System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting an image photographed by a user on a daily basis, a means for analyzing the input image and extracting related information, a means for generating new content on the basis of the extracted common point, and a means for providing the generated content 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] In modern life, discovering new things and experiencing surprises is an important factor in increasing people's vitality and improving their quality of life. However, new encounters and discoveries are often scarce in everyday life, and there are limited ways to obtain new content and experiences based on existing experiences and information. Therefore, there is a need for a system that provides novel encounters based on individual interests and experiences. [Means for solving the problem]

[0005] The present invention is a system that includes a means for inputting images taken by a user in daily life, a means for analyzing the input images and extracting related information, a means for generating novel content based on the extracted commonalities, and a means for providing the generated content to the user. Specifically, the system finds commonalities from the analyzed data and creates new content based on them. It also recommends existing content and generates novel, non-existent content. In this way, the system allows users to make new discoveries and surprises, adding color to their lives.

[0006] "User" refers to an individual or organization that uses this system.

[0007] "Daily life" refers to the range of general living environments and activities that a user experiences on a daily basis.

[0008] "Photography" refers to the act of creating an image using a camera or other photographic device.

[0009] "Image" refers to a captured still image or digital image data.

[0010] "Input" refers to the act of a user providing or transmitting images or information to the system.

[0011] "Means" refers to the function or method for achieving a particular purpose.

[0012] "Analysis" refers to the process of using technical methods to understand and break down the images and data provided and extract information.

[0013] "Related information" refers to information such as objects, colors, patterns, and themes extracted from an image.

[0014] "Commonalities" refer to features or elements that are commonly found in multiple images or data.

[0015] "Novel content" refers to new things or information that have not previously existed or are different from what has previously existed.

[0016] "Generation" refers to the process of creating new content based on existing information and data.

[0017] "Serving" refers to the act of passing, displaying, or making accessible generated content to a user.

[0018] "Existing content" refers to real information, objects, or services that already exist.

[0019] "Recommendation" refers to the act of making suggestions to users based on specific criteria or algorithms. [Brief explanation of the drawings]

[0020] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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, a 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), and an APU (Accelerated Processing Unit).

[0024] 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.

[0025] 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.

[0026] 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), Bluetooth (registered trademark), etc.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0032] 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.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server, and a specific embodiment of this system is described below.

[0042] 1. Image input

[0043] Users can use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or a web interface.

[0044] 2. Sending and receiving images

[0045] The device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[0046] 3. Image Analysis

[0047] The server retrieves the stored images and analyzes each one using image recognition algorithms, which extract relevant information such as objects, colors, patterns, and themes contained in the image.

[0048] 4. Extracting commonalities

[0049] Based on the analysis results, the server extracts common elements from multiple images, which allows it to identify user interests and themes.

[0050] 5. Creating novel content

[0051] The server creates a list of recommendations for real content based on common elements, and can also generate novel content using creative techniques, such as using AI to combine multiple landscape photos to generate a new landscape image.

[0052] 6. Formatting and sending results

[0053] The server converts the generated content (the recommendation list of existing content and novel content) into a format suitable for providing to the user, and sends the formatted results to the terminal.

[0054] 7. Provision to Users

[0055] The device displays the received results, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences, adding color to their lives.

[0056] Examples:

[0057] Example 1: Travel photos

[0058] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0059] Example 2: Food photos

[0060] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0061] In this way, Mix Machine is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] Users use their device to select images they have taken in their daily lives and upload them to Mix Machine. Through the device interface, users select multiple images and press the send button.

[0065] Step 2:

[0066] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0067] Step 3:

[0068] The server stores the received images in a database for temporary storage, which allows subsequent analysis and processing.

[0069] Step 4:

[0070] The server retrieves the stored image data and analyzes it using image recognition algorithms to extract objects (e.g., people, landscapes, food, etc.), dominant colors, patterns, and other relevant information contained in the image.

[0071] Step 5:

[0072] From the retrieved related information, the server extracts common elements and features across multiple images, such as matching objects between images, similar colors, and common themes.

[0073] Step 6:

[0074] The server generates novel content based on commonalities, creating a recommendation list of real-world content (e.g., related travel destinations or recipes) and using AI to generate novel, non-real-world content (e.g., creative combinations of landscape images or new cooking ideas).

[0075] Step 7:

[0076] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0077] Step 8:

[0078] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[0079] Step 9:

[0080] The device displays the received results on the screen, allowing the user to check and enjoy new and recommended content.

[0081] Step 10:

[0082] Users can take action based on the content provided to gain new experiences and discoveries. Through this entire process, Mix Machine adds novelty to users' daily lives and improves their quality of life.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] Conventional image analysis systems have the drawback of requiring extensive manual operations and specialized knowledge to generate new content and related information from images taken by users. Furthermore, there has been a lack of effective means to extract commonalities from the diverse images taken by users and provide new discoveries and experiences. As a result, users can only obtain limited information, making it difficult to generate new content.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for inputting images taken by a user in daily life, means for transmitting the input images to the server, means for storing the images received by the server in data storage, means for analyzing the stored images and extracting related information such as objects, colors, patterns, and themes in the images, means for extracting elements common to a plurality of images, means for generating novel content based on the common elements, and means for providing the generated content to the user. This makes it possible to automatically extract common elements from a variety of images taken by a user and generate novel content and recommendation information based on that information.

[0088] A "user" is a person who uses the system and inputs images taken in daily life into the system.

[0089] A "terminal" is a device that is operated by a user to input images and transmit them to a server.

[0090] A "server" is a computer system that stores received images, analyzes them, and provides generated content to users.

[0091] "Images" are photographs and visual data that users take in their daily lives and upload to the system.

[0092] "Data storage" refers to a storage device for storing images received by the server.

[0093] "Analysis" is the process by which the server extracts information from stored images using image recognition algorithms.

[0094] An "object" refers to a specific object present in an image and is detected by analysis.

[0095] "Color" refers to the color information present in the image that is extracted as part of the analysis.

[0096] A "pattern" is a repeating design or pattern present in an image that can be detected through analysis.

[0097] "Theme" refers to the overall mood or subject matter of an image, as identified through analysis.

[0098] "Common elements" are information such as objects, colors, patterns, and themes that are common to multiple images.

[0099] "Novel content" is new visual data or information generated based on the analysis results and common elements.

[0100] "Recommendation" refers to proposing existing content or information suitable for a user based on the extracted common elements.

[0101] This invention is a system that inputs images taken by a user in daily life, analyzes the images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server.

[0102] First, a user uses a device to select images taken during daily activities and upload them to the system via a dedicated application or web interface. The device then sends the selected images to the server, which then stores the received images in data storage.

[0103] The server retrieves the stored images and analyzes each one using image recognition algorithms, which can use image recognition software such as the Python libraries OpenCV and TensorFlow, to extract relevant information such as objects, colors, patterns, and themes contained in the image.

[0104] The server then uses the analysis results to extract common elements across multiple images, which can be done using a clustering algorithm (e.g., K-means or DBSCAN), and generates novel content based on these common elements.

[0105] Generative AI models (such as GPT-3 and MIDAS) are used to generate novel content. The server generates prompts based on common elements, and inputs these into the AI ​​model to generate new content such as landscape images and cooking recipes.

[0106] The generated content is formatted by the server and converted into a format for delivery to the user, for example, in JSON format, and sent to the device as an HTTP response. The device then displays the received results in an application or web interface, allowing the user to enjoy the new content.

[0107] Specific examples

[0108] Example 1: Travel photos

[0109] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Example prompt: "Generate a new landscape image related to mountains and lakes."

[0110] Example 2: Food photos

[0111] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Example prompt: "Analyze the food photos and suggest new dish recipes."

[0112] In this way, it becomes possible to enrich the user's daily life and provide new discoveries and experiences.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] Users use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or web interface. Users select image files and click the "Upload" button to send the image data to the system. The input is the image file selected by the user on the device, and the output is the transmission of that image file to the server.

[0116] Step 2:

[0117] The terminal sends the selected image file to the server using an HTTP request. The server saves the received image data in its data storage. The received image data is saved in a specific directory on the disk. The input is the image file sent by the user, and the output is the image file saved in the server's data storage.

[0118] Step 3:

[0119] The server retrieves the stored images and analyzes each one using image recognition algorithms. This analysis uses image recognition software such as OpenCV or TensorFlow. The server reads the image files and applies object detection algorithms to extract relevant information such as objects, colors, patterns, and themes within the image. The input is the stored image file, and the output is the image analysis results.

[0120] Step 4:

[0121] The server extracts common elements across multiple images based on the analysis results. During this process, the analyzed data is used to identify common objects, colors, patterns, and themes. A clustering algorithm (e.g., K-means or DBSCAN) is used to group the commonalities. The input is the image analysis results, and the output is the extracted common elements.

[0122] Step 5:

[0123] The server generates novel content based on the common elements. To generate new content using a generative AI model (e.g., GPT-3 or MIDAS), a prompt is created and input to the AI ​​model. The prompt contains specific instructions related to the common elements. The input is the extracted common elements and the generated prompt, and the output is the generated novel content.

[0124] Step 6:

[0125] The server converts the generated content into a format for providing it to the user and sends it to the terminal. The generated content is formatted in JSON format, for example, and sent to the terminal as an HTTP response. The input is the generated content, and the output is the formatted content.

[0126] Step 7:

[0127] The terminal displays the results received from the server in an application or web interface, allowing the user to view and enjoy newly generated content and recommended content. The input is the formatted content from the server, and the output is the content displayed on the user interface.

[0128] (Application example 1)

[0129] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] In today's world, users take a huge number of images on a daily basis, but simply storing these images does not maximize their value. In particular, if users could easily generate new video content using the images they have taken and receive recommendations for related video content, they could provide a more fulfilling digital experience. However, current systems lack the ability to seamlessly link image analysis to video content generation.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0132] In this invention, the server includes a means for inputting images taken by the user in daily life, a means for analyzing the input images and extracting related information, and a means for generating novel video content based on the extracted commonalities, thereby making it possible to generate and recommend related video content from the user's images.

[0133] "Means for users to input images they take in their daily lives" refers to a function that allows users to upload image data they have taken using devices such as smartphones or cameras to the system.

[0134] "Means for analyzing input images and extracting relevant information" refers to the process of analyzing uploaded images using image recognition algorithms or machine learning models to extract features such as objects, colors, patterns, and themes contained in the images.

[0135] "Means for generating novel video content based on extracted commonalities" refers to a process for creating new video content using commonalities between images obtained through analysis, such as a function for generating a short movie based on multiple landscape photographs.

[0136] "Means for providing generated video content to users" refers to a function for distributing generated new video content so that users can view and use it, and is provided through a smartphone application or web interface.

[0137] This invention is a system that analyzes images taken by users in their daily lives and generates and provides novel video content. This system is mainly composed of a user terminal and a server. Specific embodiments for carrying out the invention are described below.

[0138] 1. Image input

[0139] Users use devices (such as smartphones or cameras) to select images they have taken in their daily lives and upload them to the system. Images can be input via a dedicated application or a web interface. Images are in common image formats such as JPEG and PNG.

[0140] 2. Sending and receiving images

[0141] The user's device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[0142] 3. Image Analysis

[0143] The server retrieves the stored images and analyzes them using image recognition algorithms and machine learning models (using, for example, TensorFlow or OpenCV) to extract features such as objects, colors, patterns, and themes contained in the images.

[0144] 4. Extracting commonalities

[0145] Based on the analysis results, the server extracts common elements from multiple images. By identifying these common elements, it can recognize the user's interests and themes.

[0146] 5. Creation of Novel Video Content

[0147] The server generates novel video content based on the common elements. For example, it uses an AI generative model (known as a generative AI model) to create related short movies or animations. A generative AI model can generate a new short movie based on multiple landscape photos.

[0148] 6. Formatting and sending results

[0149] The server converts the generated video content into a format suitable for providing to the user, and sends the formatted result to the terminal.

[0150] 7. Provision to Users

[0151] The user's device displays the received video content, allowing the user to watch and enjoy it. Through new video content, users can gain new discoveries and experiences.

[0152] Examples:

[0153] For example, if a user uploads a landscape photo of a mountain and lake taken on a trip, the app will analyze images of similar scenery and generate and provide a new short movie, allowing users to have a new visual experience based on the photos they have taken.

[0154] Example prompt sentence:

[0155] "Based on the user's travel photos of mountains and lakes, please generate a short movie using similar scenery. Please use DeepAI's image-similarity API for image analysis."

[0156] This system allows users to enjoy new video content using images they take in their daily lives, enriching their digital experience.

[0157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0158] Step 1:

[0159] Users upload images they have taken in their daily lives from their smartphones or cameras to the application. The image files are in JPEG or PNG format. This becomes the input data. After uploading, the images are temporarily saved within the application.

[0160] Step 2:

[0161] The device sends the image selected by the user to the server. When sending, the data is sent using a communication protocol (e.g. HTTP / HTTPS). The server stores the received image data in a database and prepares it for analysis. This is the process of saving input data in the form of an image file.

[0162] Step 3:

[0163] The server then performs an analysis of the stored image data using an image recognition algorithm (for example, TensorFlow or OpenCV). The server extracts features such as objects, colors, patterns, and themes contained in the image. This is the specific operation of data analysis on the image data, and the analysis results are obtained as the output.

[0164] Step 4:

[0165] The server then uses the analysis results to extract common elements from multiple images. For example, if there are multiple landscape photos, it will identify natural features such as mountains and lakes that are common to the photos. The output of this process is a list of common elements.

[0166] Step 5:

[0167] The server generates novel video content based on the common elements. In this process, a generative AI model (e.g., image generation AI) is used to reflect the commonalities between multiple images and generate a new short movie. Based on the input data, the list of common elements, the server obtains the output of novel video content.

[0168] Step 6:

[0169] The server converts the format of the generated video content. Specifically, it converts the format of the video file into a format that users can play (e.g., MP4 format). This format conversion process results in output data that is easy for users to access.

[0170] Step 7:

[0171] The server sends the format-converted video content to the user's device, using the same communication protocol (e.g., HTTP / HTTPS). The device saves the received video content and makes it playable.

[0172] Step 8:

[0173] Users can view and enjoy video content stored on their devices through the application, providing a new video experience based on images they have taken. The output data is playable video content.

[0174] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0175] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to generate and provide more personalized content. This system is composed of a user terminal, a server, and an emotion engine, and specific embodiments thereof are described below.

[0176] 1. Image input

[0177] Users can use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[0178] 2. Sending and receiving images

[0179] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0180] 3. Image Analysis

[0181] The server stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms. This analysis extracts relevant information, such as objects, colors, patterns, and themes, contained in the image.

[0182] 4. Emotional Recognition

[0183] The emotion engine is used to recognize the user's emotions from the images uploaded by the user, including the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery.

[0184] 5. Extracting commonalities

[0185] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[0186] 6. Creating novel content

[0187] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real-world content (e.g., related travel destinations and recipes) and uses AI to generate novel, non-real-world content (e.g., creative combinations of landscape images and new cooking ideas). The emotion recognition results enable it to provide content that best suits the user's current emotional state.

[0188] 7. Formatting and sending results

[0189] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0190] 8. Provision to Users

[0191] The device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences that will add color to their lives.

