the optimal reception method.

[0037] When receiving images, the reception unit can filter them based on the user's current interests. For example, the reception unit can only accept images related to themes in which the user is currently interested. The reception unit can also preferentially accept related images based on the user's recent search history. The reception unit can also filter related images based on topics in online communities in which the user participates. This makes it possible to accept highly relevant images by filtering images based on the user's current interests. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's current interests and interest data into AI and have the AI ​​perform image filtering.

[0038] When accepting an image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user describes the image using voice, the acceptance unit can analyze the voice and accept related images. Furthermore, if the user describes the image using text, the acceptance unit can analyze the text and accept related images. Furthermore, if the user directly uploads an image, the acceptance unit can immediately accept the image. This allows for efficient acceptance of images by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into AI and have the AI ​​select the optimal acceptance means.

[0039] When accepting images, the reception unit can prioritize accepting highly relevant images by taking into account the user's geographical location information. For example, the reception unit can prioritize accepting images related to the user's current location. The reception unit can also prioritize accepting images related to nearby tourist spots and events based on the user's geographical location information. The reception unit can also prioritize accepting images related to local specialties and culture based on the user's geographical location information. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​accept highly relevant images.

[0040] The reception unit can analyze the user's social media activity and receive related images when receiving images. For example, the reception unit can preferentially receive images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related images. The reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related images can be efficiently received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​receive related images.

[0041] When accepting images, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. For example, the acceptance unit preferentially accepts image categories for which the user has previously given high ratings. The acceptance unit can also accept images by excluding image categories for which the user has previously given low ratings. The acceptance unit can also suggest an optimal acceptance method based on the user's past feedback. In this way, the optimal acceptance method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's past feedback data into AI and have the AI ​​customize the acceptance method.

[0042] During image analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on images with high importance to extract features in detail. The analysis unit can also perform a simplified analysis on images with low importance to extract only key features. The analysis unit can also analyze images with medium importance with a moderate level of detail to extract balanced features. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0043] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the image category. For example, the analysis unit can apply an algorithm that extracts buildings and natural elements to a landscape image. The analysis unit can also apply an algorithm that extracts color and shape to a food image. The analysis unit can also apply an algorithm that extracts species and movements to an animal image. By applying different analysis algorithms depending on the image category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image category data into AI and have the AI ​​apply the analysis algorithm.

[0044] During image analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the analysis algorithm by excluding analysis results that the user has previously rated poorly. The analysis unit can also suggest an optimal analysis method based on the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0045] During image analysis, the analysis unit can determine the priority of analysis based on the time the image was submitted. For example, the analysis unit prioritizes analysis of recently submitted images. The analysis unit can also postpone analysis of images that were submitted recently. The analysis unit can also analyze images that were submitted recently with a moderate priority. In this way, by determining the priority of analysis based on the time the image was submitted, analysis can be performed efficiently. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image submission time data into AI and have the AI ​​determine the analysis priority.

[0046] During image analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of images with low relevance. The analysis unit can also analyze images with medium relevance in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of images, analysis can be performed efficiently. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data into AI and have the AI ​​adjust the order of analysis.

[0047] During image analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Alternatively, the analysis unit can provide analysis results using appropriate technical terminology according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​use technical terminology.

[0048] When generating content, the generation unit can adjust the level of detail of the generation based on the importance of the extracted features. For example, the generation unit generates content by reflecting features with high importance in detail. The generation unit can also generate content by simplifying features with low importance. The generation unit can also generate content by appropriately reflecting features with medium importance. In this way, content can be generated efficiently by adjusting the level of detail of the generation based on the importance of the extracted features. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the extracted features to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0049] When generating content, the generation unit can apply different generation algorithms depending on the feature category. For example, the generation unit can apply a generation algorithm that emphasizes natural elements to landscape features. The generation unit can also apply a generation algorithm that emphasizes color and shape to food features. The generation unit can also apply a generation algorithm that emphasizes species and movements to animal features. In this way, by applying different generation algorithms depending on the feature category, more accurate content can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input feature category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0050] When generating content, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm by referring to generation results that the user has previously rated highly. The generation unit can also improve the generation algorithm by excluding generation results that the user has previously rated poorly. The generation unit can also propose an optimal generation method based on the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0051] When generating content, the generation unit can determine generation priorities based on the submission dates of the features. For example, the generation unit generates content by preferentially reflecting recently submitted features. The generation unit can also generate content by putting recently submitted features on hold. The generation unit can also generate content by reflecting features that were submitted at a moderate time with appropriate priority. In this way, content can be generated efficiently by determining generation priorities based on the submission dates of the features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input feature submission date data into the generation AI and have the generation AI determine the generation priorities.