[0192] Specific examples

[0193] Example 1: Travel photos

[0194] A user uploads three landscape photos taken at a travel destination to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. The emotion engine recognizes the user's emotion at the time of taking the photos (e.g., joy, tranquility, etc.). Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0195] Example 2: Food photos

[0196] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. The emotion engine recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0197] In this way, the Mix Machine, which combines an emotion engine, is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[0198] The processing flow will be explained below.

[0199] Step 1:

[0200] Users use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[0201] Step 2:

[0202] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0203] Step 3:

[0204] The server stores the received image data in a database, which makes it ready for analysis.

[0205] Step 4:

[0206] The server retrieves the stored images and analyzes them using image recognition algorithms to extract relevant information, such as objects, colors, patterns, and themes contained in the images.

[0207] Step 5:

[0208] The emotion engine recognizes the user's emotions from the facial expressions and behavior of people in the image, as well as the atmosphere of the landscape, and identifies emotional states such as joy, surprise, and calm.

[0209] Step 6:

[0210] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[0211] Step 7:

[0212] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real content and provides content that best suits the user's current mood based on the emotion recognition results. It also uses AI to generate novel, non-existent content.

[0213] Step 8:

[0214] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0215] Step 9:

[0216] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[0217] Step 10:

[0218] The device displays the received results on the screen, allowing users to check and enjoy new and recommended content. Users can make new discoveries and gain new experiences, adding color to their lives.

[0219] Example 2

[0220] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0221] Conventional image analysis systems generate content based on images taken by users in their daily lives without personalizing it, making it difficult to fully satisfy the user's interests and emotions. In addition, because content is generated solely based on the results of image analysis, it is difficult to recommend novel content or content that specifically matches the user's current emotions.

[0222] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0223] In this invention, the server includes means for analyzing an input image and extracting related data, means for recognizing a user's emotion using emotion recognition technology, and means for generating novel digital content based on the extracted related data and the result of the user's emotion recognition, thereby enabling the provision of personalized content based on the user's emotional state.

[0224] A "user" is an individual who utilizes the system to input images and receive analysis results and generated content.

[0225] "Image" refers to a photograph or graphic taken by a user, and is digital data that is input.

[0226] "Means of input" refers to the interface or process by which a user uses a terminal to upload images to the system.

[0227] "Remote device" refers to a server or cloud computing system that receives and analyzes images sent from a user's device.

[0228] "Means for analyzing" refers to algorithms and software used by the remote device to process the received image data and extract relevant data and features.

[0229] "Related data" refers to information such as objects, colors, patterns, and themes extracted from the analyzed image.

[0230] "Emotion recognition technology" refers to technology that analyzes images and other data to identify a user's emotional state.

[0231] "Emotion recognition result" refers to data on a user's emotional state obtained through emotion recognition technology.

[0232] "Novel digital content" refers to new digital media or content that the user has not yet experienced, which is generated based on the extracted related data and emotion recognition results.

[0233] "Means of delivery" refers to the interface or process for delivering and displaying the generated content to users.

[0234] "Real content" refers to tangible content such as real-world products, services, places, and information.

[0235] "Novel non-existent digital content" refers to fictional digital media or content that is newly generated using AI technology or creative processes.

[0236] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, and generates and provides novel digital content to the user. Furthermore, by combining emotion recognition technology that recognizes the user's emotions, it is possible to provide more personalized content. This system is composed of a user terminal, a remote device, and emotion recognition technology, and specific embodiments thereof are described below.

[0237] First, the user uses the device to select images taken in daily life and upload them to the system. The device is equipped with a dedicated application or web interface through which the user inputs image data.

[0238] The terminal transmits the selected image data to the remote device. The transmission process is carried out by uploading the image file to the remote device using an Internet connection, using the HTTP or HTTPS protocol.

[0239] The remote device stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms, using software such as TensorFlow and OpenCV. The analysis extracts relevant data, such as objects, colors, patterns, and themes, contained in the image.

[0240] Next, emotion recognition technology is used to recognize the user's emotions from the images uploaded by the user. This emotion recognition includes the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery. For example, Microsoft Azure Cognitive Services and Amazon Rekognition are used.

[0241] Based on the acquired related data and emotion recognition results, the remote device extracts common elements and features across multiple images, such as matching objects between images, similar colors, common themes, and the user's emotional state.

[0242] The remote device then generates novel digital content based on the common elements and emotion recognition results, including using generative AI models to generate relevant content (e.g., new travel destinations or recipe ideas). Generative AI models such as Hugging Face and GPT-3 are used.

[0243] The generated digital content is formatted for presentation to the user, the format including a recommendation list and novel digital content.

[0244] Finally, the device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content, leading to new discoveries and experiences.

[0245] Specific examples

[0246] Example 1: Travel photos

[0247] A user uploads three landscape photos taken during a trip to the system. A remote device analyzes these photos and extracts common natural elements, such as mountains, lakes, and forests. Emotion recognition technology recognizes the user's emotion (e.g., joy, tranquility) when taking the photos. Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses a generative AI model to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0248] Example prompt (travel photos)

[0249] Recommend new travel destinations and generate creative landscape images based on three landscape photos taken by the user. The content of the photos is mountains, lakes, and forests. The user's emotions are joy and tranquility.

[0250] Example 2: Food photos

[0251] Users upload photos of five dishes they have eaten in their daily lives. A remote device analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Emotion recognition technology recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0252] Sample prompts (food photos)

[0253] Generate new recipes and cooking ideas based on five types of food photos taken by the user. The dishes are pasta, stir-fry, and dessert. The user's emotions are enjoying cooking.

[0254] The above is a specific embodiment for carrying out the present invention.

[0255] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0256] Step 1:

[0257] A user uses a terminal to select and upload images taken in daily life.

[0258] How it works: The user selects an image from the device's photo album and presses the upload button via a dedicated application or web interface.

[0259] Input: An image file selected by the user.

[0260] Output: Images are brought into the system and ready to be uploaded.

[0261] Step 2:

[0262] The terminal transmits the selected image data to the server.

[0263] How it works: When the upload button is pressed, the device breaks the image data into packets and sends them over an internet connection to a server using the HTTP or HTTPS protocol.

[0264] Input: An image file stored on the user's device.

[0265] Output: The image data is sent to the server.

[0266] Step 3:

[0267] The server receives the image data sent from the terminal and stores it in a database.

[0268] How it works: The server reconstructs the received data packets and saves them in storage as the original image file.

[0269] Input: Image data packets sent from the terminal.

[0270] Output: Image files stored in the database.

[0271] Step 4:

[0272] The server analyzes the stored image data using an image recognition algorithm.

[0273] How it works: The server applies image analysis software such as TensorFlow or OpenCV to extract information such as objects, colors, patterns, and themes in the image.

[0274] Input: Image files stored in the database.

[0275] Output: Relevant data as a result of image analysis.

[0276] Step 5:

[0277] The server uses emotion recognition technology to recognize emotions from images uploaded by users.

[0278] How it works: The server calls the emotion recognition engine API and provides image data. The emotion engine analyzes the facial expressions and mood in the image and returns emotional data to the server.

[0279] Input: Analyzed image data.

[0280] Output: Emotion data as emotion recognition results.

[0281] Step 6:

[0282] Based on the results of image analysis and emotion recognition, the server extracts elements and features common to multiple images.

[0283] How it works: The server uses the analysis results and emotion data retrieved from the database to run algorithms that search for common elements between images (such as matching objects, similar colors, or common themes).

[0284] Input: Image related data and emotion data.

[0285] Output: Extraction of common elements and features.

[0286] Step 7:

[0287] The server generates novel digital content based on the common elements and emotion recognition results.

[0288] How it works: The server generates prompts for the generative AI model, feeds the prompts into the model, and the model generates new content and returns the results to the server.

[0289] Input: common elements, feature data, prompt sentence.

[0290] Output: The novel digital content generated.

[0291] Step 8:

[0292] The server formats the generated digital content for delivery to the user.

[0293] How it works: The server formats the results into HTML, JSON, or other formats that are easy for the user to view.

[0294] Input: Novel digital content generated.

[0295] Output: Formatted digital content.

[0296] Step 9:

[0297] The server transmits the formatted digital content to the terminal.

[0298] How it works: The server sends data to the device via the HTTPS protocol.

[0299] Input: Formatted digital content.

[0300] Output: Digital content sent to a device.

[0301] Step 10:

[0302] The terminal displays the received results on the screen.

[0303] Action: The device application or browser renders the results and displays them to the user.

[0304] Input: Digital content sent from the server.

[0305] Output: The digital content that the user sees on a screen.

[0306] (Application example 2)

[0307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] As technology advances, there is a demand for services that generate new content using images taken by users in their daily lives. However, conventional systems have difficulty generating personalized content that takes user emotions into account, making it impossible to provide users with truly valuable content. Furthermore, there has been no system that can provide users with both real content and novel, imaginary content in conjunction with emotion recognition. This has prevented systems from providing new discoveries and experiences that are in tune with users' emotions, resulting in insufficient improvements to the user experience.

[0309] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting an image taken by a user with a terminal, a means for transmitting the input image to the server, a means for analyzing the image and extracting related information in the server, a means for recognizing the user's emotion using an emotion engine, a means for generating novel content based on the extracted commonalities and emotion recognition results, and a means for providing the generated content to the user. This enables the generation of personalized novel content based on the user's emotion from the image taken by the user, thereby providing a richer user experience. In addition, by using a generative AI model based on the emotion recognition results to generate prompt sentences and generate novel content, it is possible to provide creative content that does not actually exist. This allows the user to make new discoveries and experiences.

[0310] "Means for inputting images taken by the user on a terminal" refers to a function for importing images taken by the user using a terminal that the user normally uses, such as a smartphone or digital camera, into the system.

[0311] The "means for transmitting the input image to the server" is a function for uploading the image selected from the terminal to a cloud server or a remote server via the Internet.

[0312] "Means for analyzing images on the server and extracting relevant information" refers to a function that processes image data stored on the server and recognizes and extracts features such as objects, colors, patterns, and themes contained in the images.

[0313] "Means for recognizing user emotions using an emotion engine" refers to algorithms and software for determining a user's emotions based on the content of uploaded images, the facial expressions of people photographed, the atmosphere of the scenery, etc.

[0314] "Means for generating novel content based on extracted commonalities and emotion recognition results" refers to algorithms and AI technologies for creating new content based on common elements and features from multiple images and the results of user emotion recognition.

[0315] The "means for providing generated content to the user" is a function for transmitting novel content generated by the server to the user's terminal and displaying it so that the user can view and enjoy it.

[0316] "Means for recommending real content" refers to a function that selects and suggests to users existing content (e.g., videos, music, articles, etc.) that matches their interests based on related information obtained through image analysis and emotion recognition results.

[0317] "Means for generating novel non-existent content" refers to a function that uses AI to generate creative and attractive new content that does not exist in reality (e.g., virtual landscape images, new food recipes, etc.).

[0318] "Means for generating prompts using a generative AI model" refers to a function that uses an AI model to create prompts (e.g., instructions or descriptions for new content to be generated) based on the user's emotions recognized by the emotion engine.

[0319] The present invention is a system that generates novel content using images taken by a user in their daily lives and provides the content to the user. Specific embodiments for carrying out the invention will be described below.

[0320] System configuration

[0321] The system mainly consists of the following components:

[0322] 1. User's device: a smartphone or other device

[0323] 2. Server: Image analysis and emotion engine

[0324] 3. Generative AI Model: A Novel Content Generation Engine

[0325] User's device

[0326] Users upload the images they have taken using a dedicated application. The device then sends the images to the server. Users can easily select and upload images using the smartphone application.

[0327] server

[0328] The server stores the received image data and prepares it for analysis. Specifically, it performs the following processes:

[0329] 1. Image Analysis: Use OpenCV or other image processing libraries to extract features such as objects, colors, patterns, and themes from images.

[0330] 2. Emotion Recognition: Using Keras and other deep learning models, we recognize the user's emotions from the image content. We analyze the facial expressions, behaviors, and atmosphere of the people in the images taken by the user.

[0331] 3. Content generation: Using a generative AI model, novel content is generated based on the extracted commonalities and emotion recognition results.

[0332] Generative AI Models

[0333] The model generates prompts and generates new content based on them. For example, for travel photos, it generates prompts like this:

[0334] Generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy."

[0335] Based on this prompt, the generative AI model generates a creative landscape image that corresponds to the user's emotions.

[0336] Content provider

[0337] The generated recommendation list of novel and existing content is sent from the server to the user's device and displayed through a dedicated application, allowing the user to browse this content and enjoy new discoveries and experiences.

[0338] Specific use cases

[0339] For example, if a user uploads a photo of cherry blossoms they took on a daily basis, the image is sent to the server and analyzed. As a result of the analysis, it is recognized that the image contains cherry blossoms, and the user's emotion is detected as "joy." Based on this information, the generative AI model creates a prompt to generate a new landscape image related to "spring cherry blossom viewing photos," and generates the new landscape image. This generated image is provided to the user, who can use it to get ideas for new travel destinations.

[0340] In this way, the system of the present invention enriches the user's daily life and provides new discoveries and experiences.

[0341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0342] Step 1:

[0343] The user takes a photo on their device and selects the image to upload. The user opens a dedicated application on their smartphone, selects an image they have taken in their daily life, and presses the upload button. The input is the image taken by the user, and the output is the image sent from the device to the server.

[0344] Step 2:

[0345] The device sends the selected image to the server. The image file is uploaded to the server via the Internet. The input is the image file sent from the device, and the output is the image data stored on the server.

[0346] Step 3:

[0347] The server stores the received image data and prepares it for analysis. Specifically, it stores the received images in a database. It also prepares the libraries and models required for image analysis. The input is the image data stored on the server, and the output is image data ready for analysis.

[0348] Step 4:

[0349] The server performs image analysis and extracts relevant information. It uses image processing libraries such as OpenCV to recognize objects, colors, patterns, and themes in the image. The input is the stored image data, and the information extracted as a result of the analysis is the output.

[0350] Step 5:

[0351] The server uses an emotion engine to recognize user emotions. It uses deep learning models such as Keras to analyze facial expressions of people in images and the atmosphere of the scenery to identify emotions. The input is image data and extracted related information, and the output is the recognized emotion.

[0352] Step 6:

[0353] The server generates novel content based on the extracted commonalities and emotion recognition results. Utilizing the generative AI model, it generates a prompt and then creates new content based on it. Specifically, the generative AI model receives a prompt to generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy," and the generative AI model generates novel content accordingly. The inputs are the commonalities, emotion recognition results, and the prompt, and the output is novel content.

[0354] Step 7:

[0355] The server formats the generated novel content and provides it to the user. The generated content is sent to the user's device and displayed through a dedicated application. The input is the novel content, and the output is the content displayed on the device. This allows the user to make new discoveries and gain new experiences.

[0356] 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.

[0357] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0358] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0359] [Second embodiment]

[0360] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0361] 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.

[0362] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0363] 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.

[0364] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0365] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0366] 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.

[0367] 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.

[0368] 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 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.

[0369] 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.

[0370] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0371] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0372] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server, and a specific embodiment of this system is described below.

[0373] 1. Image input

[0374] Users can use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or a web interface.

[0375] 2. Sending and receiving images

[0376] The device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[0377] 3. Image Analysis

[0378] The server retrieves the stored images and analyzes each one using image recognition algorithms, which extract relevant information such as objects, colors, patterns, and themes contained in the image.

[0379] 4. Extracting commonalities

[0380] Based on the analysis results, the server extracts common elements from multiple images, which allows it to identify user interests and themes.

[0381] 5. Creating novel content

[0382] The server creates a list of recommendations for real content based on common elements, and can also generate novel content using creative techniques, such as using AI to combine multiple landscape photos to generate a new landscape image.

[0383] 6. Formatting and sending results

[0384] The server converts the generated content (the recommendation list of existing content and novel content) into a format suitable for providing to the user, and sends the formatted results to the terminal.

[0385] 7. Provision to Users

[0386] The device displays the received results, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences, adding color to their lives.

[0387] Examples:

[0388] Example 1: Travel photos

[0389] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0390] Example 2: Food photos

[0391] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0392] In this way, Mix Machine is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[0393] The processing flow will be explained below.

[0394] Step 1:

[0395] Users use their device to select images they have taken in their daily lives and upload them to Mix Machine. Through the device interface, users select multiple images and press the send button.

[0396] Step 2:

[0397] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0398] Step 3:

[0399] The server stores the received images in a database for temporary storage, which allows subsequent analysis and processing.

[0400] Step 4:

[0401] The server retrieves the stored image data and analyzes it using image recognition algorithms to extract objects (e.g., people, landscapes, food, etc.), dominant colors, patterns, and other relevant information contained in the image.

[0402] Step 5:

[0403] From the retrieved related information, the server extracts common elements and features across multiple images, such as matching objects between images, similar colors, and common themes.

[0404] Step 6:

[0405] The server generates novel content based on commonalities, creating a recommendation list of real-world content (e.g., related travel destinations or recipes) and using AI to generate novel, non-real-world content (e.g., creative combinations of landscape images or new cooking ideas).

[0406] Step 7:

[0407] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0408] Step 8:

[0409] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[0410] Step 9:

[0411] The device displays the received results on the screen, allowing the user to check and enjoy new and recommended content.

[0412] Step 10:

[0413] Users can take action based on the content provided to gain new experiences and discoveries. Through this entire process, Mix Machine adds novelty to users' daily lives and improves their quality of life.

[0414] Example 1

[0415] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0416] Conventional image analysis systems have the drawback of requiring extensive manual operations and specialized knowledge to generate new content and related information from images taken by users. Furthermore, there has been a lack of effective means to extract commonalities from the diverse images taken by users and provide new discoveries and experiences. As a result, users can only obtain limited information, making it difficult to generate new content.

[0417] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0418] In this invention, the server includes means for inputting images taken by a user in daily life, means for transmitting the input images to the server, means for storing the images received by the server in data storage, means for analyzing the stored images and extracting related information such as objects, colors, patterns, and themes in the images, means for extracting elements common to a plurality of images, means for generating novel content based on the common elements, and means for providing the generated content to the user. This makes it possible to automatically extract common elements from a variety of images taken by a user and generate novel content and recommendation information based on that information.

[0419] A "user" is a person who uses the system and inputs images taken in daily life into the system.

[0420] A "terminal" is a device that is operated by a user to input images and transmit them to a server.

[0421] A "server" is a computer system that stores received images, analyzes them, and provides generated content to users.

[0422] "Images" are photographs and visual data that users take in their daily lives and upload to the system.

[0423] "Data storage" refers to a storage device for storing images received by the server.

[0424] "Analysis" is the process by which the server extracts information from stored images using image recognition algorithms.

[0425] An "object" refers to a specific object present in an image and is detected by analysis.

[0426] "Color" refers to the color information present in the image that is extracted as part of the analysis.

[0427] A "pattern" is a repeating design or pattern present in an image that can be detected through analysis.

[0428] "Theme" refers to the overall mood or subject matter of an image, as identified through analysis.

[0429] "Common elements" are information such as objects, colors, patterns, and themes that are common to multiple images.

[0430] "Novel content" is new visual data or information generated based on the analysis results and common elements.

[0431] "Recommendation" refers to proposing existing content or information suitable for a user based on the extracted common elements.

[0432] This invention is a system that inputs images taken by a user in daily life, analyzes the images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server.

[0433] First, a user uses a device to select images taken during daily activities and upload them to the system via a dedicated application or web interface. The device then sends the selected images to the server, which then stores the received images in data storage.

[0434] The server retrieves the stored images and analyzes each one using image recognition algorithms, which can use image recognition software such as the Python libraries OpenCV and TensorFlow, to extract relevant information such as objects, colors, patterns, and themes contained in the image.

[0435] The server then uses the analysis results to extract common elements across multiple images, which can be done using a clustering algorithm (e.g., K-means or DBSCAN), and generates novel content based on these common elements.

[0436] Generative AI models (such as GPT-3 and MIDAS) are used to generate novel content. The server generates prompts based on common elements, and inputs these into the AI ​​model to generate new content such as landscape images and cooking recipes.

[0437] The generated content is formatted by the server and converted into a format for delivery to the user, for example, in JSON format, and sent to the device as an HTTP response. The device then displays the received results in an application or web interface, allowing the user to enjoy the new content.

[0438] Specific examples

[0439] Example 1: Travel photos

[0440] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Example prompt: "Generate a new landscape image related to mountains and lakes."

[0441] Example 2: Food photos

[0442] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Example prompt: "Analyze the food photos and suggest new dish recipes."

[0443] In this way, it becomes possible to enrich the user's daily life and provide new discoveries and experiences.

[0444] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0445] Step 1:

[0446] Users use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or web interface. Users select image files and click the "Upload" button to send the image data to the system. The input is the image file selected by the user on the device, and the output is the transmission of that image file to the server.

[0447] Step 2:

[0448] The terminal sends the selected image file to the server using an HTTP request. The server saves the received image data in its data storage. The received image data is saved in a specific directory on the disk. The input is the image file sent by the user, and the output is the image file saved in the server's data storage.

[0449] Step 3:

[0450] The server retrieves the stored images and analyzes each one using image recognition algorithms. This analysis uses image recognition software such as OpenCV or TensorFlow. The server reads the image files and applies object detection algorithms to extract relevant information such as objects, colors, patterns, and themes within the image. The input is the stored image file, and the output is the image analysis results.

[0451] Step 4:

[0452] The server extracts common elements across multiple images based on the analysis results. During this process, the analyzed data is used to identify common objects, colors, patterns, and themes. A clustering algorithm (e.g., K-means or DBSCAN) is used to group the commonalities. The input is the image analysis results, and the output is the extracted common elements.

[0453] Step 5:

[0454] The server generates novel content based on the common elements. To generate new content using a generative AI model (e.g., GPT-3 or MIDAS), a prompt is created and input to the AI ​​model. The prompt contains specific instructions related to the common elements. The input is the extracted common elements and the generated prompt, and the output is the generated novel content.

[0455] Step 6:

[0456] The server converts the generated content into a format for providing it to the user and sends it to the terminal. The generated content is formatted in JSON format, for example, and sent to the terminal as an HTTP response. The input is the generated content, and the output is the formatted content.

[0457] Step 7:

[0458] The terminal displays the results received from the server in an application or web interface, allowing the user to view and enjoy newly generated content and recommended content. The input is the formatted content from the server, and the output is the content displayed on the user interface.

[0459] (Application example 1)

[0460] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0461] In today's world, users take a huge number of images on a daily basis, but simply storing these images does not maximize their value. In particular, if users could easily generate new video content using the images they have taken and receive recommendations for related video content, they could provide a more fulfilling digital experience. However, current systems lack the ability to seamlessly link image analysis to video content generation.

[0462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0463] In this invention, the server includes a means for inputting images taken by the user in daily life, a means for analyzing the input images and extracting related information, and a means for generating novel video content based on the extracted commonalities, thereby making it possible to generate and recommend related video content from the user's images.

[0464] "Means for users to input images they take in their daily lives" refers to a function that allows users to upload image data they have taken using devices such as smartphones or cameras to the system.

[0465] "Means for analyzing input images and extracting relevant information" refers to the process of analyzing uploaded images using image recognition algorithms or machine learning models to extract features such as objects, colors, patterns, and themes contained in the images.

[0466] "Means for generating novel video content based on extracted commonalities" refers to a process for creating new video content using commonalities between images obtained through analysis, such as a function for generating a short movie based on multiple landscape photographs.

[0467] "Means for providing generated video content to users" refers to a function for distributing generated new video content so that users can view and use it, and is provided through a smartphone application or web interface.

[0468] This invention is a system that analyzes images taken by users in their daily lives and generates and provides novel video content. This system is mainly composed of a user terminal and a server. Specific embodiments for carrying out the invention are described below.

[0469] 1. Image input

[0470] Users use devices (such as smartphones or cameras) to select images they have taken in their daily lives and upload them to the system. Images can be input via a dedicated application or a web interface. Images are in common image formats such as JPEG and PNG.

[0471] 2. Sending and receiving images

[0472] The user's device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[0473] 3. Image Analysis

[0474] The server retrieves the stored images and analyzes them using image recognition algorithms and machine learning models (using, for example, TensorFlow or OpenCV) to extract features such as objects, colors, patterns, and themes contained in the images.

[0475] 4. Extracting commonalities

[0476] Based on the analysis results, the server extracts common elements from multiple images. By identifying these common elements, it can recognize the user's interests and themes.

[0477] 5. Creation of Novel Video Content

[0478] The server generates novel video content based on the common elements. For example, it uses an AI generative model (known as a generative AI model) to create related short movies or animations. A generative AI model can generate a new short movie based on multiple landscape photos.

[0479] 6. Formatting and sending results

[0480] The server converts the generated video content into a format suitable for providing to the user, and sends the formatted result to the terminal.

[0481] 7. Provision to Users

[0482] The user's device displays the received video content, allowing the user to watch and enjoy it. Through new video content, users can gain new discoveries and experiences.

[0483] Examples:

[0484] For example, if a user uploads a landscape photo of a mountain and lake taken on a trip, the app will analyze images of similar scenery and generate and provide a new short movie, allowing users to have a new visual experience based on the photos they have taken.

[0485] Example prompt sentence:

[0486] "Based on the user's travel photos of mountains and lakes, please generate a short movie using similar scenery. Please use DeepAI's image-similarity API for image analysis."

[0487] This system allows users to enjoy new video content using images they take in their daily lives, enriching their digital experience.

[0488] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0489] Step 1:

[0490] Users upload images they have taken in their daily lives from their smartphones or cameras to the application. The image files are in JPEG or PNG format. This becomes the input data. After uploading, the images are temporarily saved within the application.

[0491] Step 2:

[0492] The device sends the image selected by the user to the server. When sending, the data is sent using a communication protocol (e.g. HTTP / HTTPS). The server stores the received image data in a database and prepares it for analysis. This is the process of saving input data in the form of an image file.

[0493] Step 3:

[0494] The server then performs an analysis of the stored image data using an image recognition algorithm (for example, TensorFlow or OpenCV). The server extracts features such as objects, colors, patterns, and themes contained in the image. This is the specific operation of data analysis on the image data, and the analysis results are obtained as the output.

[0495] Step 4:

[0496] The server then uses the analysis results to extract common elements from multiple images. For example, if there are multiple landscape photos, it will identify natural features such as mountains and lakes that are common to the photos. The output of this process is a list of common elements.

[0497] Step 5:

[0498] The server generates novel video content based on the common elements. In this process, a generative AI model (e.g., image generation AI) is used to reflect the commonalities between multiple images and generate a new short movie. Based on the input data, the list of common elements, the server obtains the output of novel video content.

[0499] Step 6:

[0500] The server converts the format of the generated video content. Specifically, it converts the format of the video file into a format that users can play (e.g., MP4 format). This format conversion process results in output data that is easy for users to access.

[0501] Step 7:

[0502] The server sends the format-converted video content to the user's device, using the same communication protocol (e.g., HTTP / HTTPS). The device saves the received video content and makes it playable.

[0503] Step 8:

[0504] Users can view and enjoy video content stored on their devices through the application, providing a new video experience based on images they have taken. The output data is playable video content.

[0505] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0506] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to generate and provide more personalized content. This system is composed of a user terminal, a server, and an emotion engine, and specific embodiments thereof are described below.

[0507] 1. Image input

[0508] Users can use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[0509] 2. Sending and receiving images

[0510] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0511] 3. Image Analysis

[0512] The server stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms. This analysis extracts relevant information, such as objects, colors, patterns, and themes, contained in the image.

[0513] 4. Emotional Recognition

[0514] The emotion engine is used to recognize the user's emotions from the images uploaded by the user, including the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery.

[0515] 5. Extracting commonalities

[0516] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[0517] 6. Creating novel content

[0518] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real-world content (e.g., related travel destinations and recipes) and uses AI to generate novel, non-real-world content (e.g., creative combinations of landscape images and new cooking ideas). The emotion recognition results enable it to provide content that best suits the user's current emotional state.

[0519] 7. Formatting and sending results

[0520] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0521] 8. Provision to Users

[0522] The device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences that will add color to their lives.

[0523] Specific examples

[0524] Example 1: Travel photos

[0525] A user uploads three landscape photos taken at a travel destination to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. The emotion engine recognizes the user's emotion at the time of taking the photos (e.g., joy, tranquility, etc.). Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0526] Example 2: Food photos

[0527] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. The emotion engine recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0528] In this way, the Mix Machine, which combines an emotion engine, is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[0529] The processing flow will be explained below.

[0530] Step 1:

[0531] Users use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[0532] Step 2:

[0533] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0534] Step 3:

[0535] The server stores the received image data in a database, which makes it ready for analysis.

[0536] Step 4:

[0537] The server retrieves the stored images and analyzes them using image recognition algorithms to extract relevant information, such as objects, colors, patterns, and themes contained in the images.

[0538] Step 5:

[0539] The emotion engine recognizes the user's emotions from the facial expressions and behavior of people in the image, as well as the atmosphere of the landscape, and identifies emotional states such as joy, surprise, and calm.

[0540] Step 6:

[0541] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[0542] Step 7:

[0543] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real content and provides content that best suits the user's current mood based on the emotion recognition results. It also uses AI to generate novel, non-existent content.

[0544] Step 8:

[0545] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0546] Step 9:

[0547] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[0548] Step 10:

[0549] The device displays the received results on the screen, allowing users to check and enjoy new and recommended content. Users can make new discoveries and gain new experiences, adding color to their lives.

[0550] Example 2

[0551] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0552] Conventional image analysis systems generate content based on images taken by users in their daily lives without personalizing it, making it difficult to fully satisfy the user's interests and emotions. In addition, because content is generated solely based on the results of image analysis, it is difficult to recommend novel content or content that specifically matches the user's current emotions.

[0553] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0554] In this invention, the server includes means for analyzing an input image and extracting related data, means for recognizing a user's emotion using emotion recognition technology, and means for generating novel digital content based on the extracted related data and the result of the user's emotion recognition, thereby enabling the provision of personalized content based on the user's emotional state.