[0052] When generating content, the generation unit can determine generation priorities based on the submission dates of the features. For example, the generation unit generates content by preferentially reflecting recently submitted features. The generation unit can also generate content by putting recently submitted features on hold. The generation unit can also generate content by reflecting features that were submitted at a moderate time with appropriate priority. In this way, content can be generated efficiently by determining generation priorities based on the submission dates of the features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input feature submission date data into the generation AI and have the generation AI determine the generation priorities.

[0053] When generating content, the generation unit can adjust the order of generation based on the relevance of features. For example, the generation unit generates content by preferentially reflecting highly relevant features. The generation unit can also generate content by putting off features with low relevance. The generation unit can also generate content by reflecting features with medium relevance in an appropriate order. In this way, content can be generated efficiently by adjusting the order of generation based on the relevance of features. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input feature relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0054] When generating content, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates content using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can also generate content using simple language. Furthermore, the generation unit can generate content using appropriate technical terminology according to the user's level of expertise. This allows for the provision of content that is easier to understand by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0055] When providing content, the providing unit can select the optimal providing method by referring to the user's past providing history. For example, the providing unit preferentially uses providing methods that the user has previously rated highly. The providing unit can also provide content by excluding providing methods that the user has previously rated poorly. The providing unit can also suggest the optimal providing method based on the user's past providing history. In this way, the optimal providing method can be selected by referring to the user's past providing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past providing history data into AI and have the AI ​​select the optimal providing method.

[0056] When providing content, the providing unit can customize the provided content based on the user's current interests and concerns. For example, the providing unit can prioritize providing content related to a topic in which the user is currently interested. The providing unit can also provide related content based on the user's recent search history. The providing unit can also provide related content based on topics in online communities in which the user participates. This allows the provided content to be customized based on the user's current interests and concerns, thereby providing more relevant content. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the user's current interests and concerns into AI and have the AI ​​customize the provided content.

[0057] The providing unit can improve the delivery method by reflecting user feedback when providing content. For example, the providing unit preferentially uses delivery methods that users have previously rated highly. The providing unit can also provide content by excluding delivery methods that users have previously rated poorly. The providing unit can also suggest the optimal delivery method based on user feedback. This allows the delivery method to be improved by reflecting user feedback, and more appropriate content to be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input user feedback data into AI and have the AI ​​improve the delivery method.

[0058] When providing content, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, the providing unit can prioritize providing content related to the user's current location. The providing unit can also prioritize providing content related to nearby tourist spots and events based on the user's geographical location information. The providing unit can also prioritize providing content related to local specialties and culture based on the user's geographical location information. In this way, highly relevant content can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0059] When providing content, the providing unit can analyze the user's social media activity and customize the content to be provided. For example, the providing unit can provide information related to content shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related content. The providing unit can also provide related content by referring to the activity of the user's friends on social media. In this way, highly relevant content can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and have the AI ​​customize the content to be provided.

[0060] The providing unit can customize the delivery method by reflecting the user's past feedback when providing content. For example, the providing unit preferentially uses delivery methods that the user has previously rated highly. The providing unit can also provide content by excluding delivery methods that the user has previously rated poorly. The providing unit can also suggest an optimal delivery method based on the user's past feedback. In this way, the optimal delivery method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI and have the AI ​​customize the delivery method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The reception unit can analyze a user's past behavioral patterns and predict the optimal timing for receiving images. For example, if a user tends to upload images frequently during a specific time period, the reception unit can promote image reception according to that time period. Also, if a user uploads many images on a specific day of the week, the reception unit can strengthen image reception according to that day of the week. Furthermore, if a user tends to upload images related to a specific event or season, the reception unit can optimize image reception according to that event or season. This allows for more efficient image reception by optimizing image reception based on the user's behavioral patterns.

[0063] The generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation algorithm can be adjusted by referring to generation results that the user has previously rated highly. The generation algorithm can also be improved by excluding generation results that the user has previously rated poorly. Furthermore, the optimal generation method can be proposed based on the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0064] The reception unit can preferentially accept highly relevant images in consideration of the user's geographical location information. For example, it can preferentially accept images related to the user's current location. The reception unit can also preferentially accept images related to nearby tourist spots and events based on the user's geographical location information. Furthermore, the reception unit can also preferentially accept images related to local specialties and culture based on the user's geographical location information. In this way, it is possible to preferentially accept highly relevant images by considering the user's geographical location information.

[0065] When analyzing an image, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can provide analysis results using appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0066] When providing content, the providing unit can analyze the user's social media activity and customize the content to be provided. For example, the providing unit can provide information related to content shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related content. Furthermore, the providing unit can provide related content by referring to the activity of the user's friends on social media. In this way, highly relevant content can be provided by analyzing the user's social media activity.

[0067] When generating content, the generation unit can apply different generation algorithms depending on the feature category. For example, a generation algorithm that emphasizes natural elements can be applied to landscape features. A generation algorithm that emphasizes color and shape can be applied to food features. Furthermore, a generation algorithm that emphasizes species and movements can be applied to animal features. In this way, by applying different generation algorithms depending on the feature category, more accurate content can be generated.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit receives an image from a user. The image from the user may be, for example, a photograph, an illustration, a scanned image, or the like, but is not limited to such examples. For example, the reception unit may upload an image taken by the user with a device to the system. The reception unit may also allow the user to select an existing image and upload it. Step 2: The analysis unit analyzes the images received by the reception unit and extracts features of each image. Features include, but are not limited to, color, shape, and texture. For example, the analysis unit extracts buildings and natural elements from landscape images. The analysis unit can also extract color and shape from food images. The analysis unit can also extract species and movements from animal images. Step 3: The generation unit generates novel content by combining the features extracted by the analysis unit. Novel content includes, but is not limited to, new, non-existent landscapes and animals, new cooking ideas, and the like. For example, the generation unit combines landscape elements with animal elements to generate new, non-existent landscapes and animals. The generation unit can also combine food elements to generate new cooking ideas. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate content using a generation AI model that receives the features extracted by the analysis unit as input and outputs novel content. Step 4: The providing unit presents the content generated by the generating unit to the user. For example, the providing unit displays the generated content on the user's device. The providing unit may also share the generated content on the user's social media account. In this way, the novel encounter creation system according to the embodiment allows the user to gain new discoveries and inspiration.

[0070] (Example 2) A system according to an embodiment of the present invention uses a generative AI to create novel experiences. This system accepts images from users, analyzes them, extracts features from each image, and combines the extracted features to generate novel content and present it to the user. For example, a user takes images of landscapes, food, animals, and other things they encounter in their daily lives using a device and uploads them to the system. These images are then input into the generative AI, which analyzes them and extracts features from each image. For example, features such as buildings and natural elements are extracted from landscape images, colors and shapes are extracted from food images, and species and movements are extracted from animal images. The generative AI combines the extracted features to generate novel content. For example, it can combine landscape elements with animal elements to generate new, nonexistent landscapes and animals. It can also combine food elements to suggest new cooking ideas. The generated novel content is presented to users, allowing them to discover new things and gain inspiration. This allows the system to increase novel encounters and boost people's vitality. This allows users to enjoy new discoveries in their daily lives and add color to their lives. Sharing the generated content also promotes interaction with other users.

[0071] A novel encounter creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives images from a user. Examples of images from a user include, but are not limited to, photographs, illustrations, scanned images, etc. The reception unit can, for example, allow the user to upload images taken with a device to the system. The reception unit can also allow the user to select and upload existing images. The analysis unit analyzes the images received by the reception unit and extracts features of each image. Examples of features include, but are not limited to, color, shape, texture, etc. The analysis unit can, for example, extract buildings and natural elements from landscape images. The analysis unit can also extract color and shape from food images. The analysis unit can also extract species and movements from animal images. The generation unit combines the features extracted by the analysis unit to generate novel content. Examples of novel content include, but are not limited to, new landscapes and animals that do not actually exist, new cooking ideas, etc. The generation unit, for example, combines landscape elements and animal elements to generate new landscapes and animals that do not actually exist. The generation unit can also combine food elements to generate new cooking ideas. The provision unit presents the content generated by the generation unit to the user. The provision unit, for example, displays the generated content on the user's device. The provision unit can also share the generated content on the user's social media account. This allows the novel encounter creation system according to the embodiment to enable the user to gain new discoveries and inspiration. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate content using a generation AI model that receives the features extracted by the analysis unit as input and outputs novel content.