[0555] A "user" is an individual who utilizes the system to input images and receive analysis results and generated content.

[0556] "Image" refers to a photograph or graphic taken by a user, and is digital data that is input.

[0557] "Means of input" refers to the interface or process by which a user uses a terminal to upload images to the system.

[0558] "Remote device" refers to a server or cloud computing system that receives and analyzes images sent from a user's device.

[0559] "Means for analyzing" refers to algorithms and software used by the remote device to process the received image data and extract relevant data and features.

[0560] "Related data" refers to information such as objects, colors, patterns, and themes extracted from the analyzed image.

[0561] "Emotion recognition technology" refers to technology that analyzes images and other data to identify a user's emotional state.

[0562] "Emotion recognition result" refers to data on a user's emotional state obtained through emotion recognition technology.

[0563] "Novel digital content" refers to new digital media or content that the user has not yet experienced, which is generated based on the extracted related data and emotion recognition results.

[0564] "Means of delivery" refers to the interface or process for delivering and displaying the generated content to users.

[0565] "Real content" refers to tangible content such as real-world products, services, places, and information.

[0566] "Novel non-existent digital content" refers to fictional digital media or content that is newly generated using AI technology or creative processes.

[0567] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, and generates and provides novel digital content to the user. Furthermore, by combining emotion recognition technology that recognizes the user's emotions, it is possible to provide more personalized content. This system is composed of a user terminal, a remote device, and emotion recognition technology, and specific embodiments thereof are described below.

[0568] First, the user uses the device to select images taken in daily life and upload them to the system. The device is equipped with a dedicated application or web interface through which the user inputs image data.

[0569] The terminal transmits the selected image data to the remote device. The transmission process is carried out by uploading the image file to the remote device using an Internet connection, using the HTTP or HTTPS protocol.

[0570] The remote device stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms, using software such as TensorFlow and OpenCV. The analysis extracts relevant data, such as objects, colors, patterns, and themes, contained in the image.

[0571] Next, emotion recognition technology is used to recognize the user's emotions from the images uploaded by the user. This emotion recognition includes the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery. For example, Microsoft Azure Cognitive Services and Amazon Rekognition are used.

[0572] Based on the acquired related data and emotion recognition results, the remote device extracts common elements and features across multiple images, such as matching objects between images, similar colors, common themes, and the user's emotional state.

[0573] The remote device then generates novel digital content based on the common elements and emotion recognition results, including using generative AI models to generate relevant content (e.g., new travel destinations or recipe ideas). Generative AI models such as Hugging Face and GPT-3 are used.

[0574] The generated digital content is formatted for presentation to the user, the format including a recommendation list and novel digital content.

[0575] Finally, the device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content, leading to new discoveries and experiences.

[0576] Specific examples

[0577] Example 1: Travel photos

[0578] A user uploads three landscape photos taken during a trip to the system. A remote device analyzes these photos and extracts common natural elements, such as mountains, lakes, and forests. Emotion recognition technology recognizes the user's emotion (e.g., joy, tranquility) when taking the photos. Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses a generative AI model to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0579] Example prompt (travel photos)

[0580] Recommend new travel destinations and generate creative landscape images based on three landscape photos taken by the user. The content of the photos is mountains, lakes, and forests. The user's emotions are joy and tranquility.

[0581] Example 2: Food photos

[0582] Users upload photos of five dishes they have eaten in their daily lives. A remote device analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Emotion recognition technology recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0583] Sample prompts (food photos)

[0584] Generate new recipes and cooking ideas based on five types of food photos taken by the user. The dishes are pasta, stir-fry, and dessert. The user's emotions are enjoying cooking.

[0585] The above is a specific embodiment for carrying out the present invention.

[0586] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0587] Step 1:

[0588] A user uses a terminal to select and upload images taken in daily life.

[0589] How it works: The user selects an image from the device's photo album and presses the upload button via a dedicated application or web interface.

[0590] Input: An image file selected by the user.

[0591] Output: Images are brought into the system and ready to be uploaded.

[0592] Step 2:

[0593] The terminal transmits the selected image data to the server.

[0594] How it works: When the upload button is pressed, the device breaks the image data into packets and sends them over an internet connection to a server using the HTTP or HTTPS protocol.

[0595] Input: An image file stored on the user's device.

[0596] Output: The image data is sent to the server.

[0597] Step 3:

[0598] The server receives the image data sent from the terminal and stores it in a database.

[0599] How it works: The server reconstructs the received data packets and saves them in storage as the original image file.

[0600] Input: Image data packets sent from the terminal.

[0601] Output: Image files stored in the database.

[0602] Step 4:

[0603] The server analyzes the stored image data using an image recognition algorithm.

[0604] How it works: The server applies image analysis software such as TensorFlow or OpenCV to extract information such as objects, colors, patterns, and themes in the image.

[0605] Input: Image files stored in the database.

[0606] Output: Relevant data as a result of image analysis.

[0607] Step 5:

[0608] The server uses emotion recognition technology to recognize emotions from images uploaded by users.

[0609] How it works: The server calls the emotion recognition engine API and provides image data. The emotion engine analyzes the facial expressions and mood in the image and returns emotional data to the server.

[0610] Input: Analyzed image data.

[0611] Output: Emotion data as emotion recognition results.

[0612] Step 6:

[0613] Based on the results of image analysis and emotion recognition, the server extracts elements and features common to multiple images.

[0614] How it works: The server uses the analysis results and emotion data retrieved from the database to run algorithms that search for common elements between images (such as matching objects, similar colors, or common themes).

[0615] Input: Image related data and emotion data.

[0616] Output: Extraction of common elements and features.

[0617] Step 7:

[0618] The server generates novel digital content based on the common elements and emotion recognition results.

[0619] How it works: The server generates prompts for the generative AI model, feeds the prompts into the model, and the model generates new content and returns the results to the server.

[0620] Input: common elements, feature data, prompt sentence.

[0621] Output: The novel digital content generated.

[0622] Step 8:

[0623] The server formats the generated digital content for delivery to the user.

[0624] How it works: The server formats the results into HTML, JSON, or other formats that are easy for the user to view.

[0625] Input: Novel digital content generated.

[0626] Output: Formatted digital content.

[0627] Step 9:

[0628] The server transmits the formatted digital content to the terminal.

[0629] How it works: The server sends data to the device via the HTTPS protocol.

[0630] Input: Formatted digital content.

[0631] Output: Digital content sent to a device.

[0632] Step 10:

[0633] The terminal displays the received results on the screen.

[0634] Action: The device application or browser renders the results and displays them to the user.

[0635] Input: Digital content sent from the server.

[0636] Output: The digital content that the user sees on a screen.

[0637] (Application example 2)

[0638] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0639] As technology advances, there is a demand for services that generate new content using images taken by users in their daily lives. However, conventional systems have difficulty generating personalized content that takes user emotions into account, making it impossible to provide users with truly valuable content. Furthermore, there has been no system that can provide users with both real content and novel, imaginary content in conjunction with emotion recognition. This has prevented systems from providing new discoveries and experiences that are in tune with users' emotions, resulting in insufficient improvements to the user experience.

[0640] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting an image taken by a user with a terminal, a means for transmitting the input image to the server, a means for analyzing the image and extracting related information in the server, a means for recognizing the user's emotion using an emotion engine, a means for generating novel content based on the extracted commonalities and emotion recognition results, and a means for providing the generated content to the user. This enables the generation of personalized novel content based on the user's emotion from the image taken by the user, thereby providing a richer user experience. In addition, by using a generative AI model based on the emotion recognition results to generate prompt sentences and generate novel content, it is possible to provide creative content that does not actually exist. This allows the user to make new discoveries and experiences.

[0641] "Means for inputting images taken by the user on a terminal" refers to a function for importing images taken by the user using a terminal that the user normally uses, such as a smartphone or digital camera, into the system.

[0642] The "means for transmitting the input image to the server" is a function for uploading the image selected from the terminal to a cloud server or a remote server via the Internet.

[0643] "Means for analyzing images on the server and extracting relevant information" refers to a function that processes image data stored on the server and recognizes and extracts features such as objects, colors, patterns, and themes contained in the images.

[0644] "Means for recognizing user emotions using an emotion engine" refers to algorithms and software for determining a user's emotions based on the content of uploaded images, the facial expressions of people photographed, the atmosphere of the scenery, etc.

[0645] "Means for generating novel content based on extracted commonalities and emotion recognition results" refers to algorithms and AI technologies for creating new content based on common elements and features from multiple images and the results of user emotion recognition.

[0646] The "means for providing generated content to the user" is a function for transmitting novel content generated by the server to the user's terminal and displaying it so that the user can view and enjoy it.

[0647] "Means for recommending real content" refers to a function that selects and suggests to users existing content (e.g., videos, music, articles, etc.) that matches their interests based on related information obtained through image analysis and emotion recognition results.

[0648] "Means for generating novel non-existent content" refers to a function that uses AI to generate creative and attractive new content that does not exist in reality (e.g., virtual landscape images, new food recipes, etc.).

[0649] "Means for generating prompts using a generative AI model" refers to a function that uses an AI model to create prompts (e.g., instructions or descriptions for new content to be generated) based on the user's emotions recognized by the emotion engine.

[0650] The present invention is a system that generates novel content using images taken by a user in their daily lives and provides the content to the user. Specific embodiments for carrying out the invention will be described below.

[0651] System configuration

[0652] The system mainly consists of the following components:

[0653] 1. User's device: a smartphone or other device

[0654] 2. Server: Image analysis and emotion engine

[0655] 3. Generative AI Model: A Novel Content Generation Engine

[0656] User's device

[0657] Users upload the images they have taken using a dedicated application. The device then sends the images to the server. Users can easily select and upload images using the smartphone application.

[0658] server

[0659] The server stores the received image data and prepares it for analysis. Specifically, it performs the following processes:

[0660] 1. Image Analysis: Use OpenCV or other image processing libraries to extract features such as objects, colors, patterns, and themes from images.

[0661] 2. Emotion Recognition: Using Keras and other deep learning models, we recognize the user's emotions from the image content. We analyze the facial expressions, behaviors, and atmosphere of the people in the images taken by the user.

[0662] 3. Content generation: Using a generative AI model, novel content is generated based on the extracted commonalities and emotion recognition results.

[0663] Generative AI Models

[0664] The model generates prompts and generates new content based on them. For example, for travel photos, it generates prompts like this:

[0665] Generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy."

[0666] Based on this prompt, the generative AI model generates a creative landscape image that corresponds to the user's emotions.

[0667] Content provider

[0668] The generated recommendation list of novel and existing content is sent from the server to the user's device and displayed through a dedicated application, allowing the user to browse this content and enjoy new discoveries and experiences.

[0669] Specific use cases

[0670] For example, if a user uploads a photo of cherry blossoms they took on a daily basis, the image is sent to the server and analyzed. As a result of the analysis, it is recognized that the image contains cherry blossoms, and the user's emotion is detected as "joy." Based on this information, the generative AI model creates a prompt to generate a new landscape image related to "spring cherry blossom viewing photos," and generates the new landscape image. This generated image is provided to the user, who can use it to get ideas for new travel destinations.

[0671] In this way, the system of the present invention enriches the user's daily life and provides new discoveries and experiences.

[0672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0673] Step 1:

[0674] The user takes a photo on their device and selects the image to upload. The user opens a dedicated application on their smartphone, selects an image they have taken in their daily life, and presses the upload button. The input is the image taken by the user, and the output is the image sent from the device to the server.

[0675] Step 2:

[0676] The device sends the selected image to the server. The image file is uploaded to the server via the Internet. The input is the image file sent from the device, and the output is the image data stored on the server.

[0677] Step 3:

[0678] The server stores the received image data and prepares it for analysis. Specifically, it stores the received images in a database. It also prepares the libraries and models required for image analysis. The input is the image data stored on the server, and the output is image data ready for analysis.

[0679] Step 4:

[0680] The server performs image analysis and extracts relevant information. It uses image processing libraries such as OpenCV to recognize objects, colors, patterns, and themes in the image. The input is the stored image data, and the information extracted as a result of the analysis is the output.

[0681] Step 5:

[0682] The server uses an emotion engine to recognize user emotions. It uses deep learning models such as Keras to analyze facial expressions of people in images and the atmosphere of the scenery to identify emotions. The input is image data and extracted related information, and the output is the recognized emotion.

[0683] Step 6:

[0684] The server generates novel content based on the extracted commonalities and emotion recognition results. Utilizing the generative AI model, it generates a prompt and then creates new content based on it. Specifically, the generative AI model receives a prompt to generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy," and the generative AI model generates novel content accordingly. The inputs are the commonalities, emotion recognition results, and the prompt, and the output is novel content.

[0685] Step 7:

[0686] The server formats the generated novel content and provides it to the user. The generated content is sent to the user's device and displayed through a dedicated application. The input is the novel content, and the output is the content displayed on the device. This allows the user to make new discoveries and gain new experiences.

[0687] 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.

[0688] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0689] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0690] [Third embodiment]

[0691] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0692] 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.

[0693] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0694] 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.

[0695] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0696] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0697] 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.

[0698] 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.

[0699] 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 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.

[0700] 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.

[0701] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0702] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0703] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server, and a specific embodiment of this system is described below.

[0704] 1. Image input

[0705] Users can use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or a web interface.

[0706] 2. Sending and receiving images

[0707] The device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[0708] 3. Image Analysis

[0709] The server retrieves the stored images and analyzes each one using image recognition algorithms, which extract relevant information such as objects, colors, patterns, and themes contained in the image.

[0710] 4. Extracting commonalities

[0711] Based on the analysis results, the server extracts common elements from multiple images, which allows it to identify user interests and themes.

[0712] 5. Creating novel content

[0713] The server creates a list of recommendations for real content based on common elements, and can also generate novel content using creative techniques, such as using AI to combine multiple landscape photos to generate a new landscape image.

[0714] 6. Formatting and sending results

[0715] The server converts the generated content (the recommendation list of existing content and novel content) into a format suitable for providing to the user, and sends the formatted results to the terminal.

[0716] 7. Provision to Users

[0717] The device displays the received results, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences, adding color to their lives.

[0718] Examples:

[0719] Example 1: Travel photos

[0720] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0721] Example 2: Food photos

[0722] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0723] In this way, Mix Machine is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[0724] The processing flow will be explained below.

[0725] Step 1:

[0726] Users use their device to select images they have taken in their daily lives and upload them to Mix Machine. Through the device interface, users select multiple images and press the send button.

[0727] Step 2:

[0728] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0729] Step 3:

[0730] The server stores the received images in a database for temporary storage, which allows subsequent analysis and processing.

[0731] Step 4:

[0732] The server retrieves the stored image data and analyzes it using image recognition algorithms to extract objects (e.g., people, landscapes, food, etc.), dominant colors, patterns, and other relevant information contained in the image.

[0733] Step 5:

[0734] From the retrieved related information, the server extracts common elements and features across multiple images, such as matching objects between images, similar colors, and common themes.

[0735] Step 6:

[0736] The server generates novel content based on commonalities, creating a recommendation list of real-world content (e.g., related travel destinations or recipes) and using AI to generate novel, non-real-world content (e.g., creative combinations of landscape images or new cooking ideas).