[0072] The analysis unit can extract buildings or natural elements from landscape images. Landscape images include, but are not limited to, natural landscapes and urban landscapes. For example, the analysis unit extracts the shape of buildings from landscape images. The analysis unit can also extract natural plants and terrain from landscape images. For example, the analysis unit can use image analysis technology to identify the outline and structure of buildings. The analysis unit can also use image analysis technology to identify plant types and terrain features. In this way, by extracting buildings and natural elements from landscape images, more detailed features can be grasped. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input landscape images into AI and cause the AI ​​to extract buildings and natural elements.

[0073] The analysis unit can extract colors and shapes from food images. Food images include, but are not limited to, for example, dishes and ingredients. The analysis unit, for example, extracts colors from food images. The analysis unit can also extract shapes from food images. For example, the analysis unit can use image analysis technology to identify the color range of food. The analysis unit can also use image analysis technology to identify the shape characteristics of food. In this way, by extracting colors and shapes from food images, the characteristics of food can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food images into AI and have the AI ​​extract colors and shapes.

[0074] The analysis unit can extract the type or behavior of an animal from an image of the animal. Examples of animal images include, but are not limited to, pets and wild animals. For example, the analysis unit extracts the type from the image of the animal. The analysis unit can also extract behavior from the image of the animal. For example, the analysis unit can identify the type of animal using image analysis technology. The analysis unit can also identify the animal's behavior pattern using image analysis technology. In this way, by extracting the type and behavior from the image of the animal, the characteristics of the animal can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the image of the animal into AI and have the AI ​​extract the type and behavior.

[0075] The generation unit can combine the extracted features to generate new non-existent landscapes and animals. Examples of non-existent landscapes and animals include, but are not limited to, combinations of landscape elements and animal elements. For example, the generation unit can combine landscape elements and animal elements to generate new non-existent landscapes. The generation unit can also combine animal elements to generate new non-existent animals. For example, the generation unit can use a generation AI to generate new landscapes that combine landscape elements and animal elements. The generation unit can also use a generation AI to generate new animals that combine animal elements. In this way, novel landscapes and animals can be generated by combining the extracted features. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the extracted features to the generation AI and cause the generation AI to generate novel landscapes and animals.

[0076] The generation unit can generate new dish ideas by combining the extracted features. New dish ideas include, but are not limited to, combinations of food elements, for example. The generation unit generates new dish ideas by combining food elements, for example. For example, the generation unit uses a generation AI to generate new dish ideas by combining food elements. In this way, new dish ideas can be generated by combining the extracted features. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the extracted features into the generation AI and cause the generation AI to generate new dish ideas.

[0077] The providing unit can present the generated content to the user. Examples of the generated content include, but are not limited to, images, videos, and text. The providing unit can, for example, display the generated content on the user's device. The providing unit can also share the generated content to the user's social media account. This allows the user to gain new discoveries and inspiration by presenting the generated content to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the generated content into AI and cause the AI ​​to execute a method of presenting the content to the user.

[0078] The reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. For example, if the user is excited, the reception unit can immediately receive and process images. Furthermore, if the user is relaxed, the reception unit can receive images at regular intervals. Furthermore, if the user is stressed, the reception unit can temporarily stop receiving images and wait until the user calms down. This allows images to be received at a more appropriate timing by adjusting the timing of image reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of image reception.

[0079] The reception unit can analyze the user's past image submission history and select the optimal reception method. For example, the reception unit can accept images according to the time period in which the user frequently submitted images in the past. The reception unit can also preferentially accept images of a specific category based on the types of images the user has submitted in the past. The reception unit can also predict images that will be submitted on a specific day of the week or time period from the user's past submission history and adjust the reception method. In this way, the optimal reception method can be selected by analyzing the user's past image submission history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past image submission history into AI and have the AI ​​select the optimal reception method.