[0737] Step 7:

[0738] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0739] Step 8:

[0740] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[0741] Step 9:

[0742] The device displays the received results on the screen, allowing the user to check and enjoy new and recommended content.

[0743] Step 10:

[0744] Users can take action based on the content provided to gain new experiences and discoveries. Through this entire process, Mix Machine adds novelty to users' daily lives and improves their quality of life.

[0745] Example 1

[0746] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0747] Conventional image analysis systems have the drawback of requiring extensive manual operations and specialized knowledge to generate new content and related information from images taken by users. Furthermore, there has been a lack of effective means to extract commonalities from the diverse images taken by users and provide new discoveries and experiences. As a result, users can only obtain limited information, making it difficult to generate new content.

[0748] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0749] In this invention, the server includes means for inputting images taken by a user in daily life, means for transmitting the input images to the server, means for storing the images received by the server in data storage, means for analyzing the stored images and extracting related information such as objects, colors, patterns, and themes in the images, means for extracting elements common to a plurality of images, means for generating novel content based on the common elements, and means for providing the generated content to the user. This makes it possible to automatically extract common elements from a variety of images taken by a user and generate novel content and recommendation information based on that information.

[0750] A "user" is a person who uses the system and inputs images taken in daily life into the system.

[0751] A "terminal" is a device that is operated by a user to input images and transmit them to a server.

[0752] A "server" is a computer system that stores received images, analyzes them, and provides generated content to users.

[0753] "Images" are photographs and visual data that users take in their daily lives and upload to the system.

[0754] "Data storage" refers to a storage device for storing images received by the server.

[0755] "Analysis" is the process by which the server extracts information from stored images using image recognition algorithms.

[0756] An "object" refers to a specific object present in an image and is detected by analysis.

[0757] "Color" refers to the color information present in the image that is extracted as part of the analysis.

[0758] A "pattern" is a repeating design or pattern present in an image that can be detected through analysis.

[0759] "Theme" refers to the overall mood or subject matter of an image, as identified through analysis.

[0760] "Common elements" are information such as objects, colors, patterns, and themes that are common to multiple images.

[0761] "Novel content" is new visual data or information generated based on the analysis results and common elements.

[0762] "Recommendation" refers to proposing existing content or information suitable for a user based on the extracted common elements.

[0763] This invention is a system that inputs images taken by a user in daily life, analyzes the images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server.

[0764] First, a user uses a device to select images taken during daily activities and upload them to the system via a dedicated application or web interface. The device then sends the selected images to the server, which then stores the received images in data storage.

[0765] The server retrieves the stored images and analyzes each one using image recognition algorithms, which can use image recognition software such as the Python libraries OpenCV and TensorFlow, to extract relevant information such as objects, colors, patterns, and themes contained in the image.

[0766] The server then uses the analysis results to extract common elements across multiple images, which can be done using a clustering algorithm (e.g., K-means or DBSCAN), and generates novel content based on these common elements.

[0767] Generative AI models (such as GPT-3 and MIDAS) are used to generate novel content. The server generates prompts based on common elements, and inputs these into the AI ​​model to generate new content such as landscape images and cooking recipes.

[0768] The generated content is formatted by the server and converted into a format for delivery to the user, for example, in JSON format, and sent to the device as an HTTP response. The device then displays the received results in an application or web interface, allowing the user to enjoy the new content.

[0769] Specific examples

[0770] Example 1: Travel photos

[0771] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Example prompt: "Generate a new landscape image related to mountains and lakes."

[0772] Example 2: Food photos

[0773] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Example prompt: "Analyze the food photos and suggest new dish recipes."

[0774] In this way, it becomes possible to enrich the user's daily life and provide new discoveries and experiences.

[0775] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0776] Step 1:

[0777] Users use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or web interface. Users select image files and click the "Upload" button to send the image data to the system. The input is the image file selected by the user on the device, and the output is the transmission of that image file to the server.

[0778] Step 2:

[0779] The terminal sends the selected image file to the server using an HTTP request. The server saves the received image data in its data storage. The received image data is saved in a specific directory on the disk. The input is the image file sent by the user, and the output is the image file saved in the server's data storage.

[0780] Step 3:

[0781] The server retrieves the stored images and analyzes each one using image recognition algorithms. This analysis uses image recognition software such as OpenCV or TensorFlow. The server reads the image files and applies object detection algorithms to extract relevant information such as objects, colors, patterns, and themes within the image. The input is the stored image file, and the output is the image analysis results.

[0782] Step 4:

[0783] The server extracts common elements across multiple images based on the analysis results. During this process, the analyzed data is used to identify common objects, colors, patterns, and themes. A clustering algorithm (e.g., K-means or DBSCAN) is used to group the commonalities. The input is the image analysis results, and the output is the extracted common elements.

[0784] Step 5:

[0785] The server generates novel content based on the common elements. To generate new content using a generative AI model (e.g., GPT-3 or MIDAS), a prompt is created and input to the AI ​​model. The prompt contains specific instructions related to the common elements. The input is the extracted common elements and the generated prompt, and the output is the generated novel content.

[0786] Step 6:

[0787] The server converts the generated content into a format for providing it to the user and sends it to the terminal. The generated content is formatted in JSON format, for example, and sent to the terminal as an HTTP response. The input is the generated content, and the output is the formatted content.

[0788] Step 7:

[0789] The terminal displays the results received from the server in an application or web interface, allowing the user to view and enjoy newly generated content and recommended content. The input is the formatted content from the server, and the output is the content displayed on the user interface.

[0790] (Application example 1)

[0791] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0792] In today's world, users take a huge number of images on a daily basis, but simply storing these images does not maximize their value. In particular, if users could easily generate new video content using the images they have taken and receive recommendations for related video content, they could provide a more fulfilling digital experience. However, current systems lack the ability to seamlessly link image analysis to video content generation.

[0793] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0794] In this invention, the server includes a means for inputting images taken by the user in daily life, a means for analyzing the input images and extracting related information, and a means for generating novel video content based on the extracted commonalities, thereby making it possible to generate and recommend related video content from the user's images.

[0795] "Means for users to input images they take in their daily lives" refers to a function that allows users to upload image data they have taken using devices such as smartphones or cameras to the system.

[0796] "Means for analyzing input images and extracting relevant information" refers to the process of analyzing uploaded images using image recognition algorithms or machine learning models to extract features such as objects, colors, patterns, and themes contained in the images.

[0797] "Means for generating novel video content based on extracted commonalities" refers to a process for creating new video content using commonalities between images obtained through analysis, such as a function for generating a short movie based on multiple landscape photographs.

[0798] "Means for providing generated video content to users" refers to a function for distributing generated new video content so that users can view and use it, and is provided through a smartphone application or web interface.

[0799] This invention is a system that analyzes images taken by users in their daily lives and generates and provides novel video content. This system is mainly composed of a user terminal and a server. Specific embodiments for carrying out the invention are described below.

[0800] 1. Image input

[0801] Users use devices (such as smartphones or cameras) to select images they have taken in their daily lives and upload them to the system. Images can be input via a dedicated application or a web interface. Images are in common image formats such as JPEG and PNG.

[0802] 2. Sending and receiving images

[0803] The user's device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[0804] 3. Image Analysis

[0805] The server retrieves the stored images and analyzes them using image recognition algorithms and machine learning models (using, for example, TensorFlow or OpenCV) to extract features such as objects, colors, patterns, and themes contained in the images.

[0806] 4. Extracting commonalities

[0807] Based on the analysis results, the server extracts common elements from multiple images. By identifying these common elements, it can recognize the user's interests and themes.

[0808] 5. Creation of Novel Video Content

[0809] The server generates novel video content based on the common elements. For example, it uses an AI generative model (known as a generative AI model) to create related short movies or animations. A generative AI model can generate a new short movie based on multiple landscape photos.

[0810] 6. Formatting and sending results

[0811] The server converts the generated video content into a format suitable for providing to the user, and sends the formatted result to the terminal.

[0812] 7. Provision to Users

[0813] The user's device displays the received video content, allowing the user to watch and enjoy it. Through new video content, users can gain new discoveries and experiences.

[0814] Examples:

[0815] For example, if a user uploads a landscape photo of a mountain and lake taken on a trip, the app will analyze images of similar scenery and generate and provide a new short movie, allowing users to have a new visual experience based on the photos they have taken.

[0816] Example prompt sentence:

[0817] "Based on the user's travel photos of mountains and lakes, please generate a short movie using similar scenery. Please use DeepAI's image-similarity API for image analysis."

[0818] This system allows users to enjoy new video content using images they take in their daily lives, enriching their digital experience.

[0819] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0820] Step 1:

[0821] Users upload images they have taken in their daily lives from their smartphones or cameras to the application. The image files are in JPEG or PNG format. This becomes the input data. After uploading, the images are temporarily saved within the application.

[0822] Step 2:

[0823] The device sends the image selected by the user to the server. When sending, the data is sent using a communication protocol (e.g. HTTP / HTTPS). The server stores the received image data in a database and prepares it for analysis. This is the process of saving input data in the form of an image file.

[0824] Step 3:

[0825] The server then performs an analysis of the stored image data using an image recognition algorithm (for example, TensorFlow or OpenCV). The server extracts features such as objects, colors, patterns, and themes contained in the image. This is the specific operation of data analysis on the image data, and the analysis results are obtained as the output.

[0826] Step 4:

[0827] The server then uses the analysis results to extract common elements from multiple images. For example, if there are multiple landscape photos, it will identify natural features such as mountains and lakes that are common to the photos. The output of this process is a list of common elements.

[0828] Step 5:

[0829] The server generates novel video content based on the common elements. In this process, a generative AI model (e.g., image generation AI) is used to reflect the commonalities between multiple images and generate a new short movie. Based on the input data, the list of common elements, the server obtains the output of novel video content.

[0830] Step 6:

[0831] The server converts the format of the generated video content. Specifically, it converts the format of the video file into a format that users can play (e.g., MP4 format). This format conversion process results in output data that is easy for users to access.

[0832] Step 7:

[0833] The server sends the format-converted video content to the user's device, using the same communication protocol (e.g., HTTP / HTTPS). The device saves the received video content and makes it playable.

[0834] Step 8:

[0835] Users can view and enjoy video content stored on their devices through the application, providing a new video experience based on images they have taken. The output data is playable video content.

[0836] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0837] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to generate and provide more personalized content. This system is composed of a user terminal, a server, and an emotion engine, and specific embodiments thereof are described below.

[0838] 1. Image input

[0839] Users can use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[0840] 2. Sending and receiving images

[0841] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0842] 3. Image Analysis

[0843] The server stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms. This analysis extracts relevant information, such as objects, colors, patterns, and themes, contained in the image.

[0844] 4. Emotional Recognition

[0845] The emotion engine is used to recognize the user's emotions from the images uploaded by the user, including the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery.

[0846] 5. Extracting commonalities

[0847] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[0848] 6. Creating novel content

[0849] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real-world content (e.g., related travel destinations and recipes) and uses AI to generate novel, non-real-world content (e.g., creative combinations of landscape images and new cooking ideas). The emotion recognition results enable it to provide content that best suits the user's current emotional state.

[0850] 7. Formatting and sending results

[0851] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0852] 8. Provision to Users

[0853] The device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences that will add color to their lives.

[0854] Specific examples

[0855] Example 1: Travel photos

[0856] A user uploads three landscape photos taken at a travel destination to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. The emotion engine recognizes the user's emotion at the time of taking the photos (e.g., joy, tranquility, etc.). Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0857] Example 2: Food photos

[0858] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. The emotion engine recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0859] In this way, the Mix Machine, which combines an emotion engine, is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[0860] The processing flow will be explained below.

[0861] Step 1:

[0862] Users use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[0863] Step 2:

[0864] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[0865] Step 3:

[0866] The server stores the received image data in a database, which makes it ready for analysis.

[0867] Step 4:

[0868] The server retrieves the stored images and analyzes them using image recognition algorithms to extract relevant information, such as objects, colors, patterns, and themes contained in the images.

[0869] Step 5:

[0870] The emotion engine recognizes the user's emotions from the facial expressions and behavior of people in the image, as well as the atmosphere of the landscape, and identifies emotional states such as joy, surprise, and calm.

[0871] Step 6:

[0872] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[0873] Step 7:

[0874] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real content and provides content that best suits the user's current mood based on the emotion recognition results. It also uses AI to generate novel, non-existent content.

[0875] Step 8:

[0876] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[0877] Step 9:

[0878] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[0879] Step 10:

[0880] The device displays the received results on the screen, allowing users to check and enjoy new and recommended content. Users can make new discoveries and gain new experiences, adding color to their lives.

[0881] Example 2

[0882] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0883] Conventional image analysis systems generate content based on images taken by users in their daily lives without personalizing it, making it difficult to fully satisfy the user's interests and emotions. In addition, because content is generated solely based on the results of image analysis, it is difficult to recommend novel content or content that specifically matches the user's current emotions.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0885] In this invention, the server includes means for analyzing an input image and extracting related data, means for recognizing a user's emotion using emotion recognition technology, and means for generating novel digital content based on the extracted related data and the result of the user's emotion recognition, thereby enabling the provision of personalized content based on the user's emotional state.

[0886] A "user" is an individual who utilizes the system to input images and receive analysis results and generated content.

[0887] "Image" refers to a photograph or graphic taken by a user, and is digital data that is input.

[0888] "Means of input" refers to the interface or process by which a user uses a terminal to upload images to the system.

[0889] "Remote device" refers to a server or cloud computing system that receives and analyzes images sent from a user's device.

[0890] "Means for analyzing" refers to algorithms and software used by the remote device to process the received image data and extract relevant data and features.

[0891] "Related data" refers to information such as objects, colors, patterns, and themes extracted from the analyzed image.

[0892] "Emotion recognition technology" refers to technology that analyzes images and other data to identify a user's emotional state.

[0893] "Emotion recognition result" refers to data on a user's emotional state obtained through emotion recognition technology.

[0894] "Novel digital content" refers to new digital media or content that the user has not yet experienced, which is generated based on the extracted related data and emotion recognition results.

[0895] "Means of delivery" refers to the interface or process for delivering and displaying the generated content to users.

[0896] "Real content" refers to tangible content such as real-world products, services, places, and information.

[0897] "Novel non-existent digital content" refers to fictional digital media or content that is newly generated using AI technology or creative processes.

[0898] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, and generates and provides novel digital content to the user. Furthermore, by combining emotion recognition technology that recognizes the user's emotions, it is possible to provide more personalized content. This system is composed of a user terminal, a remote device, and emotion recognition technology, and specific embodiments thereof are described below.

[0899] First, the user uses the device to select images taken in daily life and upload them to the system. The device is equipped with a dedicated application or web interface through which the user inputs image data.

[0900] The terminal transmits the selected image data to the remote device. The transmission process is carried out by uploading the image file to the remote device using an Internet connection, using the HTTP or HTTPS protocol.

[0901] The remote device stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms, using software such as TensorFlow and OpenCV. The analysis extracts relevant data, such as objects, colors, patterns, and themes, contained in the image.

[0902] Next, emotion recognition technology is used to recognize the user's emotions from the images uploaded by the user. This emotion recognition includes the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery. For example, Microsoft Azure Cognitive Services and Amazon Rekognition are used.

[0903] Based on the acquired related data and emotion recognition results, the remote device extracts common elements and features across multiple images, such as matching objects between images, similar colors, common themes, and the user's emotional state.

[0904] The remote device then generates novel digital content based on the common elements and emotion recognition results, including using generative AI models to generate relevant content (e.g., new travel destinations or recipe ideas). Generative AI models such as Hugging Face and GPT-3 are used.

[0905] The generated digital content is formatted for presentation to the user, the format including a recommendation list and novel digital content.

[0906] Finally, the device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content, leading to new discoveries and experiences.

[0907] Specific examples

[0908] Example 1: Travel photos

[0909] A user uploads three landscape photos taken during a trip to the system. A remote device analyzes these photos and extracts common natural elements, such as mountains, lakes, and forests. Emotion recognition technology recognizes the user's emotion (e.g., joy, tranquility) when taking the photos. Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses a generative AI model to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[0910] Example prompt (travel photos)

[0911] Recommend new travel destinations and generate creative landscape images based on three landscape photos taken by the user. The content of the photos is mountains, lakes, and forests. The user's emotions are joy and tranquility.

[0912] Example 2: Food photos

[0913] Users upload photos of five dishes they have eaten in their daily lives. A remote device analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Emotion recognition technology recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[0914] Sample prompts (food photos)

[0915] Generate new recipes and cooking ideas based on five types of food photos taken by the user. The dishes are pasta, stir-fry, and dessert. The user's emotions are enjoying cooking.

[0916] The above is a specific embodiment for carrying out the present invention.

[0917] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0918] Step 1:

[0919] A user uses a terminal to select and upload images taken in daily life.

[0920] How it works: The user selects an image from the device's photo album and presses the upload button via a dedicated application or web interface.

[0921] Input: An image file selected by the user.

[0922] Output: Images are brought into the system and ready to be uploaded.

[0923] Step 2:

[0924] The terminal transmits the selected image data to the server.

[0925] How it works: When the upload button is pressed, the device breaks the image data into packets and sends them over an internet connection to a server using the HTTP or HTTPS protocol.

[0926] Input: An image file stored on the user's device.

[0927] Output: The image data is sent to the server.

[0928] Step 3:

[0929] The server receives the image data sent from the terminal and stores it in a database.

[0930] How it works: The server reconstructs the received data packets and saves them in storage as the original image file.

[0931] Input: Image data packets sent from the terminal.

[0932] Output: Image files stored in the database.

[0933] Step 4:

[0934] The server analyzes the stored image data using an image recognition algorithm.

[0935] How it works: The server applies image analysis software such as TensorFlow or OpenCV to extract information such as objects, colors, patterns, and themes in the image.

[0936] Input: Image files stored in the database.

[0937] Output: Relevant data as a result of image analysis.

[0938] Step 5:

[0939] The server uses emotion recognition technology to recognize emotions from images uploaded by users.

[0940] How it works: The server calls the emotion recognition engine API and provides image data. The emotion engine analyzes the facial expressions and mood in the image and returns emotional data to the server.

[0941] Input: Analyzed image data.

[0942] Output: Emotion data as emotion recognition results.

[0943] Step 6:

[0944] Based on the results of image analysis and emotion recognition, the server extracts elements and features common to multiple images.

[0945] How it works: The server uses the analysis results and emotion data retrieved from the database to run algorithms that search for common elements between images (such as matching objects, similar colors, or common themes).

[0946] Input: Image related data and emotion data.

[0947] Output: Extraction of common elements and features.

[0948] Step 7:

[0949] The server generates novel digital content based on the common elements and emotion recognition results.

[0950] How it works: The server generates prompts for the generative AI model, feeds the prompts into the model, and the model generates new content and returns the results to the server.

[0951] Input: common elements, feature data, prompt sentence.

[0952] Output: The novel digital content generated.

[0953] Step 8:

[0954] The server formats the generated digital content for delivery to the user.

[0955] How it works: The server formats the results into HTML, JSON, or other formats that are easy for the user to view.

[0956] Input: Novel digital content generated.

[0957] Output: Formatted digital content.

[0958] Step 9:

[0959] The server transmits the formatted digital content to the terminal.

[0960] How it works: The server sends data to the device via the HTTPS protocol.

[0961] Input: Formatted digital content.

[0962] Output: Digital content sent to a device.

[0963] Step 10:

[0964] The terminal displays the received results on the screen.

[0965] Action: The device application or browser renders the results and displays them to the user.

[0966] Input: Digital content sent from the server.

[0967] Output: The digital content that the user sees on a screen.

[0968] (Application example 2)

[0969] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0970] As technology advances, there is a demand for services that generate new content using images taken by users in their daily lives. However, conventional systems have difficulty generating personalized content that takes user emotions into account, making it impossible to provide users with truly valuable content. Furthermore, there has been no system that can provide users with both real content and novel, imaginary content in conjunction with emotion recognition. This has prevented systems from providing new discoveries and experiences that are in tune with users' emotions, resulting in insufficient improvements to the user experience.

[0971] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting an image taken by a user with a terminal, a means for transmitting the input image to the server, a means for analyzing the image and extracting related information in the server, a means for recognizing the user's emotion using an emotion engine, a means for generating novel content based on the extracted commonalities and emotion recognition results, and a means for providing the generated content to the user. This enables the generation of personalized novel content based on the user's emotion from the image taken by the user, thereby providing a richer user experience. In addition, by using a generative AI model based on the emotion recognition results to generate prompt sentences and generate novel content, it is possible to provide creative content that does not actually exist. This allows the user to make new discoveries and experiences.

[0972] "Means for inputting images taken by the user on a terminal" refers to a function for importing images taken by the user using a terminal that the user normally uses, such as a smartphone or digital camera, into the system.

[0973] The "means for transmitting the input image to the server" is a function for uploading the image selected from the terminal to a cloud server or a remote server via the Internet.

[0974] "Means for analyzing images on the server and extracting relevant information" refers to a function that processes image data stored on the server and recognizes and extracts features such as objects, colors, patterns, and themes contained in the images.

[0975] "Means for recognizing user emotions using an emotion engine" refers to algorithms and software for determining a user's emotions based on the content of uploaded images, the facial expressions of people photographed, the atmosphere of the scenery, etc.

[0976] "Means for generating novel content based on extracted commonalities and emotion recognition results" refers to algorithms and AI technologies for creating new content based on common elements and features from multiple images and the results of user emotion recognition.

[0977] The "means for providing generated content to the user" is a function for transmitting novel content generated by the server to the user's terminal and displaying it so that the user can view and enjoy it.

[0978] "Means for recommending real content" refers to a function that selects and suggests to users existing content (e.g., videos, music, articles, etc.) that matches their interests based on related information obtained through image analysis and emotion recognition results.

[0979] "Means for generating novel non-existent content" refers to a function that uses AI to generate creative and attractive new content that does not exist in reality (e.g., virtual landscape images, new food recipes, etc.).

[0980] "Means for generating prompts using a generative AI model" refers to a function that uses an AI model to create prompts (e.g., instructions or descriptions for new content to be generated) based on the user's emotions recognized by the emotion engine.

[0981] The present invention is a system that generates novel content using images taken by a user in their daily lives and provides the content to the user. Specific embodiments for carrying out the invention will be described below.

[0982] System configuration

[0983] The system mainly consists of the following components:

[0984] 1. User's device: a smartphone or other device

[0985] 2. Server: Image analysis and emotion engine

[0986] 3. Generative AI Model: A Novel Content Generation Engine

[0987] User's device

[0988] Users upload the images they have taken using a dedicated application. The device then sends the images to the server. Users can easily select and upload images using the smartphone application.

[0989] server

[0990] The server stores the received image data and prepares it for analysis. Specifically, it performs the following processes:

[0991] 1. Image Analysis: Use OpenCV or other image processing libraries to extract features such as objects, colors, patterns, and themes from images.

[0992] 2. Emotion Recognition: Using Keras and other deep learning models, we recognize the user's emotions from the image content. We analyze the facial expressions, behaviors, and atmosphere of the people in the images taken by the user.

[0993] 3. Content generation: Using a generative AI model, novel content is generated based on the extracted commonalities and emotion recognition results.

[0994] Generative AI Models

[0995] The model generates prompts and generates new content based on them. For example, for travel photos, it generates prompts like this:

[0996] Generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy."

[0997] Based on this prompt, the generative AI model generates a creative landscape image that corresponds to the user's emotions.

[0998] Content provider

[0999] The generated recommendation list of novel and existing content is sent from the server to the user's device and displayed through a dedicated application, allowing the user to browse this content and enjoy new discoveries and experiences.

[1000] Specific use cases

[1001] For example, if a user uploads a photo of cherry blossoms they took on a daily basis, the image is sent to the server and analyzed. As a result of the analysis, it is recognized that the image contains cherry blossoms, and the user's emotion is detected as "joy." Based on this information, the generative AI model creates a prompt to generate a new landscape image related to "spring cherry blossom viewing photos," and generates the new landscape image. This generated image is provided to the user, who can use it to get ideas for new travel destinations.

[1002] In this way, the system of the present invention enriches the user's daily life and provides new discoveries and experiences.

[1003] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1004] Step 1:

[1005] The user takes a photo on their device and selects the image to upload. The user opens a dedicated application on their smartphone, selects an image they have taken in their daily life, and presses the upload button. The input is the image taken by the user, and the output is the image sent from the device to the server.

[1006] Step 2:

[1007] The device sends the selected image to the server. The image file is uploaded to the server via the Internet. The input is the image file sent from the device, and the output is the image data stored on the server.

[1008] Step 3:

[1009] The server stores the received image data and prepares it for analysis. Specifically, it stores the received images in a database. It also prepares the libraries and models required for image analysis. The input is the image data stored on the server, and the output is image data ready for analysis.

[1010] Step 4:

[1011] The server performs image analysis and extracts relevant information. It uses image processing libraries such as OpenCV to recognize objects, colors, patterns, and themes in the image. The input is the stored image data, and the information extracted as a result of the analysis is the output.

[1012] Step 5:

[1013] The server uses an emotion engine to recognize user emotions. It uses deep learning models such as Keras to analyze facial expressions of people in images and the atmosphere of the scenery to identify emotions. The input is image data and extracted related information, and the output is the recognized emotion.

[1014] Step 6:

[1015] The server generates novel content based on the extracted commonalities and emotion recognition results. Utilizing the generative AI model, it generates a prompt and then creates new content based on it. Specifically, the generative AI model receives a prompt to generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy," and the generative AI model generates novel content accordingly. The inputs are the commonalities, emotion recognition results, and the prompt, and the output is novel content.

[1016] Step 7:

[1017] The server formats the generated novel content and provides it to the user. The generated content is sent to the user's device and displayed through a dedicated application. The input is the novel content, and the output is the content displayed on the device. This allows the user to make new discoveries and gain new experiences.

[1018] 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.

[1019] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1020] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1021] [Fourth embodiment]

[1022] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1023] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1025] 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.

[1026] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1027] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1028] 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.

[1029] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1030] 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.

[1031] 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 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.

[1032] 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.

[1033] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1035] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server, and a specific embodiment of this system is described below.

[1036] 1. Image input

[1037] Users can use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or a web interface.

[1038] 2. Sending and receiving images

[1039] The device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[1040] 3. Image Analysis

[1041] The server retrieves the stored images and analyzes each one using image recognition algorithms, which extract relevant information such as objects, colors, patterns, and themes contained in the image.

[1042] 4. Extracting commonalities

[1043] Based on the analysis results, the server extracts common elements from multiple images, which allows it to identify user interests and themes.

[1044] 5. Creating novel content

[1045] The server creates a list of recommendations for real content based on common elements, and can also generate novel content using creative techniques, such as using AI to combine multiple landscape photos to generate a new landscape image.

[1046] 6. Formatting and sending results

[1047] The server converts the generated content (the recommendation list of existing content and novel content) into a format suitable for providing to the user, and sends the formatted results to the terminal.

[1048] 7. Provision to Users

[1049] The device displays the received results, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences, adding color to their lives.

[1050] Examples:

[1051] Example 1: Travel photos

[1052] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[1053] Example 2: Food photos

[1054] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[1055] In this way, Mix Machine is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] Users use their device to select images they have taken in their daily lives and upload them to Mix Machine. Through the device interface, users select multiple images and press the send button.

[1059] Step 2:

[1060] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[1061] Step 3:

[1062] The server stores the received images in a database for temporary storage, which allows subsequent analysis and processing.

[1063] Step 4:

[1064] The server retrieves the stored image data and analyzes it using image recognition algorithms to extract objects (e.g., people, landscapes, food, etc.), dominant colors, patterns, and other relevant information contained in the image.

[1065] Step 5:

[1066] From the retrieved related information, the server extracts common elements and features across multiple images, such as matching objects between images, similar colors, and common themes.

[1067] Step 6:

[1068] The server generates novel content based on commonalities, creating a recommendation list of real-world content (e.g., related travel destinations or recipes) and using AI to generate novel, non-real-world content (e.g., creative combinations of landscape images or new cooking ideas).

[1069] Step 7:

[1070] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[1071] Step 8:

[1072] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[1073] Step 9:

[1074] The device displays the received results on the screen, allowing the user to check and enjoy new and recommended content.

[1075] Step 10:

[1076] Users can take action based on the content provided to gain new experiences and discoveries. Through this entire process, Mix Machine adds novelty to users' daily lives and improves their quality of life.

[1077] Example 1

[1078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1079] Conventional image analysis systems have the drawback of requiring extensive manual operations and specialized knowledge to generate new content and related information from images taken by users. Furthermore, there has been a lack of effective means to extract commonalities from the diverse images taken by users and provide new discoveries and experiences. As a result, users can only obtain limited information, making it difficult to generate new content.

[1080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1081] In this invention, the server includes means for inputting images taken by a user in daily life, means for transmitting the input images to the server, means for storing the images received by the server in data storage, means for analyzing the stored images and extracting related information such as objects, colors, patterns, and themes in the images, means for extracting elements common to a plurality of images, means for generating novel content based on the common elements, and means for providing the generated content to the user. This makes it possible to automatically extract common elements from a variety of images taken by a user and generate novel content and recommendation information based on that information.

[1082] A "user" is a person who uses the system and inputs images taken in daily life into the system.

[1083] A "terminal" is a device that is operated by a user to input images and transmit them to a server.

[1084] A "server" is a computer system that stores received images, analyzes them, and provides generated content to users.

[1085] "Images" are photographs and visual data that users take in their daily lives and upload to the system.

[1086] "Data storage" refers to a storage device for storing images received by the server.

[1087] "Analysis" is the process by which the server extracts information from stored images using image recognition algorithms.

[1088] An "object" refers to a specific object present in an image and is detected by analysis.

[1089] "Color" refers to the color information present in the image that is extracted as part of the analysis.

[1090] A "pattern" is a repeating design or pattern present in an image that can be detected through analysis.

[1091] "Theme" refers to the overall mood or subject matter of an image, as identified through analysis.

[1092] "Common elements" are information such as objects, colors, patterns, and themes that are common to multiple images.

[1093] "Novel content" is new visual data or information generated based on the analysis results and common elements.