[0080] When receiving images, the reception unit can filter them based on the user's current interests. For example, the reception unit can only accept images related to themes in which the user is currently interested. The reception unit can also preferentially accept related images based on the user's recent search history. The reception unit can also filter related images based on topics in online communities in which the user participates. This makes it possible to accept highly relevant images by filtering images based on the user's current interests. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's current interests and interest data into AI and have the AI ​​perform image filtering.

[0081] When accepting an image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user describes the image using voice, the acceptance unit can analyze the voice and accept related images. Furthermore, if the user describes the image using text, the acceptance unit can analyze the text and accept related images. Furthermore, if the user directly uploads an image, the acceptance unit can immediately accept the image. This allows for efficient acceptance of images by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into AI and have the AI ​​select the optimal acceptance means.

[0082] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user emotions. For example, if the user is excited, the reception unit can preferentially receive new images to maintain the user's excitement. Furthermore, if the user is relaxed, the reception unit can preferentially receive calm images to maintain the user's relaxation. Furthermore, if the user is stressed, the reception unit can preferentially receive soothing images to reduce stress. By determining the priority of images according to the user's emotions, more appropriate images can be preferentially received. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into an AI and have the AI ​​determine the priority of images.

[0083] When accepting images, the reception unit can prioritize accepting highly relevant images by taking into account the user's geographical location information. For example, the reception unit can prioritize accepting images related to the user's current location. The reception unit can also prioritize accepting images related to nearby tourist spots and events based on the user's geographical location information. The reception unit can also prioritize accepting images related to local specialties and culture based on the user's geographical location information. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​accept highly relevant images.

[0084] The reception unit can analyze the user's social media activity and receive related images when receiving images. For example, the reception unit can preferentially receive images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related images. The reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related images can be efficiently received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​receive related images.

[0085] When accepting images, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. For example, the acceptance unit preferentially accepts image categories for which the user has previously given high ratings. The acceptance unit can also accept images by excluding image categories for which the user has previously given low ratings. The acceptance unit can also suggest an optimal acceptance method based on the user's past feedback. In this way, the optimal acceptance method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's past feedback data into AI and have the AI ​​customize the acceptance method.

[0086] The analysis unit can estimate the user's emotions and adjust the image analysis presentation method based on the estimated user's emotions. For example, if the user is excited, the analysis unit can analyze the image using vivid colors and dynamic effects. If the user is relaxed, the analysis unit can analyze the image using calm colors and static effects. If the user is stressed, the analysis unit can analyze the image using subdued colors and simple effects. This allows for adjusting the image analysis presentation method according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the image analysis presentation method.

[0087] During image analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on images with high importance to extract features in detail. The analysis unit can also perform a simplified analysis on images with low importance to extract only key features. The analysis unit can also analyze images with medium importance with a moderate level of detail to extract balanced features. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0088] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the image category. For example, the analysis unit can apply an algorithm that extracts buildings and natural elements to a landscape image. The analysis unit can also apply an algorithm that extracts color and shape to a food image. The analysis unit can also apply an algorithm that extracts species and movements to an animal image. By applying different analysis algorithms depending on the image category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image category data into AI and have the AI ​​apply the analysis algorithm.

[0089] During image analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the analysis algorithm by excluding analysis results that the user has previously rated poorly. The analysis unit can also suggest an optimal analysis method based on the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide results in a short time. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide results over time. Furthermore, if the user is stressed, the analysis unit can quickly perform an analysis and provide results. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0091] During image analysis, the analysis unit can determine the priority of analysis based on the time the image was submitted. For example, the analysis unit prioritizes analysis of recently submitted images. The analysis unit can also postpone analysis of images that were submitted recently. The analysis unit can also analyze images that were submitted recently with a moderate priority. In this way, by determining the priority of analysis based on the time the image was submitted, analysis can be performed efficiently. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image submission time data into AI and have the AI ​​determine the analysis priority.

[0092] During image analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of images with low relevance. The analysis unit can also analyze images with medium relevance in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of images, analysis can be performed efficiently. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data into AI and have the AI ​​adjust the order of analysis.