[1094] "Recommendation" refers to proposing existing content or information suitable for a user based on the extracted common elements.

[1095] This invention is a system that inputs images taken by a user in daily life, analyzes the images, generates novel content, and provides it to the user. This system is composed of a user terminal and a server.

[1096] First, a user uses a device to select images taken during daily activities and upload them to the system via a dedicated application or web interface. The device then sends the selected images to the server, which then stores the received images in data storage.

[1097] The server retrieves the stored images and analyzes each one using image recognition algorithms, which can use image recognition software such as the Python libraries OpenCV and TensorFlow, to extract relevant information such as objects, colors, patterns, and themes contained in the image.

[1098] The server then uses the analysis results to extract common elements across multiple images, which can be done using a clustering algorithm (e.g., K-means or DBSCAN), and generates novel content based on these common elements.

[1099] Generative AI models (such as GPT-3 and MIDAS) are used to generate novel content. The server generates prompts based on common elements, and inputs these into the AI ​​model to generate new content such as landscape images and cooking recipes.

[1100] The generated content is formatted by the server and converted into a format for delivery to the user, for example, in JSON format, and sent to the device as an HTTP response. The device then displays the received results in an application or web interface, allowing the user to enjoy the new content.

[1101] Specific examples

[1102] Example 1: Travel photos

[1103] A user uploads three landscape photos taken during a trip to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. Based on this, the system recommends new travel destinations such as the Swiss mountain region or Japan's Lake District, and uses AI to generate new landscape images that combine mountains and lakes. Example prompt: "Generate a new landscape image related to mountains and lakes."

[1104] Example 2: Food photos

[1105] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Based on the analysis results, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Example prompt: "Analyze the food photos and suggest new dish recipes."

[1106] In this way, it becomes possible to enrich the user's daily life and provide new discoveries and experiences.

[1107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1108] Step 1:

[1109] Users use their devices to select images they have taken in their daily lives and upload them to the system via a dedicated application or web interface. Users select image files and click the "Upload" button to send the image data to the system. The input is the image file selected by the user on the device, and the output is the transmission of that image file to the server.

[1110] Step 2:

[1111] The terminal sends the selected image file to the server using an HTTP request. The server saves the received image data in its data storage. The received image data is saved in a specific directory on the disk. The input is the image file sent by the user, and the output is the image file saved in the server's data storage.

[1112] Step 3:

[1113] The server retrieves the stored images and analyzes each one using image recognition algorithms. This analysis uses image recognition software such as OpenCV or TensorFlow. The server reads the image files and applies object detection algorithms to extract relevant information such as objects, colors, patterns, and themes within the image. The input is the stored image file, and the output is the image analysis results.

[1114] Step 4:

[1115] The server extracts common elements across multiple images based on the analysis results. During this process, the analyzed data is used to identify common objects, colors, patterns, and themes. A clustering algorithm (e.g., K-means or DBSCAN) is used to group the commonalities. The input is the image analysis results, and the output is the extracted common elements.

[1116] Step 5:

[1117] The server generates novel content based on the common elements. To generate new content using a generative AI model (e.g., GPT-3 or MIDAS), a prompt is created and input to the AI ​​model. The prompt contains specific instructions related to the common elements. The input is the extracted common elements and the generated prompt, and the output is the generated novel content.

[1118] Step 6:

[1119] The server converts the generated content into a format for providing it to the user and sends it to the terminal. The generated content is formatted in JSON format, for example, and sent to the terminal as an HTTP response. The input is the generated content, and the output is the formatted content.

[1120] Step 7:

[1121] The terminal displays the results received from the server in an application or web interface, allowing the user to view and enjoy newly generated content and recommended content. The input is the formatted content from the server, and the output is the content displayed on the user interface.

[1122] (Application example 1)

[1123] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1124] In today's world, users take a huge number of images on a daily basis, but simply storing these images does not maximize their value. In particular, if users could easily generate new video content using the images they have taken and receive recommendations for related video content, they could provide a more fulfilling digital experience. However, current systems lack the ability to seamlessly link image analysis to video content generation.

[1125] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1126] In this invention, the server includes a means for inputting images taken by the user in daily life, a means for analyzing the input images and extracting related information, and a means for generating novel video content based on the extracted commonalities, thereby making it possible to generate and recommend related video content from the user's images.

[1127] "Means for users to input images they take in their daily lives" refers to a function that allows users to upload image data they have taken using devices such as smartphones or cameras to the system.

[1128] "Means for analyzing input images and extracting relevant information" refers to the process of analyzing uploaded images using image recognition algorithms or machine learning models to extract features such as objects, colors, patterns, and themes contained in the images.

[1129] "Means for generating novel video content based on extracted commonalities" refers to a process for creating new video content using commonalities between images obtained through analysis, such as a function for generating a short movie based on multiple landscape photographs.

[1130] "Means for providing generated video content to users" refers to a function for distributing generated new video content so that users can view and use it, and is provided through a smartphone application or web interface.

[1131] This invention is a system that analyzes images taken by users in their daily lives and generates and provides novel video content. This system is mainly composed of a user terminal and a server. Specific embodiments for carrying out the invention are described below.

[1132] 1. Image input

[1133] Users use devices (such as smartphones or cameras) to select images they have taken in their daily lives and upload them to the system. Images can be input via a dedicated application or a web interface. Images are in common image formats such as JPEG and PNG.

[1134] 2. Sending and receiving images

[1135] The user's device sends the selected images to the server, which stores them in a database and prepares them for analysis.

[1136] 3. Image Analysis

[1137] The server retrieves the stored images and analyzes them using image recognition algorithms and machine learning models (using, for example, TensorFlow or OpenCV) to extract features such as objects, colors, patterns, and themes contained in the images.

[1138] 4. Extracting commonalities

[1139] Based on the analysis results, the server extracts common elements from multiple images. By identifying these common elements, it can recognize the user's interests and themes.

[1140] 5. Creation of Novel Video Content

[1141] The server generates novel video content based on the common elements. For example, it uses an AI generative model (known as a generative AI model) to create related short movies or animations. A generative AI model can generate a new short movie based on multiple landscape photos.

[1142] 6. Formatting and sending results

[1143] The server converts the generated video content into a format suitable for providing to the user, and sends the formatted result to the terminal.

[1144] 7. Provision to Users

[1145] The user's device displays the received video content, allowing the user to watch and enjoy it. Through new video content, users can gain new discoveries and experiences.

[1146] Examples:

[1147] For example, if a user uploads a landscape photo of a mountain and lake taken on a trip, the app will analyze images of similar scenery and generate and provide a new short movie, allowing users to have a new visual experience based on the photos they have taken.

[1148] Example prompt sentence:

[1149] "Based on the user's travel photos of mountains and lakes, please generate a short movie using similar scenery. Please use DeepAI's image-similarity API for image analysis."

[1150] This system allows users to enjoy new video content using images they take in their daily lives, enriching their digital experience.

[1151] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1152] Step 1:

[1153] Users upload images they have taken in their daily lives from their smartphones or cameras to the application. The image files are in JPEG or PNG format. This becomes the input data. After uploading, the images are temporarily saved within the application.

[1154] Step 2:

[1155] The device sends the image selected by the user to the server. When sending, the data is sent using a communication protocol (e.g. HTTP / HTTPS). The server stores the received image data in a database and prepares it for analysis. This is the process of saving input data in the form of an image file.

[1156] Step 3:

[1157] The server then performs an analysis of the stored image data using an image recognition algorithm (for example, TensorFlow or OpenCV). The server extracts features such as objects, colors, patterns, and themes contained in the image. This is the specific operation of data analysis on the image data, and the analysis results are obtained as the output.

[1158] Step 4:

[1159] The server then uses the analysis results to extract common elements from multiple images. For example, if there are multiple landscape photos, it will identify natural features such as mountains and lakes that are common to the photos. The output of this process is a list of common elements.

[1160] Step 5:

[1161] The server generates novel video content based on the common elements. In this process, a generative AI model (e.g., image generation AI) is used to reflect the commonalities between multiple images and generate a new short movie. Based on the input data, the list of common elements, the server obtains the output of novel video content.

[1162] Step 6:

[1163] The server converts the format of the generated video content. Specifically, it converts the format of the video file into a format that users can play (e.g., MP4 format). This format conversion process results in output data that is easy for users to access.

[1164] Step 7:

[1165] The server sends the format-converted video content to the user's device, using the same communication protocol (e.g., HTTP / HTTPS). The device saves the received video content and makes it playable.

[1166] Step 8:

[1167] Users can view and enjoy video content stored on their devices through the application, providing a new video experience based on images they have taken. The output data is playable video content.

[1168] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1169] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, generates novel content, and provides it to the user. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to generate and provide more personalized content. This system is composed of a user terminal, a server, and an emotion engine, and specific embodiments thereof are described below.

[1170] 1. Image input

[1171] Users can use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[1172] 2. Sending and receiving images

[1173] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[1174] 3. Image Analysis

[1175] The server stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms. This analysis extracts relevant information, such as objects, colors, patterns, and themes, contained in the image.

[1176] 4. Emotional Recognition

[1177] The emotion engine is used to recognize the user's emotions from the images uploaded by the user, including the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery.

[1178] 5. Extracting commonalities

[1179] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[1180] 6. Creating novel content

[1181] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real-world content (e.g., related travel destinations and recipes) and uses AI to generate novel, non-real-world content (e.g., creative combinations of landscape images and new cooking ideas). The emotion recognition results enable it to provide content that best suits the user's current emotional state.

[1182] 7. Formatting and sending results

[1183] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[1184] 8. Provision to Users

[1185] The device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content. Users can gain new discoveries and experiences that will add color to their lives.

[1186] Specific examples

[1187] Example 1: Travel photos

[1188] A user uploads three landscape photos taken at a travel destination to the system. The server analyzes these photos and extracts common natural elements such as mountains, lakes, and forests. The emotion engine recognizes the user's emotion at the time of taking the photos (e.g., joy, tranquility, etc.). Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses AI to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[1189] Example 2: Food photos

[1190] Users upload photos of five dishes they have eaten in their daily lives. The server analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. The emotion engine recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes that the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[1191] In this way, the Mix Machine, which combines an emotion engine, is a system that enriches users' daily lives and provides them with new encounters and discoveries.

[1192] The processing flow will be explained below.

[1193] Step 1:

[1194] Users use their devices to select images they have taken in their daily lives and upload them to Mix Machine, either through a dedicated application or a web interface.

[1195] Step 2:

[1196] The terminal transmits the selected image data to the server. The transmission process is carried out by uploading the image file to the server via the Internet.

[1197] Step 3:

[1198] The server stores the received image data in a database, which makes it ready for analysis.

[1199] Step 4:

[1200] The server retrieves the stored images and analyzes them using image recognition algorithms to extract relevant information, such as objects, colors, patterns, and themes contained in the images.

[1201] Step 5:

[1202] The emotion engine recognizes the user's emotions from the facial expressions and behavior of people in the image, as well as the atmosphere of the landscape, and identifies emotional states such as joy, surprise, and calm.

[1203] Step 6:

[1204] Based on the acquired related information and emotion recognition results, the server extracts common elements and features across multiple images, including matching objects between images, similar colors, common themes, and the user's emotional state.

[1205] Step 7:

[1206] The server generates novel content based on the common elements and emotion recognition results. Specifically, it creates a recommendation list of real content and provides content that best suits the user's current mood based on the emotion recognition results. It also uses AI to generate novel, non-existent content.

[1207] Step 8:

[1208] The server formats the generated content for delivery to the user, including the recommendation list and novel content.

[1209] Step 9:

[1210] The server sends the formatted results to the terminal, where they are presented in an interactive format for easy user access.

[1211] Step 10:

[1212] The device displays the received results on the screen, allowing users to check and enjoy new and recommended content. Users can make new discoveries and gain new experiences, adding color to their lives.

[1213] Example 2

[1214] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1215] Conventional image analysis systems generate content based on images taken by users in their daily lives without personalizing it, making it difficult to fully satisfy the user's interests and emotions. In addition, because content is generated solely based on the results of image analysis, it is difficult to recommend novel content or content that specifically matches the user's current emotions.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1217] In this invention, the server includes means for analyzing an input image and extracting related data, means for recognizing a user's emotion using emotion recognition technology, and means for generating novel digital content based on the extracted related data and the result of the user's emotion recognition, thereby enabling the provision of personalized content based on the user's emotional state.

[1218] A "user" is an individual who utilizes the system to input images and receive analysis results and generated content.

[1219] "Image" refers to a photograph or graphic taken by a user, and is digital data that is input.

[1220] "Means of input" refers to the interface or process by which a user uses a terminal to upload images to the system.

[1221] "Remote device" refers to a server or cloud computing system that receives and analyzes images sent from a user's device.

[1222] "Means for analyzing" refers to algorithms and software used by the remote device to process the received image data and extract relevant data and features.

[1223] "Related data" refers to information such as objects, colors, patterns, and themes extracted from the analyzed image.

[1224] "Emotion recognition technology" refers to technology that analyzes images and other data to identify a user's emotional state.

[1225] "Emotion recognition result" refers to data on a user's emotional state obtained through emotion recognition technology.

[1226] "Novel digital content" refers to new digital media or content that the user has not yet experienced, which is generated based on the extracted related data and emotion recognition results.

[1227] "Means of delivery" refers to the interface or process for delivering and displaying the generated content to users.

[1228] "Real content" refers to tangible content such as real-world products, services, places, and information.

[1229] "Novel non-existent digital content" refers to fictional digital media or content that is newly generated using AI technology or creative processes.

[1230] This invention is a system that inputs images taken by a user in their daily lives, analyzes those images, and generates and provides novel digital content to the user. Furthermore, by combining emotion recognition technology that recognizes the user's emotions, it is possible to provide more personalized content. This system is composed of a user terminal, a remote device, and emotion recognition technology, and specific embodiments thereof are described below.

[1231] First, the user uses the device to select images taken in daily life and upload them to the system. The device is equipped with a dedicated application or web interface through which the user inputs image data.

[1232] The terminal transmits the selected image data to the remote device. The transmission process is carried out by uploading the image file to the remote device using an Internet connection, using the HTTP or HTTPS protocol.

[1233] The remote device stores the received image data in a database, preparing it for analysis. It then retrieves the stored image data and analyzes each image using image recognition algorithms, using software such as TensorFlow and OpenCV. The analysis extracts relevant data, such as objects, colors, patterns, and themes, contained in the image.

[1234] Next, emotion recognition technology is used to recognize the user's emotions from the images uploaded by the user. This emotion recognition includes the facial expressions and behavior of people in the images taken by the user, as well as the atmosphere of the scenery. For example, Microsoft Azure Cognitive Services and Amazon Rekognition are used.

[1235] Based on the acquired related data and emotion recognition results, the remote device extracts common elements and features across multiple images, such as matching objects between images, similar colors, common themes, and the user's emotional state.

[1236] The remote device then generates novel digital content based on the common elements and emotion recognition results, including using generative AI models to generate relevant content (e.g., new travel destinations or recipe ideas). Generative AI models such as Hugging Face and GPT-3 are used.

[1237] The generated digital content is formatted for presentation to the user, the format including a recommendation list and novel digital content.

[1238] Finally, the device displays the received results on the screen, allowing users to enjoy newly generated content and recommended content, leading to new discoveries and experiences.

[1239] Specific examples

[1240] Example 1: Travel photos

[1241] A user uploads three landscape photos taken during a trip to the system. A remote device analyzes these photos and extracts common natural elements, such as mountains, lakes, and forests. Emotion recognition technology recognizes the user's emotion (e.g., joy, tranquility) when taking the photos. Based on this, the system recommends new travel destinations with natural scenery that matches the emotion, and uses a generative AI model to generate new landscape images that combine mountains and lakes. Users can use this content as a reference for travel ideas.

[1242] Example prompt (travel photos)

[1243] Recommend new travel destinations and generate creative landscape images based on three landscape photos taken by the user. The content of the photos is mountains, lakes, and forests. The user's emotions are joy and tranquility.

[1244] Example 2: Food photos

[1245] Users upload photos of five dishes they have eaten in their daily lives. A remote device analyzes these photos and extracts the base ingredients and format of each dish. For example, it identifies elements such as pasta, stir-fry, and dessert. Emotion recognition technology recognizes the emotions the user feels when enjoying cooking. Based on the analysis results and emotions, it recommends recipes the user may like and generates creative new dish ideas that combine each element. Users can gain inspiration for new recipes and dishes.

[1246] Sample prompts (food photos)

[1247] Generate new recipes and cooking ideas based on five types of food photos taken by the user. The dishes are pasta, stir-fry, and dessert. The user's emotions are enjoying cooking.

[1248] The above is a specific embodiment for carrying out the present invention.

[1249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1250] Step 1:

[1251] A user uses a terminal to select and upload images taken in daily life.

[1252] How it works: The user selects an image from the device's photo album and presses the upload button via a dedicated application or web interface.

[1253] Input: An image file selected by the user.

[1254] Output: Images are brought into the system and ready to be uploaded.

[1255] Step 2:

[1256] The terminal transmits the selected image data to the server.

[1257] How it works: When the upload button is pressed, the device breaks the image data into packets and sends them over an internet connection to a server using the HTTP or HTTPS protocol.

[1258] Input: An image file stored on the user's device.

[1259] Output: The image data is sent to the server.

[1260] Step 3:

[1261] The server receives the image data sent from the terminal and stores it in a database.

[1262] How it works: The server reconstructs the received data packets and saves them in storage as the original image file.

[1263] Input: Image data packets sent from the terminal.

[1264] Output: Image files stored in the database.

[1265] Step 4:

[1266] The server analyzes the stored image data using an image recognition algorithm.

[1267] How it works: The server applies image analysis software such as TensorFlow or OpenCV to extract information such as objects, colors, patterns, and themes in the image.

[1268] Input: Image files stored in the database.

[1269] Output: Relevant data as a result of image analysis.

[1270] Step 5:

[1271] The server uses emotion recognition technology to recognize emotions from images uploaded by users.

[1272] How it works: The server calls the emotion recognition engine API and provides image data. The emotion engine analyzes the facial expressions and mood in the image and returns emotional data to the server.

[1273] Input: Analyzed image data.

[1274] Output: Emotion data as emotion recognition results.

[1275] Step 6:

[1276] Based on the results of image analysis and emotion recognition, the server extracts elements and features common to multiple images.

[1277] How it works: The server uses the analysis results and emotion data retrieved from the database to run algorithms that search for common elements between images (such as matching objects, similar colors, or common themes).

[1278] Input: Image related data and emotion data.

[1279] Output: Extraction of common elements and features.

[1280] Step 7:

[1281] The server generates novel digital content based on the common elements and emotion recognition results.

[1282] How it works: The server generates prompts for the generative AI model, feeds the prompts into the model, and the model generates new content and returns the results to the server.

[1283] Input: common elements, feature data, prompt sentence.

[1284] Output: The novel digital content generated.

[1285] Step 8:

[1286] The server formats the generated digital content for delivery to the user.

[1287] How it works: The server formats the results into HTML, JSON, or other formats that are easy for the user to view.

[1288] Input: Novel digital content generated.

[1289] Output: Formatted digital content.

[1290] Step 9:

[1291] The server transmits the formatted digital content to the terminal.

[1292] How it works: The server sends data to the device via the HTTPS protocol.

[1293] Input: Formatted digital content.

[1294] Output: Digital content sent to a device.

[1295] Step 10:

[1296] The terminal displays the received results on the screen.

[1297] Action: The device application or browser renders the results and displays them to the user.

[1298] Input: Digital content sent from the server.

[1299] Output: The digital content that the user sees on a screen.

[1300] (Application example 2)

[1301] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1302] As technology advances, there is a demand for services that generate new content using images taken by users in their daily lives. However, conventional systems have difficulty generating personalized content that takes user emotions into account, making it impossible to provide users with truly valuable content. Furthermore, there has been no system that can provide users with both real content and novel, imaginary content in conjunction with emotion recognition. This has prevented systems from providing new discoveries and experiences that are in tune with users' emotions, resulting in insufficient improvements to the user experience.

[1303] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting an image taken by a user with a terminal, a means for transmitting the input image to the server, a means for analyzing the image and extracting related information in the server, a means for recognizing the user's emotion using an emotion engine, a means for generating novel content based on the extracted commonalities and emotion recognition results, and a means for providing the generated content to the user. This enables the generation of personalized novel content based on the user's emotion from the image taken by the user, thereby providing a richer user experience. In addition, by using a generative AI model based on the emotion recognition results to generate prompt sentences and generate novel content, it is possible to provide creative content that does not actually exist. This allows the user to make new discoveries and experiences.

[1304] "Means for inputting images taken by the user on a terminal" refers to a function for importing images taken by the user using a terminal that the user normally uses, such as a smartphone or digital camera, into the system.

[1305] The "means for transmitting the input image to the server" is a function for uploading the image selected from the terminal to a cloud server or a remote server via the Internet.

[1306] "Means for analyzing images on the server and extracting relevant information" refers to a function that processes image data stored on the server and recognizes and extracts features such as objects, colors, patterns, and themes contained in the images.

[1307] "Means for recognizing user emotions using an emotion engine" refers to algorithms and software for determining a user's emotions based on the content of uploaded images, the facial expressions of people photographed, the atmosphere of the scenery, etc.

[1308] "Means for generating novel content based on extracted commonalities and emotion recognition results" refers to algorithms and AI technologies for creating new content based on common elements and features from multiple images and the results of user emotion recognition.

[1309] The "means for providing generated content to the user" is a function for transmitting novel content generated by the server to the user's terminal and displaying it so that the user can view and enjoy it.

[1310] "Means for recommending real content" refers to a function that selects and suggests to users existing content (e.g., videos, music, articles, etc.) that matches their interests based on related information obtained through image analysis and emotion recognition results.

[1311] "Means for generating novel non-existent content" refers to a function that uses AI to generate creative and attractive new content that does not exist in reality (e.g., virtual landscape images, new food recipes, etc.).

[1312] "Means for generating prompts using a generative AI model" refers to a function that uses an AI model to create prompts (e.g., instructions or descriptions for new content to be generated) based on the user's emotions recognized by the emotion engine.

[1313] The present invention is a system that generates novel content using images taken by a user in their daily lives and provides the content to the user. Specific embodiments for carrying out the invention will be described below.

[1314] System configuration

[1315] The system mainly consists of the following components:

[1316] 1. User's device: a smartphone or other device

[1317] 2. Server: Image analysis and emotion engine

[1318] 3. Generative AI Model: A Novel Content Generation Engine

[1319] User's device

[1320] Users upload the images they have taken using a dedicated application. The device then sends the images to the server. Users can easily select and upload images using the smartphone application.

[1321] server

[1322] The server stores the received image data and prepares it for analysis. Specifically, it performs the following processes:

[1323] 1. Image Analysis: Use OpenCV or other image processing libraries to extract features such as objects, colors, patterns, and themes from images.

[1324] 2. Emotion Recognition: Using Keras and other deep learning models, we recognize the user's emotions from the image content. We analyze the facial expressions, behaviors, and atmosphere of the people in the images taken by the user.

[1325] 3. Content generation: Using a generative AI model, novel content is generated based on the extracted commonalities and emotion recognition results.

[1326] Generative AI Models

[1327] The model generates prompts and generates new content based on them. For example, for travel photos, it generates prompts like this:

[1328] Generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy."

[1329] Based on this prompt, the generative AI model generates a creative landscape image that corresponds to the user's emotions.

[1330] Content provider

[1331] The generated recommendation list of novel and existing content is sent from the server to the user's device and displayed through a dedicated application, allowing the user to browse this content and enjoy new discoveries and experiences.

[1332] Specific use cases

[1333] For example, if a user uploads a photo of cherry blossoms they took on a daily basis, the image is sent to the server and analyzed. As a result of the analysis, it is recognized that the image contains cherry blossoms, and the user's emotion is detected as "joy." Based on this information, the generative AI model creates a prompt to generate a new landscape image related to "spring cherry blossom viewing photos," and generates the new landscape image. This generated image is provided to the user, who can use it to get ideas for new travel destinations.

[1334] In this way, the system of the present invention enriches the user's daily life and provides new discoveries and experiences.

[1335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1336] Step 1:

[1337] The user takes a photo on their device and selects the image to upload. The user opens a dedicated application on their smartphone, selects an image they have taken in their daily life, and presses the upload button. The input is the image taken by the user, and the output is the image sent from the device to the server.

[1338] Step 2:

[1339] The device sends the selected image to the server. The image file is uploaded to the server via the Internet. The input is the image file sent from the device, and the output is the image data stored on the server.

[1340] Step 3:

[1341] The server stores the received image data and prepares it for analysis. Specifically, it stores the received images in a database. It also prepares the libraries and models required for image analysis. The input is the image data stored on the server, and the output is image data ready for analysis.

[1342] Step 4:

[1343] The server performs image analysis and extracts relevant information. It uses image processing libraries such as OpenCV to recognize objects, colors, patterns, and themes in the image. The input is the stored image data, and the information extracted as a result of the analysis is the output.

[1344] Step 5:

[1345] The server uses an emotion engine to recognize user emotions. It uses deep learning models such as Keras to analyze facial expressions of people in images and the atmosphere of the scenery to identify emotions. The input is image data and extracted related information, and the output is the recognized emotion.

[1346] Step 6:

[1347] The server generates novel content based on the extracted commonalities and emotion recognition results. Utilizing the generative AI model, it generates a prompt and then creates new content based on it. Specifically, the generative AI model receives a prompt to generate a new landscape image related to "spring cherry blossom viewing photos" that matches the emotion of "joy," and the generative AI model generates novel content accordingly. The inputs are the commonalities, emotion recognition results, and the prompt, and the output is novel content.

[1348] Step 7:

[1349] The server formats the generated novel content and provides it to the user. The generated content is sent to the user's device and displayed through a dedicated application. The input is the novel content, and the output is the content displayed on the device. This allows the user to make new discoveries and gain new experiences.

[1350] 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.

[1351] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1352] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1353] 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.

[1354] 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 includes both affect 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.

[1355] 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.

[1356] 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).

[1357] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1358] 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."

[1359] 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.

[1360] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1361] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1362] 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.

[1363] 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.

[1364] 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.

[1365] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1366] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] The following is further disclosed regarding the above embodiment.

[1372] (Claim 1)

[1373] A means for inputting images taken by a user in daily life;

[1374] means for analyzing the input image and extracting relevant information;

[1375] means for generating novel content based on the extracted commonalities;

[1376] means for providing the generated content to a user;

[1377] A system including:

[1378] (Claim 2)

[1379] 10. The system of claim 1, further comprising: means for recommending real-world content based on the extracted commonalities.

[1380] (Claim 3)

[1381] 10. The system of claim 1, further comprising means for generating novel non-existent content based on the extracted commonalities.

[1382] "Example 1"

[1383] (Claim 1)

[1384] A means for inputting images taken by a user in daily life;

[1385] means for transmitting the input image to a server;

[1386] means for storing the image received by the server in a data storage;

[1387] means for the server to analyze the stored images and extract relevant information, such as objects, colors, patterns, and themes, within the images;

[1388] A means for the server to extract elements common to a plurality of images;

[1389] a means for generating novel content based on the common elements;

[1390] means for providing the generated content to a user;

[1391] A system including:

[1392] (Claim 2)

[1393] 10. The system of claim 1, further comprising: means for recommending real-world content based on the extracted common elements.

[1394] (Claim 3)

[1395] 10. The system of claim 1, further comprising means for generating novel non-existent content based on the extracted common elements.

[1396] "Application Example 1"

[1397] (Claim 1)

[1398] A means for inputting images taken by a user in daily life;

[1399] means for analyzing the input image and extracting relevant information;

[1400] a means for generating novel video content based on the extracted common points;

[1401] means for providing the generated video content to a user;

[1402] A system including:

[1403] (Claim 2)

[1404] 10. The system of claim 1, further comprising: means for recommending real-world video content based on the extracted commonalities.

[1405] (Claim 3)

[1406] 10. The system of claim 1, further comprising means for generating novel, non-existent video content based on the extracted commonalities.

[1407] "Example 2: Combining Emotion Engines"

[1408] (Claim 1)

[1409] A means for inputting images taken by a user in daily life;

[1410] means for transmitting the input image to a remote device;

[1411] means for analyzing the received image and extracting relevant data from the remote device;

[1412] means for recognizing a user's emotion using emotion recognition technology;

[1413] means for generating novel digital content based on the extracted related data and the user's emotion recognition result;

[1414] means for providing the generated digital content to a user;

[1415] A system including:

[1416] (Claim 2)

[1417] 10. The system of claim 1, further comprising: means for recommending real-world content based on the extracted related data and emotion recognition results.

[1418] (Claim 3)

[1419] 10. The system of claim 1, further comprising: means for generating novel, non-existent digital content based on the extracted related data and emotion recognition results.

[1420] "Application example 2 when combining emotion engines"

[1421] (Claim 1)

[1422] A means for inputting an image taken by a user with a terminal;

[1423] means for transmitting the input image to a server;

[1424] means for analyzing the image on the server and extracting relevant information;

[1425] means for recognizing a user's emotion using an emotion engine;

[1426] a means for generating novel content based on the extracted commonalities and emotion recognition results;

[1427] means for providing the generated content to a user;

[1428] A system including:

[1429] (Claim 2)

[1430] 10. The system of claim 1, further comprising: means for recommending real-world content based on the extracted commonalities and emotion recognition results.

[1431] (Claim 3)

[1432] 10. The system of claim 1, further comprising: means for generating novel, non-existent content based on the extracted commonalities and emotion recognition results.

[1433] (Claim 4)

[1434] 2. The system of claim 1, further comprising: means for generating a prompt sentence using a generative AI model based on the emotion recognition result, and generating novel content based on the prompt sentence. [Explanation of symbols]

[1435] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting images taken by a user in daily life; means for analyzing the input image and extracting relevant information; means for generating novel content based on the extracted commonalities; means for providing the generated content to a user; A system including:

2. The system of claim 1 , further comprising: means for recommending real-world content based on the extracted commonalities.

3. The system of claim 1 , further comprising: means for generating novel non-existent content based on the extracted commonalities.

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