[0093] During image analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Alternatively, the analysis unit can provide analysis results using appropriate technical terminology according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​use technical terminology.

[0094] The generation unit can estimate the user's emotions and adjust the expression method of the generated content based on the estimated user's emotions. For example, if the user is excited, the generation unit can generate content using vivid colors and dynamic effects. Alternatively, if the user is relaxed, the generation unit can generate content using calm colors and static effects. Alternatively, if the user is stressed, the generation unit can generate content using subdued colors and simple effects. This allows for adjusting the expression method of the content according to the user's emotions, thereby providing more appropriate content. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into an AI and have the AI ​​adjust the expression method of the content.

[0095] When generating content, the generation unit can adjust the level of detail of the generation based on the importance of the extracted features. For example, the generation unit generates content by reflecting features with high importance in detail. The generation unit can also generate content by simplifying features with low importance. The generation unit can also generate content by appropriately reflecting features with medium importance. In this way, content can be generated efficiently by adjusting the level of detail of the generation based on the importance of the extracted features. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the extracted features to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0096] When generating content, the generation unit can apply different generation algorithms depending on the feature category. For example, the generation unit can apply a generation algorithm that emphasizes natural elements to landscape features. The generation unit can also apply a generation algorithm that emphasizes color and shape to food features. The generation unit can also apply a generation algorithm that emphasizes species and movements to animal features. In this way, by applying different generation algorithms depending on the feature category, more accurate content can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input feature category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0097] When generating content, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm by referring to generation results that the user has previously rated highly. The generation unit can also improve the generation algorithm by excluding generation results that the user has previously rated poorly. The generation unit can also propose an optimal generation method based on the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0098] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated user emotions. For example, if the user is excited, the generation unit can generate short, impactful content. Furthermore, if the user is relaxed, the generation unit can generate long, detailed content. Furthermore, if the user is stressed, the generation unit can generate short, simple content. This allows for adjusting the length of content according to the user's emotions, thereby providing more appropriate content. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the content.

[0099] When generating content, the generation unit can determine generation priorities based on the submission dates of the features. For example, the generation unit generates content by preferentially reflecting recently submitted features. The generation unit can also generate content by putting recently submitted features on hold. The generation unit can also generate content by reflecting features that were submitted at a moderate time with appropriate priority. In this way, content can be generated efficiently by determining generation priorities based on the submission dates of the features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input feature submission date data into the generation AI and have the generation AI determine the generation priorities.

[0100] When generating content, the generation unit can determine generation priorities based on the submission dates of the features. For example, the generation unit generates content by preferentially reflecting recently submitted features. The generation unit can also generate content by putting recently submitted features on hold. The generation unit can also generate content by reflecting features that were submitted at a moderate time with appropriate priority. In this way, content can be generated efficiently by determining generation priorities based on the submission dates of the features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input feature submission date data into the generation AI and have the generation AI determine the generation priorities.

[0101] When generating content, the generation unit can adjust the order of generation based on the relevance of features. For example, the generation unit generates content by preferentially reflecting highly relevant features. The generation unit can also generate content by putting off features with low relevance. The generation unit can also generate content by reflecting features with medium relevance in an appropriate order. In this way, content can be generated efficiently by adjusting the order of generation based on the relevance of features. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input feature relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0102] When generating content, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates content using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can also generate content using simple language. Furthermore, the generation unit can generate content using appropriate technical terminology according to the user's level of expertise. This allows for the provision of content that is easier to understand by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0103] The providing unit can estimate the user's emotions and adjust the content provision method based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide the content in a visually stimulating manner. Furthermore, if the user is relaxed, the providing unit can provide the content in a calm manner. Furthermore, if the user is stressed, the providing unit can provide the content in a simple and intuitive manner. This allows the content provision method to be adjusted according to the user's emotions, thereby providing the content in a more appropriate manner. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the content provision method.

[0104] When providing content, the providing unit can select the optimal providing method by referring to the user's past providing history. For example, the providing unit preferentially uses providing methods that the user has previously rated highly. The providing unit can also provide content by excluding providing methods that the user has previously rated poorly. The providing unit can also suggest the optimal providing method based on the user's past providing history. In this way, the optimal providing method can be selected by referring to the user's past providing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past providing history data into AI and have the AI ​​select the optimal providing method.

[0105] When providing content, the providing unit can customize the provided content based on the user's current interests and concerns. For example, the providing unit can prioritize providing content related to a topic in which the user is currently interested. The providing unit can also provide related content based on the user's recent search history. The providing unit can also provide related content based on topics in online communities in which the user participates. This allows the provided content to be customized based on the user's current interests and concerns, thereby providing more relevant content. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the user's current interests and concerns into AI and have the AI ​​customize the provided content.

[0106] The providing unit can improve the delivery method by reflecting user feedback when providing content. For example, the providing unit preferentially uses delivery methods that users have previously rated highly. The providing unit can also provide content by excluding delivery methods that users have previously rated poorly. The providing unit can also suggest the optimal delivery method based on user feedback. This allows the delivery method to be improved by reflecting user feedback, and more appropriate content to be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input user feedback data into AI and have the AI ​​improve the delivery method.

[0107] The providing unit can estimate the user's emotions and determine the priority of content provision based on the estimated user's emotions. For example, if the user is excited, the providing unit can prioritize providing new content to maintain the user's excitement. Furthermore, if the user is relaxed, the providing unit can prioritize providing calm content to maintain the user's relaxation. Furthermore, if the user is stressed, the providing unit can prioritize providing soothing content to reduce stress. Thus, by determining the priority of content provision according to the user's emotions, more appropriate content can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​determine the priority of content provision.

[0108] When providing content, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, the providing unit can prioritize providing content related to the user's current location. The providing unit can also prioritize providing content related to nearby tourist spots and events based on the user's geographical location information. The providing unit can also prioritize providing content related to local specialties and culture based on the user's geographical location information. In this way, highly relevant content can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0109] When providing content, the providing unit can analyze the user's social media activity and customize the content to be provided. For example, the providing unit can provide information related to content shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related content. The providing unit can also provide related content by referring to the activity of the user's friends on social media. In this way, highly relevant content can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and have the AI ​​customize the content to be provided.

[0110] The providing unit can customize the delivery method by reflecting the user's past feedback when providing content. For example, the providing unit preferentially uses delivery methods that the user has previously rated highly. The providing unit can also provide content by excluding delivery methods that the user has previously rated poorly. The providing unit can also suggest an optimal delivery method based on the user's past feedback. In this way, the optimal delivery method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI and have the AI ​​customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception 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 reception unit can receive an image from a user using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the received image. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and combines the extracted features to generate novel content. The provision unit can present the generated content to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception 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 reception unit can receive an image from a user using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the received image. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and combines the extracted features to generate novel content. The provision unit can present the generated content to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception 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 reception unit can receive an image from a user using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the received image. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and combines the extracted features to generate novel content. The provision unit can present the generated content to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception 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 reception unit can receive an image from a user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the received image. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and combines the extracted features to generate novel content. The provision unit can present the generated content to the user using the speaker 240 of the robot 414.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The reception unit can analyze a user's past behavioral patterns and predict the optimal timing for receiving images. For example, if a user tends to upload images frequently during a specific time period, the reception unit can promote image reception according to that time period. Also, if a user uploads many images on a specific day of the week, the reception unit can strengthen image reception according to that day of the week. Furthermore, if a user tends to upload images related to a specific event or season, the reception unit can optimize image reception according to that event or season. This allows for more efficient image reception by optimizing image reception based on the user's behavioral patterns.

[0113] The analysis unit can estimate the user's emotions and determine the priority of image analysis based on the estimated user's emotions. For example, if the user is excited, new images can be analyzed preferentially to maintain the user's excitement. Also, if the user is relaxed, calm images can be analyzed preferentially to maintain the user's relaxation. Furthermore, if the user is stressed, soothing images can be analyzed preferentially to reduce stress. In this way, by determining the priority of image analysis according to the user's emotions, more appropriate analysis results can be provided.

[0114] The generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation algorithm can be adjusted by referring to generation results that the user has previously rated highly. The generation algorithm can also be improved by excluding generation results that the user has previously rated poorly. Furthermore, the optimal generation method can be proposed based on the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0115] The providing unit can estimate the user's emotions and adjust the content providing method based on the estimated user's emotions. For example, if the user is excited, the content can be provided in a visually stimulating manner. If the user is relaxed, the content can be provided in a calm manner. Furthermore, if the user is stressed, the content can be provided in a simple and intuitive manner. In this way, by adjusting the content providing method according to the user's emotions, the content can be provided in a more appropriate manner.

[0116] The reception unit can preferentially accept highly relevant images in consideration of the user's geographical location information. For example, it can preferentially accept images related to the user's current location. The reception unit can also preferentially accept images related to nearby tourist spots and events based on the user's geographical location information. Furthermore, the reception unit can also preferentially accept images related to local specialties and culture based on the user's geographical location information. In this way, it is possible to preferentially accept highly relevant images by considering the user's geographical location information.

[0117] When analyzing an image, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can provide analysis results using appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0118] The generation unit can estimate the user's emotions and adjust the expression method of the generated content based on the estimated user's emotions. For example, if the user is excited, the content can be generated using vivid colors and dynamic effects. If the user is relaxed, the content can be generated using calm colors and static effects. Furthermore, if the user is stressed, the content can be generated using subdued colors and simple effects. In this way, by adjusting the expression method of the content according to the user's emotions, more appropriate content can be provided.

[0119] When providing content, the providing unit can analyze the user's social media activity and customize the content to be provided. For example, the providing unit can provide information related to content shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related content. Furthermore, the providing unit can provide related content by referring to the activity of the user's friends on social media. In this way, highly relevant content can be provided by analyzing the user's social media activity.

[0120] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is excited, the results can be provided in a short time. Alternatively, if the user is relaxed, a detailed analysis can be performed and the results can be provided over a longer period of time. Furthermore, if the user is feeling stressed, the analysis can be performed quickly and the results can be provided. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0121] When generating content, the generation unit can apply different generation algorithms depending on the feature category. For example, a generation algorithm that emphasizes natural elements can be applied to landscape features. A generation algorithm that emphasizes color and shape can be applied to food features. Furthermore, a generation algorithm that emphasizes species and movements can be applied to animal features. In this way, by applying different generation algorithms depending on the feature category, more accurate content can be generated.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit receives an image from a user. The image from the user may be, for example, a photograph, an illustration, a scanned image, or the like, but is not limited to such examples. For example, the reception unit may upload an image taken by the user with a device to the system. The reception unit may also allow the user to select an existing image and upload it. Step 2: The analysis unit analyzes the images received by the reception unit and extracts features of each image. Features include, but are not limited to, color, shape, and texture. For example, the analysis unit extracts buildings and natural elements from landscape images. The analysis unit can also extract color and shape from food images. The analysis unit can also extract species and movements from animal images. Step 3: The generation unit generates novel content by combining the features extracted by the analysis unit. Novel content includes, but is not limited to, new, non-existent landscapes and animals, new cooking ideas, and the like. For example, the generation unit combines landscape elements with animal elements to generate new, non-existent landscapes and animals. The generation unit can also combine food elements to generate new cooking ideas. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate content using a generation AI model that receives the features extracted by the analysis unit as input and outputs novel content. Step 4: The providing unit presents the content generated by the generating unit to the user. For example, the providing unit displays the generated content on the user's device. The providing unit may also share the generated content on the user's social media account. In this way, the novel encounter creation system according to the embodiment allows the user to gain new discoveries and inspiration.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an image from a user; an analysis unit that analyzes the images received by the reception unit and extracts features of each image; a generation unit that generates novel content by combining the features extracted by the analysis unit; a providing unit that presents the content generated by the generating unit to a user. A system characterized by:

2. The analysis unit Extracting buildings or natural elements from landscape images 2. The system of claim 1.

3. The analysis unit Extracting colors and shapes from food images 2. The system of claim 1.

4. The analysis unit Extracting species or behavior from animal images 2. The system of claim 1.

5. The generation unit Combining extracted features to generate new, non-existent landscapes and animals 2. The system of claim 1.

6. The generation unit Combining extracted features to generate new dish ideas 2. The system of claim 1.

7. The providing unit Present the generated content to the user 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions.

2. The system of claim 1.

Images